The design of transmission schemes and receiver techniques with high reliability and low latency for the massive machine-type communications (mMTCs) is an important challenge for the future Internet of Things (IoT) systems in the sixth generation (6G) wireless communication networks. In this article, we propose a new physical-layer transceiver scheme for mMTC, where users transmit their data based on a predesigned sparse Tanner graph, and the base station (BS) employs blind channel estimation and message-passing decoding to decode user data. In particular, low latency is achieved by the construction of the transmission Tanner graph, as well as a hybrid modulation scheme that consists of BPSK symbols to facilitate blind channel estimation, and general$M$-ary modulation to reduce the transmission delay. Moreover, high reliability is achieved by the proposed message-passing decoder that fully exploits the diversity of the transmitted signal and offers soft demodulation and decoding capabilities. We provide both performance analyzes based on density evolution, and simulation results, to demonstrate the superior performance of the proposed physical-layer transceiver solution for mMTC.

In this paper we consider two Tanner-graph-based transmission schemes for massive multiple access, which combine non-orthogonal multiple access (NOMA) and grant-based random access. Each user transmits two data streams repeatedly using several channel time-frequency resource blocks (RBs) and the transmission schedule is represented by a Tanner graph, where variable nodes and check nodes represent the transmitted signals and the RBs, respectively. In Scheme 1 each variable node represents a data stream of a user, whereas Scheme 2 employs rate splitting and each variable node represents the superimposed data streams of a user. On the receiver side, we first consider peeling decoders that serve both as pilot-based channel estimators and baseline decoders. We then develop message-passing decoders for both transmission schemes that can fully exploit the diversity afforded by the repetitive transmission across different RBs, as opposed to the peeling decoders. We also propose a neural decoder by deep unfolding the message-passing decoder and further performing a small number of training epochs using the pilots. Simulation results show that for Transmission Scheme 1, the message-passing decoder offers decoding performance improvement over the peeling decoder by orders of magnitude; and as a result, Scheme 1 substantially outperforms Scheme 2. Moreover, the neural decoder further improves upon the performance of the message-passing decoder, as more information is learned about the transmitted data over the training epochs.

In this paper, we propose new encoding and decoding schemes for the MIMO unsourced multiple access (UMA) systems. Each transmitter first encodes the information bits using an arbitrary channel code. The coded bits are divided into sub-blocks and each sub-block is mapped to a transmitted codeword using a common codebook. The codewords from all transmitters are transmitted through MIMO channels. We propose a sparsity-exploiting blind receiver algorithm that exploits the inherent codeword sparsity and a channel clustering technique to estimate the channel and decode the transmitted data. Either hard or soft estimates of the coded bits are given by the algorithm so we can apply single-user channel decoding to obtain the information bits of each transmitter. Simulation results are provided to illustrate the performance of the proposed algorithm for systems employing parity-check codes and Polar codes, respectively.

We propose novel sparse-graph-based transmission schemes and receiver algorithms for unsourced multiple access (UMA) in MIMO channels. The channel coherence interval is divided into a number of sub-slots and each active transmitter selects certain sub-slots to repeatedly transmit its codeword according to a sparse Tanner graph. We propose iterative receiver algorithms that at each iteration decode either a single codeword, or two or three codewords jointly, and then subtract the decoded codewords from received signals during all sub-slots. The keys to these decoders are novel blind channel estimation algorithms when the received signal contains one, two, or three codewords. We perform density evolution analysis on the proposed UMA systems to obtain the asymptotic upper bounds on the maximum achievable rates for different decoders under both regular and irregular Tanner graphs. Extensive simulation results are provided to illustrate the performance of the proposed UMA systems, and its advantages over existing compressed-sensing (CS)-based UMA schemes.

We propose new transmission schemes and receiver algorithms for unsourced multiple access (UMA) in MIMO and massive MIMO channels. Each active transmitter’s information bits are first channel encoded. The coded bits are divided into sub-blocks and each sub-block is modulated and transmitted. For both MIMO and massive MIMO channels, the conventional nonlinear modulation can be employed where each sub-block of coded bits is mapped to a transmitted signal vector. For the massive MIMO channel, we propose a new hybrid modulation scheme to reduce the receiver complexity, where the first sub-block is nonlinearly modulated, and the subsequent sub-blocks are linearly modulated and spread by the first sub-block signal. We also propose sparsity-exploiting blind receiver algorithms. Specifically, for the MIMO case, we exploit the codeword sparsity inherent in the UMA system, and a channel clustering technique, to estimate the channel and the transmitted signal of each transmitter. For the massive MIMO, in addition to the codeword sparsity, we further exploit the channel sparsity and user sparsity in estimating the channel and transmitted signal of each transmitter. The proposed receiver algorithms for both MIMO and massive MIMO channels output either hard or soft estimates of the coded bits, and therefore single-user channel decoding of the information bits can be performed for each transmitter. Extensive simulation results are provided to demonstrate the performances of the proposed algorithms.