Hybrid analog-digital beamforming is an effective approach for practical implementations of a massive multiple-input multiple-output (MIMO) system by reducing the number of radio frequency (RF) chains. Fully connected hybrid beam-forming (F-HBF), where each RF chain is connected to each antenna, can lower hardware complexity, power consumption, and cost compared to digital beamforming. Subarray-based hybrid beamforming (S-HBF), where a specific group of RF chains is allocated to a particular subarray, can further reduce hardware requirements. The antenna array is divided into subarrays using effective partitioning so that the optimization of analog beamforming can be shared across multiple subarrays, substantially reducing computational complexity.

The paper considers phase-only reconfigurable sparse arrays (RSAs) for receive beamforming to maximize signal-to-interference plus noise ratio (MaxSINR). We develop a design approach based on supervised deep neural network (DNN) to learn and mimic a phase-only sparse MaxSINR beamformer. The proposed approach strives to match the SINR performance of data driven sparse Capon beamformer. The problem is posed as a multi-label classification problem, where the received antenna correlations is the input to the fully connected neural network (FCNN) which outputs the optimum sensor locations for effective interference mitigation. We evaluate the performance of DNN based optimization of RSAs in terms of the ability of the classified sparse array to mitigate interference and maximize signal power using phase-only beamforming. The phase-only DNN-based sparse sensor placement reduces hardware requirements, shifts optimization algorithm complexity to satisfying training data sufficiency, and is amenable to real-time implementation.

Recent performance analysis of dual-function radar communications (DFRC) systems, which embed information using phase shift keying (PSK) into multiple-input multiple-output (MIMO) frequency hopping (FH) radar pulses, shows promising results for addressing spectrum sharing issues between radar and communications. However, the problem of decoding information at the communication receiver remains challenging, since the DFRC transmitter is typically assumed to transmit only information embedded radar waveforms and not the training sequence. We propose a novel method for decoding information at the communication receiver without using training data, which is implemented using a software-defined radio (SDR). The performance of the SDR implementation is examined in terms of bit error rate (BER) as a function of signal-to-noise ratio (SNR) for differential binary and quadrature phase shift keying modulation schemes and compared with the BER versus SNR obtained with numerical simulations.

In this paper, we consider a wireless communication system, where a full-duplex hybrid access point (HAP) transmits to a set of cellular users (CUs) in the downlink channel, while receiving data from a set of energy-constrained communication devices like user equipments (UEs) in the uplink channel. The HAP has a massive antenna array, while all CUs and UEs nodes are equipped with single antenna each. Time switching protocol is adopted, where channel estimation, wireless power transfer, and information transfer between UEs, CUs and full-duplex HAP are performed in two phases. By adopting maximum ratio combining/maximum ratio transmission (MRC/MRT) and zero-forcing (ZF) processing at the HAP, the uplink and downlink achievable rate expressions in the large-antenna limit and approximate results that hold for any finite number of antennas are derived. Moreover, the optimum energy beamformer and time-split parameter at the HAP are found to maximize the downlink sum-rate under a constraint on uplink sum-rate. Our findings reveal that our proposed energy beamforming with ZF and MRC/MRT processing for information transfer achieves up to 47% and 14% average sum rate gains as compared with the suboptimum energy beamformer, respectively.

The paper extends our earlier work on the sum of a complex Gaussian and the product of two complex Gaussians (a scalar and a vector) by incorporating multiple targets. We derive the joint probability density function (pdf) of the resulting vector, after which we derive the pdf, cumulative distribution function (cdf), and moments of the square norm of the vector (signal energy). A novel integral function is introduced to make the analysis possible. The case of high signal-to-noise ratio (SNR) and a single observation has been treated as a special case. The obtained theoretical results are then used to develop maximum likelihood and moment matching estimators, for estimating the number of targets. Numerical results validate the obtained results and characterize the performance of the proposed estimators.

