Cell-free massive multiple-input–multiple-output (CF-mMIMO) has emerged as a key architectural candidate for sixth-generation (6G) wireless networks, in which many distributed access points cooperate to serve users without cell boundaries. When combined with integrated sensing and communication (ISAC), this infrastructure evolves from a pure connectivity layer into a spatially distributed sensing–communication fabric capable of high-rate data delivery and fine-grained environmental perception. This survey provides a structured overview of CF-mMIMO– ISAC systems. We first revisit the fundamentals of CF-mMIMO and ISAC and clarify their synergies and inherent tensions. We then synthesize recent progress along several core design axes: joint maximization of communication sum-rate and sensing signal-to-noise ratio (SNR); physical-layer security and privacy-aware sensing; energy-efficient operation with stringent latency and age-of-information requirements; performance evaluation and scalability under realistic hardware and fronthaul constraints; and integration with enabling technologies such as reconfigurable intelligent surfaces (RISs), movable antennas, orthogonal time–frequency space (OTFS) modulation, and unmanned aerial vehicle (UAV) platforms. Across these themes, we compare optimization-based and learning-based methods, emphasizing how they reshape the rate–sensing trade-off, how sensitive they are to channel state information (CSI) assumptions, and how system-level coordination influences scalability. Finally, we distill cross-cutting lessons and outline open problems in distributed joint sensing–communication design. The survey is intended as both a technical reference and a roadmap for designing CF-mMIMO ISAC frameworks in 6G and beyond.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have emerged as a promising technology for enabling full-space signal manipulation and enhancing wireless network coverage and capacity. In this article, we present a comprehensive analytical comparison of STAR-RIS-assisted systems with single-input single-output (SISO), conventional RISs, and decode-and-forward (DF) relaying schemes, including both half-duplex (HD) and full-duplex (FD) modes. Closed-form expressions are derived for the achievable secrecy rates of STAR-RIS-aided communications under both the absence and presence of eavesdroppers. Unlike most existing works, the direct source–destination link is incorporated in all considered schemes, and optimal transmit power allocation is investigated for HD- and FD-DF relaying. Furthermore, we provide the conditions under which STAR-RIS outperforms HD- and FD-DF relaying and quantify the minimum number of STAR-RIS elements required to achieve superior rates. The impacts of key system parameters—including transmit power, number of elements, reflection and transmission amplitude coefficients, element-splitting factor, and deployment positions—on both achievable and secrecy performance are investigated. The results reveal that STAR-RIS systems can achieve superior rates and secrecy rates compared to all benchmark schemes.

This research paper investigates the secrecy performance in a vehicular network. A legitimate vehicular source aims to transmit confidential information to a legitimate vehicular destination, while a passive vehicular eavesdropper attempts to intercept this transmission. To enhance the practical relevance of the system model, the probabilistic existence of an obstructing vehicle is considered. This obstructing vehicle can potentially disrupt communication links between the source and the destination, the source and the eavesdropper, or both. To mitigate the adverse effects of obstruction and eavesdropping, a reconfigurable intelligent surface (RIS) is employed. Additionally, the source employs a power-splitting scheme to generate artificial noise (AN), a physical-layer security technique to degrade the wiretap channel. In this power-splitting scheme, the source’s transmission power is divided into two portions: confidential transmission and generating the AN signal. Closed-form expressions are derived for the outage probability, upper bound intercept probability, and upper bound secrecy outage probability (SOP), along with their asymptotic expressions. The influence of power-splitting ratio, transmission power, number of reflecting elements of the RIS, density of the obstructing vehicle, and the channel conditions on the performance metrics is investigated through numerical results. The results are extended to encompass practical impairments, specifically examining the impacts of node mobility, channel state information feedback delay, and AN leakage at the destination. It has been observed that an increase in the density of large obstructing vehicles leads to a reduction in the SOP. However, this degradation can be mitigated by increasing the number of passive reflectors of RIS. Additionally, based on the closed-form upper bound expression of the SOP, a power allocation optimization problem is solved to minimize the SOP in terms of the power-splitting ratio. The accuracy of the derived closed-form expressions has been verified through Monte Carlo simulations.

In this paper, we investigate a full-duplex (FD) UAV-assisted multi-user system under multiple malicious jammers and propose a robust online scheme for secure communication with mobile downlink and uplink users. A random mobility model is adopted to simulate user movement, and the problem is formulated as a two-stage online optimization framework comprising a present-point and a prediction-point problem. To tackle the non-convexity, we develop inner-approximation algorithms using successive convex approximation (SCA) and the S-procedure. Simulation results verify the effectiveness of the proposed method.

This paper studies secure and energy-efficient mobile edge computing with UAV-mounted RIS assistance, in the presence of multiple users (UEs) and an eavesdropper (EV). With an objective of maximizing the minimum energy efficiency (EE) among all UEs in the company of an EV, we jointly optimize parameters including user scheduling, phase-shifts of the programmable reflecting elements, UAV trajectory design, UEs' power allocation, and their computation task and CPU frequency allocation. To ensure secure communication, we adopt power-splitting based artificial noise transmission. We solve this challenging problem by employing successive convex approximation and block coordinate descent techniques. We showcase the superiority of our proposed algorithm over existing studies through simulation results. Particularly, our proposed algorithm manages to achieve the EE very close to the benchmark performance, which assumes the absence of an EV.

Scalable algorithms are conceived for obtaining the sum-rate capacity of the reconfigurable intelligent surface (RIS)-aided multiuser (MU) multiple-input multiple-output (MIMO) broadcast channel (BC), where a multi-antenna base station (BS) transmits signals to multi-antenna users with the help of an RIS equipped with a massive number of finite-resolution programmable reflecting elements (PREs). As a byproduct, scalable path-following algorithms emerge for determining the sum-rate capacity of the conventional MIMO BCs, closing a long-standing open problem of information theory. The paper also develops scalable algorithms for maximizing the minimum rate (max-min rate optimization) of the users achieved by the joint design of RIS’s PRE and transmit beamforming for such an RIS-aided BC. The simulations provided confirm the high performance achieved by the algorithms developed, despite their low computational complexity.

