A High-Diversity Transceiver Design for MISO Broadcast Channels

In this paper, the outage behavior and diversity order of the mixture transceiver architecture for multiple-input single-output broadcast channels are analyzed. The mixture scheme groups users with closely-aligned channels and applies superposition coding and successive interference cancellation decoding to each group composed of users with closely-aligned channels, while applying zero-forcing beamforming across semi-orthogonal user groups. In order to enable such analysis, closed-form lower bounds on the achievable rates of a general multiple-input single-output broadcast channel with superposition coding and successive interference cancellation are newly derived. By employing channel-adaptive user grouping and proper power allocation, which ensures that the channel subspaces of user groups have angle larger than a certain threshold, it is shown that the mixture transceiver architecture achieves full diversity order in multiple-input single-output broadcast channels and opportunistically increases the multiplexing gain while achieving full diversity order. Furthermore, the achieved full diversity order is the same as that of the single-user maximum ratio transmit beamforming. Hence, the mixture scheme can provide reliable communication under channel fading for ultra-reliable low latency communication. Numerical results validate our analysis and show the outage superiority of the mixture scheme over conventional transceiver designs for multiple-input single-output broadcast channels.Comment: The inner region is evaluated. The single-group SIC performance is evaluate

Beamforming Techniques for Non-Orthogonal Multiple Access in 5G Cellular Networks

In this paper, we develop various beamforming techniques for downlink transmission for multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) systems. First, a beamforming approach with perfect channel state information (CSI) is investigated to provide the required quality of service (QoS) for all users. Taylor series approximation and semidefinite relaxation (SDR) techniques are employed to reformulate the original non-convex power minimization problem to a tractable one. Further, a fairness-based beamforming approach is proposed through a max-min formulation to maintain fairness between users. Next, we consider a robust scheme by incorporating channel uncertainties, where the transmit power is minimized while satisfying the outage probability requirement at each user. Through exploiting the SDR approach, the original non-convex problem is reformulated in a linear matrix inequality (LMI) form to obtain the optimal solution. Numerical results demonstrate that the robust scheme can achieve better performance compared to the non-robust scheme in terms of the rate satisfaction ratio. Further, simulation results confirm that NOMA consumes a little over half transmit power needed by OMA for the same data rate requirements. Hence, NOMA has the potential to significantly improve the system performance in terms of transmit power consumption in future 5G networks and beyond.Comment: accepted to publish in IEEE Transactions on Vehicular Technolog

Secrecy Wireless Information and Power Transfer with MISO Beamforming

The dual use of radio signals for simultaneous wireless information and power transfer (SWIPT) has recently drawn significant attention. To meet the practical requirement that energy receivers (ERs) operate with significantly higher received power as compared to information receivers (IRs), ERs need to be deployed in more proximity to the transmitter than IRs. However, due to the broadcast nature of wireless channels, one critical issue arises that the messages sent to IRs can be eavesdropped by ERs, which possess better channels from the transmitter. In this paper, we address this new secrecy communication problem in a multiuser multiple-input single-output (MISO) SWIPT system where one multi-antenna transmitter sends information and energy simultaneously to an IR and multiple ERs, each with one single antenna. To optimally design transmit beamforming vectors and their power allocation, two problems are investigated with different aims: the first problem maximizes the secrecy rate for IR subject to individual harvested energy constraints of ERs, while the second problem maximizes the weighted sum-energy transferred to ERs subject to a secrecy rate constraint for IR. We solve these two non-convex problems optimally by reformulating each of them into a two-stage problem. First, by fixing the signal-to-interference-plus-noise ratio (SINR) target for ERs (for the first problem) or IR (for the second problem), we obtain the optimal beamforming and power allocation solution by applying the technique of semidefinite relaxation (SDR). Then, the original problems are solved by a one-dimension search over the optimal SINR target for ERs or IR. Furthermore, for each of the two studied problems, suboptimal solutions of lower complexity are also proposed in which the information and energy beamforming vectors are separately designed with their power allocation.Comment: accepted by IEEE Transactions on Signal Processing. Longer version of arXiv:1306.096

Robust Transceiver Design for MISO Interference Channel with Energy Harvesting

In this paper, we consider multiuser multiple-input single-output (MISO) interference channel where the received signal is divided into two parts for information decoding and energy harvesting (EH), respectively. The transmit beamforming vectors and receive power splitting (PS) ratios are jointly designed in order to minimize the total transmission power subject to both signal-to-interference-plus-noise ratio (SINR) and EH constraints. Most joint beamforming and power splitting (JBPS) designs assume that perfect channel state information (CSI) is available; however CSI errors are inevitable in practice. To overcome this limitation, we study the robust JBPS design problem assuming a norm-bounded error (NBE) model for the CSI. Three different solution approaches are proposed for the robust JBPS problem, each one leading to a different computational algorithm. Firstly, an efficient semidefinite relaxation (SDR)-based approach is presented to solve the highly non-convex JBPS problem, where the latter can be formulated as a semidefinite programming (SDP) problem. A rank-one recovery method is provided to recover a robust feasible solution to the original problem. Secondly, based on second order cone programming (SOCP) relaxation, we propose a low complexity approach with the aid of a closed-form robust solution recovery method. Thirdly, a new iterative method is also provided which can achieve near-optimal performance when the SDR-based algorithm results in a higher-rank solution. We prove that this iterative algorithm monotonically converges to a Karush-Kuhn-Tucker (KKT) solution of the robust JBPS problem. Finally, simulation results are presented to validate the robustness and efficiency of the proposed algorithms.Comment: 13 pages, 8 figures. arXiv admin note: text overlap with arXiv:1407.0474 by other author