Although beamforming optimization problems in full-duplex communication systems can be optimally solved with the semidefinite relaxation (SDR) approach, its computational complexity increases rapidly when the problem size increases. In order to circumvent this issue, in this paper, we propose an alternating direction of multiplier method (ADMM) which minimizes the augmented Lagrangian of the dual of the SDR and handles the inequality constraints with the use of slack variables. The proposed ADMM is then applied for optimizing the relay beamformer to maximize the secrecy rate. Simulation results show that the proposed ADMM performs as good as the SDR approach.

The joint probability density function (pdf) of a complex vector, which is the product of a complex Gaussian scalar and a complex Gaussian vector, added to another complex Gaussian vector, is derived. The pdf, cumulative distribution function, moments, and moment generating function of the squared norm and norm of this vector are then derived. The derived result is then used to characterize the performance of energy detector (square law detector) for radar system.

We consider downlink non-orthogonal multiple access transmission where an access point communicates with a set of near and far users via a full-duplex multiple antenna relay. To deal with the inter-user interference at the near user and self-interference at the relay, we propose the optimum and suboptimal beamforming schemes. In addition, we consider two different user selection criteria, namely: 1) random near user and random far user (RNRF) selection and 2) nearest near user and nearest far user (NNNF) selection, and we derive the outage probabilities of the near and far users. Our findings reveal that as compared to half-duplex operation, full-duplex relaying can reduce the outage probability of the near users up to 63% in the case of NNNF user selection. With suboptimal beamforming schemes, the NNNF user selection shows a superior performance as compared to the RNRF user selection for all choices of transmit power, while with the optimum beamforming, the performance of the RNRF user selection converges to the NNNF user selection at high transmit power. The simulation results are provided to confirm the accuracy of the developed analytical results and facilitate a better performance comparison.

In this paper, we consider a non-orthogonal multiple access cognitive radio network, where a full-duplex (FD) multi-antenna relay assists transmission from an access point (AP) to a cognitive far user, while at the same time, the AP transmits to a cognitive near user. Our objective is to maximize the rate of the near user under a constraint that the rate of the far user is above a certain threshold. To this end, a non-convex joint optimization problem of relay beamforming and the transmit powers at the AP and FD relay is solved as a semi-definite relaxation problem, in conjunction with an efficiently solvable line-search approach. We also consider a low complexity fixed beamformer design, where the optimum power allocation between the AP and FD relay is solved. Several fixed beamforming designs based on the zero-forcing criterion are proposed for which exact and asymptotic outage probability expressions corresponding to the near and far users are derived. Our results demonstrate that the proposed joint optimization can significantly reduce the self-interference impact at the FD relay and inter-user interference in the near user case.

In this paper, we consider a non-orthogonal multiple access cognitive radio network, where a full-duplex multi-antenna relay assists transmission from a base station (BS) to a cognitive far user, whereas, at the same time, the BS transmits to a cognitive near user. Our objective is to enlarge the far-near user rate region by maximizing the rate of the near user under a constraint that the rate of the far user is above a certain threshold. To this end, a non- convex joint optimization problem of relay beamforming and the transmit powers at the BS and cognitive relay is solved as a semi-definite relaxation problem, in conjunction with an efficiently solvable line-search approach. For comparisons, we also consider low complexity fixed beamformer design, where the optimum power allocation between the BS and cognitive relay is solved. Our results demonstrate that the proposed joint optimization can significantly reduce the impact of the residual self-interference at the FD relay and inter-user interference in the near user case.

We consider a multiuser wireless system with a full-duplex hybrid access point (HAP) that transmits to a set of users in the downlink channel, while receiving data from a set of energy-constrained sensors in the uplink channel. We assume that the HAP is equipped with a massive antenna array, while all users and sensor nodes have a single antenna. We adopt a time-switching protocol where in the first phase, sensors are powered through wireless energy transfer from HAP and HAP estimates the downlink channel of the users. In the second phase, sensors use the harvested energy to transmit to the HAP. The downlink-uplink sum-rate region is obtained by solving downlink sum-rate maximization problem under a constraint on uplink sum-rate. Moreover, assuming perfect and imperfect channel state information, we derive expressions for the achievable uplink and downlink rates in the large-antenna limit and approximate results that hold for any finite number of antennas. Based on these analytical results, we obtain the power-scaling law and analyze the effect of the number of antennas on the cancellation of intra-user interference and the self-interference.

