Computationally Efficient Symbol Detection Schemes in Multi-Device STBC-MIMO Systems

Abstract

The multi input multi output (MIMO) communication system has significantly higher channel capacity than the Single-Input-Single-Output (SISO) system for the same total transmission power and bandwidth (Foschini et al., 1998 & Telatar, 1999). It is known that the use of Space Time Block Code (STBC) can realize the increased capacity of MIMO systems and thus improve data throughput and spectral efficiency (Tarokh et al., 1998). In this work, we focus on the system that comprises one receiving station and multiple transmitting devices (e.g., uplinks in cellular systems). The receiver’s front end has multiple antennas, and each transmitting device has multiple transmit antennas. The system discussed in this chapter is illustrated in Fig. 1. A Space Time Block Code (STBC) is used in each transmitting device, and joint signal detection is performed at the receiver. We refer to such a system as Multi-Device (MD) STBC-MIMO system. Generally in a MD-STBC-MIMO system, the number of receive antennas is typically smaller than the number of all transmit antennas used by all transmitting devices in the system. An example of MD-STBC-MIMO would be the uplink multiple access communication system, where the number of receive antennas at the base station or the access point is smaller than the total number of transmit antennas at the mobile devices. In this work, we address symbol detection in Multi-Device (MD) STBC-MIMO systems. As will be discussed in section 3, the maximum a posteriori (MAP) detection, which reduced to the Maximum Likelihood (ML) detection in the case of a priori equally likely symbol blocks, minimizes the probability of detection error, and thus is optimal. However, a computationally efficient algorithm for achieving MAP or ML detection is not known. Some studies with sphere decoding (SD) algorithms exhibit that their expected computational complexity grows polynomially with the problem size, say m, up to some value of m for the cases of small constellation sizes (Vikalo et al., 2005), but it grows exponentially for the cases of large constellation sizes. Also, for some sphere decoding algorithms, operation at a low SNR requires inordinately high computation, although operation at a high SNR is efficient. In any case, an algorithm with polynomial growth of expected complexity for all values of the problem size, m, has not yet been found. In fact, Jalden et al. (2005) shows that even the expected computational complexity of the sphere decoding grows exponentially with the problem size in MIMO communication systems. In this work, we present two evolutionary optimization methods, Biogeography-Based Optimization (BBO) and Estimation of Distribution Algorithm (EDA) to solve the problem