Online convex optimization with periodic updates for downlink multi-cell MIMO wireless network virtualization

A method and network node for online coordinated multi-cell precoding are provided. According to one aspect, a method includes receiving from each of the plurality of service providers a virtual precoder matrix determined by the corresponding service provider. The method also includes determining a precoder matrix by minimizing a precoding deviation from a virtualization demand of the network subject to at least one power constraint, the virtualization demand being based at least in part on a product of a channel state matrix and a virtual precoder matrix. The method further includes applying the determined precoder matrix to signals applied to a plurality of antennas to achieve a sum of throughputs for the plurality of service providers that is greater than a sum of throughputs for the plurality of service providers achievable when the virtual precoder matrices are applied to the signals.

TECHNICAL FIELD

This disclosure is related to wireless communication and in particular, to online convex optimization (OCO) with periodic updates for downlink multi-cell multiple input multiple output (MIMO) wireless network virtualization.

BACKGROUND

OCO has emerged as a promising solution to many signal processing and resource allocation problems in the presence of uncertainty. In OCO, at the beginning of each time slot, an agent makes a decision from a known convex set. At the end of each time slot, the system reveals the convex loss function to the agent, and then the agent's loss is realized. The convex loss function can change arbitrarily at each time slot with unknown statistics. It is thus not possible for an OCO algorithm to make an optimal decision at each time slot due to the lack of in-time information of the convex loss function. Thus, a goal of an OCO algorithm is to make a sequence of decisions to minimize the regret, i.e., the performance gap of the accumulated loss to an optimal offline fixed strategy with all convex loss functions known in hindsight. An OCO algorithm should provide sub-linear regret, i.e., the gap from the optimal offline performance benchmark, in terms of time-averaged loss, tends to zero as time approaches infinity. With the bounded gradient or sub-gradient assumption, an online projected gradient descent algorithm achieves an O(√T) regret in a seminal work of OCO, where T is the total time horizon.

OCO with long-term constraints has been considered, in which short-term constraints are relaxed to long-term constraints to circumvent the computational challenge of the projection operator. An OCO algorithm with long-term constraints should provide sub-linear long-term constraint violations, i.e., the time-averaged violations of the long-term constraints tend to zero as time approaches infinity.

Wireless network virtualization (WNV) as an extension of wired network virtualization to the wireless domain has been proposed to share a common network infrastructure among several virtual networks. In a virtualized wireless network, an infrastructure provider (InP) owns the network infrastructure and virtualizes the network resources into virtual slices. The service providers (SPs) lease these virtual slices and serve their subscribing users under their own management and requirements, without the need to be aware of the underlying physical network infrastructure. WNV reduces the capital and operational expenses of wireless networks and makes it easier and faster to migrate to new network technologies, while ensuring the existing services are unaffected. Different from wired network virtualization, WNV concerns the sharing of both the wireless infrastructure and the radio spectrum. Due to the broadcast and random nature of the wireless medium, spectrum sharing in WNV brings new challenges in guaranteeing the isolation of virtual networks.

Existing works on OCO with long-term constraints use the standard per-time-slot settings, where the decision is made in each time slot and the convex loss function is revealed at the end of each time slot. OCO with long-term constraints has been considered in which a saddle point algorithm was proposed to achieve lower computational complexity than another known method and provides O(√T) regret and O(T{circumflex over ( )}(¾)) long-term constraint violation. A trade-off between the regret and the long-term constraint violation has been shown in a follow-up work. Variations of the online saddle point methods have been studied for distributed online problems with consensus and proximity constraints. A virtual-queue-based algorithm has been proposed and provides O(√T) regret and O(1) long-term constraint violation. The above works consider time-invariant constraints that are known in advance. Virtual-queue-based OCO algorithms have been proposed for time-varying constraints with static and dynamic regret analysis. A modified online saddle point algorithm has been proposed to deal with time-varying constraints. However, many practical systems do not allow a decision to be made at each time slot, while the system itself evolves over time.

While the known methods assume that all convex loss function feedbacks are delayed for one time slot, there are existing works on OCO with multiple-time-slot or adversarial feedback delays. However, all of these works assume decisions are made at each time slot. Algorithms dealing with a known fixed feedback delay have been studied. The standard online gradient descent algorithm has been extended to deal with a known fixed feedback delay. A delay-adaptive online gradient descent algorithm has been proposed to accommodate adversarial feedback delays, under the assumption that the feedback delays do not change the order of the received feedbacks. A unified framework has been proposed to extend the standard OCO algorithms with full-information feedbacks to handle adversarial feedback delays. Regret bound analysis of online gradient descent algorithm with adversarial feedback delays has been provided.

Inherited from wired network virtualization, most existing MIMO WNV works enforce strict physical isolation. The system throughput and energy efficiency maximization problems have been studied for WNV in orthogonal frequency division multiplexing (OFDM) massive MIMO systems. The cloud radio networks (C-RAN) and non-orthogonal multiple access (NOMA) techniques have been combined with uplink MIMO WNV in some known methods. Exclusive sub-channels are allocated to each SP through a two-level hierarchical auction architecture and antennas are allocated through pricing among the SPs for virtualized massive MIMO systems in some known works. The physical isolation approach does not fully utilize the benefit of spatial spectrum sharing enabled by MIMO beamforming. In contrast, all antennas and channel resources are shared among the SPs in some works, through stochastic robust precoding for massive MIMO WNV.

Although the above MIMO WNV works focus on offline problems, the Lyapunov optimization technique and OCO technique have been utilized in various online studies for non-virtualized MIMO systems. Under the framework of Lyapunov optimization, online power control for wireless transmission with energy harvesting and dynamic transmit covariance design for point-to-point MIMO systems have been studied. Online projected gradient descent and matrix exponential learning have been used for MIMO uplink covariance matrix design. Online MIMO WNV schemes under accurate and inaccurate CSI have been studied without considering channel sate feedback delay. Furthermore, known methods all are under the standard per-time-slot settings.

Under standard OCO settings, decision making and convex loss function feedback are in a strict per-time-slot fashion, which is restrictive for many real applications. In a practical long term evolution (LTE) network, there is only one reference or pilot symbol inserted in every six or seven transmission symbols to allow channel estimation. MIMO precoding relies on channel state feedback and is designed every six or seven symbol durations, while the underlying channel state keeps varying over those symbol durations. Existing OCO algorithms cannot be directly applied to such a system, where decisions and system states are updated per period that could last for multiple time slots.

SUMMARY

Some embodiments advantageously provide a method and system for online convex optimization with periodic updates for downlink multi-cell multiple input multiple output (MIMO) wireless network virtualization.

An OCO algorithm with periodic updates is provided. The algorithm is applied to an online downlink multi-cell MIMO WNV problem, where precoding design and channel state feedback are in a per-update-period fashion.

Also provided is a way to apply the OCO algorithm to wireless network virtualization (WNV) in the downlink coordinated multi-cell setting. In some embodiments, in the most general setting, an agent makes a decision, taken from a known convex decision space, at the beginning of each update period that can last for multiple time slots. The gradient or sub-gradient information of some convex loss functions is then revealed to the agent at the end of each update period. The convex loss function can change arbitrarily at each time slot with unknown statistics. The gradient or sub-gradient feedbacks may be delayed for multiple time slots, received out of order, and partly missing within each update period. The sequence of decisions is subject to both long-term and short-term constraints. Based only on the past gradient or sub-gradient information, the agent makes a sequence of decisions to provide updates to sub-linear regret and long-term constraint violation parameters.

In contrast to known methods, some embodiments of the algorithm allow gradient or sub-gradient feedbacks to be delayed for multiple time slots, received out of order and partly missing within each update period, while a decision is made only at the beginning of each update period.

In some embodiments, SPs are allowed to share all antennas and wireless spectrum resources simultaneously through the spatial virtualization approach.

In some embodiments, an online downlink coordinated multi-cell MIMO WNV algorithm is provided, where under the general setting a precoding design and channel state feedback are in a per-update-period fashion which, for example, is used in standard Long-Term Evolution (LTE).

In some embodiments, the OCO algorithm is implemented to design an online coordinated multi-cell precoding scheme at the infrastructure provider (InP) for downlink MIMO WNV, in which the wireless network is comprised of multiple service providers (SP) with unknown channel distribution information (CDI) and delayed channel state information (CSI). In the WNV framework discussed herein, in some embodiments, the InP communicates the corresponding channel state to each service provider (SP) after obtaining the global channel state. Each SP is allowed to utilize all antennas and wireless spectrum resources provided by the InP and design its own virtual precoding matrix in each cell, without the need to be aware of its own users in other cells and the users of other SPs. In some embodiments, at the beginning of each demand-response period, the InP coordinates the cells at the precoding level to meet the current and future virtualization demands from the SPs, based only on the past channel states and virtual precoding matrices. The InP aims to minimize the accumulated precoding deviation from the virtualization demand, accommodating both long-term and short-term transmit power constraints.

