This paper investigates UAV-enabled disaster response networks where a UAV acts as a relay between two disconnected networks. Two scenarios are compared: (i) a conventional case where the UAV relies only on its initial battery and (ii) a proposed framework where the UAV employs Simultaneous Wireless Information and Power Transfer (SWIPT) to harvest energy while relaying data. We formulate uplink/downlink communication rates, UAV energy consumption, and flight time, and develop an optimization problem to minimize mission duration while ensuring service to all users. Simulation results show that the SWIPT-based system significantly extends UAV endurance and increases the number of served users compared to the conventional approach. These results highlight SWIPT as a promising tool for reliable disaster communications.

We explore the potential of a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) to enhance the performance of wireless surveillance systems. The STAR-RIS is deployed between a full-duplex (FD) multi-antenna legitimate eavesdropper (E) and a suspicious communication pair. It reflects the suspicious signal towards the suspicious receiver (SR), while simultaneously transmitting the same signal to E for interception purposes. Additionally, it enables the forwarding of a jamming signal from E to SR, which is located on the back side of the STAR-RIS. To enhance the eavesdropping non-outage probability, we formulate a non-convex joint optimization problem to design the beamforming vectors at E and reflection/transmission phase shift matrices at the STAR-RIS. We adopt the block coordinate descent (BCD) algorithm and propose an approach, mainly based on semi-definite relaxation (SDR) and successive convex approximation (SCA), for solving the resulting decoupled sub-problems. Finally, we compare the performance of the proposed design against low-complexity zero-forcing (ZF)-based beamforming designs.

With the excessive growth in mobile users' traffic, radio resource management (RRM) techniques should undergo revolutionary changes to be competent enough to meet the ever-increasing users' demands. Virtualized wireless network (VWN) has emerged as a satisfactory solution in the fifth-generation (5G) cellular networks ensuring the required quality-of-service (QoS) of distinct slices. Yet, it seems that tackling RRM problems in VWNs using conventional optimization is not practical for real-time applications. In this paper, driven by the advancements of machine learning, we consider the throughput maximization problem in a cloud radio access network (C-RAN) assisted softly virtualized wireless network supporting different types of services and solve it with a deep Q-learning (DQL) algorithm. The performance of the proposed policy is thoroughly evaluated via simulation results with respect to the isolation rate, penalty value as well as the discount factor. It is shown that our proposed policy achieves a higher sum rate compared to the existing baseline namely a greedy search-based power allocation strategy.

In large-scale multiple-input multiple-output (LS-MIMO) systems, by exploiting hundreds of antennas at the base station, spectral efficiency, power efficiency, and link reliability can be enhanced significantly. However, by increasing the number of antennas, the computational complexity of the detectors makes the hardware implementation intractable, and therefore, LS-MIMO systems require sub-optimal low complexity detection algorithms. In this paper, two novel approaches for improving factor graphbased belief propagation with Gaussian approximation of interference (FG-BP-GAI) algorithm is proposed to reduce the computational complexity of the belief propagation (BP) based receiver without bit error rate (BER) degradation. More specifically, two novel techniques, namely odd Taylor series and odd least square, are proposed to approximate the a posteriori probability in the FG-BP-GAI policy with few polynomial terms of low degree. In the simulation results, the performance of our proposed algorithms are assessed and it is shown that our proposed improved FGBP-GAI policies can achieve lower computational complexity compared with the other approaches in the literature like MRF-BP algorithm without BER degradation.

The authors consider downlink transmission in a multi‐cell massive multiple‐input and multiple‐output (MIMO) system and focus on decreasing the feedback overhead caused by cell cooperation in constant envelope precoding (CEP). In the literature, the single‐cell case for CEP with perfect channel state information (CSI) has been studied. Here, considering full cooperation among the cells, they develop CEP for the multi‐cell case. They devise a low overhead centralised construction for CEP which employs limited cooperation among the cells, providing higher system throughput. To achieve the minimum feedback overhead, a distributed realisation of CEP is proposed where each cell locally performs CEP. Furthermore, a new optimisation problem is solved to compensate for the effects of pilot contamination. In addition, to reduce the computational complexity, a relaxed iterative form is presented for the limited cooperation and the distributed scenarios. Numerical results show that in the proposed structures, despite a tremendous decrease in the system overhead, the performance confronts merely an insignificant degradation, <10%, compared to the full‐cooperation case. Moreover, in the presence of imperfect CSI, the performance of the distributed structure whose imperfect CSI is compensated for, approaches the performance of this structure in the perfect CSI scenario.

