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We propose a novel signal multiplexing and detection method for multiple-input multipleoutput (MIMO) communication systems, especially when the number of transmitting and receiving antennas is limited. Inspired by the idea of Compressive Sensing (CS) which can recover a given signal vector from a vector of measurements with less dimensions, our proposed CS-based multiplexing scheme can deliver a modulated data vector with length l via a MIMO system with fewer transmitting/receiving antennas than l, offering higher multiplexing gain. On the receiving side, our proposed detection scheme has two steps, which resort the BCS algorithm and a Deep-Learning algorithm to recover the original modulated data vector. Analytical and simulation results show that the proposed multiplexing and detection method can achieve larger multiplexing gain while reserving good bit error rate (BER), offering a novel research paradigm to improve the utility rate of multiple antennas.

With the increasing number of Internet of Things (IoT), Industry 4.0 (I4.0), and mobile devices, it can be expected that base stations will have to serve more and more clients with a limited number of antennas. For their broadcast channels, nonorthogonal multiple access (NOMA) and blind interference alignment (BIA) are two efficient and commonly adopted transmission schemes. This paper conducts a comparison study on these techniques on a 3-user 2 × 1 multiple-input single-output (MISO) broadcast channel with a limited number of transmit antennas. Specifically, space-time block coding based NOMA (STBC-NOMA) and NOMA-assisted beamforming (NOMA-BF) are compared with BIA. Both perfect and imperfect successive interference cancellation (SIC) have been considered for NOMA-based schemes, and the theoretical achievable rates of all schemes have been derived. Furthermore, with a given fairness constraint among end users, the power allocation (PA) problems have been solved for cases when accurate channel state information is available at the transmitter (CSIT) as well as when only path loss information is available. Numerical results show the following: (1) none of the schemes under this study can always outperform the others under different SNR regions. (2) With imperfect SIC, NOMA-BF, and STBC-NOMA both suffer from a significant performance loss under a high SNR condition. (3) Fairness PA with only path loss information provides similar performance as that with perfect CSIT, thus partial CSIT is adequate for system or scheme designs in practice.

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