This paper investigates the unmanned aerial vehicle (UAV)-assisted communication network with multiple downlink users (DLUs) and uplink users (ULUs) in the presence of multiple malicious jammers. To guarantee fairness among the users and their uplink and downlink communication throughput, we aim to maximize the minimum average throughput by jointly optimizing the scheduling of ULUs/DLUs, three dimensional (3D) trajectory and the UAV transmission power. Although the optimization problem is computationally intractable due to its non-convexity, we develop an iterative algorithm based on the block coordinate descend approach and the successive convex approximation technique to solve the problem efficiently. Numerical outcomes show that our proposed algorithm can improve throughput significantly over several benchmark schemes.

Energy and spectral efficiency of Internet of Things (IoT) networks can be improved by integrating energy harvesting (EH), cognitive radio, and nonorthogonal multiple access (NOMA) techniques, while unmanned aerial vehicles (UAVs), on the other hand, are a quick and adaptable entity for improving the coverage performance. In this article, we assess the performance of a UAV-assisted overlay cognitive NOMA (OC-NOMA) system by employing an EH-based IoT-inspired cooperative spectrum sharing transmission (I-CSST) scheme. Herein, an energy-constrained UAV-borne secondary node harvests radio-frequency energy from the primary source and uses it to send both its own information signal and the primary information signal using the NOMA approach. We consider the impact of the imperfect successive interference cancellation in NOMA and the distortion noises caused by hardware impairments (HIs) in signal processing, which are unavoidable in real-world systems. We obtain the complicated expressions of outage probability (OP) for primary and secondary IoT networks using the I-CSST scheme under heterogeneous Rician and Nakagami-${m}$fading channels. We continue to investigate asymptotic analysis for OP in order to gain insightful knowledge on the high signal-to-noise ratio (SNR) slope and practicable diversity order. We also assess the system throughput and energy efficiency for the considered OC-NOMA system. Our results demonstrate the benefits of the suggested I-CSST scheme over the benchmark primary direct transmission and orthogonal multiple access schemes. We create a deep neural network (DNN) architecture for real-time OP prediction in order to combat the complications in model-based approaches.

This paper focuses on a network scenario where a multi-antenna access point serves multiple single-antenna users in the presence of multiple eavesdroppers, with the aid of a reconfigurable intelligent surface (RIS). The RIS employs low-resolution programmable reflecting elements (PREs) for cost-effective implementation. In order to establish secure links for all users, we consider the joint design of the transmit beamformers and PREs to maximize either the geometric mean of secrecy rates or the worst user’s secrecy rate. Novel computational algorithms of low computational complexity are developed for the solution of these mixed discrete continuous optimization problems. Simulations show the merit of the proposed designs in in achieving fair secrecy rate distributions and ensuring secure links for all users.

Relay-aided multi-user OFDM is investigated under which multiple sources transmit their signals to a multi-antenna relay during the first relaying stage and then the relay amplifies and forwards the composite signal to all destinations during the second stage. The signal transmission of both stages experience frequency selectivity. The relay is powered both by an energy source through the wireless power transfer as well as by the energy recycled from its own self-interference during the second stage. Accordingly, we jointly design the power allocations both at the multiple source nodes and at a common relay node for maximizing the network’s sum-throughput, which poses a large-scale nonconvex problem, regardless whether proper Gaussian signaling (PGS) or improper Gaussian signaling (IGS) is used for signal transmission to the relay. We develop new alternating descent procedures for solving our joint optimization problems, which are based on closed-forms and thus are of very low computational complexity even for large numbers of subcarriers. The results show the superiority of IGS over PGS in terms of both its sum-rate and individual user-rate. Another benefit of IGS over PGS is that the former promises fairer rate distribution across the subcarriers. Moreover, the recycled self-interference also provides a beneficial complementary energy source.

A multi-antenna aided base station (BS) supporting several multi-antenna downlink users with the aid of a reconfigurable intelligent surface (RIS) of programmable reflecting elements (PREs) is considered. Low-resolution PREs constrained by a set of sparse discrete values are used for reasons of cost-efficiency. Our challenging objective is to jointly design the beamformers at the BS and the RIS’s PREs for improving the throughput of all users by maximizing their geometric-mean, under a variety of different access schemes. This constitutes a computationally challenging problem of mixed continuous-discrete optimization, because each user’s throughput is a complicated function of both the continuous-valued beamformer weights and of the discrete-valued PREs. We develop low-complexity algorithms, which iterate by directly evaluating low-complexity closed-form expressions. Our simulation results show the advantages of non-orthogonal multiple access-aided signaling, which allows the users to decode a part of the multi-user interference for enhancing their throughput.

This paper considers a cell-free massive multiple-input multiple-output network (cfm-MIMO) with a massive number of access points (APs) distributed across an area to deliver information to multiple users. Based on only local channel state information, conjugate beamforming is used under both proper and improper Gaussian signalings. To accomplish the mission of cfm-MIMO in providing fair service to all users, the problem of power allocation to maximize the geometric mean (GM) of users’ rates (GM-rate) is considered. A new scalable algorithm, which iterates linear-complex closed-form expressions and thus is practical regardless of the scale of the network, is developed for its solution. The problem of quality-of-service (QoS) aware network energy-efficiency is also addressed via maximizing the ratio of the GM-rate and the total power consumption, which is also addressed by iterating linear-complex closed-form expressions. Intensive simulations are provided to demonstrate the ability of the GM-rate based optimization to achieve multiple targets such as a uniform QoS, a good sum rate, and a fair power allocation to the APs.

In this paper, an unmanned aerial vehicle (UAV) enabled secure downlink communication is considered, where a single-antenna UAV serves multiple ground users facilitated by orthogonal frequency-division multiple access (OFDMA), in the presence of an eavesdropper (EV) with imperfect channel state information. To enhance the secrecy rate (SR), we employ a power splitting approach, where part of the transmit power is used for communication while the rest is used for jamming. We maximize the average secrecy rate (ASR) by jointly optimizing the bandwidth, trajectory, power allocation, and power splitting ratio. To tackle this non-convex and computationally intractable optimization problem, we propose a novel algorithm by employing successive convex approximation, block coordinate descend and$S$-procedure. Numerical results show that our proposed joint optimization algorithm outperforms the benchmark schemes.

