Miracle-dz/UniFAR-FTUs huggingface dataset:
Outage Probability and Average Rate Analysis of User-Centric Ultra-Dense Networks

User-centric ultra-dense network (UUDN) is a promising research direction for 5G, which organizes a dynamic transmission point group (TPG) consisting of potential transmission points (TPs) for each user. This paper is the first to give a general result of UUDN performance to characterize the gain obtained from the increased TP diversity within TPG, without considering cell coordination. Specifically, the outage probability and average achievable rate under general assumptions are analyzed and simple closed-form expressions for Rayleigh fading are derived, which gives a theoretical performance bound as the TPG's member is adjusted adaptively with the change of the instantaneous channel conditions. The numerical results show that the outage performance can be improved by 75% with low signal-to-interference-plus-noise ratio (SINR) threshold and the average achievable rate can be improved by 16% in comparison with ultra-dense network (UDN). In addition, the optimal TPG radius can be decided according to the requirements of users, which can provide guidance for TPG forming/reforming in UUDN.
Downlink NOMA Without SIC for Fast Fading Channels: Lattice Partitions with Algebraic Rotations

The problem of downlink non-orthogonal multiple access (NOMA) scheme over fast fading channels is studied. A new class of downlink NOMA scheme is proposed, where each user's signals are encoded to a constellation corresponding to the same algebraic lattices from number fields and the transmitter sends the superposition of users' signals. The minimum product distance achieved by the proposed scheme with an arbitrary power allocation factor is investigated and its upper bounds are derived. Within this class, a family of NOMA schemes based on lattice partitions of the underlying ideal lattice is identified, whose minimum product distances can be easily controlled. Numerical results show that the scheme based on lattice partitions always results in the largest possible minimum product distance among the proposed class. Simulation results further indicate that the proposed scheme significantly outperforms the conventional NOMA scheme and the current state-of-the-art.
Hybrid Joint Transmission and Coordinated Beamforming in Millimeter-Wave Cellular Networks

The downlink performance of a millimeter-wave cellular network comprising multiple-antenna-remote radio heads (RRHs) that service users under a hybrid Joint Transmission and Coordinated Beamforming (JT-CB) scheme is analyzed. Using the tools of stochastic geometry, the signal-to-interference-plus noise ratio (SINR) distribution at a typical user is derived and analytical expressions are provided for the coverage probability and network area spectral efficiency (ASE) under the proposed scheme. In addition, the average rate gain under the proposed scheme is characterized as a function of the network load, file request and caching distributions, and the spatial distribution of the RRHs. Numerical results reveal up to 68% coverage probability gain under the hybrid JT-CB scheme, compared to a CB-only scheme in the low coverage regime (>10 dB SINR threshold), while a CB-only scheme outperforms the hybrid JT-CB scheme by up to 5% in the high coverage regime (<10 dB SINR threshold). Moreover, the hybrid scheme is shown to outperform a JT-only scheme in all coverage regimes, attaining 52% at 30 dB SINR threshold. In addition, up to 25%, 35%, and 120% gains in ASE are shown to be achieved, compared to the JT-only, CB-only, and non-coordinating schemes, respectively.
Absolute Value Layered ACO-OFDM for Intensity-Modulated Optical Wireless Channels

Both enhanced unipolar orthogonal frequency division multiplexing (eU-OFDM) and layered asymmetrically clipped optical OFDM (LACO-OFDM) are among the most spectrally efficient modulation techniques for intensity modulated links that layer multiple non-negative signals. In this paper, we propose absolute value layered asymmetrically clipped optical OFDM (ALACO-OFDM) which further improves the spectral efficiency while using a smaller number of layers and no direct current (DC) bias. In ALACO-OFDM, asymmetrically clipped optical OFDM (ACO-OFDM) signals are sent at the first L layers and absolute value optical OFDM (AVO-OFDM) is used for the remaining subcarriers and transmitted simultaneously. Analysis indicates that ALACO-OFDM achieves higher spectral efficiency and smaller peak-to-average power ratio (PAPR) compared to eU-OFDM, LACO-OFDM and asymmetrically clipped absolute value optical OFDM (AAO-OFDM). Monte Carlo simulation results also indicate that ALACO-OFDM achieves significant bit error rate (BER) performance gains compared to its counterparts at the same spectral efficiency.
Spectral Efficiency Maximization of Multiuser Massive MIMO with a Finite Dimensional Channel

