Transmission schemes for device communications

A base station communicates with a plurality of user equipments (UEs) using a method. The method includes receiving, by the base station, a plurality of signals from a plurality of user equipments (UE) in communication with the base station. The method also includes using an iterative algorithm to estimate a matrix Λ of channel coefficients based on the received signals. The method further includes decoding, at the base station, the received signals using the estimated matrix Λ.

TECHNICAL FIELD

The present disclosure is generally directed to wireless network communications, and more particularly to transmission schemes, such as for use in future networks including 5G networks.

BACKGROUND

Recently, H. Nikpour and H. Baligh proposed Sparse Code Multiple Access for beyond 4G communications. The gist of this approach is to use sparse codebooks for communications to, and communications by, various users, and use the sparsity to recover the transmitted sequences from superposed codebooks.

In the future, there will be millions of devices that need to be connected. These devices will transmit with very low duty cycles and often will not transmit any information. Thus, at any time slot within a cell/sector, only a few of these devices will be active. Consider a system where a channel (e.g., a subcarrier or resource block) is dedicated to transmission by these devices to the base station.

A practical method for these devices to transmit efficiently to the base station and provide decoding without coordination is desired.

SUMMARY

This disclosure is directed to a new transmission scheme for device communications in a network.

In one example embodiment, a method for use in a wireless communication network is provided. The method includes receiving, by the base station, a plurality of signals from a plurality of user equipments (UE) in communication with the base station. The method also includes using an iterative algorithm to estimate a matrix Λ of channel coefficients based on the received signals. The method further includes decoding, at the base station, the received signals using the estimated matrix Λ.

In another example embodiment, a base station configured to operate in a wireless network is provided. The base station includes at least one memory and at least one processing unit. The at least one processing unit is configured to receive a plurality of signals from a plurality of user equipments (UEs) in communication with the base station; use an iterative algorithm to estimate a matrix Λ of channel coefficients based on the received signals; and decode the received signals using the estimated matrix Λ.

In another example embodiment, a wireless network system is provided that includes a plurality of user equipments (UEs) and a base station configured to communicate with the plurality of UEs. The base station is configured to receive a plurality of signals from a plurality of user equipments (UEs) in communication with the base station; use an iterative algorithm to estimate a matrix Λ of channel coefficients based on the received signals; and decode the received signals using the estimated matrix.

DETAILED DESCRIPTION

FIG. 1illustrates an example communication system100that may be used for implementing the devices and methods disclosed herein. In general, the system100enables multiple wireless users to transmit and receive data and other content. The system100may implement one or more channel access methods, such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), or single-carrier FDMA (SC-FDMA).

In this example, the communication system100includes user equipment (UE)110a-110c, radio access networks (RANs)120a-120b, a core network130, a public switched telephone network (PSTN)140, the Internet150, and other networks160. While certain numbers of these components or elements are shown inFIG. 1, any number of these components or elements may be included in the system100.

The UEs110a-110care configured to operate and/or communicate in the system100. For example, the UEs110a-110care configured to transmit and/or receive wireless signals or wired signals. Each UE110a-110crepresents any suitable end user device and may include such devices (or may be referred to) as a user equipment/device (UE), wireless transmit/receive unit (WTRU), mobile station, fixed or mobile subscriber unit, pager, cellular telephone, personal digital assistant (PDA), smartphone, laptop, computer, touchpad, wireless sensor, or consumer electronics device.

The RANs120a-120bhere include base stations170a-170b, respectively. Each base station170a-170bis configured to wirelessly interface with one or more of the UEs110a-110cto enable access to the core network130, the PSTN140, the Internet150, and/or the other networks160. For example, the base stations170a-170bmay include (or be) one or more of several well-known devices, such as a base transceiver station (BTS), a Node-B (NodeB), an evolved NodeB (eNodeB), a Home NodeB, a Home eNodeB, a site controller, an access point (AP), or a wireless router, or a server, router, switch, or other processing entity with a wired or wireless network.