Distributed‐compressed sensing (DCS)‐based channel estimation of multiple‐input multiple‐output (MIMO) orthogonal frequency division multiplexing for relay communication is considered in this study. Specifically, the pilot allocation is addressed to optimise the channel estimation performance. All the existing works on DCS‐based channel estimation pilot placement discussed on the mean square error (MSE) of the estimation probabilistically based on the mutual coherence. On the contrary, the authors try to address the MSE of the estimation directly and optimise the MSE directly and design a pilot pattern to optimise the performance of the estimation. By taking into account the optimisation approach, two combinatorial stochastic algorithms have been presented. These two algorithms utilise cross entropy approach in sequential and parallel form. Simulation results represent that the DCS‐based MIMO relay channel estimation using optimised pilot placements will increase the performance from 3 to 12 dB as compared with the conventional least squares (LS) method. Furthermore, parallelism has demonstrated the performance gain. Moreover, the DCS‐based MIMO relay channel estimation shows improvement in spectrum efficiency under the same bit error rate performance over the traditional LS‐based channel estimation approach, respectively.

In this paper, a novel stochastic robust method is proposed for multiuser distributed relay beamforming systems. All of the channels are subject to complex Gaussian uncertainty. The robust problem is formulated to minimize the total power transmission of the relays, which are subject to the outage probabilistic quality of service (QoS) constraint at each receiver. Based on a new quadratic type of confidence interval inequality for the quadratic form of the Gaussian random variable, the original outage-based problem is reformulated to a nonconvex semidefinite programming (SDP) with a rank constraint. By using the semidefinite relaxation technique, this issue turns into a convex optimization problem, consisting of mixed semidefinite and conic quadratic constraints. A customized stochastic programming technique is proposed to obtain a solution to the original problem. Three other competitive approaches are numerically compared to the proposed method, which are: 1) the robust stochastic method which is based on the central limit theorem (CLT) approximation; 2) the Berenstain inequality approach; and 3) the worst-case robust approach, which is adapted to our stochastic scenario. The first and second methods do not meet the outage constraints whereas the third method qualifies the constraints but it is outperformed by our proposed method in terms of its transmission power.

A cooperative spectrum‐sensing problem for a cognitive radio (CR) system is investigated, where CR users collaborate to sense and track the primary users’ (PUs) activities in frequency‐selective fading channels. To sense PUs activities, channel gain estimation is performed by CRs through space–time extended Kalman filtering (STEKF). The STEKF method captures the channel gain from any point in space to each CR at each frame for a specific range of frequencies. The proposed channel gain tracking enables CRs to detect the transmit power, location and the number of active subcarriers of each PU via a time spatial weighted non‐negative Lasso (TSWNL) algorithm. The TSWNL exploits the sparsity of the PUs activities in a geographical area to track PUs activities in frequency‐selective fading channels. Numerical results indicate that the proposed spectrum sensing based on STEKF significantly improves the performance of CRs in tracking of PUs activities.

Two closed-form expressions for the end-to-end cumulative distribution function (CDF) and outage probability of two-way interference-limited amplify-and-forward (AF) relaying were recently presented in Equations (19) and (24) in the above titled paper (ibid., vol. 61, no. 8 pp. 3156-3169, Aug. 2013). However, the expressions contain a notational error. A correction is presented here.

In this paper, we present the performance of selective combining decode-and-forward relay networks in independent and non-identically distributed Nakagami-n and Nakagami-q fading channels by using the best-worse and the decoding-set approaches. The outage probability, moment generation function, symbol error probability and average channel capacity are derived in closed-form using the signal to noise ratio SNR statistical characteristics. After that, we analyze the outage probability at high SNRs, and then, we optimize it. Beside the optimum method, we have proposed a sub-optimum adaptive method. Also, we derive the outage probability for the selection-combining case with the direct link between the source and the destination. Finally, for comparison with analytical formulas, we perform some Monte-Carlo simulations. Copyright © 2012 John Wiley & Sons, Ltd.