The paper is the first work that considers a constrained feedback control strategy to navigate an unmanned aerial vehicle (UAV) from a given starting point to a given terminal point while harvesting solar energy and providing a wireless communication service for ground users. Wireless communication channels are stochastic and cannot be known off-line, making the problem of off-line UAV path planning for wireless communication as considered in most existing works less meaningful. We consider the problem of navigating a solar-powered UAV from a starting point to a terminal point to harvest solar energy while serving the two-way communication between multiple pairs of ground users in a complex terrain. The objective is to jointly optimize the UAV’s flight time and its flight path by trading-off between the harvested energy and power consumption subject to the ground users’ minimum throughput requirement. We develop a new model predictive control (MPC) technique to address this problem. Namely, based on the well-known statistics of the air-to-ground (A2G) and ground-to-air (G2A) wireless channels, a predictive control model is proposed at each time-instant, which leads to an optimization problem over a receding horizon for the control design. This problem is non-convex due to the involvement of various optimization variables, which is then solved via novel convex iterations. Simulation results show the merits of the proposed algorithm. The results obtained by the proposed algorithm match with the benchmark non-MPC and offline-MPC approaches.

Under both long (infinite) and short (finite) blocklength transmissions, this paper considers physical layer security for a wireless interference network of multiple transmitter-user pairs, which is overheard by multiple eavesdroppers (EVs). The EVs are assumed to have better channel conditions than the legitimate users (UEs), making the conventional transmission unsecured. The paper develops a novel time-fraction based transmission, under which the information is transmitted to the UEs within a fraction of the time slot and artificial noise (AN) is transmitted within the remaining fraction to counter the strong EVs' channels. Based on channel distribution information of UEs and EVs, the joint design of transmit beamforming, time fractions and AN power allocation to maximize the worst users' secrecy rate is formulated in terms of nonconvex problems. Path-following algorithms of low complexity and rapid convergence are proposed for their solution. Simulations are provided to demonstrate the viability of the proposed methodology.

Providing ultra reliable and low-latency communication (URLLC) is considered one of the major challenges for wireless communication networks. This article considers a downlink URLLC system in which a base station (BS) serves multiple single-antenna users in the short blocklength regime. With the objective of maximizing the users' minimum rate, three different optimization problems are considered: (i) joint design of bandwidth and power allocation for the case of a single-antenna BS; (ii) beamforming design for the case of a multiple-antenna BS; and (iii) design of power allocation with regularized zero-forcing beamforming for the case of a multiple-antenna BS. In the short blocklength regime, the achievable rate is a complicated function of bandwidth and power allocation coefficients or beamforming vectors, which makes these max-min rate optimization problems challenging to solve. This work develops path-following algorithms, which generate a sequence of improved feasible points and converge at least to a locally optimal solution, to solve these three optimization problems. Performance of the proposed algorithms is analyzed through extensive simulations under various settings of transmit power budget, number of users, total bandwidth, transmission time, and number of transmit antennas at the BS. Simulation results clearly demonstrate the merits of the proposed algorithms.

This paper considers cell-free massive MIMO (cfm-MIMO) for downlink ultra reliable and low-latency communication (URLLC). At the time of writing, cfm-MIMO has only been considered for communication in the long blocklength regime (LBR), whose throughput is determined by the Shannon capacity with the interference treated as Gaussian noise. Conjugate beamforming (CB) is often used as it requires only local channel state information (CSI) for implementation but its design is based on a large-scale nonconvex problem, which is computationally intractable. The rate function in URLLC is much more complex than the Shannon rate function. The paper proposes a special class of CB, which admits a low-scale optimization formulation for computational tractability. Accordingly, a new path-following algorithm, which generates a sequence of better feasible points and converges at least to a locally optimal solution, is developed for optimizing URLLC rates and cfm-MIMO energy efficiency. Furthermore, the paper also develops improper Gaussian signaling to improve both the Shannon rate and URLLC rate.

This paper considers a wireless interference network in which the communication between multiple transmitter-user pairs is overheard by multiple eavesdroppers (EVs). Based on knowledge of the channel distribution, the goal is to maximize the worst users' secrecy rate under both long (infinite) blocklength and short (finite) blocklength transmissions. Under long blocklength transmission, the performance of the existing algorithms is unsatisfactory when the wiretapped channels are sufficiently strong. To address this drawback, we adopt a time-fraction based information and artificial noise (AN) transmission, under which first the information is transmitted within the initial fraction of the time slot and then AN is transmitted within the remaining fraction. Accordingly, the problem of join optimization of the time fractions, transmit power, and AN power to maximize the minimum secrecy rate is proposed and computed by a path-following algorithm, which iterates feasible points and converges at least to a locally optimal solution. A similar problem under short blocklength transmission is also proposed and computed. The provided simulations results clearly show the merits of the proposed approach.

This paper considers a network consisting of a multiple antenna array access point serving multiple single antenna downlink users with the assistance of a reconfigurable intelligent surface (RIS). The reflecting coefficients of the RIS can be programmed to ensure that the signals reflected from the RIS elements add coherently at the users. The joint design of these programmable reflecting coefficients and transmit beamforming to maximize the users' worst rate is addressed. Under either proper Gaussian signaling (PGS) or improper Gaussian signaling (IGS), the design poses a very computationally challenging nonconvex problem. Based on their exactly penalized optimization reformulation, which incorporates the computationally intractable unit-modulus constraints on the reflecting coefficients into the optimization objectives, new iterative algorithms of low computational complexity, which converge at least to a locally optimal solution, are developed. The provided simulations show not only the benefit of using the RIS, but also the advantage of IGS over PGS in delivering higher rates to users.