An OCO algorithm with periodic updates is described below. The OCO algorithm makes a decision at the beginning of each update period that can last for multiple time slots. The convex loss function can change arbitrarily at each time slot with unknown statistics. The gradient or sub-gradient feedbacks of the convex loss functions are allowed to be delayed for multiple time slots, received out of order, and partly missing within each update period. A sequence of decisions is made based only on the past gradient or sub-gradient information, subject to both long-term and short-term constraints. In some embodiments, the OCO algorithm provides O(√T) regret defined with partial feedbacks and O(1) long-term constraint violation.

An online downlink coordinated multi-cell MIMO WNV algorithm, with unknown CDI and delayed CSI under standard LTE transmission frame settings is disclosed. In the WNV framework, the InP may communicate the corresponding channel state to each SP after receiving the global channel state feedback. Each SP is allowed to utilize all antennas and wireless spectrum resources and design its own virtual precoding matrix in each cell, without the need to be aware of its own users in other cells and the other SPs. At the beginning of each demand-response period, in some embodiments, the InP coordinates the cells at the precoding level to meet the current and future virtualization demands based on the past channel states and virtual precoding matrices. In some embodiments, the InP optimizes MIMO precoding to minimize the accumulated precoding deviation from the virtualization demand, accommodating both long-term and short-term transmit power constraints. In such a case, the online coordinated multi-cell precoding problem has a distributed closed-form solution with low computational complexity.

Extensive simulation studies have been performed to validate the performance of the proposed online downlink coordinated multi-cell MIMO WNV algorithm under standard LTE network settings. Simulation studies show a fast convergence of time-averaged performance. The algorithm is able to track channel variations and is applicable to massive MIMO. Comparisons with an optimal offline fixed precoding strategy and OCO algorithm with long-term constraints under the standard settings demonstrate system performance advantage of the method proposed herein. Performance studies under different demand-response periods, precoding strategies adopted by the SPs, long-term transmit power limits, numbers of cells and antennas, and per-antenna transmit power constraints are also provided.

According to one aspect, a method of online coordinated multi-cell precoding for a network node configurable at least in part by an infrastructure provider is provided. The network node is configured to facilitate sharing of wireless network infrastructure resources by a plurality of service providers. The method includes obtaining a global channel state, the global channel state being based at least in part on a channel state in each cell of a plurality of cells and communicating a channel state to a corresponding service provider. The method also includes receiving a virtual precoding matrix from each service provider of the plurality of services providers, each virtual precoding matrix being based at least in part on the channel state communicated to the corresponding service provider and being further based at least in part on a condition that each service provider of the plurality of services providers is allowed to use all of a plurality of available antennas and available wireless spectrum resources. The method further includes executing an optimization procedure to periodically determine a multiple input multiple output, MIMO, precoding matrix that minimizes an accumulated precoding deviation from a virtualization demand subject to constraints, the virtualization demand and the MIMO precoding matrix being based at least in part on the virtual precoding matrices received from the plurality of service providers. The method includes applying the determined MIMO precoding matrix to signals applied to the plurality of available antennas to achieve a sum of throughputs for the plurality of service providers that is greater than a sum of the throughputs for the plurality of service providers achievable when the virtual precoder matrices are applied to the signals.

According to this aspect, in some embodiments, the optimization procedure includes periodically determining a gradient of a convex loss function and providing the determined gradient as feedback to the determination of the MIMO precoding matrix. In some embodiments, feedback in the optimization procedure is allowed to be delayed for multiple time slots, received out of order and/or partly missing within an update period. In some embodiments, the optimization procedure includes an online projected gradient ascent algorithm that provides O(√{square root over (T)}) regret and O(1) long term constraint violation, where T is a total time horizon over which multiple updates of the determined MIMO precoding matrix occur. In some embodiments, the accumulated precoding deviation is determined according toft(xt) where T is a time horizon, xtis a decision in a sequence of decisions made by the network node, ft(xt) is a convex loss function,ft(xt) is an accumulated loss and xoarg minx∈X0ft(x) is the argument offt(x) that produces a minimum value offt(x). In some embodiments, the method further includes dividing a total time horizon T into update periods, each update period having a duration of Totime slots, Tobeing at least one time slot, and updating the MIMO precoding matrix at a beginning or end of each update period. In some embodiments, at a beginning of each update period, a decision is taken from a known convex decision space and a loss is determined by an end of the duration of Totime slots based at least in part on the decision, the loss being based at least in part on a convex loss function. In some embodiments, the virtualization demand is further based at least in part on past channel states. In some embodiments, the constraints include long term transmit power constraints and short term transmit power constraints. In some embodiments, the MIMO precoding matrix is determined to provide sub-linear T-slot regret with partial feedback on the accumulated precoding deviation from the virtualization demand, where T is a total time horizon.

According to another aspect, a network node configured for online coordinated multi-cell precoding, the network node configurable at least in part by an infrastructure provider is provided. The network node is configured to facilitate sharing of wireless network infrastructure resources by a plurality of service providers. The network node includes processing circuitry configured to: obtain a global channel state, the global channel state being based at least in part on a channel state in each cell of a plurality of cells and communicate a channel state to a corresponding service provider. The processing circuitry is further configured to receive a virtual precoding matrix from each service provider of the plurality of services providers, each virtual precoding matrix being based at least in part on the channel state communicated to the corresponding service provider and being further based at least in part on a condition that each service provider of the plurality of services providers is allowed to use all of a plurality of available antennas and available wireless spectrum resources. The processing circuitry is further configured to execute an optimization procedure to periodically determine a multiple input multiple output, MIMO, precoding matrix that minimizes an accumulated precoding deviation from a virtualization demand subject to constraints, the virtualization demand and the MIMO precoding matrix being based at least in part on the virtual precoding matrices received from the plurality of service providers. The processing circuitry is further configured to apply the determined MIMO precoding matrix to signals applied to the plurality of available antennas to achieve a sum of throughputs for plurality of service providers that is greater than a sum of the throughputs for the plurality of service providers achievable when the virtual precoder matrices are applied to the signals.

According to this aspect, in some embodiments, the optimization procedure includes periodically determining a gradient of a convex loss function and providing the determined gradient as feedback to the determination of the MIMO precoding matrix. In some embodiments, feedback in the optimization procedure is allowed to be delayed for multiple time slots, received out of order and/or partly missing within an update period. In some embodiments, the optimization procedure includes an online projected gradient ascent algorithm that provides O(√{square root over (T)}) regret and O(1) long term constraint violation, where T is a total time horizon over which multiple updates of the determined MIMO precoding matrix occur. In some embodiments, the accumulated precoding deviation is determined according toft(xt) where T is a time horizon, xtis a decision in a sequence of decisions made by an agent, ft(xt) is a convex loss function,ft(xt) is an accumulated loss and xoarg minx∈X0ft(x) is the argument offt(x) that produces a minimum value offt(x). In some embodiments, the processing circuitry is further configured to divide a total time horizon T into update periods, each update period having a duration of Totime slots, Tobeing at least one timeslot, and updating the MIMO precoding matrix at a beginning or end of each update period. In some embodiments, at a beginning of each update period, a decision is taken from a known convex decision space and a loss is determined by an end of the duration of Totime slots based at least in part on the decision, the loss being based at least in part on a convex loss function. In some embodiments, the virtualization demand is further based at least in part on past channel states. In some embodiments, the constraints include long term transmit power constraints and short term transmit power constraints. In some embodiments, the MIMO precoding matrix is determined to provide sub-linear T-slot regret with partial feedback on the accumulated precoding deviation from the virtualization demand, where T is a total time horizon.

According to yet another aspect, a network node configured for sharing of wireless network resources among a plurality of service providers is provided. The network node includes processing circuitry configured to receive from each of the plurality of service providers a virtual precoder matrix determined by the corresponding service provider. The processing circuitry is further configured to determine a precoder matrix by minimizing a precoding deviation from a virtualization demand of the network subject to at least one power constraint, the virtualization demand being based at least in part on a product of a channel state matrix and a virtual precoder matrix. The processing circuitry is further configured to apply the determined precoder matrix to signals applied to a plurality of antennas to achieve a sum of throughputs for the plurality of service providers that is greater than a sum of throughputs for the plurality of service providers achievable when the virtual precoder matrices are applied to the signals.