Although radar and communication systems so far have been considered separately, recent advances in passive radar systems have motivated us to propose a unified system, capable of fulfilling the requirements of both radar and communications. In this paper, we provide performance tradeoff analysis for a system consisting of a transmitter, a passive radar receiver, and a communication receiver (CR). The total power is allocated for transmitting the radar waveforms and information signals in such a way that the probability of detection (PD) is maximized, while satisfying the information rate requirement of the CR. An exact closed-form expression for the probability of false alarm (PFA) is derived, whereas PD is approximated by assuming that the signal-to-noise ratio corresponding to the reference channel is often much larger than that corresponding to the surveillance channel. The performance tradeoff between the radar and communication subsystems is then characterized by the boundaries of the PFA-rate and PD-rate regions.

We propose techniques for optimizing transmit beamforming in a full-duplex multiple-input-multiple-output wireless-powered communication system, which consists of two phases. In the first phase, the wireless-powered mobile station (MS) harvests energy using signals from the base station (BS), whereas in the second phase, both MS and BS communicate to each other in a full-duplex mode. When complete instantaneous channel state information (CSI) is available, the BS beamformer and the time-splitting (TS) parameter of energy harvesting are jointly optimized in order to obtain the BS-MS rate region. The joint optimization problem is non-convex, however, a computationally efficient optimum technique, based upon semidefinite relaxation and line-search, is proposed to solve the problem. A sub-optimum zero-forcing approach is also proposed, in which a closed-form solution of TS parameter is obtained. When only the second-order statistics of transmit CSI is available, we propose to maximize the ergodic information rate at the MS while maintaining the outage probability at the BS below a certain threshold. An upper bound for the outage probability is also derived and an approximate convex optimization framework is proposed for efficiently solving the underlying non-convex problem. Simulations demonstrate the advantages of the proposed methods over the sub-optimum and half-duplex ones.

We consider a full-duplex (FD) decode-and-forward system in which the time-switching protocol is employed by the multiantenna relay to receive energy from the source and transmit information to the destination. The instantaneous throughput is maximized by optimizing receive and transmit beamformers at the relay and the time-split parameter. We study both optimum and suboptimum schemes. The reformulated problem in the optimum scheme achieves closed-form solutions in terms of transmit beamformer for some scenarios. In other scenarios, the optimization problem is formulated as a semidefinite relaxation problem and a rank-one optimum solution is always guaranteed. In the suboptimum schemes, the beamformers are obtained using maximum ratio combining, zero-forcing, and maximum ratio transmission. When beamformers have closed-form solutions, the achievable instantaneous and delay-constrained throughput are analytically characterized. Our results reveal that beamforming increases both the energy harvesting and loop interference suppression capabilities at the FD relay. Moreover, simulation results demonstrate that the choice of the linear processing scheme as well as the time-split plays a critical role in determining the FD gains.

Although partial relay selection (PRS) for amplify-and-forward relay systems requires only the knowledge of source-relay (S-R) channels, in general, it incurs a significant performance loss. In cooperative systems with all single-antenna nodes, irrespective of the number of relays, the diversity order of PRS is limited to only one. This paper proposes a novel relay selection method for improving the performance of PRS scheme. In particular, as in the conventional PRS scheme, the relay that gives the best first-hop signal-to-noise ratio (SNR) is selected. However, this selection is made from only a subset of relays, for which the corresponding S-R and relay-destination (R-D) links are not in outage. An R-D link is considered to be in outage if its SNR is below the predefined threshold value of the end-to-end SNR plus some adjustable margin. The additional overhead required for implementing the proposed scheme is comparable to that of the conventional PRS method. For conciseness and better exposition of the proposed method, we limit our theoretical analysis to a system with two to three relays. The exact expressions of the end-to-end outage probability are derived and it is shown that full diversity order is achieved. Simulation results verify theoretical analysis and show that the proposed method significantly outperforms the conventional PRS method. Moreover, the results demonstrate that, for properly selected margin, the performance of the proposed method is very close or comparable to the method with full channel state information.

