In large scale dynamic wireless networks, the amount of overhead caused by channel estimation (CE) is becoming one of the main performance bottlenecks. This is due to large number of users whose channels should be estimated and the user mobility. This work proposes a new hybrid channel estimation/prediction (CEP) scheme to reduce overhead in time-division duplex (TDD) wireless cell-free massive multiple-input-multiple-output (mMIMO) systems. The scheme proposes sending a pilot signal from each user only once in a given number (window) of coherence intervals (CIs). Then minimum mean-square error (MMSE) estimation is used to estimate the channel of this CI, while a deep neural network (DNN) is used to predict the channels of the remaining CIs in the window, exploiting the temporal correlation between the consecutive CIs. By doing so, CE overhead is reduced by at least 50 percent at the expense of negligible CE error for practical user mobility settings. Consequently, the proposed CEP scheme improves the spectral efficiency compared to the conventional MMSE CE approach, which is demonstrated numerically.

Abstract Particle swarm optimization (PSO) is one of the most well-regard metaheuristics with remarkable performance when solving diverse optimization problems. However, PSO faces two main problems that degrade its performance: slow convergence and local optima entrapment. In addition, the performance of this algorithm substantially degrades on high-dimensional problems. In the classical PSO, particles can move in each iteration with either slower or faster speed. This work proposes a novel idea called velocity pausing where particles in the proposed velocity pausing PSO (VPPSO) variant are supported by a third movement option that allows them to move with the same velocity as they did in the previous iteration. As a result, VPPSO has a higher potential to balance exploration and exploitation. To avoid the PSO premature convergence, VPPSO modifies the first term of the PSO velocity equation. In addition, the population of VPPSO is divided into two swarms to maintain diversity. The performance of VPPSO is validated on forty three benchmark functions and four real-world engineering problems. According to the Wilcoxon rank-sum and Friedman tests, VPPSO can significantly outperform seven prominent algorithms on most of the tested functions on both low- and high-dimensional cases. Due to its superior performance in solving complex high-dimensional problems, VPPSO can be applied to solve diverse real-world optimization problems. Moreover, the velocity pausing concept can be easily integrated with new or existing metaheuristic algorithms to enhance their performances. The Matlab code of VPPSO is available at: https://uk.mathworks.com/matlabcentral/fileexchange/119633-vppso .

In a cell-free wireless network, distributed access points (APs) jointly serve all user equipments (UEs) within their coverage area by using the same time/frequency resources. In this paper, we develop a novel downlink cell-free multiple-input multiple-output (MIMO) millimeter wave (mmWave) network architecture that enables all APs and UEs to dynamically self-partition into a set of independent cell-free subnetworks in a time-slot basis. For this, we propose several network partitioning algorithms based on deep reinforcement learning (DRL). Furthermore, to mitigate interference between different cell-free subnetworks, we develop a novel hybrid analog beamsteering-digital beamforming model that zero-forces interference among cell-free subnetworks and at the same time maximizes the instantaneous sum-rate of all UEs within each subnetwork. Specifically, the hybrid beamforming model is implemented by using a novel mixed DRL-convex optimization method in which analog beamsteering between APs and UEs is conducted based on DRL while digital beamforming is modeled and solved as a convex optimization problem. The DRL models for network clustering and hybrid beamsteering are combined into a single hierarchical DRL design that enables exchange of DRL agents’ experiences during both network training and operation. We also benchmark the performance of DRL models for clustering and beamsteering in terms of network performance, convergence rate, and computational complexity. Results show a significant rate enhancement due to the proposed hybrid beamforming scheme compared to its conventional all-digital counterpart. This performance enhancement becomes more significant as the number of network partitions increases. For DRL-based network clustering, the policy gradient (PG) algorithm offers the best possible performance in terms of stability and convergence rate while the state-action-reward-state-action (SARSA) algorithm suffers from significant variance, slower convergence, and slightly inferior performance than other algorithms. For DRL-based beamsteering, the soft actor-critic (SAC) algorithm with continuous action space shows the best performance. Also, online training of the agents with varying channel state information (CSI) is observed to increase the variance of the Q-values and decrease the convergence rate, with no significant effect on the average reward. The simulation codes are available at:https://github.com/yasser-aleryani/mmWaveCellFree.git

