Large-scale multiuser SM-MIMO versus massive MIMO

Abstract

Abstract-Spatial modulation (SM) is attractive for multiantenna wireless communications. SM uses multiple transmit antenna elements but only one transmit radio frequency (RF) chain. In SM, in addition to the information bits conveyed through conventional modulation symbols (e.g., QAM), the index of the active transmit antenna also conveys information bits. In this paper, we establish that SM has significant signal-to-noise (SNR) advantage over conventional modulation in large-scale multiuser (multiple-input multiple-output) MIMO systems. Our new contribution in this paper addresses the key issue of largedimension signal processing at the base station (BS) receiver (e.g., signal detection) in large-scale multiuser SM-MIMO systems, where each user is equipped with multiple transmit antennas (e.g., 2 or 4 antennas) but only one transmit RF chain, and the BS is equipped with tens to hundreds of (e.g., 128) receive antennas. Specifically, we propose two novel algorithms for detection of large-scale SM-MIMO signals at the BS; one is based on message passing and the other is based on local search. The proposed algorithms achieve very good performance and scale well. For the same spectral efficiency, multiuser SM-MIMO outperforms conventional multiuser MIMO (recently being referred to as massive MIMO) by several dBs. The SNR advantage of SM-MIMO over massive MIMO can be attributed to: (i) because of the spatial index bits, SM-MIMO can use a lower-order QAM alphabet compared to that in massive MIMO to achieve the same spectral efficiency, and (ii) for the same spectral efficiency and QAM size, massive MIMO will need more spatial streams per user which leads to increased spatial interference.

On the Capacity and Performance of Generalized Spatial Modulation

“…Figure 6 presents a performance comparison between the proposed LaMP detection, ML detection, and CSR based detection for different (N, M, R)-GSM MIMO systems with BPSK. It can be seen that the LaMP performance is away from ML performance by about 3 dB and 1.6 dB for (8,8,4)and (16, 16, 4)-GSM MIMO systems, respectively, at 10 −5 BER. Thus, the performance of LaMP improves as the system dimensions increase.…”

“…Gaussian approximation: To compute u i (b), we require N − 1 probability mass functions, i.e., q i s. We note that q i is a Bernoulli distribution with probability masses for a i = 0 and a i = 1, i.e., Pr(a i = 0|x) = q i (0) and Pr(a i = 1|x) = q i (1). The evaluation of φ i requires the computation of the distribution of the random variable Bit error rate (8,8,4), LaMP (prop.) (8,8,4), CSR in […”

“…The evaluation of φ i requires the computation of the distribution of the random variable Bit error rate (8,8,4), LaMP (prop.) (8,8,4), CSR in […”

“…Mapping: Let b = [0010011] be the bit sequence that is to be transmitted. The decimal equivalent of b is d(b) = 19, and the combinadic of 19 is (N 1 , N 2 , N 3 , N 4 ) = (0,1,4,6). The corresponding indices of the activated antennas are(1,2,5,7).…”

Cooperative multi‐cellular large MIMO over asynchronous channel training

“…Therefore, there is a considerable number of techniques for MIMO communications, including different schemes, precoding, and symbol detectors. The most popular schemes are the space-time block codes (e.g., Alamouti [2]) offering diversity gain and the Bell Laboratories Layered Space Time (BLAST) scheme [3] and spatial modulation [4] offering multiplexing gains. Because high-order space-time block codes are not straightforward and BLAST scheme can offer higher data rates, this one became the most popular among MIMO transmission schemes aiming to COOPERATIVE MULTI-CELLULAR LARGE MIMO OVER DESYNCHRONIZED CHANNELS 2331 realize large multiplexing gains.…”