In the embodiment shown inFIG. 1, the base station170aforms part of the RAN120a, which may include other base stations, elements, and/or devices. Also, the base station170bforms part of the RAN120b, which may include other base stations, elements, and/or devices. Each base station170a-170boperates to transmit and/or receive wireless signals within a particular geographic region or area, sometimes referred to as a “cell.” In some embodiments, multiple-input multiple-output (MIMO) technology may be employed having multiple transceivers for each cell.

The base stations170a-170bcommunicate with one or more of the UEs110a-110cover one or more air interfaces190using wireless communication links. The air interfaces190may utilize any suitable radio access technology. In some embodiments, the UEs110a-110cmay transmit signals to one or more of the base stations170a-170bwithout coordination between the UEs110a-110c.

It is contemplated that the system100may use multiple channel access functionality, including such schemes as described herein. In particular embodiments, the base stations and UEs implement LTE, LTE-A, and/or LTE-B. Of course, other multiple access schemes and wireless protocols may be utilized.

The RANs120a-120bare in communication with the core network130to provide the UEs110a-110cwith voice, data, application, Voice over Internet Protocol (VoIP), or other services. Understandably, the RANs120a-120band/or the core network130may be in direct or indirect communication with one or more other RANs (not shown). The core network130may also serve as a gateway access for other networks (such as PSTN140, Internet150, and other networks160). In addition, some or all of the UEs110a-110cmay include functionality for communicating with different wireless networks over different wireless links using different wireless technologies and/or protocols.

AlthoughFIG. 1illustrates one example of a communication system, various changes may be made toFIG. 1. For example, the communication system100could include any number of UEs, base stations, networks, or other components in any suitable configuration, and can further include the EPC illustrated in any of the figures herein.

FIGS. 2A and 2Billustrate example devices that may implement the methods and teachings according to this disclosure. In particular,FIG. 2Aillustrates an example UE110, andFIG. 2Billustrates an example base station170. These components could be used in the system100or in any other suitable system.

As shown inFIG. 2A, the UE110includes at least one processing unit200. The processing unit200implements various processing operations of the UE110. For example, the processing unit200could perform signal coding, data processing, power control, input/output processing, or any other functionality enabling the UE110to operate in the system100. The processing unit200also supports the methods and teachings described in more detail below. Each processing unit200includes any suitable processing or computing device configured to perform one or more operations. Each processing unit200could, for example, include a microprocessor, microcontroller, digital signal processor, field programmable gate array, or application specific integrated circuit.

The UE110also includes at least one transceiver202. The transceiver202is configured to modulate data or other content for transmission by at least one antenna204. The transceiver202is also configured to demodulate data or other content received by the at least one antenna204. Each transceiver202includes any suitable structure for generating signals for wireless transmission and/or processing signals received wirelessly. Each antenna204includes any suitable structure for transmitting and/or receiving wireless signals. One or multiple transceivers202could be used in the UE110, and one or multiple antennas204could be used in the UE110. Although shown as a single functional unit, a transceiver202could also be implemented using at least one transmitter and at least one separate receiver.

The UE110further includes one or more input/output devices206. The input/output devices206facilitate interaction with a user. Each input/output device206includes any suitable structure for providing information to or receiving information from a user, such as a speaker, microphone, keypad, keyboard, display, or touch screen.

In addition, the UE110includes at least one memory208. The memory208stores instructions and data used, generated, or collected by the UE110. For example, the memory208could store software or firmware instructions executed by the processing unit(s)200and data used to reduce or eliminate interference in incoming signals. Each memory208includes any suitable volatile and/or non-volatile storage and retrieval device(s). Any suitable type of memory may be used, such as random access memory (RAM), read only memory (ROM), hard disk, optical disc, subscriber identity module (SIM) card, memory stick, secure digital (SD) memory card, and the like.

