Broadcast in MIMO Systems Based on a Generalized QR Decomposition: Signaling and Performance Analysis

Abstract

Abstract-A simple signaling method for broadcast channels with multiple-transmit multiple-receive antennas is proposed. In this method, for each user, the direction in which the user has the maximum gain is determined. The best user in terms of the largest gain is selected. The corresponding direction is used as the modulation vector (MV) for the data stream transmitted to the selected user. The algorithm proceeds in a recursive manner where in each step, the search for the best direction is performed in the null space of the previously selected MVs. It is demonstrated that with the proposed method, each selected MV has no interference on the previously selected MVs. Dirty-paper coding is used to cancel the remaining interference. For the case that each receiver has one antenna, the presented scheme coincides with the known scheme based on Gram-Schmidt orthogonalization (QR decomposition). To analyze the performance of the scheme, an upper bound on the cumulative distribution function (CDF) of each subchannel is derived which is used to establish the diversity order and the asymptotic sum-rate of the scheme. It is shown that using fixed rate codebooks, the diversity order of the jth data stream, 1 j M, is equal to N(M 0 j + 1)(K 0 j + 1), where M, N, and K indicate the number of transmit antennas, the number of receive antennas, and the number of users, respectively. Furthermore, it is proven that the throughput of this scheme scales as M log log(K) and asymptotically (K 0! 1) tends to the sum-capacity of the multiple-input multiple-output (MIMO) broadcast channel. The simulation results indicate that the achieved sum-rate is close to the sum-capacity of the underlying broadcast channel.Index Terms-Dirty-paper coding, multiple-antenna arrays, multiple-input multiple-output (MIMO) broadcast channels, multiuser diversity, multiuser systems, QR decomposition, space-division-multiple access.

Eigen-Based Transceivers for the MIMO Broadcast Channel With Semi-Orthogonal User Selection

“…It is still not clear how much advantage can be gained by employing multiple-antennas at the user terminals. Some recent exceptions which deal with the multiple-antenna user scenario are presented in [14] and [15]. Particularly, [14] proposed a generalized G-ZFDPC approach, based on the idea of eigenmode transmission (eigen-beamforming).…”

“…Some recent exceptions which deal with the multiple-antenna user scenario are presented in [14] and [15]. Particularly, [14] proposed a generalized G-ZFDPC approach, based on the idea of eigenmode transmission (eigen-beamforming). A limitation of 1 More specifically, two complex vectors u and v, with unit norm, are said to be semi-orthogonal if |u H v| 2 < δ, where δ is referred to as the semi-orthogonality parameter.…”

“…The second technique is a generalization of the algorithm proposed in [13], which we refer to as ZFBF-SUS. For both techniques, we present an asymptotic performance analysis of the sum rate (as in [6,[13][14][15][16][17]) as the number of users grows large.…”

Low-complexity multiuser MIMO downlink system based on a small-sized CQI quantizer

“…where Q a : The number of antennas in the ath user selection process Proof: Appendix 4 In Theorem 1, γ a:Q a is the approximated value of the CQI when 1 < N < M. Since the cdf in Lemma 3 can be changed to follow the type (iii) distribution in [ [18], Theorem 4], the closed form expression of CQI k,a can be analyzed using the studies of extreme value theory when N ≠ M. Q a used under the RBF and SUS-ee system will be presented in the next section.…”

“…Therefore, g' can be analyzed using the studies of extreme value theory in order statistics. According to [18,19], the distribution of g' satisfies the following inequality …”