This paper considers wireless-powered relaying multiple-input-multiple-output (MIMO) communication, where all four nodes (information source, energy source, relay, and destination) are equipped with multiple antennas. Orthogonal frequency division multiplexing (OFDM) is applied for information processing to compensate the frequency selectivity of communication channels between the information source and the relay and between the relay and the destination as these nodes are assumed to be located far apart from each. The relay is equipped with a full-duplexing interface for harvesting energy not only from the wireless transmission of the dedicated energy source but also from its own transmission while relaying the source information to the destination. The problem of designing the optimal power allocation over OFDM subcarriers and transmit antennas to maximize the overall spectral efficiency is addressed. Due to a very large number of subcarriers, this design problem poses a large-scale nonconvex optimization problem involving a few thousand variables of power allocation, which is very computationally challenging. A novel path-following algorithm is proposed for computation. Based on the developed closed-form calculation of linear computational complexity at each iteration, the proposed algorithm rapidly converges to an optimal solution. Compared to the best existing solvers, the computational complexity of the proposed algorithm is reduced at least 105times, making it very efficient and practical for online computation while existing solvers are ineffective. Numerical results for a practical simulation setting show promising results by achieving high spectral efficiency.

Recent studies of single-cell two-user networks have shown that a higher network throughput is achieved by using a common message to be decoded by both users and conveying partial information for both users, rather than using the common message to convey the entire information for one of the two users. The latter is essentially the conventional non-orthogonal multiple access (NOMA), which performs better than orthogonal multiple access (OMA) only under users' dissimilar channel conditions. Unlike NOMA, the former performs consistently better than OMA. This paper generalizes such a signaling strategy to a general multi-cell multiuser network, which leads to a new NOMA approach (called n-NOMA) in which each pair of users decodes a message that conveys partial information for one of them only. Unlike the conventional NOMA, whose performance is dependent on the users' pairing strategy, the proposed n-NOMA consistently outperforms both NOMA and OMA schemes. Both proper and improper Gaussian signaling is considered for all the concerned schemes and it is shown that the latter is clearly more advantageous than the former.

This paper proposes Han-Kobayashi signaling (HKS), under which each pair of users decodes a common message to improve their throughput, for UAV-enabled multi-user communication. Given that only a single transmit antenna is used and thus there is no null space of users' channels for inserting an artificial noise that would effectively help to jam an eavesdropper without interfering the users' desired signals, a new information and artificial noise transfer scheme to address physical layer security (PLS) for the considered networks is investigated. Under this scheme, the UAV sends the confidential information to its users within a fraction of the time slot and sends the artificial noise within the remaining fraction. Accordingly, the problem of jointly optimizing the time-fraction, bandwidth and power allocation to maximize the users' worst secrecy throughput is formulated. New inner approximations are proposed for developing path-following algorithms for its computation. Simulation shows that the proposed information and artificial noise transfer enables not only HKS but also orthogonal multi-access and nonorthogonal multi-access to provide PLS for UAV-enabled communication even when the eavesdropper is in the best channel condition. HKS outperforms the other two schemes in terms of users' worst secrecy throughput.

For a multi-user multi-cell network, which suffers both intra-cell and inter-cell interference, this letter considers improper Gaussian signaling (IGS) as a means to improve the achievable rate. The problem of interest is designing of improper Gaussian signals' augmented covariance matrices to maximize the users' minimum rate subject to transmit power constraints. This problem is seen as a nonconvex matrix optimization problem, which cannot be solved by conventional techniques, such as weighted minimum mean square error minimization or alternating optimization. A path-following algorithm, which iterates a sequence of improved feasible points, is proposed for its computation. The provided simulation results for three cells serving 18 users show that IGS offers a much better max-min rate compared with that achieved by conventional proper Gaussian signaling. Another problem of maximizing the energy efficiency in IGS is also considered.

An energy harvesting (EH) enabled network is capable of delivering energy to users, who are located sufficiently close to the base stations. However, wireless energy delivery requires much more transmit power than what the normal information delivery does. It is very challenging to provide the quality of wireless information and power delivery simultaneously. It is of practical interest to employ non-orthogonal multiple access (NOMA) to improve the network throughput, while fulfilling the EH requirements. To realize both the EH and information decoding, this paper considers a transmit time-switching (transmit-TS) protocol. Two important problems of users' max-min throughput optimization and energy efficiency maximization under power constraint and EH thresholds, which are non-convex in beamforming vectors, are addressed by efficient path-following algorithms. In addition, the conventional power splitting (PS)-based EH receiver is also considered. The provided numerical results confirm that the proposed transmit-TS-based algorithms clearly outperform the PS-based algorithms in terms of throughput and energy efficiency.

Unmanned aerial vehicles (UAVs) can be deployed as flying base stations (BSs) to leverage the strength of line-of-sight connections and effectively support the coverage and throughput of wireless communication. This paper considers a multiuser communication system, in which a single-antenna UAV-BS serves a large number of ground users by employing non-orthogonal multiple access (NOMA). The max-min rate optimization problem is formulated under total power, total bandwidth, UAV altitude, and antenna beamwidth constraints. The objective of max-min rate optimization is non-convex in all optimization variables, i.e., UAV altitude, transmit antenna beamwidth, power allocation, and bandwidth allocation for multiple users. A path-following algorithm is proposed to solve the formulated problem. Next, orthogonal multiple access (OMA) and dirty paper coding (DPC)-based max-min rate optimization problems are formulated and respective path-following algorithms are developed to solve them. The numerical results show that NOMA outperforms OMA and achieves rates similar to those attained by DPC. In addition, a clear rate gain is observed by jointly optimizing all the parameters rather than optimizing a subset of parameters, which confirms the desirability of their joint optimization.

Considering a multi-user interference network with an eavesdropper, this paper aims at the power allocation to optimize the worst secrecy throughput among the network links or the secure energy efficiency in terms of achieved secrecy throughput per Joule under link security requirements. Three scenarios for the access of channel state information are considered: the perfect channel state information; partial channel state information with channels from the transmitters to the eavesdropper exponentially distributed; and not perfectly known channels between the transmitters and the users with exponentially distributed errors. The paper develops various path-following procedures of low complexity and rapid convergence for the optimal power allocation. Their effectiveness and viability are illustrated through numerical examples. The power allocation schemes are shown to achieve both high secrecy throughput and energy efficiency.