According to this aspect, in some embodiments, determining the precoder matrix includes solving a minimization problem that includes determining a gradient of a convex loss function. In some embodiments, determining the precoder matrix includes decomposing the minimization problem into a number of subproblems, each subproblem involving a gradient of a convex loss function of a local precoding matrix. In some embodiments, the minimization problem includes comparing a function of the determined precoder matrix to an offline fixed precoding strategy. In some embodiments, the virtual precoder matrices received from the plurality of service providers are each based at least in part on a condition that each service provider is allowed to use all of a plurality of available antennas and wireless spectrum resources. In some embodiments, the precoding deviation from virtualization demand is determined at least in part by:
ft(V)∥HtV−Dt∥F2
where V is a past precoding matrix, Htis the channel state and Dtis the virtualization demand.

According to another aspect, a method of online coordinated multi-cell precoding for a network node configurable at least in part by an infrastructure provider is provided, where the network node is configured to facilitate sharing of wireless network infrastructure resources by a plurality of service providers. The method includes receiving from each of the plurality of service providers a virtual precoder matrix determined by the corresponding service provider. The method includes determining a precoder matrix by minimizing a precoding deviation from a virtualization demand of the network subject to at least one power constraint, the virtualization demand being based at least in part on a product of a channel state matrix and a virtual precoder matrix. The method also includes applying the determined precoder matrix to signals applied to a plurality of antennas to achieve a sum of throughputs for the plurality of service providers that is greater than a sum of throughputs for the plurality of service providers achievable when the virtual precoder matrices are applied to the signals.

According to this aspect, in some embodiments, determining the precoder matrix includes solving a minimization problem that includes determining a gradient of a convex loss function. In some embodiments, determining the precoder matrix includes decomposing the minimization problem into a number of subproblems, each subproblem involving a gradient of a convex loss function of a local precoding matrix. In some embodiments, the minimization problem includes comparing a function of the determined precoder matrix to an offline fixed precoding strategy. In some embodiments, the virtual precoder matrices received from the plurality of service providers are each based at least in part on a condition that each service provider is allowed to use all of a plurality of available antennas and wireless spectrum resources. In some embodiments, the precoding deviation from virtualization demand is determined at least in part by:
ft(V)∥HtV−Dt∥F2
where V is a past precoding matrix, Htis the channel state and Dtis the virtualization demand.

DETAILED DESCRIPTION

Before describing in detail exemplary embodiments, it is noted that the embodiments reside primarily in combinations of apparatus components and processing steps related to online convex optimization with periodic updates for downlink multi-cell multiple input multiple output (MIMO) wireless network virtualization. Accordingly, components have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.

A method and network node for online coordinated multi-cell precoding are provided. According to one aspect, a method includes receiving from each of the plurality of service providers a virtual precoder matrix determined by the corresponding service provider. The method also includes determining a precoder matrix by minimizing a precoding deviation from a virtualization demand of the network subject to at least one power constraint, the virtualization demand being based at least in part on a product of a channel state matrix and a virtual precoder matrix. The method further includes applying the determined precoder matrix to signals applied to a plurality of antennas to achieve a sum of throughputs for the plurality of service providers that is greater than a sum of throughputs for the plurality of service providers achievable when the virtual precoder matrices are applied to the signals.

Problem Formulation

In this subsection, a constrained OCO problem is formulated. Then, for a performance metric, a regret with partial feedback and long-term constraint violations is defined. Finally, an OCO algorithm with periodic updates is derived.

Under standard OCO settings, at the beginning of each time slot t, an agent makes a decision xt∈X0, where X0∈nis a known compact convex set. At the end of each time slot t, the system reveals the convex loss function ft(x):nto the agent, who then suffers a loss ft(xt). A goal of an OCO algorithm is to select a sequence {xt} such that the accumulated lossft(xt) is comparable with the one yielded by the optimal offline fixed strategy defined as xoarg minx∈X0ft(x), with all convex loss functions known in hindsight, where={0, . . . ,T−1}. A desired OCO algorithm should provide sub-linear regret defined asft(xt)−ft(xo). With the assumption of bounded gradient or sub-gradient, it is known that the online projected gradient ascent algorithm provides O(√{square root over (T)}) regret. Note that the compact convex set X0can be expressed as a set of short-term constraints,c(xt)≤0, wherec(xt):n, c∈with={1, . . . , C}.

The standard OCO formulation is useful in many applications. However, it does not accommodate long-term constraints. OCO with long-term constraints was formulated that does not requirec(xt)≤0 to be satisfied at each time slot t, but only requires the long-term constraint violation, defined asc(xt), to grow sub-linearly. Recently, a virtual-queue-based OCO algorithm in provides O(√{square root over (T)}) regret and O(1) long-term constraint violation under the per-time-slot OCO settings.

Thus an OCO problem formulation with periodic updates as shown inFIG.1is provided. The total time horizon T is segmented into I update periods, indexed by the set={0, . . . , I−1}, each having a duration of T0≥1 time slots. At the beginning of each update period i∈, an agent is required to make a decision xifor the next T0time slots, from a known compact convex set X0that incorporates the short-term constraints. The variable tidenotes the first time slot of update period i. Let ft(x):nbe a convex loss function at time slot t, which is determined by the system (e.g., sum rate of a wireless network determined by the wireless channel conditions). Within each update period i, the agent is sent feedback multiple times of the gradient or sub-gradient information ∇ft(xi) for ti≤t<ti+1. Leti={1, . . . , Si} be the index set of such feedback. Let τisdenote the time slot when feedback s, s∈Siis sent. Then, at the end of time slot τis+dis−1, ∇fτis(xi) is revealed to the agent, where disis the delay of the feedback at time slot τis.

While the set X0accommodates short-term constraints, long-term constraints in may also be considered in the problem formulation. Let gc(x):n, c∈be the known convex long-term constraint function. The agent tries to enforce the long-term constraintT0(xi)0 over I periods, where(x)[1(x), . . . ,c(x)]Tanddenotes element-by-element inequality.

In some embodiments, the following two conventional goals of an OCO algorithm, with slight modification for the system model are achieved. One goal of an OCO algorithm is to select a sequence {xi}∈X0, such that the accumulated loss is competitive with an optimal offline fixed strategy defined with partial feedback

xp∘=△argminx∈𝒳∑i∈𝒥⁢T0Si⁢∑s∈𝒮i⁢fτis(x),
where X{x∈X0: g(x)0}. To capture this, define the regret with partial feedback as follows:

REp(T)=△T0Si⁢∑i∈𝒥⁢∑s∈𝒮i⁢fτis(xi)-fτis(xp∘).(1)
Another goal of an OCO algorithm is to provide sub-linear long-term constraint violation defined for any c as follows:
V0c(T)T0c(xi)  (2)
Note that the long-term constraints introduce correlation on the decisions over time, and lead to a more complicated online problem when the decision is fixed for one update period that lasts for multiple time slots, while the underlying system evolves over time.

Assume that each feedback ∇fτis(xi), s∈Siis delayed for at least one time slot and received by the agent before it makes the next decision xi+1, i.e., 1≤dis≤ti+1−τis∀s∈Si, ∀i∈. Also assume that there is at least one feedback received by the agent within each update period, i.e., 1≤Si, ∀i∈. Note that the feedback within each update period is not assumed to be received in order, i.e., even if r<s, the following statement is not necessarily true: τir+dir≤τis+dis, ∀r, ∈i.

When the feedback received for the gradient or sub-gradient of the convex loss functions is incomplete, i.e.,Si<T, it is unfair to compare the algorithm described herein with the standard optimal offline fixed strategy xothat knows all convex loss functions in hindsight. Let={τi1, . . . , τisi} be the set of time slots with feedback andi={t: ti≤t<ti+1} be the set of time slots where decision xiis made. For the offline setting, assume the convex loss function at time slot t∈i\is the average of the convex loss functions received within update period i, i.e.,

ft(x)=1Si⁢∑s∈𝒮i⁢fτis(x),∀t∈ℱi\𝒯i,∀i∈𝒥.
As such, the OCO algorithm derived herein is compared with an optimal offline fixed strategy xpothat has the same partial feedback information. Note that when all gradient or sub-gradient feedback for all time slots in each period are received, i.e., Si=T0, ∀i∈, xpobecomes the standard optimal offline fixed strategy x0.

OCO Algorithm with Periodic Updates

The OCO algorithm described herein with periodic updates is presented as Algorithm 1, summarized below. It selects a decision sequence {xi} and a virtual queue sequence {Qi}, which can be viewed as the primal and dual variables in the saddle-point-typed OCO algorithms. The main difference is that Algorithm 1 uses the virtual queues as the dual variables to incorporate the violations of long-term constraints. The virtual queue sequence {Qi} serves as a queue backlog of the violations of long-term constraints, and an upper bound on the virtual queue vector can be readily translated to an upper bound on the long-term constraint violations. It has been shown that a virtual-queue-based algorithm has some advantages over the saddle-point-typed algorithms regarding performance guarantees under the standard OCO settings.

