No abstract
In this paper, Sphere Decoding (SD) algorithms for Spatial Modulation (SM) are developed to reduce the computational complexity of Maximum-Likelihood (ML) detectors. Two SDs specifically designed for SM are proposed and analysed in terms of Bit Error Ratio (BER) and computational complexity. Using Monte Carlo simulations and mathematical analysis, it is shown that by carefully choosing the initial radius the proposed sphere decoder algorithms offer the same BER as ML detection, with a significant reduction in the computational complexity. A tight closed form expression for the BER performance of SM-SD is derived in the paper, along with an algorithm for choosing the initial radius which provides near to optimum performance. Also, it is shown that none of the proposed SDs are always superior to the others, but the best SD to use depends on the target spectral efficiency. The computational complexity trade-off offered by the proposed solutions is studied via analysis and simulation, and is shown to validate our findings. Finally, the performance of SM-SDs are compared to Spatial Multiplexing (SMX) applying ML decoder and applying SD. It is shown that for the same spectral efficiency, SM-SD offers up to 84% reduction in complexity compared to SMX-SD, with up to 1 dB better BER performance than SMX-ML decoder.Index Terms-Multiple-input-multiple-output (MIMO) systems, spatial modulation (SM), spatial multiplexing (SMX), sphere decoding (SD), large scale MIMO.
Abstract-In this work we seek to characterise the performance of spatial modulation (SM) and spatial multiplexing (SMX) with an experimental testbed. Two National Instruments (NI)-PXIe devices are used for the system testing, one for the transmitter and one for the receiver. The digital signal processing that formats the information data in preparation for transmission is described along with the digital signal processing that recovers the information data. In addition, the hardware limitations of the system are also analysed. The average bit error ratio (ABER) of the system is validated through both theoretical analysis and simulation results for SM and SMX under line of sight (LoS) channel conditions.
International audienceIn this paper, Sphere Decoding (SD) algorithms for Spatial Modulation (SM) are developed to reduce the computational complexity of Maximum-Likelihood (ML-) optimum detectors, which foresee an exhaustive search of the whole search space and have a complexity that linearly increases with the product of number of transmit-antenna, receive-antenna, and size of the modulation scheme. Three SDs specifically designed for SM are proposed and analyzed in terms of Bit Error Probability (BEP) and computational complexity. By judiciously choosing some key parameters, e.g., the radius of the sphere centered around the received signal, it is shown that the proposed algorithms offer the same BEP as ML-optimum detection, with a significant reduction of the computational complexity. Also, it is shown that none of the proposed SDs is always superior to the others, but the best SD to use depends on the system setup, i.e., the number of transmit-antenna, receive-antenna, and the size of the modulation scheme. The computational complexity trade-off offered by the proposed solutions is studied via analysis and simulation, and numerical results are shown to validate our findings
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