In this paper, we study bit allocation and pairing methods based on zero forcing beamforming for downlink multiuser distributed antenna systems with limited feedback. Before assigning the feedback bit for each distributed antenna (DA) port, we need to solve the pairing issue which determines the set of DA ports to support a user. To this end, we first analyze an upper bound of a mean rate loss between perfect channel state information systems and limited feedback systems. Since minimizing the obtained bound is a joint optimization problem with respect to the pairing and the bit allocation, it is difficult to identify a solution analytically. Instead, we propose a two-step algorithm which derives the pairing based on the bound of the rate loss, and then obtain the feedback bit allocation method independently. From simulation results, we confirm that the proposed algorithms offer about 135% performance gains over a conventional equal bit allocation scheme for 5 DA ports systems.

In this paper, we study a feedback bit allocation algorithm with signal-to-leakage plus noise ratio maximizing beamforming (FA-SMB) for distributed antenna systems (DAS) presented in work [1]. We first investigate a scaling law of feedback bits for both the FA-SMB scheme and equal bit allocation. Through this analysis, we confirm that the required number of feedback bits to satisfy the maximum allowable rate gap between DAS with perfect channel state information (CSI) and limited feedback linearly increases with signal-to-noise ratio. Also, it is verified that the FA-SMB scheme saves the feedback bits by up to 30% over the equal bit allocation with the same rate gap at SNR = 40 dB for three-user DAS. Moreover, we show that the FA-SMB scheme substantially reduces the computational complexity compared to exhaustive search. Finally, we provide simulation results to demonstrate the efficacy of the FA-SMB scheme.

In this paper, we study bit allocation and pairing methods based on distributed zero forcing beamforming for downlink multi-user distributed antenna (DA) systems with limited feedback. Before assigning the feedback bit for each DA port, we need to solve the pairing issue that determines the set of DA ports to support a user. To this end, we first analyze an upper bound of a mean rate loss between perfect channel state information systems and limited feedback systems. Since minimizing the obtained bound is a joint optimization problem with respect to the pairing and the bit allocation, it is difficult to identify a solution analytically. Instead, we propose a two-step algorithm that derives the pairing based on the bound of the rate loss and then obtain the non-iterative bit allocation method independently. To further improve the performance, an enhanced feedback bit allocation algorithm is also proposed by applying an iterative optimization technique. In addition, we investigate a scaling law of limited feedback systems to maintain a constant rate loss as signal-to-noise ratio increases. From simulation results, we confirm that the proposed algorithms offer about 135% performance gains over a conventional scheme for five DA port systems and verify that our analysis is well matched with the numerical results.

In this paper, we optimize antenna locations for a distributed antenna system (DAS) with distributed antenna (DA) ports equipped with multiple antennas under per-DA port power constraint. Maximum ratio transmission and scaled zero-forcing beamforming are employed for single-user and multi-user DAS, respectively. Instead of maximizing the cell average ergodic sum rate, we focus on a lower bound of the expected signal-to-noise ratio (SNR) for the single-cell scenario and the expected signal-to-leakage ratio (SLR) for the two-cell scenario to determine antenna locations. For the single-cell case, optimization of the SNR criterion generates a closed form solution in comparison to conventional iterative algorithms. Also, a gradient ascent algorithm is proposed to solve the SLR criterion for the two-cell scenario. Simulation results show that DAS with antenna locations obtained from the proposed algorithms achieves capacity gains over traditional centralized antenna systems.

In this paper, we propose new algorithms to determine the antenna location for downlink distributed antenna systems (DASs) in single-cell and two-cell environments. We consider the composite fading channel which includes small and large scale fadings. First, for the single-cell DAS, we formulate the optimization problem of distributed antenna (DA) port locations by maximizing the lower bound of the expected signal to noise ratio (SNR). In comparison to the conventional algorithm based on the squared distance criterion which requires an iterative method, our problem generates a closed form solution. Next, for the two-cell DAS, we propose a gradient ascent algorithm which determines the optimum DA locations by maximizing the lower bound of the expected signal to leakage ratio (SLR). In our work, we consider selection transmission, maximal ratio transmission and zero-forcing beamforming (ZFBF) under sum power constraint and study equal gain transmission and scaled ZFBF under per-antenna power constraint. Simulation results show that our proposed algorithms based on both the SNR and the SLR criteria offer a capacity gain over the conventional centralized antenna systems.

In this paper we propose new algorithms to determine the antenna location for downlink distributed antenna systems (DAS) with selection transmission (ST). ST has some advantages for DAS since the feedback overhead is quite small and other-cell interference can be reduced compared to other transmission schemes. For the single-cell case, we consider a circular antenna layout with or without a center antenna and divide a cell into regions with the same physical area. Then, we formulate the optimization problem of distributed antenna (DA) port locations which maximizes the lower bound of the expected signal to noise ratio in each region. Also, for the two-cell DAS, we maximize the lower bound of the expected signal to leakage ratio to identify the optimum DA positions. In order to solve the problem, we propose an iterative method by deriving the gradient of the cost function for a gradient ascent algorithm. The DA locations obtained from our proposed method are compared with conventional solutions. Simulation results show that the proposed algorithms offer a large capacity gain over the centralized antenna systems in single-cell and two-cell environments.

This paper considers both one-way and two-way relaying systems with multiple relays between two terminal nodes where all nodes have multiple-input multiple-output (MIMO) antennas. We propose a unified algorithm which computes the optimal linear transceivers jointly at the source node and the relay nodes for amplify-and-forward (AF) protocols. First, optimization designs based on the sum-rate and the mean-square error (MSE) criteria are formulated for the two-way AF relaying channel. Due to non-convexity of the given problems, the proposed schemes iteratively identify local-optimal source and relay filters by deriving the gradients of the cost functions for a gradient descent algorithm. Then, the proposed algorithm can optimize a one-way multiple relay system as a special case of the two-way channel. Finally, we prove the global optimality of the maximum sum-rate scheme under an asymptotically large antenna assumption. From simulation results, it is confirmed that the proposed methods yield the near optimum result for the MIMO multiple relay channel even with a moderate number of antennas. Consequently, we show that the proposed algorithm outperforms conventional schemes in terms of the sum-rate and the error performance for both one-way and two-way protocols.

This paper considers amplify-and-forward (AF) relaying systems with multiple relay nodes between the source and the destination node, where all nodes are equipped with multiple antennas. Using multiple relay nodes to transmit a message, a distributed diversity gain can be obtained. In this paper, we address the optimization problem of relay weighting matrices which minimizes the mean-square-error (MSE). For general relaying systems which have an arbitrary number of relay nodes, the given problem has a non-convex cost function. Thus, we propose an iterative scheme which identifies a local optimal relay precoder by deriving the gradient of the MSE and applying the gradient descent algorithm. Simulation results show that the proposed iterative scheme outperforms the conventional schemes for multiple MIMO relay systems with a small number of iterations in terms of both the MSE and the bit error rate.