In this paper, we develop a closed-form lower bound for the achievable uplink rate of users in a single-cell massive multiple-input multiple-output (MIMO) system using zero-forcing (ZF) receiver. The base station (BS) is equipped with a large number of uniformly and linearly spaced antennas and serves single-antenna users. It estimates channel state information (CSI) using uplink pilot symbols. The channel model is also supposed to have a finite dimension. We then define the spectral efficiency as the sum of derived uplink rates. Through power allocation between data and pilot symbols, we further optimize this approximate expression of spectral efficiency. Simulation results are presented to evaluate the approximation of spectral efficiency and to show the advantage of our power allocation scheme in comparison to equal power allocation. The results also demonstrate that in low signal-to-noise ratios, more power should be allocated to the pilot symbols.
Achieving Fairness in IEEE 802.11ah Networks for IoT Applications with Different Requirements

The IEEE 802.11ah standard can provide cost-effective Internet access to a large number of devices in newly evolving Internet-of-Things (IoT) and machine-to-machine (M2M) networks. To handle high collision probability caused by a large number of devices, it adopts a group-based protocol at the MAC layer and divides nodes (or sensors) into a number of groups. The formed groups may not be uniform in terms of data rate requirements, since each group is a combination of sensors with different traffic characteristics. To achieve fair resource utilization across the groups which in turn maximizes the channel utilization, this paper formulates fair grouping in IEEE 802.11ah networks as an optimization problem, and we develop a heuristic method to solve the problem in real-time. In addition, to ensure fair channel utilization by the nodes in each group, a contention window selection and adjustment method is proposed. Results from extensive simulations conducted in a dense IoT network show that the proposed fairness model achieves a superior performance than the existing methods in terms of throughput, packet delay, energy efficiency, and fairness.
Joint Computation Offloading and Resource Allocation in D2D Enabled MEC Networks

The mobile edge computing (MEC) and device-to-device (D2D) communications take advantage of the proximity for supporting high-speed mobile computing and high-rate data communications, respectively. In this paper, we integrate both techniques to further improve the computation capacity of the cellular networks by proposing the D2D-MEC technique. We aim to maximize the number of supported devices and formulate a mixed integer non-linear problem. To solve it, we decouple it into two subproblems and prove that the optimal solutions to the two subproblems compose the optimal solution to the original problem. The first one minimizes the required edge computation resource for a given D2D pair while the second one maximizes the number of supported devices via optimal D2D pairing. Then, by solving two subproblems, the optimal algorithm is developed and some insightful results are also highlighted. Finally, numerical results show that combining D2D communications with MEC can significantly enhance the computation capacity of the system.
Slice Management in Radio Access Network via Iterative Adaptation

In the context of 5G systems, the emergence of various use cases with diverse requirements has attracted great attention to network slicing. In a single physical network, several instances of logical end-to-end networks, i.e. slices, will be instantiated to fulfill these requirements. To this end, slices should share the resources of the physical network, which consist of Core Network (CN) and Radio Access Network (RAN) resources. Herein, we focus on the Radio Resource Management (RRM) in the context of network slicing. To maximize the pooling gains, dynamic resource sharing is preferred over static sharing. However, dynamic resource sharing can lead to undesirable inter-slice influences, in particular, the contention on radio resources. In this article, we show that, although the radio resources are dynamically shared among the users of different slices, proper slice management can realize slice protection. This is achieved by adjusting the fraction of radio resources allocated to the different slices by the Packet Scheduler (PS) and by limiting the number of users admitted to the network via Admission Control (AC). We propose an iterative algorithm to optimize the parameters of PS and AC in order to ensure that the service level agreements are satisfied. Extensive system-level simulations have shown that a central entity that tunes these control parameters can greatly increase the network's performance.
Preamble Reservation Based Access for Grouped mMTC Devices with URLLC Requirements

