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The multi-antenna non-orthogonal multiple access (NOMA) technique is a promising method to enhance energy and spectrum efficiencies of wireless communication systems through advanced precoding algorithms. However, conventional NOMA schemes encounter high complexity issues due to the successive interference cancellation (SIC) process at the receiver end. Moreover, traditional precoding designs for multi-antenna NOMA systems only utilize user channel state information and overlook the modulation details of transmitted data symbols, which may result in suboptimal performance. To overcome these disadvantages, we propose a symbol-level precoding (SLP) scheme to maximize the energy efficiency of the downlink NOMA systems. In particular, by the proposed SLP scheme, the “interference signals” is designed to fall within the decoding region of the “desired signal”, eliminating the need for an SIC receiver, thereby reducing the complexity of the NOMA system in practical applications. For ease of illustrating the idea of the SLP-based NOMA designs, we first focus on a single user group case with two users, and solve the associated problem by developing a fractional programming-based algorithm. Then, we extend the idea to the multi-user scenarios and focus on the joint user grouping and SLP design. For the purpose of computational complexity reduction, a low-complexity (adaptive) user grouping scheme is proposed based on the idea of unsupervised clustering. Finally, numerical simulations are carried out to validate the effectiveness of the proposed SLP scheme and algorithms in improving the energy efficiency of the system.

Hierarchical federated learning (HFL) shows great advantages over conventional two-layer federated learning (FL) in reducing network overhead and interaction latency while still retaining the data privacy of distributed FL clients. However, the communication and energy overhead still pose a bottleneck for HFL performance, especially as the number of clients raises dramatically. To tackle this issue, we propose a non-orthogonal multiple access (NOMA) enabled HFL system under semi-synchronous cloud model aggregation in this paper, aiming to minimize the total cost of time and energy at each HFL global round. Specifically, we first propose a novel fuzzy logic based client selection policy considering client heterogeneity in multiple aspects, including channel quality, data quantity and model staleness. Subsequently, given the fuzzy based client-edge association, a joint edge server scheduling and resource allocation problem is formulated. Utilizing problem decomposition, we firstly derive the closed-form solution for the edge server scheduling subproblem via the penalty dual decomposition (PDD) method. Next, a deep deterministic policy gradient (DDPG) based algorithm is proposed to tackle the resource allocation subproblem considering time-varying environments. Finally, extensive simulations demonstrate that the proposed scheme outperforms the considered benchmarks regarding HFL performance improvement and total cost reduction.

Spatial non-stationarity channel estimation for uplink massive MIMO systems can be formulated as a non-uniform block sparse signal recovery problem, in which the hierarchical sparsity of the channel matrix is classified as row sparsity and in-row sparsity. This paper proposes an efficient threshold-enhanced hierarchical estimation (TEHE) algorithm without prior information. More precisely, the non-zero rows of spatial non-stationarity channel matrix are estimated according to the in-row correlation in the first layer; while the non-zero elements of the estimated non-zero rows are further refined in the second layer. Different from the existing two-layer iteration algorithms, an adaptive threshold is designed to estimate the non-zero elements replacing the iterative algorithm in the second layer. In the proposed TEHE algorithm, row-wise sparse adaptive matching pursuit (SAMP) is used to find the non-zero rows in the first layer, which has high precision and lower complexity, compared to the conventional SAMP. To further improve the efficiency of the row estimation for larger antenna array, an adaptive threshold-enhanced hierarchical estimation (A-TEHE) algorithm is proposed. In addition, a sufficient condition and a halting condition for theoretical guarantee to obtain accurate row estimation are developed. Finally, the computation complexity is analyzed and compared. The simulation results demonstrate that the proposed threshold-enhanced hierarchical spatial non-stationary channel estimation algorithms achieve better performance compared to various state-of-the-art baselines in terms of support set estimation, channel coefficient estimation, and computational efficiency. Specifically, the proposed algorithms are robust to the in-row sparsity.