Residual self-interference cancellation is an important practical requirement for realizing the full potential of full-duplex (FD) communication. Traditionally, the residual selfinterference is cancelled via digital processing at the baseband, which requires accurate knowledge of channel estimates of the desired and self-interference channels. In this work, we consider point-to-point FD communication and propose a superimposed signaling technique to cancel the residual self-interference and detect the data without estimating the unknown channels. We show that when the channel estimates are not available, data detection in FD communication results in ambiguity if the modulation constellation is symmetric around the origin. We demonstrate that this ambiguity can be resolved by superimposed signalling, i.e., by shifting the modulation constellation away from the origin, to create an asymmetric modulation constellation. We compare the performance of the proposed detection method to that of the conventional channel estimation-based detection method, where the unknown channels are first estimated and then the data signal is detected. Simulations show that for the same average energy over a transmission block, the bit error rate performance of the proposed detection method is better than that of the conventional method. The proposed method does not require any channel estimates and is bandwidth efficient.

Millimeter wave (mmWave) technology is gaining momentum because of its ability to provide high data rates. However, in addition to other challenges in the operation of mmWave systems, developing cell search algorithms is a challenge due to high path loss, directional transmission, and excessive sensitivity to blockage at mmWave frequencies. Thus, the cell search schemes of long term evolution (LTE) cannot be used with mmWave networks. Exhaustive and iterative search algorithms have been proposed in literature for carrying out cell search in mmWave systems. The exhaustive search offers high probability of detection with high discovery delay while the iterative approach offers low probability of detection with low discovery delay. In this paper, we propose a hybrid algorithm that combines the strengths of exhaustive and iterative methods. We compare the three algorithms in terms of misdetection probability and discovery delay and show that hybrid search is a smarter algorithm that achieves a desired balance between probability of detection performance and discovery delay.

Cooperative spatial diversity allows for more robust wireless networks with higher capacity and data rates. These diversity gains, however, diminish with the multiple carrier frequency offsets (CFOs) that arise due to distributed transmissions over independent channels. A number of techniques have been proposed for multiple CFO estimation, however, their empirical performance remains to be analyzed. This paper seeks to experimentally analyze the performance of the iterative MUltiple SIgnal Characterization (I-MUSIC) algorithm for decode-and-forward (DF) relaying on the universal software radio peripheral (USRP) platforms. The results show that the aforementioned algorithm performs reasonably well, in both line-of-sight (LoS) and non-line-of-sight (NLoS) channels, in terms of mean squared error (MSE) of the estimated CFOs. However, the performance margin greatly depends on the type of environment and the number of data symbols used for estimation.

Information and energy can be transferred over the same radio-frequency channel. In the power-splitting (PS) mode, they are simultaneously transmitted using the same signal by the base station (BS) and later separated at the user (UE)'s receiver by a power splitter. In the time-switching (TS) mode, they are either transmitted separately in time by the BS or received separately in time by the UE. In this paper, the BS transmit beamformers are jointly designed with either the receive PS ratios or the transmit TS ratios in a multicell network that implements wireless information and power transfer (WIPT). Imposing UE-harvested energy constraints, the design objectives include: 1) maximizing the minimum UE rate under the BS transmit power constraint, and 2) minimizing the maximum BS transmit power under the UE data rate constraint. New iterative algorithms of low computational complexity are proposed to efficiently solve the formulated difficult nonconvex optimization problems, where each iteration either solves one simple convex quadratic program or one simple second-order-cone-program. Simulation results show that these algorithms converge quickly after only a few iterations. Notably, the transmit TS-based WIPT system is not only more easily implemented but outperforms the receive PS-based WIPT system as it better exploits the beamforming design at the transmitter side.

Impairments such as time varying phase noise (PHN) and carrier frequency offset (CFO) result in loss of synchronization and poor performance of multi-relay communication systems. Joint estimation of these impairments is necessary in order to correctly decode the received signal at the destination. In this paper, we address spectrally efficient multi-relay transmission scenarios where all the relays simultaneously communicate with the destination. We propose an iterative pilot-aided algorithm based on the expectation conditional maximization for joint estimation of multipath channels, Wiener PHNs, and CFOs in decode-and-forward-based multi-relay orthogonal frequency division multiplexing systems. Next, a new expression of the hybrid Cramér-Rao lower bound (HCRB) for the multi-parameter estimation problem is derived. Finally, an iterative receiver based on an extended Kalman filter for joint data detection and PHN tracking is employed. Numerical results show that the proposed estimator outperforms existing algorithms and its mean square error performance is close to the derived HCRB at different signal-to-noise ratios for different PHN variances. In addition, the combined estimation algorithm and the iterative receiver can significantly improve average bit-error rate (BER) performance compared with existing algorithms. In addition, the BER performance of the proposed system is close to the ideal case of perfect channel impulse responses, PHNs, and CFOs estimation.

Some next-generation wireless networks will likely involve the energy-efficient transfer of information and energy over the same wireless channel. Moreover, densification of such networks will make the physical layer more vulnerable to cyber attacks by potential multi-antenna eavesdroppers. To address these issues, this paper considers transmit time-switching (TS) mode, in which energy and information signals are transmitted separately in time by the base station (BS). This protocol is not only easy to implement but also delivers the opportunity for multi-purpose beamforming, in which energy beamformers can be used to jam eavesdroppers during wireless power transfer. In the presence of imperfect channel estimation and multiantenna eavesdroppers, the energy and information beamformers and the transmit TS ratio are jointly optimized to maximize the worst-case user secrecy rate subject to energy constrained users' harvested energy thresholds and a BS transmit power budget. New robust path-following algorithms, which involve one simple convex quadratic program at each iteration are proposed for computational solutions of this difficult optimization problem and also the problem of secure energy efficiency maximization. The latter adds further complexity due to additional optimization variables appearing in the denominator of the secrecy rate function. Numerical results confirm that the performance of the proposed computational solutions is robust against channel uncertainties.

This paper considers the efficient design of precoding matrices for sum throughput maximization under throughput quality of service (QoS) constraints and energy harvesting (EH) constraints for energy-constrained devices in a full-duplex (FD) multicell multi-user multiple-input-multiple-output network. Both time splitting (TS) and power splitting are considered to ensure practical EH and information decoding. These problems are quite complex due to non-concave objectives and nonconvex constraints. Especially, with TS, which is implementation-wise quite simple, the problem is even more challenging because the time splitting variable is not only coupled with the downlink throughput function but also coupled with the self-interference in the uplink throughput function. New path-following algorithms are developed for their solutions, which require only a single convex quadratic program for each iteration and ensure rapid convergence. Moreover, the FD EH maximization problem under throughput QoS constraints with TS is also considered. The performance of the proposed algorithms is compared with that of the modified problems assuming half-duplex systems. Finally, the merit of the proposed algorithms is demonstrated through extensive simulations.

