Downlink Training in Cell-Free Massive MIMO: A Blessing in Disguise

Cell-free Massive MIMO (multiple-input multiple-output) refers to a distributed Massive MIMO system where all the access points (APs) cooperate to coherently serve all the user equipments (UEs), suppress inter-cell interference and mitigate the multiuser interference. Recent works demonstrated that, unlike co-located Massive MIMO, the \textit{channel hardening} is, in general, less pronounced in cell-free Massive MIMO, thus there is much to benefit from estimating the downlink channel. In this study, we investigate the gain introduced by the downlink beamforming training, extending the previously proposed analysis to non-orthogonal uplink and downlink pilots. Assuming single-antenna APs, conjugate beamforming and independent Rayleigh fading channel, we derive a closed-form expression for the per-user achievable downlink rate that addresses channel estimation errors and pilot contamination both at the AP and UE side. The performance evaluation includes max-min fairness power control, greedy pilot assignment methods, and a comparison between achievable rates obtained from different capacity-bounding techniques. Numerical results show that downlink beamforming training, although increases pilot overhead and introduces additional pilot contamination, improves significantly the achievable downlink rate. Even for large number of APs, it is not fully efficient for the UE relying on the statistical channel state information for data decoding.Comment: Published in IEEE Transactions on Wireless Communications on August 14, 2019. {\copyright} 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other use

Physical Layer Security in Integrated Sensing and Communication Systems

The development of integrated sensing and communication (ISAC) systems has been spurred by the growing congestion of the wireless spectrum. The ISAC system detects targets and communicates with downlink cellular users simultaneously. Uniquely for such scenarios, radar targets are regarded as potential eavesdroppers which might surveil the information sent from the base station (BS) to communication users (CUs) via the radar probing signal. To address this issue, we propose security solutions for ISAC systems to prevent confidential information from being intercepted by radar targets. In this thesis, we firstly present a beamformer design algorithm assisted by artificial noise (AN), which aims to minimize the signal-to-noise ratio (SNR) at the target while ensuring the quality of service (QoS) of legitimate receivers. Furthermore, to reduce the power consumed by AN, we apply the directional modulation (DM) approach to exploit constructive interference (CI). In this case, the optimization problem is designed to maximize the SINR of the target reflected echoes with CI constraints for each CU, while constraining the received symbols at the target in the destructive region. Apart from the separate functionalities of radar and communication systems above, we investigate sensing-aided physical layer security (PLS), where the ISAC BS first emits an omnidirectional waveform to search for and estimate target directions. Then, we formulate a weighted optimization problem to simultaneously maximize the secrecy rate and minimize the Cram\'er-Rao bound (CRB) with the aid of the AN, designing a beampattern with a wide main beam covering all possible angles of targets. The main beam width of the next iteration depends on the optimal CRB. In this way, the sensing and security functionalities provide mutual benefits, resulting in the improvement of mutual performances with every iteration of the optimization, until convergence. Overall, numerical results show the effectiveness of the ISAC security designs through the deployment of AN-aided secrecy rate maximization and CI techniques. The sensing-assisted PLS scheme offers a new approach for obtaining channel information of eavesdroppers, which is treated as a limitation of conventional PLS studies. This design gains mutual benefits in both single and multi-target scenarios

Energy Efficient Cooperative Communication

This dissertation studies several problems centered around developing a better understanding of the energy efficiency of cooperative wireless communication systems. Cooperative communication is a technique where two or more nodes in a wireless network pool their antenna resources to form a virtual antenna array . Over the last decade, researchers have shown that many of the benefits of real antenna arrays, e.g. spatial diversity, increased range, and/or decreased transmission energy, can be achieved by nodes using cooperative transmission. This dissertation extends the current body of knowledge by providing a comprehensive study of the energy efficiency of two-source cooperative transmission under differing assumptions about channel state knowledge, cooperative protocol, and node selfishness. The first part of this dissertation analyzes the effect of channel state information on the optimum energy allocation and energy efficiency of a simple cooperative transmission protocol called orthogonal amplify-and-forward (OAF). The source nodes are required to achieve a quality-of service (QoS) constraint, e.g. signal to noise ratio or outage probability, at the destination. Since a QoS constraint does not specify a unique transmit energy allocation when the nodes use OAF cooperative transmission, minimum total energy strategies are provided for both short-term and long-term QoS constraints. For independent Rayleigh fading channels, full knowledge of the channel state at both of the sources and at the destination is shown to significantly improve the energy efficiency of OAF cooperative transmission as well as direct (non-cooperative) transmission. The results also demonstrate how channel state knowledge affects the minimum total energy allocation strategy. Under identical channel state knowledge assumptions, the results demonstrate that OAF cooperative transmission tends to have better energy efficiency than direct transmission over a wide range of channel conditions. The second part of this dissertation focuses on the development of an opportunistic hybrid cooperative transmission protocol that achieves increased energy efficiency by not only optimizing the resource allocation but also by selecting the most energy efficient cooperative transmission protocol from a set of available protocols according to the current channel state. The protocols considered in the development of the hybrid cooperative transmission protocol include compress-and-forward (CF), estimate-and-forward (EF), non-orthogonal amplify-and-forward (NAF), and decode-and-forward (DF). Instantaneous capacity results are analyzed under the assumption of full channel state knowledge at both of the sources and the destination node. Numerical results are presented showing that the delay limited capacity and outage probability of the hybrid cooperative transmission protocol are superior to that of any single protocol and are also close to the cut-set bound over a wide range of channel conditions. The final part of this dissertation focuses on the issue of node selfishness in cooperative transmission. It is common to assume in networks with a central authority, e.g. military networks, that nodes will always be willing to offer help to other nodes when requested to do so. This assumption may not be valid in ad hoc networks operating without a central authority. This section of the dissertation considers the effect selfish behavior on the energy efficiency of cooperative communication systems. Using tools from non-cooperative game theory, a two-player relaying game is formulated and analyzed in non-fading and fading channel scenarios. In non-fading channels, it is shown that a cooperative equilibrium can exist between two self-interested sources given that the end of the cooperative interaction is uncertain, that the sources can achieve mutual benefit through cooperation, and that the sources are sufficiently patient in the sense that they value future payoffs. In fading channels, a cooperative conditional trigger strategy is proposed and shown to be an equilibrium of the two-player game. Sources following this strategy are shown to achieve an energy efficiency very close to that of a centrally-controlled system when they are sufficiently patient. The results in this section show that cooperation can often be established between two purely self-interested sources without the development of extrinsic incentive mechanisms like virtual currency