During the last few decades, wireless communication technologies and services have radically changed the way we live and interact at the personal, social, local and global levels. Such changes were mainly driven by the continuous emergence of innovative wireless communication services and products. These services and products represents a direct upshot of enduring research outcomes within the area. Nevertheless, the blessing of such innovation was accompanied by extremely high demands in forms of data traffic, per-user transmission rate, minimum transmission delay and in the number of wireless devices per unit area. Tackling these issues through cellular network densification was faced by many technical issues related to high interference levels, tedious user scheduling processes, and complicated network resource allocation algorithms. Trying to address these imperative technical issues in future wireless networks, this thesis develops several innovative enabling techniques for massive wireless multiple access. Specifically, we commence this work by introducing a new concept of partial spectrum overlapping among active users equipment (UEs). The proposed scheme represents a trade-off between fully orthogonal multiple access schemes (e.g. time division multiple access [TDMA], frequency division multiple access (FDMA) and orthogonal frequency division multiple access (OFDMA)) and that of non-orthogonal multiple access (NOMA). Second, we develop several innovative dynamic cell-free network architectures that support massive wireless connectivity through adaptive access points (APs)/base stations (BSs) coordination and/or cooperation. The proposed network models are then evaluated under different state-of-the-art enabling wireless techniques such as millimeter wave (mmWave) channel links and massive multiple-input multiple-output (mMIMO) systems. Furthermore, the performance of the proposed architectures is investigated through the derivation of several closed-form expressions of exact and/or asymptotic performance metrics (example, probability of outage, asymptotic outage, instantaneous rate and outage-capacity). Finally, for practical control and monitoring of the proposed access techniques and network models, we develop several low-complexity deep reinforcement learning (DRL)-based modeling frameworks that can efficiently learn the solution of several combinatorial optimization problems related to network partitioning (clustering) and uplink/downlink beamforming. This is achieved through innovative nested DRL designs that utilizes continuous and discrete deep neural networks (DNN) agents based on the nature of the problem. Several operating scenarios of the proposed techniques are evaluated through extensive Monte-Carlo simulations (Matlab and Python) with practical parameters and assumptions.

We propose a novel antenna clustering-based method for simultaneous wireless information and power transfer (SWIPT) in a multiple-input multiple-output (MIMO) full-duplex (FD) system. For a point-to-point communication set up, the proposed method enables a wireless device with multiple antennas to simultaneously transmit information and harvest energy using the same time-frequency resources. And the energy transmitting device with multiple antennas simultaneously receives information from the energy harvesting (EH) device. This is achieved by clustering the antennas into two MIMO subsystems: one for information transmission (IT) and another for EH. Furthermore, the self-interference (SI) signal at the EH device caused by the FD mode of operation is harvested by the device. For implementation-friendly antenna clustering and MIMO precoding, we propose two methods: (i) a sub-optimal method based on relaxation of objective function in a combinatorial optimization problem, and (ii) a hybrid deep reinforcement learning (DRL)-based method. For the proposed DRL solution, we design a hybrid discrete/continuous action agent that jointly clusters the MIMO antennas between EH and IT, and at the same time, find the best values for MIMO precoding matrices at both devices. This is achieved by using two interacting agent learning subsystems, namely, deep double Q-learning (DDQN), for antenna clustering and deep deterministic policy gradient (DDPG), for MIMO precoding. The effect of imperfect CSI is also studied and investigated. Finally, we study the performances of the two implementation methods and compare them with the conventional time switching-based simultaneous wireless information and power transfer (SWIPT) technique. Our findings show that the proposed MIMO clustering-based SWIPT method gives a significant improvement in spectral efficiency compared to the time switching-based SWIPT method. In particular, the DRL-based method provides the highest spectral efficiency. Besides, the numerical results show that, for the considered system set up, the number of antennas in each device should exceed three to mitigate self-interference to an acceptable level.

A novel generalized coordinated multi-point transmission (GCoMP)-enabled non-orthogonal multiple access (NOMA) scheme is proposed. In particular, distributed base stations (BSs) in a network coverage area cooperate on the downlink to serve a set of user equipments (UEs) using the same transmission frequency band. Furthermore, all UEs associated to a BS and using a particular frequency band forms a single NOMA cluster. The number of BSs serving a UE in a particular frequency band is referred to as theorder of clustering(or order of BS cooperation). To evaluate the proposed scheme, we derive a closed-form expression for the probability of outage for a UE with different orders of BS cooperation. To obtain important insights on the performance of the proposed system, approximate (asymptotic) expressions for the probability of outage and outage capacity are derived considering both perfect and imperfect channel state information (CSI) estimation. We observe that improved spectral efficiency with a large number of UEs per NOMA cluster can be achieved by increasing the clustering order (i.e., number of cooperating BSs per UE). Furthermore, an optimal transmission power allocation scheme that jointly allocates transmission power fractions from all cooperating BSs to all connected UEs is developed.

