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One of the most promising technologies to be employed in beyond 5G networks is based on the concept of cell-free (CF) massive MIMO, in which a predetermined set of access points (APs) jointly cooperate in the data transmission and reception to/from the user equipment (UE). In order to efficiently manage radio resources, the CF central processing unit implement uplink power control policies. These policies aim to optimize a given network utility function. In this paper, we investigate the max-min fairness optimization problem, in which the spectral efficiency performance of the UE with the worst channel conditions is prioritized, taking into account a per-UE power constraint and assuming linear maximum ratio combining at APs. Existing solutions have typically relied on second-order cone programming with convex approximations, which exhibit high computational complexity and scalability issues. Therefore, meta-heuristics (MHs) are explored as alternative optimization schemes, capable of providing (near)-optimal solutions with reasonable computational effort. Three MH approaches with different operation principles are compared. Numerical results show that the differential evolution algorithm exhibits the best trade-off between solution quality and run time, being able to reach (near)-optimal solutions faster than the bisection approach while coping with the scalability issues of the geometric programming-based algorithm. [ABSTRACT FROM AUTHOR]

Massive machine-type communications (mMTC) is one of the three generic services for the fifth-generation (5G) wireless communications system. To utilize fully the high rate transmission feature of the 5G system to support massive MTC devices (MTCDs), we propose a group-based random access and data transmission scheme, where the data packets of MTCDs are first aggregated by the MTC gateways (MTCGs) and then forwarded to the base station. The access process of the MTC network is divided into two phases, namely the intra-group transmission phase and MTCG forwarding phase. The entire resources are also partitioned for the two phases. We employ the discrete-time nonhomogenous Markov model to characterize the joint queue-length evolution of multiple MTCGs, which cannot be analyzed directly due to the exponential complexity and time-nonhomogeneity. To facilitate the analysis, we approximately decompose the joint nonhomogenous queue-length evolution process into multiple independent nonhomogenous queue-length evolution processes with the same state transition probabilities. Then, we establish an equivalent single queue-length evolution based homogenous Markov chain by constructing a virtual queue and determine the corresponding stationary distribution by using the Gauss-Jordan elimination method. An optimization problem is formulated to maximize the average network throughput subject to the constraints on the resource partition for the two phases and the MTCG forwarding threshold. By developing a modified differential evolution algorithm, we provide an efficient solution to the formulated problem, which can be arbitrarily close to the optimal solution. Simulation results show that the proposed scheme can efficiently improve the network performance over the existing schemes. [ABSTRACT FROM AUTHOR]

The joint base matrix ${{{{B}}_{{\mathrm{J}}}}}$ of the joint source-channel coding (JSCC) system based on double protograph low-density parity-check (DP-LDPC) codes consists of four components, namely, the source code ${{{{B}}_{{\mathrm{s}}}}}$ , the channel code ${{{{B}}_{{\mathrm{c}}}}}$ , the type-1 connection edge ${{{{B}}_{{ \textit {L1}}}}}$ and the type-2 connection edge ${{{{B}}_{{ \textit {L2}}}}}$ , each having a non-negligible influence on the system performance. Different from the traditional component-specific design approach, we propose a joint design and optimization algorithm based on the idea of multi-objective differential evolution (MODE). Specifically, we consider the optimization of the DP-LDPC JSCC system through joint design of three components ${{{{B}}_{{\mathrm{s}}}}}$ , ${{{{B}}_{{\mathrm{c}}}}}$ , ${{{{B}}_{{ \textit {L1}}}}}$ and all four components ${{{{B}}_{{\mathrm{s}}}}}$ , ${{{{B}}_{{\mathrm{c}}}}}$ , ${{{\mathbf{B}}_{{ \textit {L1}}}}}$ , ${{{{B}}_{{ \textit {L2}}}}}$ , respectively. The proposed algorithm has low search complexity due to the reduction in size and element value of base matrices. The joint protograph extrinsic information transfer (JPEXIT) analyses and the simulation results demonstrate that the resulting JSCC system is free from a high error floor, requires fewer number of iterations for reaching the same bit error rate (BER) and achieves significant coding gains as compared to the state-of-the-art. Our DP-LDPC JSCC system is also shown to outperform its separation-based counterpart by a wide margin. [ABSTRACT FROM AUTHOR]

