Symbol Based Search Space Constraining for Complexity/Performance Scalable Near ML Detection in Spatial Multiplexing MIMO OFDM Systems

For both outdoor and indoor wireless systems there is an increasing demand of high spectral efficiency at a very low cost and power consumption. In this context, MIMO wireless system adopting spatial multiplexing offer a way of increasing the spectral efficiency of the system. In order to fully exploit this capacity non linear MIMO detectors such as maximum likelihood detectors are required. However, when high order modulation schemes are applied, the complexity of this kind of detector becomes prohibitive for a practical implementation. As a solution to this problem, low complexity maximum likelihood detectors such as sphere detectors are appealing as a low complexity solution for high spectral efficiency transmission. Although sphere decoding provides a lower complexity solution than a classical ML detector, its complexity still remains unpredictable and exponentially dependent on channel propagation conditions. This variability in complexity makes the implementation of sphere decoders not practical. In this paper, a new approach for constraining the ML search space is proposed which provides a predictable upper bound for complexity, hence facilitating its implementation. Moreover, the new approach for computing the constrained search space significantly reduces the complexity of the detection while offering scalability in terms of performance and complexity. Simulation results in a cellular system demonstrate the scalability of our detector and the performance/complexity trade-off that it enables.