In some embodiments, algorithm 1 may be summarized as follows:A. Let γ, α>0 be constant algorithm parameters. At the beginning of period i=0, initialize Q0[Q01, . . . , Q0C]T=0 and x0∈X0. By the end of period 0, receive ∇fτ0s(x0), s∈0.B. If i+1≥I, then End.C. If i+1≤I, then:D. At the beginning of each i+1-st update period ti+1, use ∇fτis(xi), s∈ito do the following:Update the virtual queue for all c∈
Qi+1c=max{−T0c(xi),Qic+T0c(xi)}  (3)
wherec(xi)γc(xi).E. Solve P1 for xi+1as

Note that the disclosed OCO formulation and Algorithm 1 account for the impact of multiple-time-slot update period on the regret bound and long-term constraint violation bound, which has not been studied in the literature before. Compared with the virtual-queue-based OCO algorithm under standard OCO settings, Algorithm 1 only uses the gradient or sub-gradient information instead of the loss function information. Furthermore, when the long-term and short-term constraint functions are separable with respect to blocks of x, P1 in Algorithm 1 can be equivalently decomposed into several sub-problems, each corresponding to a block of xi+1and can be solved with lower computational complexity.

Performance Analysis

In this subsection, performance bounds are provided for the OCO algorithm described herein. Impacts of the update period on the regret and long-term constraint violation bounds are explicitly analyzed.

First, bounds are provided on the virtual queues in the following lemma.

Li=△12⁢Qi22
as the quadratic Lyapunov function and ΔiLi+1−Lias the corresponding Lyapunov drift. Leveraging results in Lemma 1, an upper bound on the Lyapunov drift is provided in the following lemma.
Lemma 2. The Lyapunov drift Δiat each update period i∈is upper bounded by
Δi≤QiT[T0{tilde over (g)}(xi)]+∥T0{tilde over (g)}(xi)∥22(13)
From the definition of strong convexity, function f(x) is said to be strongly convex on a convex set X∈nwith modulus α, if there exists a constant α>0 such that

f⁡(x)-α2⁢x22
is convex on X. Based on the properties of strongly convex functions, the following lemma is commonly used in the literature of OCO for regret analysis.
Lemma 3. Let X∈nbe a convex set. Let function f(x) be strongly convex on X with modulus α and xoptbe a global minimum of f(x) on X. Then, for any x∈X:

f⁡(xopt)≤f⁡(x)-α2⁢xopt-x22.
Leveraging results in Lemmas 1-3 and OCO techniques, the following theorem provides regret bounds for Algorithm 1.
Theorem 4. If

γ=T14,and⁢α=12⁢(T0+T02⁢β2)⁢T
in Algorithm 1, then for all I>0:

REp(T)≤D2+(T0+T02⁢β2)⁢R2+T02⁢G22⁢T=O⁡(T).(14)
Next, an upper bound on the long-term constraint violation in (2) is provided for Algorithm 1. The virtual queue is related to the long-term constraint violation in the following lemma.
Lemma 5. For the virtual queue Qiproduced by Algorithm 1, for any c∈

VOc(T)≤1γ⁢Qic.(15)
From Lemma 5, the long-term constraint violation VOc(T) can be bound through an upper bound on the virtual queue Qic. The following theorem provides an upper bound on the long-term constraint violation over any given I>0 update periods.

Theorem 6. If

γ=T14,and⁢α=12⁢(T0+T02⁢β2)⁢T
in Algorithm 1, then for all I>0 and any c∈:

VOc(T)≤2⁢T0⁢G+2⁢DR+(1+T0⁢β2)⁢R2+4⁢T0⁢G22⁢ϵ=O⁡(1).(16)
Theorems 4 and 6 show that Algorithm 1 provides O(√{square root over (T)}) regret defined with partial feedback in (1) and O(1) long-term constraint violation in (2). Performance analysis explicitly considers the impacts of periodic updates on the performance guarantees of OCO, which has not been studied in the literature before. The above results can be extended to a more general system model with time-varying update periods and unknown time horizon. This is explained in the following remarks.

Define the variation of update period as VT=(Ti−Ti+1)2, where Tiis the i-th update period. Under the assumptions that Ti≤Tmax, ∀i∈and VT≤O(1), i.e., both the update period and the variation of update period are upper bounded, it can be shown that Algorithm 1 still yields O(√{square root over (T)}) regret and O(1) long-term constraint violation.

The parameters γ and α in Algorithm 1 depend on the time horizon T. When T is unknown, the standard doubling trick can be applied to preserve the O(√{square root over (T)}) regret bound of Algorithm 1 when T is known. It can be easily shown that applying the doubling trick to Algorithm 1 yields O(log2T) long-term constraint violation.

In this subsection, an online coordinated multi-cell precoding problem for downlink MIMO WNV with unknown CDI and delayed CSI is disclosed. Both long-term and short-term transmit power constraints are accommodated in the formulation. Leveraging the OCO algorithm described herein, an online downlink coordinated multi-cell MIMO WNV algorithm is disclosed, with a distributed closed-form precoding solution and guaranteed performance bounds.

Idealized MIMO WNV Model

In the system under consideration, an InP performs WNV to a MIMO cellular network that consists of C cells, each cell c∈has one BS equipped with Ncantennas, for a total of N=Ncantennas in the network. The InP serves M SPs, each SP m∈has Kcmusers, for a total of Kc=Kcmusers in cell c and a total of K=Kcusers in the network. Let={1, . . . , C},={1, . . . , M} and={1, . . . , K}. Consider a time-slotted system with time indexed by t, and denote Ĥtlcm∈KlmNcas the local channel state between the Klmusers of SP m in cell l and the BS in cell c at time slot t.

For ease of exposition, first consider an idealized WNV framework, with channel state feedback in each time slot without channel state feedback delay, as shown inFIG.2, where the double arrowed lines extending from SP1 to SP2 indicating sharing between these two SPs. At each time slot t, in each cell c, the InP shares the corresponding channel state Ĥtccm∈Kcm×Ncwith SP m, and allocates transmit power Pcmto the SP, such thatPcm≤Pmaxcwith Pmaxcbeing the maximum transmit power limit of cell c. Each SP m designs its own precoding matrix Ŵtccm∈Nc×Kcmbased on the service needs of its subscribing users, subject to the transmit power limit ∥Ŵtccm∥F2≤Pcm, and then sends this matrix to the InP as a virtual precoding matrix. Note that each SP m designs Ŵtccmwithout the need to be aware of its own users in the other cells and the other SPs. With the virtual precoding matrices designed by the SPs, the desired received signal vector (noiseless) for all Kcusers in cell c is
{tilde over (y)}tc′={tilde over (D)}tcxtc
where {tilde over (D)}tcblkdiag{Dtc1, . . . ,DtcM} is the virtualization demand from cell c withDtcmĤtccmŴtccmbeing the virtualization demand from SP m in cell c, and xtc=[xtc1H, . . . , xtcMH]His the transmitted signal vectors for the Kcusers in cell c, with xtcmbeing the transmitted signal vector for the Kcmusers of SP m in cell c. The desired received signal vector at all K users in the network is
y′t=Dtxt
where Dtblkdiag{{tilde over (D)}t1, . . . , {tilde over (D)}tc} is the virtualization demand from the network, xt=[xt1H, . . . , xtCH]H, and y′t=[{tilde over (y)}tc′H]H. Without loss of generality, assume the transmitted signal to each user is zero-mean with unit power and uncorrelated to each other at each time slot t, i.e.,{xt}=0 and{xtxtH}=I, ∀t∈.

The InP coordinates the cells at the precoding level to serve all the users directly. In each cell c, with local channel state {tilde over (H)}tc=[Ht1cH, . . . ,HtCcH]H∈K×Nc, the InP designs the actual downlink precoding matrix {tilde over (V)}tc∈Nc×Kcto serve the Kcusers in cell c, whereHtlc=[Ĥtlc1H, . . . , ĤtlcMH]H∈Kl×Ncis the channel state between the Klusers in cell l and the BS in cell c. The actual received signal vector at all K users (for now without considering noise) is
yt=HtVtxt
where Ht=[{tilde over (H)}t1, . . . , {tilde over (H)}tC]∈K×Nis the global channel state, and Vt=blkdiag{{tilde over (V)}t1, . . . , {tilde over (V)}tC}∈N×Kis the global precoding matrix designed by the InP. The expected deviation of the received signal vector at all K users via the InP's actual global precoding matrix from that via the SPs' local virtual precoding matrices is given by
{∥yt−y′t∥22}={∥HtVt−Dt∥F2}
where ∥·∥Frepresents the Frobenius norm.

