In this paper, we present a novel and robust nonlinear precoding (NLP) design and detection structure specifically tailored for multiple-input multiple-output space division multiple access (MIMO-SDMA) systems toward 6G wireless. Our approach aims to effectively mitigate the impact of imperfect channel estimation by leveraging the channel fluctuation mean square error (MSE) for reconstructing a highly accurate precoding matrix at the transmitter. Furthermore, we introduce a simplified receiver structure that eliminates the need for equalization, resulting in reduced interference and notable enhancements in overall system performance. We conduct both computer simulations and experimental tests to validate the efficacy of our proposed approach. The results reveals that the proposed NLP scheme offers significant performance improvements, making it particularly well-suited for the forthcoming 6G wireless.

The hybrid carrier (HC) system rooted in the carrier fusion concept is gradually garnering attention. In this paper, we study the extended hybrid carrier (EHC) multiple access scheme to ensure reliable wireless communication. By employing the EHC modulation, a power layered multiplexing framework is realized, which exhibits enhanced interference suppression capability owing to the more uniform energy distribution design. The implementation method and advantage mechanism are explicated respectively for the uplink and downlink, and the performance analysis under varying channel conditions is provided. In addition, considering the connectivity demand, we explore the non-orthogonal multiple access (NOMA) method of the EHC system and develop the EHC sparse code multiple access scheme. The proposed scheme melds the energy spread superiority of EHC with the access capacity of NOMA, facilitating superior support for massive connectivity in high mobility environments. Simulation results have verified the feasibility and advantages of the proposed scheme. Compared with existing HC multiple access schemes, the proposed scheme exhibits robust bit error rate performance and can better guarantee multiple access performance in complex scenarios of next-generation communications.

This paper considers the frame-asynchronous grant-free rateless multiple access (FA-GF-RMA) scenario, where users can initiate access at any symbol time, using shared channel resources to transmit data to the base station. Rateless coding is introduced to enhance the reliability of the system. Previous literature has shown that FA-GF-RMA can achieve lower access delay than frame-synchronous grant-free rateless multiple access (FS-GF-RMA), with extreme reliability enabled by rateless coding. To support FA-GF-RMA in more practical scenarios, a joint activity and data detection (JADD) scheme is proposed. Exploiting the feature of sporadic traf?c, approximate message passing (AMP) is exploited for transmission signal matrix estimation. Then, to determine the packet start points, a maximum posterior probability (MAP) estimation problem is solved based on the recovered transmitted signals, leveraging the intrinsic power pattern in the codeword. An iterative power-pattern-aided AMP algorithm is devised to enhance the estimation performance of AMP. Simulation results verify that the proposed solution achieves a delay performance that is comparable to the performance limit of FA-GF-RMA.

Massive machine type communication aims to support the connection of massive devices, which is still an important scenario in 6G. In this paper, a novel cluster-based massive access method is proposed for massive multiple input multiple output systems. By exploiting the angular domain characteristics, devices are separated into multiple clusters with a learned cluster-specific dictionary, which enhances the identification of active devices. For detected active devices whose data recovery fails, power domain nonorthogonal multiple access with successive interference cancellation is employed to recover their data via re-transmission. Simulation results show that the proposed scheme and algorithm achieve improved performance on active user detection and data recovery.

This paper investigates the low earth orbit (LEO) satellite-enabled coded compressed sensing (CCS) unsourced random access (URA) in orthogonal frequency division multiple access (OFDMA) framework, where a massive uniform planar array (UPA) is equipped on the satellite. In LEO satellite communications, unavoidable timing and frequency offsets cause phase shifts in the transmitted signals, substantially diminishing the decoding performance of current terrestrial CCS URA receiver. To cope with this issue, we expand the inner codebook with predefined timing and frequency offsets and formulate the inner decoding as a tractable compressed sensing (CS) problem. Additionally, we leverage the inherent sparsity of the UPA-equipped LEO satellite angular domain channels, thereby enabling the outer decoder to support more active devices. Furthermore, the outputs of the outer decoder are used to reduce the search space of the inner decoder, which cuts down the computational complexity and accelerates the convergence of the inner decoding. Simulation results verify the effectiveness of the proposed scheme.

In this paper, ambient IoT is used as a typical use case of massive connections for the sixth generation (6G) mobile communications where we derive the performance requirements to facilitate the evaluation of technical solutions. A rather complete design of unsourced multiple access is proposed in which two key parts: a compressed sensing module for active user detection, and a sparse interleaver-division multiple access (SIDMA) module are simulated side by side on a same platform at balanced signal to noise ratio (SNR) operating points. With a proper combination of compressed sensing matrix, a convolutional encoder, receiver algorithms, the simulated performance results appear superior to the state-of-the-art benchmark, yet with relatively less complicated processing.