In this paper, we propose an unmanned aerial vehicle (UAV)-enabled massive multiple-input multiple-out (MIMO) non-orthogonal multiple access (NOMA) two-way relay (TWR) system, where multiple pairs of ground users (GUs) aim to exchange their information via an amplify-and-forward (AF) UAV relay. We consider that deploy a hovering rotary-wing UAV equipped with multiple antennas and operating in full-duplex (FD) mode, to provide reliable links to the GUs with emergency communications needs during periods of temporary damage to ground base stations. Minimum mean-square error (MMSE) channel estimation is employed by the UAV to obtain the air-ground channel state information (CSI), while maximum ratio combining/maximum ratio transmission (MRC/MRT) is adopted to process the signals of GUs. Analytical general closed-form expressions are derived for the sum spectrum efficiency (SE) and total energy efficiency (EE) of the proposed system in the case of imperfect CSI, as well as their respective asymptotic expressions. Based on the obtained expressions, the power scaling laws are presented. The numerical results suggest that the proposed system can achieve higher SE performance than terrestrial massive MIMO relay system due to strong line-of-sight (LoS) links, and the advantage is more obvious with more number of antennas. Furthermore, the optimal hovering height of the UAV is explored, which increases as the number of antennas increases. Also, to achieve a trade-off between SE and EE, it is desirable to increase the transmit power of the GUs when increasing the number of antennas at the UAV. Finally, we confirm the proposed scheme can better enhance the SE of multiple GU pairs communication system by comparing NOMA with orthogonal multiple access (OMA), FD and half duplex (HD) schemes.

In order to achieve rate-compatible RM codes, two most effective ways are puncturing and shortening. In this paper, puncturing schemes are studied. Different from the existing spherical puncturing, puncturing schemes which do no change the Plotkin structure of RM codes are proposed, enabling existing decoding techniques be readily accessible. The proposed puncturing schemes are based on Hamming weights of column indices of the generator matrix of RM codes. When Hamming weights of column indices (also determining the column weights, CW) are the same, different strategies are proposed, producing four different puncturing strategies, called CW-IV, CW-LSB, CW-MSB, and CW-MSB-Sym, respectively. Analysis is performed to show the union bound on error performance of RM codes with puncturing. Theoretically, it also shows that the punctured first 1st-order subcode can have potentially better performance than the non-puncturing case if puncturing is properly designed. Simulation results show that the proposed puncturing strategies outperform random and quasi-uniform puncturing (QUP) schemes in terms of block error rate (BLER). Union bound results also confirm that the first 1st-order subcode shows better performance than the original non-puncturing case. This fact indicates a more efficient transmission scheme of RM codes: transmitting part of the original codeword to increase the spectrum efficiency while achieving a better BLER performance under recursive list decoding.

Faster-than-Nyquist (FTN) technique promises higher capacity and spectral efficiency for wireless communications. However, existing FTN studies over doubly-selective fading (DSF) channels separate channel-induced inter-symbol interference (channel-ISI) and FTN-induced ISI (FTN-ISI) to simplify cancellation. In practical DSF scenarios, the inherent coupling between FTN-ISI and channel-ISI causes significant performance degradation in conventional detection algorithms. To address this limitation, we first derive a practical FTN transmission model over DSF channels and construct the corresponding coupled interference matrix. Considering that data detection relies on efficient channel estimation, we propose a channel estimation algorithm with joint recovery of resolvable subpath parameters. This algorithm decomposes propagation paths into resolvable subpaths with independent delay-Doppler characteristics, achieving enhanced estimation accuracy through joint gain-phase optimization. Finally, building on the derived transceiver model and coupled interference matrix, we propose a whitening-enhanced orthogonal approximate message passing (WE-OAMP) algorithm that suppresses coupled interference through iterative linear-nonlinear estimation while maintaining spectral compactness. This algorithm constructs a whitening matrix using the FTN-ISI matrix to suppress noise correlation, and then performs detection through iterative linear and nonlinear estimation. Simulation results validate that the WE-OAMP algorithm outperforms benchmark algorithms in terms of bit error rate performance, especially in coded systems. Furthermore, we derive the achievable capacity of FTN signaling with WE-OAMP detection, demonstrating capacity improvement compared to Nyquist systems.