Millimeter-wave (mm-wave) systems suffer from an assortment of propagation and hardware challenges such as extremely high pathloss/shadowing and amplifier non-linearity/phase noise, respectively. In this paper, we demonstrate via simulations that non-coherent frequency shift keying (FSK) can utilize the vast bandwidth at mm-wave frequencies to combat significant pathloss and shadowing in this band, while being robust to amplifier non-linearity and phase noise. To support our findings, we establish a comprehensive simulation setup and set of parameters that consider the impact of pathloss, shadowing, amplifier non-linearity, and phase noise, at mm-wave frequencies. Our results indicate that non-coherent FSK outperforms other modulation schemes such as phase shift keying and quadrature amplitude modulation. This outcome combined with the low detection complexity of non-coherent FSK make it an attractive modulation for achieving multi Gbps wireless links at mm-wave frequencies. The proposed comprehensive simulation setup can also be applied to investigate and validate the performance of various mm-wave systems in practical settings.

We consider the downlink of a dense multicell network where each cell region is divided into two zones. The users nearby their serving base station (BS) in the inner zone implement simultaneous wireless information and power transfer (SWIPT), thus harvest energy and decode information using the power splitting approach. Further, they try to eavesdrop the information intended for other users within the same cell. The users in the outer zone of each cell only implement information decoding. Our objective is to maximize the minimum user equipment (UE) signal-to-interference-and-noise ratio (SINR) under constraints on the BS transmit power, minimum energy harvesting levels of near-by users, and maximum SINR of eavesdroppers in the presence of multi-cell interference. For such a highly non-convex problem, semidefinite relaxation (SDR) may even fail to locate a feasible solution. We propose two methods to address such a difficult problem. In the spectral optimization, we express the rank-one constraints as a single reverse convex nonsmooth constraint and incorporate it into the optimization objective. In the difference-of-convex-functions iteration method, we directly solve for the beamforming vectors via quadratic programming (QP), avoiding the matrix rank constraints. In each iteration of the proposed algorithms, we only solve one simple convex semidefinite program (SDP) or QP. Our simulation results confirm that the proposed algorithms converge quickly after a few iterations. More importantly, our algorithms yield the performance that is very close to the theoretical bound given by SDP relaxation with comparable computational complexity.

This paper proposes a centralized decision making framework at the macro base station (MBS) for device-to-device (D2D) communication underlaying a two-tier cellular network. We consider a D2D pair in the presence of an MBS and a femto access point, each serving a user, with quality of service constraints for all users. Our proposed solution encompasses mode selection (choosing between cellular or reuse or dedicated mode), resource allocation (in cellular and dedicated mode), and power control (in reuse mode) within a single framework. The framework prioritizes D2D dedicated mode if the D2D pair is close to each other and orthogonal resources are available. Otherwise, it allows D2D reuse mode if the D2D satisfies both the maximum distance and an additional interference criteria. For reuse mode, we present a geometric vertex search approach to solve the power allocation problem. We analytically prove the validity of this approach and show that it achieves near optimal performance. For cellular and dedicated modes, we show that frequency sharing maximizes sum rate and solve the resource allocation problem in a closed form. Our simulations demonstrate the advantages of the proposed framework in terms of the performance gains achieved in the D2D mode.

This paper considers a heterogeneous multicell network where the base station (BS) in each cell communicates with its cell-edge user with the assistance of an amplify-and-forward relay node. Equipped with a power splitter and a wireless energy harvester, the relay scavenges RF energy from the received signals to process and forward the information. In the face of strong intercell interference and limited radio resources, we develop a resource allocation scheme that jointly optimizes (i) BS transmit powers, (ii) power splitting factors for energy harvesting and information processing at the relays, and (iii) relay transmit powers. To solve the highly non-convex problem formulation of sum-rate maximization, we propose to apply the successive convex approximation (SCA) approach and devise an iterative algorithm based on geometric programming. The proposed algorithm transforms the nonconvex problem into a sequence of convex problems, each of which is solved very efficiently by the interior-point method. We prove that our developed algorithm converges to an optimal solution that satisfies the Karush-Kuhn-Tucker conditions of the original nonconvex problem. Numerical results confirm that our joint optimization solution substantially improves the network performance, compared to the existing solution wherein only the received power splitting factors at the relays are optimized.

We consider wireless-powered amplify-and-forward relaying in cooperative communications and propose block-wise time-switching based energy harvesting protocol to implement wireless energy harvesting (EH) and information transmission (IT) at the energy constrained relay node. The time-switching EH protocol switches the relay operation between EH and IT such that during EH, relay harvests energy through the received radio-frequency signal from the source and during IT, the relay receives information signal from the source and uses the harvested energy to amplify and forward source signal to the destination. In our proposed block-wise time-switching EH protocol, the whole transmission block time is used for either EH or IT. The attractive feature of our proposed protocol is that the relay transmits at preset fixed transmit power and no channel state information is required either by the source or relay node. We derive exact expression of the analytical throughput for the proposed protocol and verify it through simulation. In addition, we show that our proposed protocol outperforms the existing time-switching EH protocol because it allows efficient use of resources by intelligently switching between EH and IT in an online fashion.

This paper develops power allocation schemes to manage the signal interference in a multiuser multicell network, where full-duplex transceivers are implemented at all base stations (BSs) and user equipment units (UEs). Beside the acute intracell and intercell interferences, the significant residual self-interference at the full-duplex receivers is the limiting factor for any network performance enhancement. To help control such severe interferences, we propose to associate each bidirectional full-duplex link with a net utility function, which consists of a utility and a flexible price. While the utility corresponds to the link throughput, the proposed logarithmic cost function allows for a moderate penalty. Our aim is to maximize the sum network utility subject to the power constraints at the BSs and UEs. To solve the highly nonconvex problem formulation, we propose two successive convex approximation (SCA) algorithms based on the difference-of-convex-functions programming and the geometric programming. In each algorithm, we specifically tailor the generic SCA framework and transform our formulated nonconvex problem into a sequence of convex power allocation programs. We prove that the developed iterative algorithms converge to locally optimal solutions that satisfy the Karush-Kuhn-Tucker conditions of the original problem. Numerical results confirm that our utility-based solutions markedly improve the network throughput by effectively managing the interferences.