Problem Formulation

Define the convex set for InP's global precoding matrix V∈N×Kas0{V=blkdiag{{tilde over (V)}1, . . . , {tilde over (V)}c}: ∥{tilde over (V)}c∥F2≤Pmaxc, ∀c ∈}, where {tilde over (V)}c∈Nc×Kcis the local precoding matrix for cell c. Define the precoding deviation from virtualization demand of the network as

ft(V)=△Ht⁢V-DtF2=∑c∈𝒞{H_tcc⁢V~c-D~tcF2+∑l∈𝒞,l≠cH_tlc⁢V~cF2}(17)
where the first term is the precoding deviation from virtualization demand of cell c, and the second term is the leakage from cell c to all the other cells. Define the long-term transmit power constraint function for cell c∈as
gc({tilde over (V)}c)∥{tilde over (V)}c∥F2−Pc(18)
wherePc≤Pmaxcis the long-term transmit power limit set by the InP. The InP coordinates the cells at the precoding level to minimize the accumulated precoding deviation from the virtualization demand, subject to both long-term and short-term transmit power constraints.

Consider a periodic demand-response mechanism between the InP and the SPs. At the beginning of each time slot ti=iT0, i∈, the InP coordinates the C cells at the precoding level and designs a fixed global precoding matrix Vi∈N×Kfor the next T0time slots. The InP then schedules channel state feedback to itself (with any channel estimation and feedback transmission method) at time slot τis, s∈i, and receives the global channel state Hτisat the end of time slot τis+dis−1, where disis the channel state feedback delay that satisfies 1≤dis≤ti+1−τis. The InP is allowed to schedule partial channel state feedbacks within each demand-response period, i.e., 1≤Si≤T0, ∀i∈. In each cell c, the InP shares Ĥτisccmwith SP m, who then designs its own precoding matrix Ŵτisccmon the fly and sends it to the InP before it designs the next global precoding matrix Vi+1.

The InP designs {Vi}∈0to provide sub-linear T-slot regret defined with partial feedback on the precoding deviation from the virtualization demand

Online Downlink Coordinated Multi-Cell MIMO WNV Algorithm

The OCO algorithm described herein provides a solution to the afore-mentioned online coordinated multi-cell precoding problem for downlink MIMO WNV. From the first-order condition of a real-valued scalar convex function with respect to the complex-valued matrix variable, for any V, Vi∈0
ft(V)≥ft(Vi)+2Re{tr{∇Vi*ftH(Vi)(V−Vi)}}  (21)
where ∇Vi*ft(Vi)=HtH(HtVi−Dt) is the partial derivative of ft(Vi) with respect to Vi*(the complex conjugate of Vi).

Leveraging the OCO algorithm described herein, at the beginning of each demand-response period ti+1, the InP solves P2 for the global precoding matrix Vi+1as follows:

P3 is a convex optimization problem satisfying the Slater's condition, thus strong duality holds. P3 can be solved by studying the Karush-Kuhn-Tucker (KKT) conditions. The Lagrange function for P3 is

L⁡(V~c,λc)=T0Si⁢∑s∈𝒮i⁢2⁢Re⁢{tr⁢{∇V~ic*fτisH(Vi)⁢(V~c-V~ic)}}+α⁢V~c-V~icF2+[Qi+1c+T0⁢ℊ~c(V~ic)][T0⁢ℊc~(V~c)]+λc(V~cF2-Pmaxc)
where λcis the Lagrangian multiplier associated with constraint (22). Taking partial derivative of L({tilde over (V)}c, λc) with respect to {tilde over (V)}c*:

∇V~c*L⁡(V~c,λc)=T0Si⁢∑s∈𝒮i⁢∇V~ic*fτis(Vi)+α⁡(V~c-V~ic)+[Qi+1c+T0⁢ℊ~c(V~ic)]⁢T0⁢γ⁢V~c+λc⁢V~c.
The KKT conditions for ({tilde over (V)}co, λco) being globally optimal are given by

V~c∘=α⁢V~ic-T0Si⁢∑s∈𝒮i⁢∇V~ic*fτis(Vi)α+[Qi+1c+T0⁢ℊ~c(V~ic)]⁢T0⁢γ,(27)
Equation (27) can be categorized into two subcases: 2.1) If {tilde over (V)}coin (27) satisfies (24), then it is the optimal solution. 2.2) If {tilde over (V)}coin (27) cannot satisfy (24), which means the condition in Case 2) does not hold in optimality, i.e., λco>0, and the optimal solution is given by Case 1).

An online downlink coordinated multi-cell MIMO WNV algorithm in accordance with the present disclosure and referred to herein as algorithm 2, is summarized as follows:A. For large T, set

γ=T14⁢and⁢α=12⁢(T0+T02⁢β2)⁢T;
For intermediate value of T, solve P4 for γ and α.B. At the beginning of period i=0, initialize Q0=0 and V0∈0. By the end of period 0, receive Hτosand Dτos, s∈0.C. If, i+1≥I then end;D. If, i+1≤I then:E. At the beginning of each demand-response period i+1, use Hτisand Dτis, s∈ito do the following:Update virtual queue for all c∈
Qi+1c=max{−T0{tilde over (g)}c({tilde over (V)}ic),Qic+T0{tilde over (g)}c({tilde over (V)}ic)}  (28)where {tilde over (g)}c({tilde over (V)}ic)γgc({tilde over (V)}ic).Update {tilde over (V)}i+1c, ∀c∈as

Performance Analysis

The following assumption is made that the channel gain is upper bounded for theoretical performance bound analysis.

Assumption 6. There exists a constant B>0 such that the channel gain is upper bounded for any t∈as
∥Ht∥F≤B.(29)
The following lemma shows that Assumptions 1-5 hold for the formulated online downlink MIMO WNV problem.
Lemma 7. Under Assumption 6, the formulated online downlink MIMO WNV problem with decision set0, loss function ft(V), and long-term constraint function gc({tilde over (V)}j), c∈, satisfies Assumptions 1-5, with constants D, β, G, R, ∈ given as follows:

γ=T14⁢and⁢α=12⁢(T0+T02⁢β2)⁢T,
Algorithm 2 yields an O(√{square root over (T)}) upper bound given in (15) on the regret defined in (19) and an O(1) upper bound given in (16) on the long-term constraint violation defined in (20), with constants D, β, G, R, ∈ given in Lemma 7.
Furthermore, three remarks regarding the algorithm parameter selection, the optimal offline fixed precoding strategy, and the short-term per-antenna transmit power constraints are provided below.

Algorithm 2 provides O(√{square root over (T)}) regret and O(1) long-term constraint violation from Theorem 8 for a large T. However, the constants hidden behind the O(√{square root over (T)}) and O(1) upper bounds can be relatively large for an intermediate value of T. The following problem can be solved to obtain the algorithm parameters γ and α that yield the best regret bound, subject to a constraint on the long-term constraint violation upper bound:

P⁢4:minγ>0,α>0D22⁢T+α⁢R2+12⁢T02⁢γ2⁢G2s.t.2⁢T0⁢G+T0⁢DR+α⁢R2+2⁢T02⁢γ2⁢G2T0⁢γ2⁢ϵ≤min⁢{Pmaxc-P_c,∀c∈𝒞}⁢T,12⁢(T0⁢T+T02⁢β2⁢γ2)≤α
where D, β, G, R, ∈ are given in Lemma 7. P4 is a geometric programming (GP) problem that can be easily solved by a standard convex program solver such as CVX.