Existing channel estimation and data detection methods for faster-than-Nyquist (FTN) transmission over frequency-selective fading channels primarily face three key challenges: high pilot and guard interval overhead, low channel estimation accuracy, and long distances in satellite communication systems. To address the first two issues, we design a low-overhead frame structure based on circular convolution and, accordingly, propose a low-overhead precoding-driven channel estimation (PD-CE) algorithm. The proposed algorithm leverages circular convolution to suppress inter-block interference (IBI) from the channel with minimal guard intervals and eliminate FTN-induced IBI without guard intervals, significantly reducing pilot and guard overhead. Meanwhile, the limited guard interval mitigates noise enhancement, enabling PD-CE to achieve superior channel estimation accuracy over existing estimation methods. The third challenge arises from the imperfect channel state information obtained at the transmitter. To enhance the robustness in satellite communication systems, we design a precoding matrix based on the minimum mean square error (MMSE) criterion, introducing a low-overhead precoding-driven channel estimation and data detection (MMSE-PD-CEDD) algorithm for interference suppression. Simulation results indicate that, even under channel estimation error, the proposed MMSE-PD-CEDD algorithm exhibits superior interference resistance compared to existing algorithms, while its bit error rate performance loss remains within an acceptable range relative to the Nyquist criterion.

This letter presents a Direction-of-arrival (DOA) estimation algorithm in passive sensing systems with Reconfigurable Intelligent Surface (RIS). In order to improve the estimation accuracy in non-Gaussian noise and access point (AP) interference environments, a joint logarithmic function and atomic norm (LFAN) constrained DOA estimation method is proposed, analyzed, and discussed in detail. The proposed LFAN effectively mitigates impulsive noise and AP interference to achieve precise estimation through a minimization problem using logarithmic function and atomic norm constraints. Simulation results show that the proposed LFAN outperforms existing algorithms in terms of estimation performance.

Reconfigurable Intelligent Surface (RIS) can obtain huge passive beamforming gains. However, due to imperfect hardware and deployment environments, RIS is subject to hardware impairments (HWI) and partial elements blockage, resulting in a significant loss of gain. Hence, RIS array diagnosis is of great significance for normal operations of the RIS system. We consider a RIS assisted millimeter-wave (mmWave) communication system where some RIS elements suffer from HWI. A conjugate gradient complex soft threshold (CG-CST) algorithm is proposed to diagnose the RIS array, reducing the overhead and increasing the diagnostic accuracy. Furthermore, the proposed CG-CST algorithm is effective in both single-input single-output (SISO) systems and systems with multiple antennas. Numerical results confirm that the presented CG-CST algorithm has superior performance compared to existing methods.

Compared to the linear MIMO detectors, the Belief Propagation (BP) detector has shown greater capabilities in achieving near-optimal performance and better nature to iteratively cooperate with channel decoders. Aiming at real applications, recent works mainly fall into the category of reducing the complexity by simplified calculations, at the expense of performance sacrifice. However, the complexity is still unsatisfactory with exponentially increasing complexity or required exponentiation operations. Furthermore, the state-of-the-art (SOA) BP detectors persistently encounter error floor in high signal-to-noise ratio (SNR) region, which becomes even worse with calculation approximation. This work aims at a revised BP detector, named Belief-selective Propagation (BsP) detector by selectively utilizing the trusted incoming messages with sufficiently large a priori probabilities for updates. Two proposed strategies: symbol-based truncation (ST) and edge-based simplification (ES) squeeze the complexity (orders lower than the BP detector), while greatly relieving the error floor issue over a wide range of antenna and modulation combinations. For the 256-QAM$128 \times 64$uplink massive multiuser MIMO (MU-MIMO) system, the$\mathcal {B}(1,1)$BsP detector achieves more than 1dB performance gain (@$\text {BER}=10^{-4}$) with lower complexity than the state-of-the-art (SOA) BP detector. Trade-off between performance and complexity towards different application requirements can be conveniently obtained by tuning the parameters of the ST and ES strategies.

Puncturing and shortening are two general ways to obtain an arbitrary code length and code rate for polar codes. When some of the coded bits are punctured or shortened, it is equivalent to a situation in which the underlying channels of polar codes are different. This fact calls for a general polar code construction, which is not yet available. In this article, a general construction of polar codes is studied in two aspects: 1) the theoretical foundation of the general construction and 2) the hardware implementation of general polar codes encoders. In contrast to the original identical and independent binary-input, memoryless, symmetric (BMS) channels, these underlying BMS channels can be different. The proposed general construction of polar codes is based on the existing Tal-Vardy's procedure. The symmetric property and the degradation relationship are shown to be preserved under the general setting, rendering the possibility of a modification of Tal-Vardy's procedure. Simulation results clearly show improved error performance with reordering using the proposed new procedures. Also, a novel encoding hardware architecture is proposed, which supports puncturing and shortening modes. Implementation results show the proposed encoder achieves approximately 30% throughput improvement when one quarter of bits are punctured/shortened.