The practical NAND flash memory suffers from various non-stationary noises that are difficult to be predicted. For example, the data retention noise induced channel offset is unknown during the readback process, and hence severely affects the reliability of data recovery from the memory cell. In this paper, we first propose a novel recurrent neural network (RNN)-based detector to effectively detect the data stored in the multi-level-cell (MLC) flash memory without the prior knowledge of the channel. However, compared with the conventional threshold detector, the proposed RNN detector introduces much longer read latency and more power consumption. To tackle this problem, we further propose an RNN-aided (RNNA) dynamic threshold detector, whose detection thresholds can be derived based on the outputs of the RNN detector. We thus only need to activate the RNN detector periodically when the system is idle. Moreover, to enable soft-decision decoding of error-correction codes, we first show how to obtain more read thresholds based on the hard-decision read thresholds derived from the RNN detector. We then propose integer-based reliability mappings based on the designed read thresholds, which can generate the soft information of the channel. Finally, we propose to apply density evolution (DE) combined with the differential evolution algorithm to optimize the read thresholds for low-density parity-check (LDPC) coded flash memory channels. Computer simulation results demonstrate the effectiveness of our proposed RNNA dynamic read thresholds design, for both the uncoded and LDPC-coded flash memory channels, without any prior knowledge of the channel. [ABSTRACT FROM AUTHOR]

In research on spatially-coupled low-density parity-check (SC-LDPC) codes, rate-loss of SC-LDPC codes is one of the main issues to be addressed. One way to mitigate the rate-loss is to attach additional variable nodes with an irregular degree distribution, where the degree distribution is optimized with a constraint that the belief propagation (BP) threshold should not be degraded by attaching variable nodes. However, it is observed that the degree distribution obtained with the BP threshold constraint induces degradation of the finite-length performance. In order to address the problem, we propose new optimization methods to attach additional variable nodes while minimizing performance degradation. The proposed optimization methods are based on several design techniques including the scaling law, local threshold, expected graph evolution, differential evolution algorithms, the use of a protograph structure, and puncturing codewords. Using the optimized structure for additional variable nodes, the rate-loss of SC-LDPC codes can be reduced by more than 53% without sacrificing the finite-length performance. It is also shown that the rate-loss mitigation can be translated into a performance improvement if the proposed and the conventional SC-LDPC codes are compared at the same code rate. [ABSTRACT FROM AUTHOR]

Since multi-edge type low-density parity-check (MET-LDPC) codes were first proposed, the design of MET-LDPC codes has been extensively studied for various applications. However, the existing design rules assume that check node degrees are in the so-called concentrated form which enables one to conveniently find pairs of edge and node distributions satisfying the socket count equalities (SCEs). However, it in return makes the code design conducted in a limited search space, which may lead to a sub-optimal design. This paper proposes a novel design rule for MET-LDPC codes together with a scheme to efficiently sort out invalid distributions, i.e., ones not satisfying the SCEs, in the design process. The proposed design rule does not require check node degrees to be in the concentrated form and can find out optimized MET-LDPC codes without any restriction on the search space. Based on the proposed design rule, MET-LDPC codes are designed across a wide range of code rates on binary-erasure channel and binary-input additive-white Gaussian channel. The designed codes are compared with ones reported in the open literature, which confirms that MET-LDPC codes with the proposed design rule have better threshold performances regardless of channel and code rate even with sets of limited code parameters. [ABSTRACT FROM AUTHOR]