This paper investigates performance limits of a two-hop multi-antenna amplify-and-forward (AF) relay system in the presence of a multi-antenna energy harvesting receiver. The source and relay nodes of the two-hop AF system employ orthogonal space-time block codes for data transmission. We derive joint optimal source and relay precoders to achieve different tradeoffs between the energy transfer capability and the information rate, which are characterized by the boundary of the so-called rate-energy (R-E) region. Numerical results demonstrate the effect of different parameters on the boundary of the R-E region.

In this paper, we examine the resource optimization problem in a broadcast relay network where a source broadcasts signals to the user receivers, which are distributed over a service region. The source does not have direct line-of-sight to the service area and the information is delivered through a relay. The objective of this paper is to jointly optimize the relay position as well as the power allocation between the source and relay so that the outage probability of the signal received at the user nodes is minimized. Two different optimization criteria, which respectively minimize the worst-case outage probability and the average outage probability, are used. The analyses are verified by simulation results.

The source and relay precoders are jointly optimized for an amplify-and-forward multiple-input multiple-output relay system. Both the source and relay nodes employ orthogonal space-time block codes (OSTBC), and have imperfect channel state information of the source-relay and relay-destination channels, respectively. Using the worst-case robust design approach, we show that the problem of maximizing the minimum signal-to-noise ratio at the destination can be exactly reformulated as a convex optimization problem. Further, we provide an approximate semi-analytical approach which significantly reduces the computational cost of solving the convex problem. Numerical results show that this approximation is accurate and the proposed design outperforms OSTBC with eigen beamforming, OSTBC with equal power allocation, and currently available worst-case robust beamforming design without OSTBC.

This paper considers the problem of jointly designing the precoders for the downlink multiuser multiple-input multiple-output (MIMO) systems where imperfect channel state information (CSI) is available at the base station (BS). The multi-antenna users are assumed to employ the linear minimum-mean-square-error (MMSE) receivers. Our objective is to maximize the achievable sum rate under a total BS power constraint and provide robustness against the channel uncertainties. We apply the familiar worst-case performance optimization approach, which in our case turns to be the problem of maximizing the worst-case sum rate constrained with the total BS power. The problem is examined as follows. We first reformulate the expression for the MMSE matrix so that it is conveniently utilized for the worst-case sum rate maximization problem. Then, we solve the resulting robust design problem efficiently by employing the existing mean-square-error (MSE) uplink/downlink duality approach. Computer simulations verify the robustness of the proposed design when compared to the non-robust/naive design.

A new approach to multi-antenna downlink beam- forming is proposed that provides an improved robustness against uncertainty in the downlink channel covariance matrices caused by errors between the actual and estimated channel values. The proposed method uses the knowledge of the statistical distribution of such a covariance uncertainty to minimize the total downlink transmit power under the constraint that the outage probability does not exceed a certain threshold value. Although our approach initially leads to a non-convex optimization problem, it can be reformulated in a convex form using the semidefinite relaxation technique. The resulting convex optimization problem can be solved efficiently using the well-established interior point methods. Computer simulations verify performance improvements of the proposed technique as compared to the robust transmit beamforming method based on the worst-case performance optimization with judicious selection of the upper bounds on channel covariance errors.

A multi-user multiple-input-multiple-output (MIMO) system with orthogonal space-time block coding (OSTBC) is analyzed in a Rayleigh fading environment for the uplink of a wireless communication system. The outage probability of the signal-to-interference-and-noise ratio (SINR) for the user of interest is evaluated by using the following linear receivers; 1) a sub-optimum matched filter (MF) and 2) an optimum minimum variance (MV) receiver. Simulation results show that for an outage probability of 0.2, the MV receiver provides a gain of about 3 dB over the MF in terms of the SINR threshold value when a full-rate OSTBC like the Alamouti code is used. However, for low-rate OSTBCs like a 1/2-rate 8times3 OSTBC, the difference of this gain between the MF and the MV receiver is above 8 dB

