We consider the multiuser detection (MUD) problem, i.e., how to separate and decode colliding data streams, in the uplink of massive Machine Type Communications (mMTC) at millimeter wave (mmWave). Operating on factor-graphs by passing messages, the sum-product algorithm and its variants are widely applied in many other scenarios. However, in this paper, we find that their performance in mMTC at mmWave could be dramatically degraded due to the ill-conditioned MUD channel gain matrix and the existence of enormous short cycles in their corresponding factor-graphs, which are caused by the limited scattering of mmWave and the sharing of a same codebook for error correction among densely located user equipments. Assuming LDPC codes are used for error correction, we further propose a novel sum-product based approach to dealing with the MUD problem in mMTC at mmWave. It first leverages the propagation characteristics of mmWave to optimize the factor-graph for MUD by removing short cycles based on node-split and node-contraction, and then takes a dynamic-programming based method to approximate the messages passing on the resulted factor-graph, which can achieve a higher decoding accuracy. Extensive simulation results show that our approach outperforms the state-of-the-art sum-product based approaches significantly.

Federated learning is a very promising machine learning paradigm where a large number of clients cooperatively train a global model using their respective local data. In this paper, we consider the application of federated learning in wireless networks featuring uplink multiuser multiple-input and multiple-output (MU-MIMO), and aim at optimizing the communication efficiency during the aggregation of client-side updates by exploiting the inherent superposition of radio frequency (RF) signals. We propose a novel approach named Physical-Layer Arithmetic (PhyArith), where the clients encode their local updates into aligned digital sequences which are converted into RF signals for sending to the server simultaneously, and the server directly recovers the exact summation of these updates as required from the superimposed RF signal by employing a customized sum-product algorithm. PhyArith is compatible with commodity devices due to the use of full digital operation in both the client-side encoding and the server-side decoding processes, and can also be integrated with other updates compression based acceleration techniques. Simulation results show that PhyArith further improves the communication efficiency by 1.5 to 3 times for training LeNet-5, compared with solutions only applying updates compression.

In conventional 802.11, MAC protocol data units (MPDUs) that a receiver fails to decode will be simply discarded even when only a very few bits are corrupted. This makes conventional 802.11 hardly work when the channel fading or the interference is very severe. In this paper, we propose Rateless802.11, a novel scheme that is fully compatible with common commodity 802.11 devices, which can be used as a complement of 802.11 used in terrible channels. By concatenating LT codes with 802.11 convolutional codes, Rateless802.11 can introduce redundancy properly without the need of accurate channel status estimation, and exploit the uncorrupted bits of MPDUs adequately. To improve the decoding performance as well as reducing the decoding delay, we introduce an integrated decoder (IntBP) in Rateless802.11, which decodes convolutional codes and LT codes compactly and can be performed in an incremental manner. Evaluations based on numerical simulation and driven by universal software radio peripheral (USRP) captured real trace show that IntBP can dramatically improve decoding reliability compared with natural serial decoding approaches and compared with state-of-the-art solutions, throughput of 802.11 in terrible channels can be improved many times by Rateless802.11.

Physical-layer network coding (PNC) is a communications paradigm that exploits overlapped transmissions to boost the throughput of wireless relay networks. A high point of PNC research was a theoretical proof that PNC that makes use of nested lattice codes could approach the information-theoretic capacity of a two-way relay network (TWRN), where two end nodes communicate via a relay node. The capacity cannot be achieved by conventional methods of time-division or straightforward network coding. Many practical challenges, however, remain to be addressed before the full potential of lattice-coded PNC can be realized. Two major challenges are: (1) for good performance in lattice-coded PNC, channels of simultaneously transmitting nodes must be aligned; (2) for lattice-coded PNC to be practical, the complexity of lattice encoding at the transmitters and lattice decoding at the receiver must be reduced. We address these challenges and implement a first lattice-coded PNC system on a software-defined radio (SDR) platform. Specifically, we design and implement a low-overhead channel precoding system that accurately aligns the channels of distributed nodes. In our implementation, the nodes use low-cost temperature-compensated oscillators (TCXO) only-a consequent challenge is that the channel alignment must be done more frequently and more accurately compared with the use of expensive oscillators. The low overhead and accurate channel alignment are achieved by (1) a channel precoding system implemented over FPGA to realize fast feedback of channel state information; (2) a highly-accurate carrier frequency offset (CFO) estimation method; and (3) a partial-feedback channel estimation method that significantly reduces the amount of feedback information from the receiver to the transmitters for channel precoding at the transmitters. To reduce lattice encoding and decoding complexities, we adapt the low-density lattice code (LDLC) for use in PNC systems. Experiments show that our implemented lattice-coded PNC achieves better bit error rate performance compared with timedivision and straightforward network coding systems. It also has good throughput performance in mobile non-LoS scenarios.

In this paper, we investigate the minimization problem of average delay of multi-antenna AP based SIMO (single-input and multiple-output) system by optimizing the antenna allocation. We first obtain the upper bound of transmission bandwidth by deriving the expected error probability of coherent detection for SIMO under Rayleigh fading, and then develop the expression of average delay of terminals sharing the same channel via contention by applying CTMC (continuous-time Markov chains). Upon the above results, we formulate the optimization of antenna allocation as a non-linear integer programming problem and present a polynomial time algorithm based on integer partition and minimum matching to support optimized allocation. We prove the correctness of our theoretical derived results by extensive simulations. The performance comparison results show that the proposed antenna allocation algorithm outperforms other heuristic based algorithms under different traffic models.