Algorithm 2 provides an O(√{square root over (T)}) regret compared with the optimal offline fixed precoding solution Vp0defined with the same partial feedbacks for fair comparison. However, for algorithm performance evaluation in Subsection 6.4, the performance of Algorithm 2 is compared with the one yielded by the standard optimal offline fixed strategy Vopt0, that solves the following optimization problem with complete feedback
P5: minvopt∥HtVopt−Dt∥F2
s.t.∥{tilde over (V)}optc∥F2≤Pc,∀c∈(35)
where Vopt=blkdiag{{tilde over (V)}opt1, . . . , {tilde over (V)}optc}. Note that Voptois a more knowledgeable fixed off-line strategy than Vpo. Since P5 is a convex optimization problem satisfying the Slater's condition, the strong duality holds. P5 can be solved by studying the KKT conditions. The Lagrange function for P5 is
L(Vopt,λ)=∥HtVopt−Dt∥F2+λc(∥{tilde over (V)}optc∥F2−Pc)
where λc, c∈is the Lagrangian multiplier associated with constraint (35). Taking partial derivative of L(Vopt, λ) with respect to {tilde over (V)}optc*:
∇{tilde over (V)}optc*L(Vopt,λ)={tilde over (H)}tcH({tilde over (H)}tc{tilde over (V)}optc−{tilde over (D)}tc′)+λc{tilde over (V)}optc
where {tilde over (D)}tc′=[0, . . . , {tilde over (D)}tcH, . . . , 0]H. The KKT conditions for ({tilde over (V)}optcoλco), c∈being globally optimal are given by
(Ac+λcoI){tilde over (V)}optco=Bc(36)
∥{tilde over (V)}optco∥F2−Pc≤0  (37)
λco≥0  (38)
λco(∥{tilde over (V)}optco∥F2−Pc)=0  (39)
where (36) follows by setting ∇{tilde over (V)}optcoL(Vopt, λ)=0, Ac=Σt∈τ{tilde over (H)}tcH{tilde over (H)}tcand Bc={tilde over (H)}tcH{tilde over (D)}tc′. Consider the following cases.1) λco>0: From (36), Ac+λcoI>0, which implies that it is invertible, and:
{tilde over (V)}optco=(Ac+λcoI)−1Bc(40)
in which λco>0 can be found by bisection search such that ∥(Ac+λcoI)−1Bc∥F2=Pc.2) λco=0: From (36), the optimal solution must satisfy
Ac{tilde over (V)}optco=Bc(41)
categorize (41) into two subcases: 2.1) If Acis a rank deficient matrix, there are infinitely many solutions for {tilde over (V)}optco. {tilde over (V)}optcois chosen to minimize ∥{tilde over (V)}optco∥F2subject to (41), which is an under-determined least-square problem with a closed-form solution:
{tilde over (V)}optco=AcH(AcAcH)−1Bc(42)
By (37), if ∥AcH(AcAcH)−1Bc∥F2≤Pc, then {tilde over (V)}optcoin (42) is the optimal solution. 2.2) If Acis of full rank, there is a unique solution:
{tilde over (V)}optco=Ac−1Bc(43)
Again, if ∥Ac−1Bc∥F2≤Pc, then {tilde over (V)}optcoin (43) is the optimal solution. For both subcases 2.1) and 2.2), {tilde over (V)}optcoin (42) or (43) cannot satisfy (37), which means the condition in Case 2) does not hold in optimality, i.e., λco>0, and the optimal solution is given by Case 1).

Short-term per-antenna transmit power constraints can be incorporated into the problem formulation, by redefining the convex decision set as0{V=blkdiag{{tilde over (V)}1, . . . , {tilde over (V)}c}: ∥vcn∥F2≤Pmaxcn,∀n∈}, where {tilde over (V)}c=[vc1, . . . , vcNc]H∈Nc×Kcis the precoding matrix for cell c, vcn∈Kc×1is the precoding vector and Pmaxcnis the maximum transmit power limit for the n-th antenna in cell c. Leveraging the OCO algorithm described herein, the InP solves P6 to obtain {tilde over (V)}i+1cas follows:

P⁢7:minvcnT0Si⁢∑s∈𝒮i⁢2⁢Re⁢{tr⁢{∇vicn*fτisH(Vi)⁢(vcn-vicn)}}+α⁢vcn-vicnF2(45)+[Qi+1c+T0⁢ℊ~c(V~ic)][T0⁢γ⁢vcnF2]s.t.vcnF2-Pmaxcn≤0
where ∇vicn*fτis(Vi)=hτiscnH({tilde over (H)}τisc{tilde over (V)}ic−{tilde over (D)}τisc′) and hτiscnis the n-th column vector of {tilde over (H)}τisc. By studying the KKT conditions, a closed-form solution for vi+1cnºis given by

Numerical Performance Evaluation

Consider an InP that owns a cellular network consisting of C=7 urban hexagon micro-cells, each cell c is of 500 m radius and has a BS at the center equipped with Nc=32 antennas by default. The InP serves M=4 SPs, each SP m serves Kcm=2 users, for a total of Kc=8 users in each cell c. Set maximum transmit power limit Pmaxc=33 dBm, long-term transmit power limitPc=32 dBm, noise spectral density N0=−174 dBm/Hz, noise figure NF=10 dB, and transmission channel bandwidth BW=15 kHz.

The fading channel is modeled as a first-order Gauss-Markov process
ht+1ck=αhhtck+ztck
where htck˜(0, βckI) is the channel state between user k and BS antennas in cell c, βck[dB]=−31.54−33 log10dck−ψckcaptures path-loss and shadowing, dckis the distance in kilometers from the BS in cell c to user k, ψck˜(0, σØ2) accounts for shadowing with a σø=8 dB, αhis the channel correlation coefficient, and ztck˜(0, (1−αh2)βckI) is independent of htck. For a coherence time of

Tc=916⁢π⁢fd
and transmission channel bandwidth Bw, αhis determined by

αhTc⁢BW=∅⁢where⁢fd=vvc⁢fc
is the Doppler spread, v is the user speed assumed to be the same for all K users, fc=2.5 GHz is the central frequency, vcis the speed of light, and Ø=0.1 is the level of de-correlation. For αh=0.995 as default in the simulation, v≈3 km/h.FIG.3shows a timeline illustrating online precoding design for downlink MIMO WNV. Under the standard LTE transmission frame structure, one symbol is transmitted with symbol duration

Δ⁢t=1BW=66.67us.
Define Δt as one time slot and set the demand-response period T0=8Δt (533.6 us) as default in the simulation, such that T0can be seen as one slot (500 us) that consists of 6 or 7 symbol durations plus cyclic prefix under the standard LTE transmission frame settings. An overview of the default simulation parameters is shown in Table 1.

TABLE 1ParameterValueNumber of cells C7Number of SPs M4Number of users per cell Kc8Number of users per cell Nc32Transmission channel bandwidth BW15kHzCentral frequency fc2.5GHzNoise spectral density N0−174dBm/HzNoise figure NF10dBUser speed v3km/hMaximum transmit power per cell Pmaxc33dBmLong-term transmit power limit per cellPc32dBmDemand-response period T08Number of channel feedbacks per period Si1Time horizon T600

For performance study, consider the cases where each SP m uses MRT or ZF precoding, which are two commonly used precoding schemes in current MIMO systems, to design their own virtual precoding matrices given by

W^tccmF2=PmaxcM,
i.e., all SPs are allocated equal proportions of the maximum transmit power in each cell. Based on {Vi} produced by Algorithm 2, define the normalized time-averaged precoding deviation from the virtualization demand as

ρ_(T)=△1T⁢∑i∈𝒥⁢∑t∈ℱi⁢ft(Vi)DtF2,
the time-averaged transmit power as

P_(T)=△T0TC⁢∑i∈𝒥⁢ViF2,
and the time-averaged rate per user as

Performance vs. Demand-Response Period To

First evaluate the performance of the disclosed algorithm described herein with different values of the demand-response period T0. Consider the case where there is only one channel state feedback at the beginning of each demand-response period, i.e.,={iT0}, ∀i∈, which is the same as the standard LTE transmission frame structure.FIGS.4A,4B,4C,5A,5B and5Cshowρ(T),P(T), andR(T) versus time horizon T for different values of T0, when all SPs adopt MRT and ZF precoding, respectively. Observe fast convergence of the disclosed algorithm (within 600 time slots). Then use the averaged value over the last 100 time slots as the steady-state value in the remaining simulation results. It can be seen that the system performance deteriorates as T0increases, which is because the underlying channel state keeps varying over time slots while Viis fixed for each update period. As can be seen fromFIGS.5A,5B and5C, as T0increases,P(T) converges slower since Viis fixed for a longer period and thus it is harder for the virtual queue vector Qito converge. When all SPs adopt ZF precoding, the steady-state value ofρ(T) is more sensitive to T0compared with the MRT precoding case, as ZF precoding is known to be sensitive to channel noise and inaccuracy.

Performance Comparison

A performance comparison between the disclosed online downlink coordinated MIMO WNV algorithm is provided herein with a different, but generally related arrangement that discloses standard per-time-slot OCO settings and does not provide performance guarantees under the periodic update scenario. However, the information of the update period T0may be ignored. The steady-state precoding deviationρand rate per userRversus the number of antennas Nc, between the two algorithms as shown inFIGS.6A and6B, are compared. It can be seen that the performance advantage of the disclosed algorithm over the known arrangement becomes more substantial as Nc, increases, indicating the advantage of applying the disclosed algorithm to massive MIMO.