As shown inFIG. 2B, the base station170includes at least one processing unit250, at least one transmitter252, at least one receiver254, one or more antennas256, and at least one memory258. The processing unit250implements various processing operations of the base station170, such as signal coding, data processing, power control, input/output processing, or any other functionality. The processing unit250can also support the methods and teachings described in more detail below. Each processing unit250includes any suitable processing or computing device configured to perform one or more operations. Each processing unit250could, for example, include a microprocessor, microcontroller, digital signal processor, field programmable gate array, or application specific integrated circuit.

Each transmitter252includes any suitable structure for generating signals for wireless transmission to one or more UEs or other devices. Each receiver254includes any suitable structure for processing signals received wirelessly from one or more UEs or other devices. Although shown as separate components, at least one transmitter252and at least one receiver254could be combined into a transceiver. Each antenna256includes any suitable structure for transmitting and/or receiving wireless signals. While a common antenna256is shown here as being coupled to both the transmitter252and the receiver254, one or more antennas256could be coupled to the transmitter(s)252, and one or more separate antennas256could be coupled to the receiver(s)254. Each memory258includes any suitable volatile and/or non-volatile storage and retrieval device(s).

Additional details regarding UEs110and base stations170are known to those of skill in the art. As such, these details are omitted here for clarity.

Embodiments of this disclosure provide a transmission scheme for device communications in an advanced wireless network.

Consider N low duty UEs110in a sector. Let the transmission channel from the i-th UE110to the base station170be αi. The channels are assumed to be static during each transmission. This is a reasonable assumption as most of the UEs110are fixed or quasi-static. Suppose k of the UEs110(e.g., users i1, i2, . . . , ik) are active at some time and are transmitting on the dedicated channel (resource block), where k<=N. It is not known which k users are transmitting but it is known that there are at most K of these active UEs110where K<<N.

One way to separate these UEs110is to allocate a (spreading) sequence:

pi=(p1,ip2,i⋮pm,i)
to user i. To transmit the symbol si, UE i can then send sipi. The received signal at the base station170is then given by:

r=∑j=1k⁢αij⁢sij⁢pij+n
where n is independent and identically distributed (i.i.d.) Gaussian noise.

One way to detect these UEs110is to choose m=N and select orthogonal signature sequences Pi, i=1, 2, . . . , N for all of the UEs110. Then, by correlating r with these signature sequences, noisy estimates of αijsijcan be obtained. However, this may not be bandwidth efficient as N can be quite large.

An alternative approach is to choose m<<N. In this way, the underlying signature sequences are not orthogonal. The fact that K<<N can be used to resolve these UEs110. This can be performed as in multiuser detection. However if K<<N, then the correlation of signature sequences may not be small. A matched filter to determine the active UEs110may not have good performance due to the interference by other UEs110and near-far problems. Multiuser detection of all UEs110may also be computationally complex.

Thus, an alternative approach is desired. To model the problem, it is assumed that all UE110transmissions are codewords of length M. If a codeword (c1,i, c2,i, . . . , cM,i) is to be transmitted, UE i transmits (c1,ipi, c2,ipi, . . . , cm,ipi). Any inactive UE can be thought as transmitting an all zero codeword.

Let Λ be an N×M matrix of channel coefficients whose j-th row Λ[j,·] is (c1,jαj,c2,jαj, . . . , cM,jαj), where (c1,j, c2,j, . . . , cM,j) is the codeword transmitted by UEj. Then, the matrix R, which represents the received signals, can be determined according to the following:
R=PΛ+n(1)
where P=[pi,j] is an m×N matrix whose i,j-th element is given by pi,j, and n is an m×M matrix modeled as an i.i.d. complex Gaussian noise distributed according to N(0, σ2I), where I denotes the identity matrix.

The vector n captures interference and noise at the base station receiver j during the transmission of coded signature sequences. In the above, the values in the j-th column of R represent the received signals at the base station170at times m*(j−1)+1, m*(j−1)+2, . . . m*j for j=1, 2, . . . , M.