Ultra-reliable low latency communication (URLLC) and massive machine type communications (mMTC) are two of the three major technological pillars in 5G. For medium access in mMTC scenarios, e.g., smart cities, a major bottleneck for achieving reliable access is channel congestion due to LTE-A based random access. Hence, priority-based access schemes are preferred in order to provide reliable and low latency access for mMTC devices in 5G networks. In this paper, we categorize the devices covered inside a cell into grouped and non-grouped sets and propose a preamble reservation based 2-step access scheme where grouped devices gain network access via a designated group leader using reserved preambles. Through analysis and simulations, we demonstrate that the proposed scheme enables ultra-reliable and low latency access for grouped devices while improving the performance of non-grouped devices through proper configuration of parameters.
Satellite Image Prediction Relying on GAN and LSTM Neural Networks

Satellite image is an important resource for weather forecast. It can indicate the evolution of weather systems and is beneficial in terms of guiding people to make accurate weather forecasting. However, the use of satellite images is encountered with the dilemma of such as small data volume and of poor real-time performance. Hence it is important to make accurate prediction for satellite images. The goal of satellite image prediction is to predict the next few images of the image sequence. Essentially, it is a a spatiotemporal sequence prediction problem, where the prediction of satellite images is difficult due to its large-scale observation area. In this paper, we propose a generative adversarial networks-long short-term memory (GAN-LSTM) model for the satellite image prediction by combining the generating ability of the GAN with the forecasting ability of the LSTM network. For evaluation, we conduct our experiments on the FY-2E satellite cloud maps. In addition, we use a score correct rate (CR) to measure the degree of similarity between predictions and ground truth. Experiment results show that the proposed GAN-LSTM network is capable of efficiently capturing the evolution rules of weather systems, which outperforms the traditional autoencoder-LSTM.
Performance Comparison of Transcoding and Bitrate-Aware Caching in Adaptive Video Streaming

Video traffic is growing in dominance in today's Internet, prompting new challenges in timely delivery of video content. As Dynamic Adaptive Streaming (DAS) is becoming the de facto paradigm for video delivery, there is growing evidence on how caching improves users' Quality of Experience (QoE) in DAS. However, there is no consensus on how to maximize the utilization of in-network caching resources. Specifically, there are conflicting proposals on the impact of caching based on its distance (in hops) from the network edge. That is, contrasting ubiquitous network-wide caching to edge-caching. Supporters of the ubiquitous caching paradigm propose bitrate-aware caching schemes for optimizing video streaming, while counter-proposals suggest that edge-caching only the highest bitrate with online transcoding, may offer superior performance to ubiquitous caching. In this paper, we answer a contentious question: Can transcoding at the edge outperform bitrate-aware ubiquitous caching for DAS? We devise an extensive simulation environment using NS-3 to contrast both paradigms, experimenting with different bandwidth fluctuation patterns, under the FESTIVE user-based bitrate adaptation protocol. Caching performance was evaluated under five established QoE metrics, gauging delivered video quality, playback freezing and bitrate oscillation. We further assume zero processing delay for online transcoding at the network edge, to contrast to an upper bound performance from the edge-caching paradigm. Our experiments demonstrate that neither transcoding nor bitrate-aware caching offer a silver bullet for all cases. We present our insights on networking scenarios where each model would dominate in performance, and present our concluding remarks on their development.
Uplink Fractional Power Control for Cell-Free Wireless Networks

This paper proposes a power control policy for the uplink of cell-free wireless networks. Such policy, which generalizes the fractional power control used extensively in cellular networks, relies only on large-scale quantities, is fully distributed, and features a single control parameter. By adjusting this parameter, the SIR distribution experienced by the users can be compressed or expanded, effecting a tradeoff between average performance and fairness.
Fast Reconfigurable Antenna State Selection with Hierarchical Thompson Sampling