A multi-user Multiple Input Multiple Output (MIMO) system with beamforming based on Maximum Ratio Transmission (MRT) is analyzed in a Rayleigh fading environment. A simple closed and compact form approximate expression for the outage probability of a mobile station for the downlink of such a system is obtained by considering that all the mobile stations are power controlled by a single base station. An interesting observation of the analysis is that an optimum power control problem can be formulated based on the outage probability of each mobile station in the downlink because the outage probability computation is easily simplified for a cellular environment where all the mobile stations are power controlled by the single base station. The objective is to minimize the base station transmission power by keeping the outage probability of all mobile stations below a specified threshold value. The optimization problem is non-convex and non-linear. We use an Evolutionary Strategy (EV) [1] to solve such a non-convex optimization problem.

In this paper, we propose a new uplink beamforming algorithm that provides robustness against the uncertainty of the assumed spatial covariance matrix at a base station (BS) which employs antenna arrays. This uncertainty arises due to the difference between the presumed and the actual array response as well as the estimation errors of the fading coefficients. In our method, we exploit the assumed statistical distribution of the uncertainty to minimize the outage probability of each mobile station in the uplink. Our approach leads to a semidefinite relaxation problem that can be solved efficiently using the well-established interior point methods. Simulation results show that our method slightly outperforms the robust adaptive beamforming based upon worst-case performance optimization. Moreover, the proposed technique avoids the necessity of the knowledge of the norm bounds of the uncertainty of spatial covariance matrices.

We propose a new uplink to downlink spatial covariance matrix transformation technique that provides robustness against uplink steering vector mismatches for frequency duplex systems (FDD) in a frequency selective fading environment. Our approach leads to a matrix robust least squares (RLS) problem that can be formulated in a convex form as a second order cone (SOC) program and solved efficiently using the well-established interior point methods. The performance of the proposed method is compared with that of (K. Hugl et al., 1999) and (T. Aste et al., 1998) using a dynamic system level simulator developed for investigating the capacity improvement of a UMTS-FDD system with smart antennas at the base stations. Downlink capacity is used as a measure of system performance by finding the number of users for which a certain quality of service (QoS) requirement is fulfilled. System level simulations are carried out for 4-element uniform circular antenna (UCA) array. Simulation results show that our method outperforms both (K. Hugl et al., 1999) and (T. Aste et al., 1998) by supporting about 48% and 15% more average number of users than that of (K. Hugl et al., 1999) and (T. Aste et al., 1998) , respectively without any significant degradation in performance when compared to (T. Aste et al., 1998) with known uplink steering vector.

This paper presents a comparison of two different uplink to downlink covariance matrix transformation methods that can be used in downlink beamforming for frequency selective FDD channels. The performance improvement obtained from the transformation methods is compared with that from the method, which directly uses the uplink covariance matrix for downlink beamforming. For this purpose, a dynamic system level simulator developed for investigating the capacity improvement of a UMTS-FDD system employing smart antennas at the base stations is used. Downlink capacity is used as a measure of the performance of the system by finding the number of users for which a certain quality of service (QoS) requirement is fulfilled. System level simulations are carried out for antenna arrays with 2, 4 and 8 elements. Simulation results show that the downlink capacity can be improved significantly with higher number of antenna elements only if a transformation method is used in order to overcome the effect of different uplink and downlink carrier frequencies.

A new dynamic system level simulator is developed for investigating the capacity improvement in a UMTS-FDD system employing smart antennas at the base stations. The simulator is used to analyze the performance of this system by finding the number of users for which a certain quality of service (QoS) requirement is fulfilled. In this paper, analyses of the downlink capacity for different numbers of array elements with different adaptive beamforming strategies are presented. When using smart antennas, the real amount of performance improvement can only be estimated by system level simulations. Hence, dynamic system level simulations for UMTS-FDD are carried out which take into account the statistical nature of almost all parameters, e.g. wave propagation, user motion and traffic scenarios. Simulation results show a significant system capacity increase when beamforming is utilized.