Diffusion models (DMs) have recently achieved significant success in wireless communications systems due to their denoising capabilities. The broadcast nature of wireless signals makes them susceptible not only to Gaussian noise, but also to unaware interference. This raises the question of whether DMs can effectively mitigate interference in wireless semantic communication systems. In this paper, we model the interference cancellation problem as a maximum a posteriori (MAP) problem over the joint posterior probability of the signal and interference, and theoretically prove that the solution provides excellent estimates for the signal and interference. To solve this problem, we develop an interference cancellation diffusion model (ICDM), which decomposes the joint posterior into independent prior probabilities of the signal and interference, along with the channel transition probability. The log-gradients of these distributions at each time step are learned separately by DMs and accurately estimated through deriving. ICDM further integrates these gradients with advanced numerical iteration method, achieving accurate and rapid interference cancellation. Extensive experiments demonstrate that ICDM significantly reduces the mean square error (MSE) and enhances perceptual quality compared to schemes without ICDM. For example, on the CelebA dataset under the Rayleigh fading channel with a signal-to-noise ratio (SNR) of 20 dB and signal to interference plus noise ratio (SINR) of 0 dB, ICDM reduces the MSE by 4.54 dB and improves the learned perceptual image patch similarity (LPIPS) by 2.47 dB. The code is available at https://github.com/Wireless3C-SJTU/ICDM.

Lightweight and efficient neural network models for deep joint source-channel coding (JSCC) are crucial for semantic communications. In this paper, we propose a novel JSCC architecture, named MambaJSCC, that achieves great performance with low computational and parameter overhead. MambaJSCC utilizes the visual state space model with channel adaptation (VSSM-CA) blocks as its backbone for transmitting images over wireless channels, where the VSSM-CA primarily consists of the generalized state space models (GSSM) and the zero-parameter, zero-computational channel adaptation method (CSI-ReST). We design the GSSM module, leveraging reversible matrix transformations to express generalized scan expanding operations, and theoretically prove that two GSSM modules can effectively capture global information. We discover that GSSM inherently possesses the ability to adapt to channels, a form of endogenous intelligence. Based on this, we design the CSI-ReST method, which injects channel state information (CSI) into the initial state of GSSM to utilize its native response, and into the residual state to mitigate CSI forgetting, enabling effective channel adaptation without introducing additional computational and parameter overhead. Experimental results on different devices, including IoT device JETSON AGX ORIN, show that MambaJSCC not only outperforms existing JSCC methods (e.g., SwinJSCC) across various scenarios but also significantly reduces parameter size, computational overhead, and inference delay. We have released our code and pre-trained models at https://github.com/Wireless3C-SJTU/MambaJSCC, allowing full reproduction of our results.

In this paper, we propose a novel multi-task, multi-link relay semantic communications (MTML-RSC) scheme that enables the destination node to simultaneously perform image reconstruction and classification with one transmission from the source node. In the MTML-RSC scheme, the source node broadcasts a signal using semantic communications, and the relay node forwards the signal to the destination. We analyze the coupling relationship between the two tasks and the two links (source-to-relay and source-to-destination) and design a semantic-focused forward method for the relay node, where it selectively forwards only the semantics of the relevant class while ignoring others. At the destination, the node combines signals from both the source node and the relay node to perform classification, and then uses the classification result to assist in decoding the signal from the relay node for image reconstructing. Experimental results demonstrate that the proposed MTML-RSC scheme achieves significant performance gains, e.g., 1.73 dB improvement in peak-signal-to-noise ratio (PSNR) for image reconstruction and increasing the accuracy from 64.89% to 70.31 % for classification.

Federated semi-supervised learning (FSSL) is primarily challenged by two factors: the scarcity of labeled data across clients and the non-independent and identically distribution (non-IID) nature of data among clients. In this paper, we propose a novel approach, diffusion model-based data synthesis aided FSSL (DDSA-FSSL), which utilizes a diffusion model (DM) to generate synthetic data, bridging the gap between heterogeneous local data distributions and the global data distribution. In DDSA-FSSL, clients address the challenge of the scarcity of labeled data by employing a federated learning-trained classifier to perform pseudo labeling for unlabeled data. The DM is then collaboratively trained using both labeled and precision-optimized pseudo-labeled data, enabling clients to generate synthetic samples for classes that are absent in their labeled datasets. This process allows clients to generate more comprehensive synthetic datasets aligned with the global distribution. Extensive experiments conducted on multiple datasets and varying non-IID distributions demonstrate the effectiveness of DDSA-FSSL, e.g., it improves accuracy from 38.46% to 52.14% on CIFAR-10 datasets with 10% labeled data.