This paper considers full data exchange for massive uncoordinated multiway relay networks (mu-mRN) applying coded random access for flexible topology changes and capable of serving massive number of users. We aim to maximize the normalized throughput of the mu-mRN using the multiuser detection (MUD) technique with MUD capability of K > 1. Specifically, our goal is multiple times improvement, i.e., ten times improvement, from the maximum normalized throughput of the mu-mRN applying slotted ALOHA (SA). First, we present a network capacity bound of the mu-mRN with general K. Then, we optimize many degree distributions for the MUD-based mu-mRN. However, as K increases, the distributions are no longer optimal to achieve the network capacity bound. We propose doubly irregular coded SA (dir-CSA) to solve the problem. Asymptotically, the mu-mRN applying dir-CSA with K = 4 can achieve the ten times throughput improvement. However, for finite number of time slots, we propose to exploit side information (SI) from neighboring users to improve the normalized throughput of the mu-mRN. The results show that the mu-mRN applying dir-CSA with SI and having optimal physical encoding can achieve ten times throughput improvement, compared with the conventional SA scheme. [ABSTRACT FROM PUBLISHER]

In this paper, a new detector is proposed for an amplify-and-forward (AF) relaying system. The detector is designed to minimize the symbol-error-rate (SER) of the system. The SER surface is non-linear and may have multiple minimas, therefore, designing an SER detector for cooperative communications becomes an optimization problem. Evolutionary based algorithms have the capability to find the global minima, therefore, evolutionary algorithms such as particle swarm optimization (PSO) and differential evolution (DE) are exploited to solve this optimization problem. The performance of proposed detectors is compared with the conventional detectors such as maximum likelihood (ML) and minimum mean square error (MMSE) detector. In the simulation results, it can be observed that the SER performance of the proposed detectors is less than 2 dB away from the ML detector. Significant improvement in SER performance is also observed when comparing with the MMSE detector. The computational complexity of the proposed detector is much less than the ML and MMSE algorithms. Moreover, in contrast to ML and MMSE detectors, the computational complexity of the proposed detectors increases linearly with respect to the number of relays. [ABSTRACT FROM PUBLISHER]

We consider a noisy Slepian-Wolf problem where two correlated sources are separately encoded (using codes of fixed rate) and transmitted over two independent binary memoryless symmetric channels. The capacity of each channel is characterized by a single parameter that is not known at the transmitter. System performance is evaluated by computing the set of channel parameters for which the system can successfully decode. This set is called the achievable channel parameter region (ACPR). The goal is to design systems whose ACPRs are as large as possible. The main result is the design of irregular low-density parity-check (LDPC) ensembles whose ACPRs are significantly larger than previous designs. Some previous attempts to achieve large ACPRs with LDPC codes failed because systematic codes were used. In this work, we start with systematic encoders but puncture all the systematic bits before transmission. We also show that additional gains are possible using a staggered structure which enables codes optimized for single-user channels to perform well under symmetric channel conditions. The main analysis tool is a generic density-evolution framework for the analysis of joint iterative decoding for this problem. [ABSTRACT FROM PUBLISHER]

A differential evolution (DE) algorithm aided iterative channel estimation and turbo multi-user detection (MUD) scheme is proposed for multi-user multi-input multiple-output aided orthogonal frequency-division multiplexing / space-division multiple-access (OFDM/SDMA) systems. The proposed scheme iteratively exchanges the estimated channel information and the detected data between the channel estimator and MUD employing a turbo technique, which gradually improves the accuracy of the channel estimation and the MUD, especially for the first iteration. Quadrature amplitude modulation (QAM) is employed in most wireless standards by virtue of providing a high throughput. However, the optimal maximum likelihood (ML)-MUD becomes extremely complex for employment in QAM-aided multi-user systems. Hence, two different DE aided MUD schemes, the DE aided minimum symbol error rate (MSER)-MUD as well as the discrete DE aided ML-MUD, were developed, and their achievable performance versus complexity was characterized. The simulation results demonstrate that the proposed DE aided channel estimator is capable of approaching the Cramer-Rao lower bound with just two or three iterations. The ultimate bit error rate lower-bound of the single-user additive white Gaussian noise scenario has been approached in the range of Eb / N0 ≥ 10 dB and Eb / N0 ≥ 6 dB for the DE aided MSER-MUD and the discrete DE aided ML-MUD, respectively. [ABSTRACT FROM PUBLISHER]