Performance vs. Optimal Offline Fixed Precoding Strategy

FIGS.7A and7Bshow the performance of the disclosed algorithm compared with the one achieved with the offline fixed precoding strategy Voptothat solves P5 and the one yielded by Yu et al., with different values of the channel correlation coefficient αhand all SPs adopting MRT precoding. It can be seen that the disclosed algorithm achieves better system performance compared with the other two schemes, which shows that the disclosed algorithm is able to better suppress the interference for coordinated multi-cell MIMO precoding.

The impact of the long-term transmit power limitPcon the disclosed algorithm has been studied. SeeFIGS.8A, and8Bwhich show that there is a natural trade-off between the steady-state rate per user R and the long-term transmit power limitPc, which allows the InP to balance the system performance and the energy consumption. The disclosed algorithm is applicable to massive MIMO, since the steady-state precoding deviationsρvary within 10% as the number of antennas Nc, increasing from 32 to 256. The time-averaged rate per user R is 2.06 bpcu when Nc=32 compared with 4.48 bpcu when Nc=256, with the same long-term transmit power limitPc=32 dBm, indicating substantial performance gain of WNV in massive MIMO.

Performance vs. Number of Cells C

The impact of the number of cells C on the performance of the algorithm provided herein is described with reference toFIGS.9A and9B. As C increasing, the inter-cell interference becomes more severe, and the steady-state precoding deviationρincreases. However, the steady-state rate per userRis robust to the number of cells C, indicating the effectiveness of the coordinated multi-cell precoding design in handling the inter-cell interference. The impact of the number of cells C becomes smaller as the number of antennas Ncincreases, indicating the advantages of coordinated multi-cell WNV in massive MIMO.

Per-Antenna Transmit Power Constraint

The impact of short-term per-antenna transmit power constraints on the system performance of the disclosed algorithm has been studied. As shown inFIGS.10A,10B and10C, the number of antennas is set to Nc=256, the per-antenna maximum transmit power

Pmaxcn=PmaxcNc,
and long-term transmit power limitPc=32.5 dBm, such that the per-antenna constraint is much stricter than the per-cell transmit power constraint. It can be seen that the system performance deteriorates after adding the per-antenna transmit power constraint, since it further limits the convex decision set0for the sequence of global precoding matrices {Vi}. The per-antenna constraint is observed to have negligible impact on the algorithm performance.

Thus, an OCO algorithm with periodic updates is provided herein. In existing standard OCO settings, decision making and convex loss function feedback are in a strict per-time-slot fashion. In contrast, the OCO algorithm described herein makes a decision at the beginning of each update period that can last for multiple time slots. The convex loss function can change arbitrarily at each time slot with unknown statistics. The gradient or sub-gradient feedbacks are allowed to be delayed for multiple time slots, received out of order, and missing for some time slots within each update period. Based only on the past gradient or sub-gradient information, the OCO algorithm described herein provides O(√{square root over (T)}) regret defined with partial feedbacks and O(1) long-term constraint violation.

An online downlink coordinated multi-cell MIMO WNV algorithm is disclosed, with unknown CDI and delayed CSI under standard LTE transmission frame settings. In the WNV framework, each SP is allowed to utilize all antennas and wireless spectrum resources simultaneously and design its own virtual precoding matrix in a cell without the need to be aware of the other SPs, or to consider the inter-cell interference created by its own users. The InP may coordinate the cells at the precoding level to minimize the current and future precoding deviations from the virtualization demands, based only on the past channel states and virtual precoding matrices. The sequence of global precoding matrices designed by the InP may subject to both long-term and short-term transmit power constraints. In such a case, the online coordinated multi-cell MIMO precoding problem has a distributed closed-form solution with low computational complexity. The disclosed algorithm provides theoretically guaranteed performance, as it yields O(√{square root over (T)}) regret on the precoding deviation from the virtualization demand, and O(1) long-term transmit power constraint violation.

The performance of the disclosed online downlink coordinated multi-cell MIMO WNV algorithm under typical LTE cellular network settings has been numerically validated. Simulation studies show fast convergence of the time-averaged performance. The disclosed algorithm shows substantial system performance advantage over an optimal offline fixed precoding strategy, and over the existing standard OCO algorithm with long-term constraints. Extensive simulation results are provided to demonstrate the impact of different values of demand-response period, precoding strategies adopted by the SPs, long-term transmit power limits, numbers of cells and antennas, and per-antenna transmit power constraint.

Referring toFIG.11. one or more blocks described herein may be performed by one or more elements of network node2such as by one or more of processing circuitry10(including the OCO algorithm12), processor8, and radio interface4. Network node2such as via processing circuitry10and/or processor8and radio interface4, is configured to implement the OCO algorithm described herein. In some embodiments, the network node2is configurable by an infrastructure provider. A memory6stores data and computer code to cause the processor8, which is part of processing circuitry10, to execute an OCO algorithm as described above.

The radio interface4may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, the processing circuitry10may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions. The processor8may be configured to access (e.g., write to and/or read from) the memory6, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory). The processing circuitry10may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by network node2. Processor8corresponds to one or more processors for performing network node2functions described herein. The memory6is configured to store data, programmatic software code and/or other information described herein. In some embodiments, the OCO algorithm12may include instructions that, when executed by the processor8and/or processing circuitry10, causes the processor8and/or processing circuitry10to perform the processes described herein with respect to network node2. The network node2, configured to execute the algorithm disclosed herein, may be operated by the infrastructure provider. The network node2may be in radio communication with one or more other network nodes operated or used by the service providers.

FIG.12is a flowchart of an exemplary process for determining a MIMO precoding matrix that minimizes an accumulated deviation from a virtualization demand subject to constraints. The process may be performed by the radio interface4and processing circuitry10. The process includes obtaining a global channel state, the global channel state being based at least in part on a channel state in each cell of a plurality of cells (Block S100). The process also includes communicating a channel state to a corresponding service provider (Block S102). The process further includes receiving a virtual precoding matrix from each service provider of the plurality of services providers, each virtual precoding matrix being based at least in part on the channel state communicated to the corresponding service provider and being further based at least in part on a condition that each service provider of the plurality of services providers is allowed to use all of a plurality of available antennas and available wireless spectrum resources (Block S104). The process also includes executing an optimization procedure to periodically determine a multiple input multiple output, MIMO, precoding matrix that minimizes an accumulated precoding deviation from a virtualization demand subject to constraints, the virtualization demand and the MIMO precoding matrix being based at least in part on the virtual precoding matrices received from the plurality of service providers (Block S106). The process also includes applying the determined MIMO precoding matrix to signals applied to the plurality of available antennas to achieve a sum of throughputs for the plurality of service providers that is greater than a sum of the throughputs for the plurality of service providers achievable when the virtual precoder matrices are applied to the signals (Block S108).

FIG.13is a flowchart of an alternative exemplary process for determining a MIMO precoding matrix that minimizes an accumulated deviation from a virtualization demand subject to constraints. The process may be performed by the radio interface4and processing circuitry10. The process includes receiving from each of the plurality of service providers a virtual precoder matrix determined by the corresponding service provider (Block S110). The process also includes determining a precoder matrix by minimizing a precoding deviation from a virtualization demand of the network subject to at least one power constraint, the virtualization demand being based at least in part on a product of a channel state matrix and a virtual precoder matrix (Block S112). The process further includes applying the determined precoder matrix to signals applied to a plurality of antennas to achieve a sum of throughputs for the plurality of service providers that is greater than a sum of throughputs for the plurality of service providers achievable when the virtual precoder matrices are applied to the signals (Block S114).

According to one aspect, a method of online coordinated multi-cell precoding for a network node2configurable at least in part by an infrastructure provider is provided. The network node2is configured to facilitate sharing of wireless network infrastructure resources by a plurality of service providers. The method includes obtaining, via the processing circuitry10a global channel state, the global channel state being based at least in part on a channel state in each cell of a plurality of cells and communicating a channel state to a corresponding service provider. The method also includes receiving, via the radio interface4, a virtual precoding matrix from each service provider of the plurality of services providers, each virtual precoding matrix being based at least in part on the channel state communicated to the corresponding service provider and being further based at least in part on a condition that each service provider of the plurality of services providers is allowed to use all of a plurality of available antennas and available wireless spectrum resources. The method further includes executing, via the processing circuitry10, an optimization procedure to periodically determine a multiple input multiple output, MIMO, precoding matrix that minimizes an accumulated precoding deviation from a virtualization demand subject to constraints, the virtualization demand and the MIMO precoding matrix being based at least in part on the virtual precoding matrices received from the plurality of service providers. The method includes applying, via the processing circuitry10, the determined MIMO precoding matrix to signals applied to the plurality of available antennas to achieve a sum of throughputs for the plurality of service providers that is greater than a sum of the throughputs for the plurality of service providers achievable when the virtual precoder matrices are applied to the signals.