What is known is that most of the rows of Λ are zeros as most UEs110are not active at each time. Thus, it is desired to determine the transmitting UEs110and compute their transmitted codewords using this information but with m<<N.

The above scenario is similar to compressed sensing. However, there are some differences between compressed sensing and this scenario. First, Λ is a matrix and not a vector. Second, in compressed sensing, a sparse vector must be recovered. However, in this scenario, the matrix Λ is row-sparse, i.e., many rows of the matrix Λ are zeros. Thus, this disclosure provides new algorithms for reconstructing Λ.

v2=∑j=1M⁢vi2
be the Euclidean norm and

v1=∑j=1M⁢vi
be respectively the L1norm of v. ∥v∥ is referred to as the length of v. ∥v∥o is defined as the L0norm of v, which is the number of nonzero elements of v.

It is known that the L1norm of the vector is a sparsifying regularizer. A regularizer is needed that is sparsifying for the rows of matrix Λ. One such sparsifier is given by

An important observation can be made about this regularizer. Since this regularizer is based on the L1norm, it sparsifies the elements of the above matrix. This forces the elements of Λ to go to zero. However, it is desired that some of the rows (vectors) of Λ go to zero. Thus, the L0norms of the rows of Λ are brought into consideration.

The regularized decoder minimizes the objective function

C⁡(Λ)=1σ2⁢R-P⁢⁢Λ22+λ⁢∑i,j⁢Λ⁡[i,j]
subject to modifications that forces some of the rows (vectors) of Λ to go to zero.

In the above scenario, metric λ>0 is a regularization parameter that can be fine-tuned. The minimization of C(Λ) is not generally easy. The metric C(Λ) can be written as the negative of a log-likelihood metric given by a product of independent Gaussians and Poisson-like distributions (plus some constant terms that are dropped from the maximization) parameterized by Λ, as shown in the following log-likelihood:

This means that to minimize C(Λ), it suffices to find the parameter Λ that maximizes the above log-likelihood (with some constants eliminated from the equation). A standard approach to maximizing the log-likelihood function is the expectation-maximization (EM) algorithm, where a hidden auxiliary variable is revealed.

Let s1be the maximal eigenvalue of PP*. Let n1and n2be independent Gaussian N×M and m×M matrices whose columns are distributed i.i.d. according to N(0,I) and N(0,σ2I−β2PP*), where β>0 is chosen such that

β2σ2<1s1
and P* denotes the Hermitian of P. In fact, after P is designed, s1can be computed as the maximum eigenvalue of PP* and set to

β2σ2≤12⁢s1.
Also, in this implementation, no knowledge of σ is needed, and only the value of

β2σ2⁢
is needed, which is set as just described.

The hidden auxiliary random variable is revealed in the following:
v=Λ+βn1.

Then, it is easy to see that statistically speaking
R=Pv+n2.

The expectation (E) and maximization (M) steps of the EM algorithm can now be computed. However, embodiments of this disclosure add a row sparsification step that is not present in a conventional EM algorithm. The iterative algorithm for computing Λ is given below.

Estimation Algorithm for Λ

The estimation algorithm for Λ starts by determining an initial estimate Λ1for the channel coefficients Λ (step 1). Next, a number of iterations Niter is selected (step 2). For each value l=1, 2, . . . , Niter, the following steps 3 through 5 are performed. For the E-Step (step 3), the following is computed:

For the M-Step (step 4), the following is computed:

sgn⁡(x)=xx⁢⁢for⁢⁢x≠0
and ½ for x=0, ones (m, M) is an m×M matrix whose i,j-th element is one, and x+=x for x>=0 and is zero otherwise.

In step 5, the row sparsification step is performed as follows: If any row of Λl+1has more than L zeros (where L is an integer predefined and optimized by the designer), then replace that row with the all-zero vector.