We consider wireless-powered amplify-and-forward and decode-and-forward relaying in cooperative communications, where an energy constrained relay node first harvests energy through the received radio-frequency signal from the source and then uses the harvested energy to forward the source information to the destination node. We propose time-switching based energy harvesting (EH) and information transmission (IT) protocols with two modes of EH at the relay. For continuous time EH, the EH time can be any percentage of the total transmission block time. For discrete time EH, the whole transmission block is either used for EH or IT. The proposed protocols are attractive because they do not require channel state information at the transmitter side and enable relay transmission with preset fixed transmission power. We derive analytical expressions of the achievable throughput for the proposed protocols. The derived expressions are verified by comparison with simulations and allow the system performance to be determined as a function of the system parameters. Finally, we show that the proposed protocols outperform the existing fixed time duration EH protocols in the literature, since they intelligently track the level of the harvested energy to switch between EH and IT in an online fashion, allowing efficient use of resources.

In this paper, we consider a decode-and-forward (DF) relaying network based on wireless energy harvesting. The energy constrained relay node first harvests energy through radio-frequency (RF) signals from the source node. Next, the relay node uses the harvested energy to forward the decoded source information to the destination node. The source node transfers energy and information to the relay node through two mechanisms, i) time switching-based relaying (TSR) and ii) power splitting-based relaying (PSR). Considering wireless energy harvesting constraint at the relay node, we derive the exact analytical expressions of the achievable throughput and ergodic capacity of a DF relaying network for both TSR and PSR schemes. Through numerical analysis, we study the throughput performance of the overall system for different system parameters, such as energy harvesting time, power splitting ratio, and signal-to-noise-ratio (SNR). In particular, the throughput performance of the PSR scheme outperforms the throughput performance of the TSR scheme for a wide range of SNRs.

Cooperative communication systems employ cooperation among nodes in a wireless network to increase data throughput and robustness to signal fading. However, such advantages are only possible if there exist perfect synchronization among all nodes. Impairments like channel multipath, time varying phase noise (PHN) and carrier frequency offset (CFO) result in the loss of synchronization and diversity performance of cooperative communication systems. Joint estimation of these multiple impairments is necessary in order to correctly decode the received signal in cooperative systems. In this paper, we propose an iterative pilot-aided algorithm based on expectation conditional maximization (ECM) for joint estimation of multipath channels, Wiener PHNs, and CFOs in amplify-and-forward (AF) based cooperative orthogonal frequency division multiplexing (OFDM) system. Numerical results show that the proposed estimator achieves mean square error performance close to the derived hybrid Cramer-Rao lower bound (HCRB) for different PHN variances.

In this paper, we study the problem of decoding delay reduction for instantly decodable network coding (IDNC) in broadcast cooperative systems, where a group of closely located clients cooperate with each other to obtain their missing packets. In such cooperative systems, one of the clients (referred to as the leader) decides the transmitting client and the packet combination for each transmission. We consider intermittent system status update (SSU) at the leader such that all other clients feed back their packet reception status to the leader after several cooperative transmissions. We first introduce an intermittent local IDNC (IL-IDNC) graph to represent all potential packet combinations for a transmitting client. We then formulate the joint client and packet selection problem that results in the minimum expected decoding delay in each cooperative transmission as a maximum weight clique problem over all the IL-IDNC graphs. Since solving the formulated problem is computationally complex, we propose a heuristic algorithm to select the transmitting client and the packet combination that can reduce the decoding delay. Simulation results show that the proposed heuristic algorithm can achieve a tolerable degradation compared to the full SSU performance while using a smaller number of SSUs.

The problem of joint oscillator phase noise (PHN) estimation and data detection for multi‐input multi‐output (MIMO) systems using bit‐interleaved‐coded modulation is analysed. A new MIMO receiver that iterates between the estimator and the detector, based on the expectation‐maximisation (EM) framework, is proposed. It is shown that at high signal‐to‐noise ratios, a maximum a posteriori (MAP) estimator can be used to carry out the maximisation step of the EM algorithm. Moreover, to reduce the computational complexity of the proposed EM algorithm, a soft decision‐directed extended Kalman filter‐smoother (EKFS) is applied instead of the MAP estimator to track the PHN parameters. The numerical results show that by combining the proposed EKFS‐based approach with an iterative detector that employs low‐density parity check codes, PHN can be accurately tracked. The simulations also demonstrate that compared to the existing algorithms, the proposed iterative receiver can significantly enhance the performance of MIMO systems in the presence of PHN.

Oscillator phase noise (PHN) and carrier frequency offset (CFO) can adversely impact the performance of orthogonal frequency division multiplexing (OFDM) systems, since they can result in inter carrier interference and rotation of the signal constellation. In this paper, we propose an expectation conditional maximization (ECM) based algorithm for joint estimation of channel, PHN, and CFO in OFDM systems. We present the signal model for the estimation problem and derive the hybrid Cramér-Rao lower bound (HCRB) for the joint estimation problem. Next, we propose an iterative receiver based on an extended Kalman filter for joint data detection and PHN tracking. Numerical results show that, compared to existing algorithms, the performance of the proposed ECM-based estimator is closer to the derived HCRB and outperforms the existing estimation algorithms at moderate-to-high signal-to-noise ratio (SNR). In addition, the combined estimation algorithm and iterative receiver are more computationally efficient than existing algorithms and result in improved average uncoded and coded bit error rate (BER) performance.

