Abstract-In this paper, the problem of distributed beamforming is considered for a wireless network which consists of a transmitter, a receiver, and relay nodes. For such a network, assuming that the second-order statistics of the channel coefficients are available, we study two different beamforming design approaches. As the first approach, we design the beamformer through minimization of the total transmit power subject to the receiver quality of service constraint. We show that this approach yields a closed-form solution. In the second approach, the beamforming weights are obtained through maximizing the receiver signal-to-noise ratio (SNR) subject to two different types of power constraints, namely the total transmit power constraint and individual relay power constraints. We show that the total power constraint leads to a closed-form solution while the individual relay power constraints result in a quadratic programming optimization problem. The later optimization problem does not have a closed-form solution. However, it is shown that using semidefinite relaxation, this problem can be turned into a convex feasibility semidefinite programming (SDP), and therefore, can be efficiently solved using interior point methods. Furthermore, we develop a simplified, thus suboptimal, technique which is computationally more efficient than the SDP approach. In fact, the simplified algorithm provides the beamforming weight vector in a closed form. Our numerical examples show that as the uncertainty in the channel state information is increased, satisfying the quality of service constraint becomes harder, i.e., it takes more power to satisfy these constraints. Also our simulation results show that when compared to the SDP-based method, our simplified technique suffers a 2-dB loss in SNR for low to moderate values of transmit power.Index Terms-Convex feasibility problem, distributed beamforming, distributed signal processing, relay networks, semidefinite programming.

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The problem of distributed beamforming is considered for a network which consists of a transmitter, a receiver, and r relay nodes. Assuming that the second order statistics of the channel coefficients are available, we design a distributed beamforming technique via maximization of the receiver signal-to-noise ratio (SNR) subject to individual relay power constraints. We show that using semi-definite relaxation, this SNR maximization can be turned into a convex feasibility semi-definite programming problem, and therefore, it can be efficiently solved using interior point methods. We also obtain a performance bound for the semi-definite relaxation and show that the semi-definite relaxation approach provides a c-approximation to the (nonconvex) SNR maximization problem, where c = O((log r) −1 ) and r is the number of relays.Index Terms-Distributed beamforming, relay networks, semidefinite programming, convex feasibility problem, distributed signal processing.

We consider a relay network which consists of two transceivers and r relay nodes. We study a half-duplex two-way relaying scheme. First, the two transceivers transmit their information symbols simultaneously and the relays receive a noisy mixture of the two transceiver signals. Then each relay adjusts the phase and the amplitude of its received signal by multiplying it with a complex beamforming coefficient and transmits the so-obtained signal. Aiming at optimally calculating the beamforming weight vector as well as the transceiver transmit powers, we minimize the total transmit power subject to two constraints on the receive signal-to-noise ratios (SNRs) at the two transceivers. We show that the optimal weight vector can be obtained through a simple iterative algorithm which enjoys a linear computational complexity per iteration.Index Terms-Distributed beamforming, two-way relaying, distributed signal processing, bi-directional relaying, optimal power allocation, wireless relay networks.

We consider a relay network which consists of two transceivers and r relay nodes. Assuming that the transceivers and the relays are all equipped with single antennas, we devise a two-way amplify-andphase-adjust relaying scheme. In this scheme, each relay multiplies its received signal by a complex weight and transmits the so-obtained signal thereby participating in a distributed beamforming process. We deploy an SNR balancing technique where the smallest of the two transceiver SNRs is maximized while the total transmit power is kept below a certain power budget. We show that this problem has a unique solution which can be obtained through an iterative procedure with a linear computational complexity per iteration. We also prove that for any channel realization, this approach leads to a power allocation scheme where half of the maximum power budget is allocated to the two transceivers and the remaining half will be shared among all the relay nodes. We further devise a distributed implementation of our proposed scheme which requires a minimal cooperation among the two transceivers and the relays. In fact, we show that our technique can be implemented such that the bandwidth required to obtain the beamforming weights in a distributed manner remains constant as the size of the network grows.

We consider a wireless network consisting of a singleantenna transmitter, a single-antenna receiver, and a multi-antenna relay node. For such a network, we introduce the novel approach of general rank beamforming. In this approach, the relay multiplies the vector of its received signals by a general-rank complex matrix to obtain a new vector. Each entry of this new vector is then transmitted on one of the antennas available at the relay. We show that maximizing the receiver SNR subject to total relay power constraint yields a closedform solution for the beamforming matrix. We also prove that if the channel coefficients from the transmitter to the relay antennas and those from the relay antennas to the receiver are statistically independent, the general rank beamforming approach results in a rank-one solution for the beamforming matrix.