Resource allocation and secure communication design in simultaneous wireless information and power transfer systems

Radio frequency (RF) energy transfer techniques have been regarded as the key enabling solutions to supply continuous and stable energy for the energy-constrained wireless devices. Simultaneous wireless information and power transfer (SWIPT) has been developed as a more promising RF energy transfer technique since it enables wireless information and wireless energy to access users from a same transmitted signal. Therefore, SWIPT has received remarkable attention. This thesis provides an investigation on applications and security issues of this emerging technology in various wireless communication scenarios. First, this thesis examines the application of SWIPT to a multi-user cooperative network in which the amplify-and-forward (AF) relay protocol is employed at the multi-antenna relay. A power splitting (PS) receiver architecture is utilized at each destination node to implement energy harvesting (EH) and information decoding (ID) simultaneously. The aim of this chapter is to minimize the relay transmit power by jointly designing relay beamforming vectors and PS ratios based on channel uncertainty models. The non-convex problem is converted into a semidefinite programming (SDP) problem by using the semidefinite relaxation (SDR) approach. In addition, a rank-one proof presents that the solution generated by the relaxed problem is optimal to the original problem. Second, a security issue about the SWIPT system is investigated in a cooperative network in the presence of potential eavesdroppers. The AF relay protocol and a PS receiver architecture are adopted at the multi-antenna relay and the desired destination node, respectively. Based on the system setup and the assumption of perfect channel state information (CSI), a transmit power minimization problem combined with the secrecy rate and harvested energy constraints is proposed to jointly optimize the beamforming vector and the PS ratio. The proposed optimization problem is non-convex and hard to tackle due to the issues of the quadratic terms and the coupled variables. To deal with this non-convex problem, two algorithms are proposed. In the first algorithm case, the proposed problem can be globally solved by using a two-level optimization approach which involves the SDR method and the one-dimensional (1-D) line search method. In addition, a rank reduction theorem is introduced to guarantee the tightness of the relaxation of the proposed scheme. In the second algorithm case, the proposed problem can be locally solved by exploiting a low complexity iterative algorithm which is embedded in the sequential parametric convex approximation (SPCA) method. Furthermore, the proposed optimization problem is extended to the imperfect CSI case. Third, a secure communication case is studied in an underlay multiple-input multiple-output (MIMO) cognitive radio (CR) network where the secondary transmitter (ST) provides SWIPT to receivers. In this chapter, two uncertainty channel models are proposed. One is based on the assumption that the ST has the perfect channel knowledge of the secondary information receiver (SIR) and the imperfect channel knowledge of secondary energy receivers (SERs) and primary receivers (PUs). The other one assumes that the ST only has the imperfect channel knowledge of all receivers. In each uncertainty channel model, an outage-constrained secrecy rate maximization (OC-SRM) problem combined with probability constraints is proposed to jointly optimizing the transmit covariance matrix and the artificial noise (AN)- aided covariance matrix. The designed OC-SRM problem for both models is non-convex due to the unsolvable probabilistic constraints. To solve this non-convex problem, the log determinant functions are first approximated to the easy handle the functions that the channel error terms are included in the trace function. Then, the probability constraints are converted into the deterministic constraints by exploiting the Bernstein-type inequality (BTI) approach. Finally, the reformulated problem for both models is solvable by using the existing convex tools. Last, a novel security issue is investigated in a MIMO-SWIPT downlink network where nonlinear energy receivers (ERs) are considered as the potential eavesdroppers. In this chapter, two uncertainty channel models, namely partial channel uncertainty (PCU) and full channel uncertainty (FCU), are proposed. An OC-SRM problem of each model is proposed to design the transmit signal covariance matrix while satisfying probabilistic constraints of the secrecy rate and the harvested energy. To surmount the non-convexity of the proposed OC-SRM problem in each model, several transformations and approximations are utilized. In the PCU model, the OC-SRM problem is first converted into two subproblems by introducing auxiliary variables. Then, three conservative approaches are adopted to obtain the safe approximation expressions of the probabilistic constraints, which are deterministic constraints. Moreover, an alternating optimization (AO) algorithm is proposed to iteratively solve two convex conic subproblems. In the FCU model, log determinant functions are first approximated to the trace functions. Then, the three approaches aforementioned are employed to convert probabilistic constraints into deterministic ones. The bisection method is utilized to solve the reformulated problem. Finally, the computational complexity of the proposed three approaches based on the PCU and FCU model is analyzed