Joint Transmitter-Receiver Design for the Downlink Multiuser Spatial Multiplexing MIMO System

Reading time: 5 minute
...

📝 Original Info

  • Title: Joint Transmitter-Receiver Design for the Downlink Multiuser Spatial Multiplexing MIMO System
  • ArXiv ID: 0811.0241
  • Date: 2009-09-29
  • Authors: Researchers from original ArXiv paper

📝 Abstract

This paper proposes a joint transmitter-receiver design to minimize the weighted sum power under the post-processing signal-to-interference-and-noise ratio (post-SINR) constraints for all subchannels. Simulation results demonstrate that the algorithm can not only satisfy the post-SINR constraints but also easily adjust the power distribution among the users by changing the weights accordingly. Hence the algorithm can be used to alleviates the adjacent cell interference by reducing the transmitting power to the edge users without performance penalty.

💡 Deep Analysis

Deep Dive into Joint Transmitter-Receiver Design for the Downlink Multiuser Spatial Multiplexing MIMO System.

This paper proposes a joint transmitter-receiver design to minimize the weighted sum power under the post-processing signal-to-interference-and-noise ratio (post-SINR) constraints for all subchannels. Simulation results demonstrate that the algorithm can not only satisfy the post-SINR constraints but also easily adjust the power distribution among the users by changing the weights accordingly. Hence the algorithm can be used to alleviates the adjacent cell interference by reducing the transmitting power to the edge users without performance penalty.

📄 Full Content

arXiv:0811.0241v2 [cs.IT] 4 Nov 2008 Joint Transmitter-Receiver Design for the Downlink Multiuser Spatial Multiplexing MIMO System Pengfei Ma, Wenbo Wang, Xiaochuan Zhao and Kan Zheng Wireless Signal Processing and Network Lab, Key Laboratory of Universal Wireless Communication Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China Abstract—In the multiuser spatial multiplexing multiple-input multiple-output (MIMO) system, the joint transmitter-receiver (Tx-Rx) design is investigated to minimize the weighted sum power under the post-processing signal-to-interference-and-noise ratio (post-SINR) constraints for all subchannels. Firstly, we show that the uplink-downlink duality is equivalent to the Lagrangian duality in the optimization problems. Then, an iterative algorithm for the joint Tx-Rx design is proposed according to the above result. Simulation results show that the algorithm can not only satisfy the post-SINR constraints, but also easily adjust the power distribution among the users by changing the weights accordingly. So that the transmitting power to the edge users in a cell can be decreased effectively to alleviate the adjacent cell interference without performance penalty. Index Terms—spatial multiplexing, MIMO, power allocation, Lagrangian duality. I. INTRODUCTION Spatial multiplexing for the multiple-input multiple-output (MIMO) systems, employing multiple transmit and receive antennas, has been recognized as an effective way to improve the spectral efficiency of the wireless link [1]. More recently, the multiuser schemes have been investigated for the spatial multiplexing MIMO systems. This paper focuses on the down- link multiuser schemes in which each user can not cooperate with the others thus suffers from the interference from them. Mainly, there are two kinds of multiuser schemes. One is the precoder or the transmit beamforming, such as the dirty-paper coding (DPC) [2] and the zero-forcing (ZF) [3], etc., which mitigates the multiuser interference only by processing at the transmitter. The other is the joint transmitter-receiver (Tx-Rx) design, such as the nullspace-directed SVD (Nu-SVD) [4] and the minimum total mean squared error (TMMSE) [5], etc. In general, the former possesses lower complexity but more performance penalty. With the great development of signal processors, the latter gradually draws more attention. For the joint Tx-Rx design, the schemes proposed in [4][5] minimize mean squared error (MMSE), or maximize the capacity under the transmit power constraint. Whereas on some occasions, such as the multimedia communication, it is required to minimize the total transmit power while guarantee the quality of service (QoS). [6][7] investigate the beamforming and the power allocation policy when all users are subjected to a set of post-processing signal-to-interference- and-noise ratio (post-SINR) constrains in the uplink SIMO and the downlink MISO. [8][9] extend this work to the downlink MIMO and the MIMO network, however the MIMO systems discussed in [8][9] are assumed that there is only one substream between each pair of the transmitter and re- ceiver. In other words, only the multiuser interference appears in the so-called diversity MIMO system in [8][9]. For the multiuser spatial multiplexing MIMO system, however, both the multiuser interference between individual users and self- interference between individual substreams of a user should be mitigated. For the downlink, the transmit beamforming affects the interference signature of all receivers, whereas the receive beamforming only affects that of the corresponding user. [7][8] construct a dual system, called the virtual uplink, and indicate that the virtual uplink can obtain the same post-SINR as the primary downlink. Moreover, the receive beamforming matrix of the virtual uplink is identical with the transmit beamforming matrix of the primary downlink. The design of the downlink, therefore, can resort to the virtual uplink. In this paper, we extend the duality derived for MIMO network in [9] to the multiuser spatial multiplexing MIMO system. According to the uplink-downlink duality, we propose a joint Tx-Rx scheme to minimize the weighted sum power under the post-SINR constraints of all the subchannels. Notation: Boldface upper-case letters denote matrices, and boldface lower-case letters denote column vectors. tr(·), (·)∗, (·)H, ||·||2 and ||·||F denote trace, conjugate, conjugate trans- position, Euclidian norm and Frobenius norm, respectively. diag(x) denotes a diagonal matrix with diagonal elements drawn from the vector x. [·]i,j, [·]:,j denote the (i,j)-th element and j-th column of a matrix, respectively. II. SYSTEM MODEL We consider a base station (BS) with M antennas and K mobile stations (MS’s) each having Ni(i = 1, . . . , K) antennas. There are Li(i = 1, . . . , K) substreams between BS and MSi(i = 1, . . . , K), that is to say, BS transmits Li symbols to MSi simultaneously. T

…(Full text truncated)…

Reference

This content is AI-processed based on ArXiv data.

Start searching

Enter keywords to search articles

↑↓
ESC
⌘K Shortcut