We investigate the performance of two-way interference-limited amplify-and-forward relaying systems over independent but non-identically distributed (i.n.i.d.) Nakagami-m fading channels. A tight closed-form lower bound on the outage probability of the system is derived, along with a simplified expression in the asymptotically low outage regime. Some special cases of practical interest (e.g., no interference power and interference-limited case) are also examined. The theoretical and numerical results provide important physical insights into the implications of the model parameters on the system performance.

We analyze the performance of a dual-hop selection combining amplify-and-forward cooperative system over independent and identically distributed Nakagami-m fading channels, where multiple antennas are deployed in the receive side of relays and the destination. The outage probability, symbol error probability and average channel capacity are derived at arbitrary signal to noise ratios (SNRs). In order to obtain additional physical insights, we derive the above mentioned performance metrics in the high SNR regime; this enables us to parameterize the performance of the system in terms of diversity order and coding gain. Furthermore, some special cases of interest (e.g., Nakagami-0.5 and Rayleigh fading channels) are also studied. It is demonstrated that the analytical expressions, expressed via infinite series, match precisely with the Monte-Carlo simulations using only a small number of terms.

In this paper, we investigate the performance of two-way interference-limited amplify-and-forward relaying systems over independent, non-identically distributed Nakagami-m fading channels. Our analysis generalizes several previous results, since it accounts for interference affecting all network nodes. In particular, tight lower bounds on the end-to-end outage and symbol error probability are derived in closed-form, while a useful expression is presented for the asymptotically low outage regime. Some special cases of practical interest (e.g., no interference power and interference-limited case) are also studied. Using the derived lower bounds as a starting point and for the case of Rayleigh fading, we formulate and solve analytically three practical optimization problems, namely, power allocation under fixed location for the relay, optimal relay position with fixed power allocation, and joint optimization of power allocation and relay position under a transmit power constraint. The numerical results provide important physical insights into the implications of model parameters on the system performance; for instance, it is demonstrated that relay position optimization offers significant performance enhancement over the non-optimized case for an asymmetric interference power profile, whilst the optimization gains are marginal for a symmetric one.

We study information rate penalties for single-relay amplify and forward (AF) cooperative communication in the presence of unknown and time-varying fading. The penalty is defined as the gap between the information rate with perfect channel state information and that when the source-destination, source-relay and relay-destination channels are unknown at the receiving nodes. We prove that this gap in the cooperative system is the sum of gaps in the source-destination (single-hop) and source-relay-destination (dual-hop) channels. Under the assumption that the source and destination are mobile and the relay is stationary, we derive a closed-form accurate approximation for the asymptotic penalty of the mobile-to-mobile single-hop channel, which can also be used in computing asymptotic penalties for the mobile-fixed-mobile dual-hop channel. AF relaying induces non-Gaussian noise at the destination and hence, the penalty of the dual-hop channel is computed semi-analytically. We discuss a numerical technique for evaluation of destination noise entropy. One main observation of this paper is that non-negligible amplified relay noise can increase the cooperation information rate penalty of up to 1.5 times, as compared to non-relayed transmission. We provide extensive simulation results that characterize the behavior of penalty and include other mobility models for the source, relay and destination.

Adaptive radio resource allocation in a cellular and multiuser orthogonal frequency-division multiple access (OFDMA)-based relay network is studied here. The authors have considered a margin adaptive optimisation problem to minimise the total transmitted power of the base and relay stations subject to the requested data rates of the users. In this study, a novel algorithm is presented for subcarrier allocation to the users with frequency reusing in the network. The interference among users in non-adjacent cells has been considered in the proposed algorithm which results in interference aware power allocation. Tolerable bit error rate is satisfied as quality of service (QoS) factor for each subcarrier. Linear programming approach has been proposed to solve this problem, such that power allocation problem is isolated for each of the subcarriers and total transmitted power is minimised in the network. According to this technique, the cost of interference in the system in terms of the excess consumed power to satisfy QoS constraints can be computed. The gain achieved in the power consumption is demonstrated through the simulations and the results are compared with the previous works.