For distributed multi-user and multi-relay cooperative networks, the received signal may be affected by multiple timing offsets (MTOs) and multiple channels that need to be jointly estimated for successful decoding at the receiver. This paper addresses the design of optimal training sequences for efficient estimation of MTOs and multiple channel parameters. A new hybrid Cramer-Rao lower bound (HCRB) for joint estimation of MTOs and channels is derived. Subsequently, by minimizing the derived HCRB as a function of training sequences, three training sequence design guidelines are derived and according to these guidelines, two training sequences are proposed. In order to show that the proposed design guidelines also improve estimation accuracy, the conditional Cramer-Rao lower bound (ECRB), which is a tighter lower bound on the estimation accuracy compared to the HCRB, is also derived. Numerical results show that the proposed training sequence design guidelines not only lower the HCRB, but they also lower the ECRB and the mean-square error of the proposed maximum a posteriori estimator. Moreover, extensive simulations demonstrate that application of the proposed training sequences significantly lowers the bit-error rate performance of multi-relay cooperative networks when compared to training sequences that violate these design guidelines.

An emerging solution for prolonging the lifetime of energy constrained relay nodes in wireless networks is to avail the ambient radio-frequency (RF) signal and to simultaneously harvest energy and process information. In this paper, an amplify-and-forward (AF) relaying network is considered, where an energy constrained relay node harvests energy from the received RF signal and uses that harvested energy to forward the source information to the destination. Based on the time switching and power splitting receiver architectures, two relaying protocols, namely, i) time switching-based relaying (TSR) protocol and ii) power splitting-based relaying (PSR) protocol are proposed to enable energy harvesting and information processing at the relay. In order to determine the throughput, analytical expressions for the outage probability and the ergodic capacity are derived for delay-limited and delay-tolerant transmission modes, respectively. The numerical analysis provides practical insights into the effect of various system parameters, such as energy harvesting time, power splitting ratio, source transmission rate, source to relay distance, noise power, and energy harvesting efficiency, on the performance of wireless energy harvesting and information processing using AF relay nodes. In particular, the TSR protocol outperforms the PSR protocol in terms of throughput at relatively low signal-to-noise-ratios and high transmission rates.

In cooperative networks, multiple carrier frequency offsets (MCFOs) and multiple timing offsets (MTOs) originate due to multiple distributed nodes. In this paper, algorithms for joint estimation of these parameters and channels in amplify-and-forward (AF) relaying networks are proposed. A new training model and transceiver structure at the relays for achieving synchronization throughout the network is devised. New exact closed-form expressions for the Cramér-Rao lower bounds (CRLBs) for the multi-parameter estimation problem are derived. An estimation method is proposed for jointly estimating MCFOs, MTOs, and channel gains at the destination based on space-alternating generalized expectation maximization (SAGE) and compared to a computationally-intensive least squares (LS) approach. The proposed estimator's performance is shown to be close to the CRLB at mid-to-high signal-to-noise ratio (SNR) resulting in significant cooperative performance gains in the presence of practical impairments.

This paper proposes a framework for joint blind timing and carrier offset estimation and data detection using a Sequential Importance Sampling (SIS) particle filter in Additive White Gaussian Noise (AWGN) channels. We assume baud rate sampling and model the intractable posterior probability distribution functions for sampling timing and carrier offset particles using beta distributions. To enable the SIS approach to estimate static synchronization parameters, we propose new resampling guidelines for dealing with the degeneracy problem and fine tuning the estimated values. We derive the Weighted Bayesian Cramer Rao Bound (WBCRB) for joint timing and carrier offset estimation, which takes into account the prior distribution of the estimation parameters and is an accurate lower bound for all considered Signal to Noise Ratio (SNR) values. Simulation results are presented to corroborate that the Mean Square Error (MSE) performance of the proposed algorithm is close to optimal at higher SNR values (above 20 dB). In addition, the bit error rate performance approaches that of the perfectly synchronized case for small unknown carrier offsets and any unknown timing offset. The advantage of our particle filter algorithm, compared to existing techniques, is that it can work for the full range acquisition of carrier offsets.

This paper proposes a new blind algorithm, based on Mixture Kalman Filtering (MKF), for joint carrier recovery and channel estimation in time-selective Rayleigh fading channels. MKF is a powerful tool for estimating unknown parameters in non-linear, non-Gaussian, real-time applications. We use a combination of Kalman filtering and Sequential Monte Carlo Sampling to estimate the channel fading coefficients and joint posterior probability density of the unknown carrier offset and transmitted data respectively. We study the effect of Signal to Noise Ratio (SNR) and doppler shift on Mean Square Error (MSE) and Bit Error Rate (BER) performance of the proposed algorithm through computer simulations. The results show that BER of the proposed algorithm achieves the theoretical performance slope for the full acquisition range of normalized carrier frequency offset.

Synchronization in Decode and Forward (DF) cooperative communication systems is a complex and challenging task requiring estimation of many independent timing and carrier offsets at each relay in the broadcasting phase and multiple timing and carrier offsets at the destination in the relaying phase. This paper presents a scheme for blind channel, timing and carrier offset estimation in a DF cooperative system with one source, M relays and one destination equipped with N antennas. In particular, we exploit blind source separation at the destination to convert the difficult problem of jointly estimating multiple synchronization parameters in the relaying phase into more tractable sub-problems of estimating many individual timing and carrier offsets for the independent relays. We also modify and propose a criteria for best relay selection at the destination. Simulation results demonstrate the excellent end-to-end Bit Error Rate (BER) performance of the proposed blind scheme with relay selection, which is shown to achieve the maximum diversity order with M = 4 relays using N = 5 antennas at the destination. The presented work is a complete solution to blind synchronization and channel estimation in DF cooperative communication systems.

This study addresses the problem of joint blind timing and carrier synchronisation in a (distributed-M)×N antenna system where the objective is to estimate the M carrier offsets, the M timing offsets and to recover the transmitted symbols for each of the M users given only the measured signal at the N antennas of the receiver. The authors propose a modular receiver structure that exploits blind source separation to reduce the problem into more tractable sub-problems of estimating individual timing and carrier offsets for multiple users. This leads to a robust solution of low complexity. The authors investigate the performance of the estimators analytically using modified Cramer–Rao bounds and computer simulations. The results show that the proposed receiver exhibits robust performance over a wide range of parameter values, even with worst-case Doppler of 200–300 Hz and frame size as small as 400 symbols. This work is relevant to future wireless networks and is a complete solution to the problem of estimating multiple timing and carrier offsets in distributed multiple input multiple output (MIMO) communication systems.