Reconfigurable intelligent surface (RIS)-assisted symbiotic radio is a spectrum- and energy-efficient communication paradigm, in which an RIS performs passive beamforming to enhance active transmission, while using the electromagnetic waves from the active transmission for additional information transfer (i.e., passive transmission). In this paper, a hybrid RIS-based modulation, termed hybrid phase and code modulation (HPCM), is proposed to improve the reliability of RIS-assisted symbiotic radio. In RIS-HPCM, the RIS simultaneously performs direct sequence spread spectrum and passive beamforming on incident signals. Moreover, both the spreading code and phase offset are exploited to carry the RIS's own information. A low-complexity detector is designed, in which the receiver first detects the spreading codes and then demodulates the constellation symbols. We analyze the bit error rate (BER) performance of RIS-HPCM over Rician fading channels. BER upper bounds and approximate BER expressions are derived in closed-form for maximum-likelihood and low-complexity detectors, respectively. Simulation results in terms of BER verify the analysis and show the superiority of RIS-HPCM over the existing RIS-based modulation.

Unmanned Aerial Vehicle (UAV) networks have huge potential for a variety of military and civilian uses, such as intelligent transportation system, smart city, and so on. The 6th Generation (6G) communication technology is expected to provide 3-Dimensional (3D) wireless coverage and greatly improve the performance of UAV networks. The UAV-to-UAV (U2U) message exchange (or message exchange for short) is an important basis of multi-UAV cooperation. However, due to the unique characteristics of UAV networks, the U2U messages (or messages for short) are vulnerable to both the external and internal attackers. In this work, we propose a Lightweight Trustworthy Message Exchange (LTME) scheme for UAV networks by efficiently aggregating the cryptography and trust management technologies. In the LTME scheme, a centralized Ground Control Station (GCS) periodically updates the reputation levels of registered UAVs (or UAVs for short) and securely distributes secret values to the UAVs. Based on the received secret values, each trustworthy broadcasting UAV can generate its encrypted messages so that only trustworthy receiving UAVs can decrypt them, and each trustworthy receiving UAV can accurately judge whether the received messages and the corresponding broadcasting UAVs are trustworthy in a lightweight manner. Furthermore, we present a simplified LTME (sLTME) scheme and conduct a comprehensive theoretical analysis and simulation evaluation for the LTME and sLTME schemes. The results demonstrate that the proposed schemes can provide rich functionality and strong robustness with low computation and communication overheads, and are significantly superior to the existing schemes in several aspects.

Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have been considered promising auxiliary devices to enhance the performance of the wireless network, where users located at different sides of the surfaces can be simultaneously served by the transmitting or reflecting signals. In this article, an energy efficiency (EE) optimization problem for non-orthogonal multiple access (NOMA) assisted STAR-RIS downlink network is investigated. Due to the fractional form of the objective function, it is challenging to solve the EE optimization problem using traditional convex optimization solutions. This article proposes a deep deterministic policy gradient (DDPG)-based algorithm to maximize the EE by jointly optimizing the transmission beamforming vectors at the base station and the coefficients matrices at the STAR-RIS. Simulation results demonstrate that the proposed algorithm can effectively maximize the system EE considering the time-varying channels.

Transposon (de)repression and heterochromatin reorganization are dynamically regulated during cell fate determination and are hallmarks of cellular senescence. However, whether they are sequence specifically regulated remains unknown. Here we uncover that the KCNQ1OT1 lncRNA, by sequence-specific Hoogsteen base pairing with double-stranded genomic DNA via its repeat-rich region and binding to the heterochromatin protein HP1α, guides, induces and maintains epigenetic silencing at specific repetitive DNA elements. Repressing KCNQ1OT1 or deleting its repeat-rich region reduces DNA methylation and H3K9me3 on KCNQ1OT1-targeted transposons. Engineering a fusion KCNQ1OT1 with an ectopically targeting guiding triplex sequence induces de novo DNA methylation at the target site. Phenotypically, repressing KCNQ1OT1 induces senescence-associated heterochromatin foci, transposon activation and retrotransposition as well as cellular senescence, demonstrating an essential role of KCNQ1OT1 to safeguard against genome instability and senescence.