According to this aspect, in some embodiments, the optimization procedure includes periodically determining a gradient of a convex loss function and providing the determined gradient as feedback to the determination of the MIMO precoding matrix. In some embodiments, feedback in the optimization procedure is allowed to be delayed for multiple time slots, received out of order and/or partly missing within an update period. In some embodiments, the optimization procedure includes an online projected gradient ascent algorithm that provides O(√{square root over (T)}) regret and O(1) long term constraint violation, where T is a total time horizon over which multiple updates of the determined MIMO precoding matrix occur. In some embodiments, the accumulated precoding deviation is determined, via the processing circuitry10, according toft(xt) where T is a time horizon, xtis a decision in a sequence of decisions made by the network node2, ft(xt) is a convex loss function,ft(xt) is an accumulated loss and x0arg minx∈ X0ft(x) is the argument offt(x) that produces a minimum value offt(x). In some embodiments, the method further includes dividing, via the processing circuitry10, a total time horizon T into update periods, each update period having a duration of Totime slots, Tobeing at least one time slot, and updating the MIMO precoding matrix at a beginning or end of each update period. In some embodiments, at a beginning of each update period, a decision is taken, via the processing circuitry10, from a known convex decision space and a loss is determined by an end of the duration of Totime slots based at least in part on the decision, the loss being based at least in part on a convex loss function. In some embodiments, the virtualization demand is further based at least in part on past channel states. In some embodiments, the constraints include long term transmit power constraints and short term transmit power constraints. In some embodiments, the MIMO precoding matrix is determined to provide sub-linear T-slot regret with partial feedback on the accumulated precoding deviation from the virtualization demand, where T is a total time horizon.

According to another aspect, a network node2configured for online coordinated multi-cell precoding, the network node2configurable at least in part by an infrastructure provider is provided. The network node2is configured to facilitate sharing of wireless network infrastructure resources by a plurality of service providers. The network node2includes processing circuitry10configured to: obtain a global channel state, the global channel state being based at least in part on a channel state in each cell of a plurality of cells and communicate a channel state to a corresponding service provider. The processing circuitry10is further configured to receive a virtual precoding matrix from each service provider of the plurality of services providers, each virtual precoding matrix being based at least in part on the channel state communicated to the corresponding service provider and being further based at least in part on a condition that each service provider of the plurality of services providers is allowed to use all of a plurality of available antennas and available wireless spectrum resources. The processing circuitry10is further configured to execute an optimization procedure to periodically determine a multiple input multiple output, MIMO, precoding matrix that minimizes an accumulated precoding deviation from a virtualization demand subject to constraints, the virtualization demand and the MIMO precoding matrix being based at least in part on the virtual precoding matrices received from the plurality of service providers. The processing circuitry10is further configured to apply the determined MIMO precoding matrix to signals applied to the plurality of available antennas to achieve a sum of throughputs for plurality of service providers that is greater than a sum of the throughputs for the plurality of service providers achievable when the virtual precoder matrices are applied to the signals.

According to this aspect, in some embodiments, the optimization procedure includes periodically determining a gradient of a convex loss function and providing the determined gradient as feedback to the determination of the MIMO precoding matrix. In some embodiments, feedback in the optimization procedure is allowed to be delayed for multiple time slots, received out of order and/or partly missing within an update period. In some embodiments, the optimization procedure includes an online projected gradient ascent algorithm that provides O(√{square root over (T)}) regret and O(1) long term constraint violation, where T is a total time horizon over which multiple updates of the determined MIMO precoding matrix occur. In some embodiments, the accumulated precoding deviation is determined, via the processing circuitry10, according toft(xt) where T is a time horizon, xtis a decision in a sequence of decisions made by an agent, ft(xt) is a convex loss function,ft(xt) is an accumulated loss and x0arg minx∈X0ft(x) is the argument offt(x) that produces a minimum value offt(x). In some embodiments, the processing circuitry10is further configured to divide a total time horizon T into update periods, each update period having a duration of Totime slots, Tobeing at least one timeslot, and updating the MIMO precoding matrix at a beginning or end of each update period. In some embodiments, at a beginning of each update period, a decision is taken, via the processing circuitry10, from a known convex decision space and a loss is determined by an end of the duration of Totime slots based at least in part on the decision, the loss being based at least in part on a convex loss function. In some embodiments, the virtualization demand is further based at least in part on past channel states. In some embodiments, the constraints include long term transmit power constraints and short term transmit power constraints. In some embodiments, the MIMO precoding matrix is determined to provide sub-linear T-slot regret with partial feedback on the accumulated precoding deviation from the virtualization demand, where T is a total time horizon.

According to yet another aspect, a network node2configured for sharing of wireless network resources among a plurality of service providers is provided. The network node2includes processing circuitry10configured to receive from each of the plurality of service providers a virtual precoder matrix determined by the corresponding service provider. The processing circuitry10is further configured to determine a precoder matrix by minimizing a precoding deviation from a virtualization demand of the network subject to at least one power constraint, the virtualization demand being based at least in part on a product of a channel state matrix and a virtual precoder matrix. The processing circuitry10is further configured to apply the determined precoder matrix to signals applied to a plurality of antennas to achieve a sum of throughputs for the plurality of service providers that is greater than a sum of throughputs for the plurality of service providers achievable when the virtual precoder matrices are applied to the signals.

According to this aspect, in some embodiments, determining the precoder matrix includes solving a minimization problem that includes determining a gradient of a convex loss function. In some embodiments, determining the precoder matrix includes decomposing the minimization problem into a number of subproblems, each subproblem involving a gradient of a convex loss function of a local precoding matrix. In some embodiments, the minimization problem includes comparing a function of the determined precoder matrix to an offline fixed precoding strategy. In some embodiments, the virtual precoder matrices received from the plurality of service providers are each based at least in part on a condition that each service provider is allowed to use all of a plurality of available antennas and wireless spectrum resources. In some embodiments, the precoding deviation from virtualization demand is determined, via the processing circuitry10, at least in part by:
ft(V)∥HtV−Dt∥F2
where V is a past precoding matrix, Htis the channel state and Dtis the virtualization demand.

According to another aspect, a method of online coordinated multi-cell precoding for a network node2configurable at least in part by an infrastructure provider is provided, where the network node2is configured to facilitate sharing of wireless network infrastructure resources by a plurality of service providers. The method includes receiving, via the radio interface4, from each of the plurality of service providers a virtual precoder matrix determined by the corresponding service provider. The method includes determining, via the processing circuitry10, a precoder matrix by minimizing a precoding deviation from a virtualization demand of the network subject to at least one power constraint, the virtualization demand being based at least in part on a product of a channel state matrix and a virtual precoder matrix. The method also includes applying, via the processing circuitry10, the determined precoder matrix to signals applied to a plurality of antennas to achieve a sum of throughputs for the plurality of service providers that is greater than a sum of throughputs for the plurality of service providers achievable when the virtual precoder matrices are applied to the signals.

According to this aspect, in some embodiments, determining the precoder matrix includes solving a minimization problem that includes determining a gradient of a convex loss function. In some embodiments, determining the precoder matrix includes decomposing the minimization problem into a number of subproblems, each subproblem involving a gradient of a convex loss function of a local precoding matrix. In some embodiments, the minimization problem includes comparing a function of the determined precoder matrix to an offline fixed precoding strategy. In some embodiments, the virtual precoder matrices received from the plurality of service providers are each based at least in part on a condition that each service provider is allowed to use all of a plurality of available antennas and wireless spectrum resources. In some embodiments, the precoding deviation from virtualization demand is determined, via the processing circuitry10, at least in part by:
ft(V)∥HtV−Dt∥F2
where V is a past precoding matrix, Htis the channel state and Dtis the virtualization demand.

ABBREVIATIONS

5G: Fifth Generation

CDI: Channel Distribution Information

CSI: Channel State Information

C-RAN: Cloud Radio Networks

InP: Infrastructure Provider

MIMO: Multiple Input Multiple Output

OCO: Online Convex Optimization

OFDM: Orthogonal Frequency Division Multiplexing

SP: Service Provider

WNV: Wireless Network Virtualization

The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. It is to be understood that the functions/acts noted in the blocks may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Although some of the diagrams include arrows on communication paths to show a primary direction of communication, it is to be understood that communication may occur in the opposite direction to the depicted arrows. Computer program code for carrying out operations of the concepts described herein may be written in an object oriented programming language such as Java® or C++. However, the computer program code for carrying out operations of the disclosure may also be written in conventional procedural programming languages, such as the “C” programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer. In the latter scenario, the remote computer may be connected to the user's computer through a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).