The Research Of The Multiple Symbol Differential Space-time Detection Algorithms

Space time coding is one of the core technologies in MIMO system. Itprocesses the signals in spatial and time domains. It effectively improves systemcapacity without sacrificing the frequency spectrum. But in the environment such asthe bad channel conditions or fast-moving, the receiver is hard to estimate channelbased on pilot symbol, even impossible to estimate. Fortunately, blind space-timecoding can effectively solve the problem. It decodes completely without channelstate information, therefore, blind space-time coding technology is the researchhotspot in wireless communication. However, the traditional blind space-timetechnology has3dB loss in performance. In order to overcome this shortcoming, themaximum likelihood (ML) detection is proposed, but at the same time bringing highcomputational complexity. This paper studied three improved multiple-symboldifferential space-time detection algorithms based on the space-time coding.Firstly, a low-complexity dynamic K-Best algorithm is proposed. The algorithmis base on the traditional K-Best algorithm. It dynamically changes value K, andcombines with efficient sorting scheme to select K branch. The simulation resultsshow that the performance of the dynamic K-best algorithm with K cycles sort isbetter than bubble sort at the high signal to noise ratio region and the complexitygreatly declines. At the same time, derived from theoretical analysis, the dynamicK-best algorithm with K cycles sort can achieve a better balance between resourceconsumption and throughput, and easier to implement in hardware.Secondly, we propose a low complexity M-SD algorithm. It combines with theM-algorithm using the linear radius. Simulation results show that M-SD detectionwith the constraint of linear radius significantly reduces the overall complexitycompared with regular SD and its complexity is more stable. Especially thecomplexity of M-SD can get more than90%reduction when SNR<5dB, while the biterror rate (BER) performance is very close to SD within0.1dB loss.Finally, we propose pruning memory constraints-tree search (PMCTS) algorithmin MIMO-OFDM system. With the constraint of the pruning radius, the algorithm reduces the computational complexity with the smaller storage space. Simulationresults show that at low signal to noise ratio region under RA channel model, whenthe observation window size is6, the number of visited nodes for the proposedPMCTS algorithm can reduce up to47.8%,36%,87.8%than MCTS, PASD and MLrespectively, but its performance loss is less0.2dB than ML.