This paper proposes some low complexity algorithms for active user detection (AUD), channel estimation (CE) and multi-user detection (MUD) in uplink non-orthogonal multiple access (NOMA) systems, including single-carrier and multi-carrier cases. In particular, we first propose a novel algorithm to estimate the active users and the channels for single-carrier based on complex alternating direction method of multipliers (ADMM), where fast decaying feature of non-zero components in sparse signal is considered. More importantly, the reliable estimated information is used for AUD, and the unreliable information will be further handled based on estimated symbol energy and total accurate or approximate number of active users. Then, the proposed algorithm for AUD in single-carrier model can be extended to multi-carrier case by exploiting the block sparse structure. Besides, we propose a low complexity MUD detection algorithm based on alternating minimization to estimate the active users' data, which avoids the Hessian matrix inverse. The convergence and the complexity of proposed algorithms are analyzed and discussed finally. Simulation results show that the proposed algorithms have better performance in terms of AUD, CE and MUD. Moreover, we can detect active users perfectly for multi-carrier NOMA system.

In this letter, we study the optimum tradeoff between the spectral efficiency (SE) and energy efficiency (EE) for the two-user Gaussian interference channel (GIC). The achievable SE–EE tradeoff region of two-user GIC is obtained by studying the interactions between the two links due to their mutual interference. Each point in the SE–EE tradeoff region corresponds to a possible scheduling strategy between the two links. The optimum SE–EE tradeoff is then identified by maximizing the EE for a given sum SE, where the maximum EE is expressed as a closed-form function of the SE. The result analytically quantifies the impact of mutual interference on the SE–EE tradeoff. It is shown that the optimum scheduling strategy depends on the interference coefficients between the two links and the target SE.

We derive a tight lower bound for symbol error rate (SER) performance of the sparse code multiple access (SCMA) uplink operated over Rayleigh fading channels. In particular, based on the working principle of message passing algorithm receiver and the special structure of the sparse binary matrix, which is used to control the collisions of SCMA system, the system can be simplified to a single user single input multiple output (SU-SIMO) system employing maximal ratio combining (MRC) reception. In this way, the average SER lower bound of SCMA is obtained by calculating the average SER of SU-SIMO system employing MRC reception. Our results show that the derived lower bound is not only an efficient means of analyzing the SER performance, but also is tight, especially in the high signal-to-noise ratio regime.

This paper investigates multiple access schemes for uplink and downlink transmissions in cellular networks with massive Internet of Things (IoT) devices. Recall that single-carrier frequency division multiple access and orthogonal frequency division multiple access, which are orthogonal multiple access (OMA) schemes, have been conventionally adopted for uplink and downlink transmissions in narrow-band IoT, respectively. Unlike these OMA schemes, we propose two non-orthogonal multiple access (NOMA) schemes for cellular IoT with short-packet transmissions. Especially, a generalized expectation consistent signal recovery-based algorithm is proposed to estimate active devices, channel state information and data in uplink transmission, where all of the active devices are allowed to transmit their pilots and data through the same resource block without authorization. On the other hand, the active devices estimated during uplink transmission are grouped for downlink transmission with a trade-off between performance and detection complexity. Additionally, the data error rates are analysed for both uplink and downlink transmissions with low-resolution analog-to-digital converters (ADCs), where the effects of critical parameters such as the estimation error, ADC bits, packet length, and message bits are revealed. Both simulation and analytical results are provided to demonstrate the excellent performance of the proposed NOMA schemes and algorithms, especially for active device, channel, and data estimations. More importantly, the obtained results show that the data error rate performance of downlink NOMA is superior to that of OMA when the message bits of devices in one group are selected following the proposed strategy.

This paper investigates the existence of random attractor for stochastic Boussinesq equations driven by multiplicative white noises in both the velocity and temperature equations and estimates the Hausdorff dimension of the random attractor.