Reconfigurable antennas (RAs) arised as a promising antenna technology which can adapt to channel variations and enhance wireless link capacity. To fully take advantage of RA's benefits, optimal antenna states need to be selected on-the-fly. However the channel statistics are unknown a priori. Multi-armed bandit (MAB) algorithms have been adopted to cope with this challenge, however the main drawback of existing approaches is that their regret scales linearly with the number of candidate antenna states and converges slowly with time. In this paper, we propose a novel Hierarchical Thompson Sampling (HTS) algorithm. HTS divides the arms into multiple clusters, first uses TS to sample a cluster and then samples an individual arm inside that cluster. Then we apply HTS to anntena state selection, and propose a K-means based antenna state clustering strategy by exploiting antenna radiation pattern correlation. Simulation results using a real-world RA's radiation patterns show that our HTS algorithm can substantially improve the convergence rate and enjoys much lower expected regret than existing schemes, especially for a large number of antenna states.
Secrecy Outage Performance of a Unified Non-Orthogonal Multiple Access Framework

This paper investigates the impact of physical layer secrecy on the performance of a unified non-orthogonal multiple access (NOMA) framework. The spatial locations of legitimate users (LUs) and eavesdroppers are governed by invoking stochastic geometry. To characterize the secrecy performance of the unified framework, new exact and asymptotic expressions of secrecy outage probabilities are derived, in which both imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC) schemes are taken into account. The secrecy diversity orders achieved by a pair of LUs (the n-th user and m-th user) for CD/PD-NOMA are obtained. Analytical results make known that the secrecy diversity orders of LUs with ipSIC/pSIC for CD/PD-NOMA are equal to zero/K and zero/one, respectively. Finally, numerical results are present to confirm the accuracy of the developed analytical results.
Quantization with Combined Codebook for Hybrid Array using Two-Phase-Shifter Structure

We propose a novel joint quantization scheme for hybrid antenna array systems using the two-phase-shifter (2-PS) structure, where two phase shifters are combined to represent one beamforming weight. Conventional quantization using a single phase shifter for each beamforming weight cannot represent the magnitude. We propose a new codebook design that combines the two codebooks of the two phase shifters in the recently proposed 2-PS structure. We also study the scaling problem of the beamforming vector and propose a low-complexity searching algorithm for finding a near-optimal scalar based on element-wise quantization. The mean squared quantization error and signal-to-noise ratio (SNR) degradation are derived analytically. Simulation results validate the accuracy of the analytical results and the effectiveness of the proposed quantization methods.
Low Complexity Two-Stage Decoders for BAWGN

The object of the paper is the design of two-stage co-decoders (codecs) with one hard decoded stage to reduce the decoder complexity. It will be shown that properly designed schemes with that structure can yield performance close to the information-theoretical limits while decreasing the decoder complexity. We focus the attention on the design of schemes for the binary input AWGN channel (BAWGN) in a large SNR range. The structure of the designed codec includes two stage encoders and one inner “channel polarizing code”, whose aim is to turn an AWGN channel into a worse and a better channel. The rationale of this operation is that both “very bad channels” and “very good channels” admit simpler hard-input decoders. After showing that for the positive (high) SNR and negative (low) SNR regime the optimal codec has a different optimal structure, the focus is concentrated on the case of low SNR, where binary modulation is indeed the best one. For low SNR regimes, the best solution consists of a very low rate (possibly zero) encoder at first stage coupled with a hard-input decoder, followed by a capacity-achieving encoder associated to a soft-input iterative decoder. The rate splitting between the two stages allows to construct flexible and low complexity encoding schemes for very large SNR ranges.
Non Contiguous Frequency Hopping Aided OFDM-DCSK Design Without Requiring CSI