In this paper, we consider the problem of distributed network beamforming for a bidirectional cognitive relay network which consists of two secondary transceivers and K cognitive relay nodes and a primary network with a transmitter and receiver, over the same spectrum. For effective use of spectrum, we propose a two-step two-way relaying scheme for cognitive networks. Our aim is to design the beamforming coefficients to maximize quality of service (QoS) for the secondary network while satisfying tolerable interference constraint for the primary network. We show that this approach yields a closed-form solution. Our simulation results show that the maximum achievable SINR improved while the tolerable interference on primary receiver increased.

In this paper, we present the performance of selective combining decode-and-forward relay networks in independent and non identical distributed Rician fading channels. The outage probability, moment generation function, symbol error probability and average channel capacity are derived in closed-form using the SNR statistical characteristics. After that, we analyze the outage probability at high SNR and then we optimize it. Finally, for comparison with analytical formulas, we perform some Monte-Carlo simulations.

Motivated by the fact that full diversity order is achieved using the “best-relay” selection technique, we consider opportunistic amplify-and-forward and decode-and-forward relaying systems. We focus on the outage probability of such a systems and then derive closed-form expressions for the outage probability of these systems over independent but non-identical imperfect Log-normal fading channels. We consider the error of channel estimation as a Gaussian random variable. As a result the estimated channels distribution are not Log-normal either as would be in the case of the Rayleigh fading channels. This is exactly the reason why our simulation results do not exactly matched with analytical results. However, this difference is negligible for a wide variety of situations.

The problem of joint maximum-likelihood estimation of the carrier-frequency offset (CFO) and channel coefficients in multiple-input multiple-output orthogonal frequency-division multiplexing systems is investigated, assuming that a training sequence is available. The exact solution to this problem turns out to be too complex for practical purposes. To overcome this difficulty, the authors resort to the expectation–maximisation (EM) algorithm and propose an iterative scheme which iterates between estimating the channel parameters and the frequency offset. This results in an estimation algorithm of a reasonable complexity which is suitable for practical applications. Moreover, the Cramer–Rao bounds (CRB) for both CFO and channel estimators are developed to evaluate the performance of the proposed scheme. Simulation results show that the proposed algorithm achieves almost ideal performance compared with the CRBs in all ranges of signal-to-noise ratio for both channel and frequency offset estimates.

The design of expectation–maximisation (EM)-based turbo receivers for low-density parity-check-coded multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems with the presence of carrier-frequency offset (CFO) is studied. First, starting from the maximum-likelihood principle, a novel EM-based CFO estimator for MIMO-OFDM systems is devised. This estimator iteratively provides the CFO estimate with the aid of pilot symbols. It is also capable of accommodating any space–time-coded-OFDM transmission. Then this CFO estimator is incorporated into the initialisation step of the turbo receiver. Simulation results show the effectiveness of the receiver design in combating CFO over unknown frequency-selective fading channels.

Recognition of digital signal type is an important topic for various applications. In this paper a method is presented that identifies different types of digital signals. This method utilizes a radial basis function neural network as the classifier which an evolutionary algorithm, i.e. swarm intelligence (SI), is used to construct it. As the features of signals, this method uses fourth and sixth and eighth order of moments and cumulants, i.e. a combination of higher orders of statistics (HOS). In conjunction with neural network it is used K-fold cross validation to improve the generalization ability. Experimental results indicate that this method has high percentage of the correct classification to discriminate different types of signal even at low SNRs.

In this paper, we propose a new scheme for joint maximum- likelihood (ML) estimation of carrier-frequency offset (CFO) and channel coefficients in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. To reduce the prohibitive computational complexity of joint ML estimation problems, the expectation- maximization (EM) algorithm is employed. Moreover, the Cramer-Rao bounds (CRB) for both CFO and channel estimators are developed to evaluate the performance of the proposed scheme. Computer simulations show that the proposed algorithm achieves almost ideal performance compared with the CRB for both channel and frequency offset estimations.