In the above, the value of

β2σ2
and λσ2can be fine-tuned. Additionally, depending on the initial guess of Λ1, faster or slower convergence of the optimum value of Λ can be achieved. A possible initial guess is given next assuming that columns of P (UE signatures) are normalized to all have length 1. Each column R[·,j] is projected on the subspace W spanned by columns of P for j=1, 2 . . . , M. Or stated in mathematical notation, the Projw(R[·,j]) is computed. The value of the initial guess is then computed for Λ[·,j] as P*Projw(R[·,j]) for j=1, 2 . . . , M. This produces an initial, rough estimate of Λ. Other initial guesses are also possible.

Estimation Algorithm for the Transmitted Codewords

Once the estimate {tilde over (Λ)} of Λ are computed in the above, then the j-th row of {tilde over (Λ)} is an estimate of the j-th row of Λ for j=1, 2, . . . , N. This means that (c1,jαj,c2,jαj, . . . , cM,jαj) is an estimate at hand for j=1, 2, . . . , N. For the nonzero rows, this estimate is fed to a standard decoder in the base station170for the code book assigned to UE j to decode the transmitted signals.

To construct the UE signatures, any matrix suitable for compressed sensing (such as Random Gaussian matrices) can be applied as a signature sequence.

FIG. 4illustrates a flowchart of an embodiment method400for decoding received signals. As shown, the method400begins at step410, where a base station receives a plurality of signals from a plurality of user equipments (UE) in communication with the base station. Thereafter, the method400proceeds to step420, where the base station uses an iterative algorithm to estimate a matrix Λ of channel coefficients based on the received signals. The iterative algorithm may be a modified expectation and maximization (EM) algorithm that includes row sparsification. Subsequently, the method400proceeds to step430, where the base station decodes the received signals using the estimated matrix Λ.

FIG. 5illustrates a flowchart of an embodiment method500for using an iterative algorithm to estimate a matrix of channel coefficients, as may be performed by a base station. As shown, the method500begins at step510, where the base station determines an initial estimate Λ1for a channel coefficient matrix Λ, for example based on a plurality of signals received from a plurality of user equipments (UE). Thereafter, the method500proceeds to step520, where for each iteration l (starting from 1), it is determined whether or not Λlis an optimum value. When Λlis determined to be optimum, the method500ends. On the other hand, when Λlis determined not to be optimum, the method500proceeds to step530, where the base station determines a received signal matrix Rlbased on the estimated Λl. At step540, the base station determines a matrix Λl+1based on the received signal matrix Rl. At step550, for any row in Λl+1that has more than L zeros, the base station replaces the row with an all-zero vector, where L is a predefined integer. The method500further proceeds to step520after step550.

Simulation Results

InFIG. 3, simulation results of communications using the disclosed embodiments are shown. As can be seen, the transmission scheme according to this disclosure performs as well as the no multiple access interference case, but is six times more bandwidth efficient.

The performance of this disclosure shown inFIG. 3is simulated for 60 UEs110on a dedicated channel. At any given time, four UEs are active, but it is not known at the base station170which four UEs110these are. Each UE110is assigned a randomized signature of length10created according to an i.i.d. Gaussian distribution and normalized to have length one. The transmission uses encoded binary phase shift keying (BPSK) with packets of length100. The channel is Rayleigh fading. The value SNR in the plot ofFIG. 3is the collected Energy per transmitted bit to noise power spectral density Eb/No. In the classical case, each UE110uses a signature of length60, and these signatures are assumed to be orthogonal to each other. Thus, there is no multiple access interference. The bandwidth efficiency of this case is ⅙ of the conventional case described above. In both cases, the channels of each UE110are assumed to be perfectly estimated at the receiver of base station170.

In this disclosure, a new method is provided for systems beyond 4G systems. In the disclosed embodiments, the receiver at the base station170does not need to know which transmitters of the UEs are active but the receiver knows an upper bound on the number of transmitting UEs. The disclosed embodiments use a spreading technique using short signatures and a modified Expectation Maximization (EM) algorithm to decode the transmitting UEs at the base station170. Simulation results provided demonstrate the performance of the scheme of this disclosure.