In this paper, we present a frequency hopping (FH) orthogonal frequency division multiplexing-aided differential chaos shift keying (OFDM-DCSK) modulation system for cognitive radio (CR) systems operating over non contiguous bands. Our aim is to provide reliable transmissions for CR transceivers which may hardly get the exact and perfect channel state information (CSI). In our design, we utilize the natural high security of chaotic sequences and the frequency diversity brought by the FH. The information bits are firstly modulated by chaotic chips, then both reference chips and chaotic modulated symbols are carried out FH operations and hopped non-repetitively. After performing the inverse fast Fourier transform (IFFT) over available non-contiguous spectrum bands, the resultant OFDM symbols are transmitted and the non-repetitive reference chips are respectively transmitted over different subcarriers. Subsequently, the receiver would retrieve the information using the received reference chaotic chips which naturally embed the channel frequency response (CFR) of all subcarriers. The bit error rate (BER) expressions are derived and simulations results verify the effectiveness of our derivations, which demonstrate that the presented system can achieve better BER than counterpart systems, especially when the CSI is not exact or unknown.
Path-Based Optimization of NFV-Resource Allocation in SDN Networks

Network Function Virtualization (NFV) and Software-defined Networking (SDN) enable flexible and scalable placement of Virtual Network Functions (VNFs). Existing approaches for optimal VNF selection, placement, and traffic routing use link-based approaches. In this paper, we introduce a path-based mathematical optimization model for the NFV Resource Allocation (NFV-RA-PB) problem. The output is an optimal routing based on paths in conjunction with selection and placement of Virtual Network Functions (VNFs). As the problem is known to be hard and not efficiently to solve, we further introduce a more efficient version of the problem (NFV-RA-KSP) that uses k-shortest paths as input. Our evaluation results indicate that our heuristic is able to reduce the solution space of the optimization problem significantly, and eliminates the exponential complexity of NFV-RA-PB. It allows the computation of near-optimal assignments in reasonable time.
FlowStalker: Comprehensive Traffic Flow Monitoring on the Data Plane using P4

Programmability has been extensively investigated to enable a more flexible operation of computer networks, and in this context the P4 language was designed entirely for programming the data plane. With programmable data planes comes the possibility of revisiting many inefficient approaches to networking problems, for example how we use the data plane to create an understanding of the network state. Traditional switches expose only a bare minimum of what happens in their forwarding plane, forcing us to resort to inefficient methods, such as snapshotting, to acquire the state of the network. We advocate that by combining the programmable hardware on switches with every switch specific view over its traffic, we are able to accomplish the same tasks in a more efficient and comprehensive manner. In this paper we present an efficient monitoring mechanism using programmable data planes. Our mechanism capitalizes on data plane programmability to perform tasks that are usually performed solely by the control plane (e.g. traffic monitoring and information gathering). Firstly, we present a monitoring system based on a two-phase monitoring scheme that runs directly on the data plane. Secondly, we introduce a flexible method for network data gathering, enabling “control-plane-free” consolidation of data from switches. Finally, we show techniques for using both the monitor and the gathering system to create constant, snapshot-free analysis of network traffic.
Fair-Energy Trajectory Planning for Cooperative UAVs to Locate Multiple Targets

Due to the flexibility and affordability, multi-target positioning based on cooperation of Unmanned Aerial Vehicles (UAVs) becomes attractive in recent years. Trilateration is popular and easy to implement, but still faces challenges in multi-UAV scenario. First, multiple distance measurements from a single UAV on same targets will lead to large accumulated errors. Second, the time interval between successive distance measurements on the same target cannot be long due to the mobility of the target. Finally, UAVs have limited onboard energy which constrains the flight duration and the mission will fail when some UAVs reach the limitation. In this paper, to complete multi-target positioning mission, we aim at minimizing the maximum energy consumption among all UAVs, which can be decomposed into two subproblems after dividing all UAVs into groups of three. Then we propose a two-stage heuristic algorithm, in which we first use adjusted Genetic Algorithm (GA) to plan the trajectories of all groups with bounded maximum energy consumption and then we pursue to minimize the maximum energy consumption among UAVs in a group. Compared to two other algorithms, extensive simulations show that the proposed algorithm can reduce up to 24.9% and 11.8% in terms of maximum and average energy consumption, respectively.