This paper studies the performance of two-way multiuser mixed radio frequency/free space optical (RF/FSO) relay networks with opportunistic user scheduling1and asymmetric fading channels. The considered system consists of multiple users communicating with a destination node through a decode-and forward (DF) relay in a two-way fashion. The links between the users and relay are assumed to be Rayleigh distributed RF channels, while the link between the relay and destination is Gamma-Gamma fading FSO channel. First, exact closed-form and asymptotic (high signal-to-noise ratio (SNR)) expressions are derived for the outage probability. Then, the asymptotic results are used to conduct a power optimization algorithm where exact expressions for the optimal transmission powers are provided. Additionally, performance comparisons between the considered two-way relaying (TWR) and one-way relaying (OWR) schemes under different network parameters are provided and discussed. The results show that the opportunistic user scheduling in TWR networks does not affect the network diversity order, but it enhances the system coding gain. Additionally, it is found that severe pointing error may result in a total service blockage. Additionally, the proposed power allocation scheme is found to enhance the network outage performance significantly compared to the scheme of equal power allocation.

In this paper, the performance of two-way relaying (TWR) multiuser mixed radio frequency/free space optical (RF/FSO) relay networks with opportunistic user scheduling and asymmetric channel fading is studied. First, closed-form expressions for the exact outage probability, asymptotic (high signal-to-noise ration (SNR)) outage probability, and average ergodic channel capacity are derived assuming heterodyne detection (HD) scheme. Additionally, impacts of several system parameters including number of users, pointing errors, and atmospheric turbulence conditions on the overall network performance are investigated. All the theoretical results are validated by Monte-Carlo simulations. The results show that the TWR scheme almost doubles the network ergodic capacity compared to that of one-way relaying (OWR) scheme with the same outage performance. Additionally, the overall diversity order of the network is shown to be affected not only by the number of users, but it is also a function of the pointing error and atmospheric turbulence conditions.

In this paper, we consider bidirectional decode-and-forward (DF) buffer-aided relay selection and transmission power allocation schemes for underlay cognitive radio (CR) relay networks. First, a low complexity delay-constrained bidirectional relaying protocol is proposed. The proposed protocol maximizes the single-hop normalized sum of the primary network (PN) and secondary network (SN) rates and controls the maximum packet delay caused by physical layer buffering at relays. Second, optimal transmission power expressions that maximize the single-hop normalized sum rate are derived for each possible transmission mode. Additionally, the impacts of several system parameters including maximum buffer size, interference threshold, maximum packet delay and number of relays on the network performance are also investigated. The results reveal that the proposed bidirectional relaying protocol and antenna transmission power allocation schemes introduce a satisfactory performance with much lower complexity compared to the optimal relay selection and power allocation schemes and provide an application dependent delay-controlling mechanism. It is also found that the network performance degrades as the delay constraint is more restricted until it matches the performance of conventional unbuffered relaying with delay constraints of three. Additionally, findings show that using buffer-aided relaying significantly enhances the SN performance while slightly weakens the performance of the PN.

In this paper, we consider decode-and-forward (DF) buffer-aided relay selection and transmission power allocation for underlay cognitive radio (CR) network that is equipped with multiple-input multiple-output (MIMO) antenna scheme. We propose a low complexity MIMO-based relay selection scheme that maximizes the single-hop normalized rate of the secondary network (SN). Also, we propose a sub-optimal antenna transmission power allocation scheme that maximizes the overall normalized sum rate of the primary network (PN) and the SN. We first derive optimal expressions for antenna transmission power of both the PN and the SN. The derived expressions are then used in an iterative algorithm to produce a near-optimum solution that maximizes the normalized sum rat per time slot. Simulation results are provided to evaluate the performance of the proposed MIMO-based relay selection and antenna transmission power allocation schemes and compare their performance with that of the optimal scenario. Impact of several system parameters including buffer maximum size, interference threshold and number of antennas on network performance are also investigated. Results reveal that the proposed sub-optimal relay selection and antenna transmission power allocation schemes introduce a satisfactory performance with much lower complexity compared to optimal relay selection and power allocation schemes. Findings also show that using buffer-aided relays significantly enhances the SN performance while slightly weakens the performance of the PN.