As envisioned in the sixth-generation (6G) networks, connectivity density of users and/or machines is expected to surge in future Internet of Everything. However, most current unsourced multiple access schemes experience a swift decline in performance as the number of active users increases due to higher level of interference, limited symbol representation space and available spectrum resources. To tackle these, we propose in this paper a generalized finite-field unsourced multiple access (FFUMA) with either explicit or implicit pilots. The first several information bits in each user message are used to select the spreading pattern, and the remaining information bits are encoded and spread over the finite field. For the explicitly-piloted transmission, the first few bits are mapped to a low-correlated or compressed-sensing pilot which is then transmitted through the channel. At the receiver, a two-stage algorithm consisting of pilot detection and finite-field data decoding is proposed. For the implicitly-piloted transmission, the first few bits are not physically transmitted, but they are in fact delivered to the receiver once the spreading pattern is correctly recognized. We establish the algorithm for blindly detecting the active finite-field spreading patterns and set forth the joint iterative data detection and decoding over the finite field. Error analyses of the proposed finite-field UMA, including error event decomposition and extrinsic information transfer (EXIT) analysis, are provided to evaluate the performance. The advantages of the proposed FFUMA have been demonstrated by the simulation results in terms of error rates and energy-efficiency.

We propose a neural min-sum (MS) decoder for non-binary LDPC codes by applying deep learning to the decoding with short to moderate code lengths. In this algorithm, the iterations are unfolded into hidden layers of the neural network, and trainable weights are added to the check node updates to mitigate the correlation in the message passing due to short girths in the Tanner graph. To avoid gradient vanishing and to converge faster, we adopt an iterative training mechanism for parameter tuning. Furthermore, we investigate the impact of girth distribution on the proposed neural MS decoder, which reveals the reasons for the expected performance improvement. Numerical results in terms of bit error rate are provided, which show that the neural MS decoder achieves significant gains for decoding non-binary LDPC codes.

Toward next-generation multiple access, we propose a multiuser transmission framework via non-binary factor graphs defined over Galois field GF(q). The non-orthogonal multiuser transmission is realized by applying the GF(q) multiplication and superposition. In coordination with the high-order modulation, the proposed GF(q) codebook improves the cardinality of the superimposed constellation and reduces interference at each resource element. The proposed structure also characters for flexibility by spatially coupling the base graph when applying to different user scales. We have theoretically proved that both the system capacity and the error performance are improved due to the GF(q) operations. We utilize the simulated annealing algorithm to optimize both the base factor graph and the spatially-coupled graph, therefore short girths are eliminated or mitigated. The detection of the proposed scheme can be performed through iterative belief-propagation (BP) based on symbol-level log-domain probability vectors derived on the GF(q) factor graph. The numerical results in terms of upper bounds of maximum likelihood (ML) detection and symbol error rates through BP detection are both provided, which demonstrate the superiority of the GF(q) factor graphs to their binary counterparts. The performance can be further improved by increasing the field order q and/or by spatially coupling the non-binary factor graph.

Unsourced multiple access (UMA) is a promising technique for the Internet-of-Things systems with a large number of users. In this paper, we explore the good algebraic properties of finite fields and propose a non-binary coding scheme for the UMA. The scheme is based on the slotted structure and T-fold ALOHA, and each active user randomly selects one slot to transmit its message. Finite-field operations are performed for both the channel coding and the multiple access. Compared to binary-field operations, the finite-field sparse spreading and multiuser superposition lead to improved diversity and less interference on each channel use. In addition, the non-binary LDPC code boosts the performance under short/moderate code length. We derive the joint belief-propagation decoding algorithm for the concatenated finite-field factor graph, which is then verified by the extrinsic information transfer (EXIT) analysis. The numerical results in terms of energy-efficiency performance are provided, which clearly demonstrate the superiority to the existing schemes in the region with large user numbers. The proposed scheme has the potential to carry out larger-scale user connections, and the energy-efficiency performance can be further improved by optimizing the coding parameters.

In this paper we propose a novel non-orthogonal multiple access (NOMA) scheme via non-binary factor graphs by introducing operations over Galois field GF(q). By utilizing sparse GF(q) spreading, the proposed scheme benefits from an improved diversity of the superimposed symbols and accordingly a lower probability of constellation overlapping. Meanwhile, the coordination with high-order modulation helps to reduce the interference at the receiver, and therefore a better error performance can be expected. Iterative belief-propagation (BP) detection based on log-domain probabilities is derived for the GF(q) NOMA. The upper bounds of maximum likelihood (ML) detection and simulation results in terms of symbol error rate are provided, which demonstrate the superiority of the proposed scheme to its binary counterparts.

Inspired by the shoulder pole, a kind of Chinese archaic folk transporting tool, the mechanism of Anti Gravity System (AGS) is to create a regular bouncing reaction when the user is walking. It thus provides immediate relief regularly and a feeling of reduced heaviness to the user, and in theory, a slower progression of muscle fatigue can be detected from users carrying AGS backpack. In this paper, we made a comparative study to test the effect of AGS backpack on relieving fatigue of neck, shoulder and back muscles. Two trails were conducted to respectively extract the surface electromyography (sEMG) signals of 10 healthy male subjects carrying a loaded AGS backpack and an ordinary (non-AGS) backpack on their backs under the walking state. The comparison of three primary sEMG features (iEMG, MPF and MF) between test group and control group reveals that AGS backpack is advanced in aspects of less work done and energy expenditure of the main stress-bearing muscles and easier recovery of MPF and MF after the occurrence of muscular fatigue.