In the visionary ideals of “Internet of Everything” and “Digital Twins”, the future 6G will deeply integrate diverse heterogeneous networks such as satellite and aerial networks to support seamless connectivity and efficient interoperability, also known as space-air-ground integrated networks (SAGIN), in which the grant-free uplink random access based on Slotted ALOHA (S-ALOHA) can reduce access latency and complexity for massive Internet of Things (IoT) devices. However, with the increasing number of IoT users, the collision probability of S-ALOHA escalates and further degrades the system performance. In this paper, we focus on the massive IoT device uplink access in SAGIN aided by high altitude platform stations (HAPS), investigating power allocation for IoT devices to maximize system access capability and spectral efficiency (SE). Specifically, we first optimize 3D deployment of HAPS. Then the resilient massive access (RMA) based on flexible fusion of S-ALOHA and non-orthogonal multiple access methods is proposed. To maximize system SE with device power constraints, we model the sequential decision problem as a Markov decision process and solve it with the Advantage Actor-Critic (A2C) algorithm. Simulation results demonstrate the proposed RMA can significantly improve the IoT terminal successful access probability and the resource scheduling based on A2C also significantly increases the system SE with low complexity.

Lightweight and efficient neural network models for deep joint source-channel coding (JSCC) are crucial for semantic communications. In this paper, we design a novel JSCC scheme named MambaJSCC, which utilizes a visual state space model with channel adaptation (VSSM-CA) block as its backbone for transmitting images over wireless channels. The VSSM-CA block utilizes VSSM to integrate images with the state space, enabling feature extraction and encoding processes to operate with linear complexity. It also incorporates channel state information (CSI) via a newly proposed CSI embedding method. This method deploys a shared CSI encoding module within both the encoder and decoder to encode and inject the CSI into each VSSM-CA block, improving the adaptability of a single model to varying channel conditions. Experimental results show that MambaJSCC not only outperforms Swin Transformer based JSCC (SwinJSCC) but also significantly reduces parameter size, computational overhead, and inference delay (ID). In particular, with employing an equal number of the VSSM-CA blocks and the Swin Transformer blocks, MambaJSCC achieves a 0.48 dB gain in peak-signal-to-noise ratio (PSNR) while requiring only 53.3% multiply-accumulate operations, 53.8% of the parameters, and 44.9% of ID.

Diffusion models (DM) can gradually learn to remove noise, which have been widely used in artificial intelligence generated content (AIGC) in recent years. The property of DM for eliminating noise leads us to wonder whether DM can be applied to wireless communications to help the receiver mitigate the channel noise. To address this, we propose channel denoising diffusion models (CDDM) for semantic communications over wireless channels in this paper. CDDM can be applied as a new physical layer module after the channel equalization to learn the distribution of the channel input signal, and then utilizes this learned knowledge to remove the channel noise. We derive corresponding training and sampling algorithms of CDDM according to the forward diffusion process specially designed to adapt the channel models and theoretically prove that the well-trained CDDM can effectively reduce the conditional entropy of the received signal under small sampling steps. Moreover, we apply CDDM to a semantic communications system based on joint source-channel coding (JSCC) for image transmission and design a three-stage training algorithm for combining them. Extensive experimental results demonstrate that CDDM can further reduce the mean square error (MSE) after minimum mean square error (MMSE) equalizer, and the joint CDDM and JSCC system achieves better performance than the JSCC system, the traditional JPEG2000 with low-density parity-check (LDPC) code approach and other benchmarks in diverse scenarios.

Diffusion models (DM) can gradually learn to re-move noise, which have been widely used in artificial intelligence generated content (AIGC) in recent years. The property of DM for removing noise leads us to wonder whether DM can be applied to wireless communications to help the receiver eliminate the channel noise. To address this, we propose channel denoising diffusion models (CDDM) for wireless communications in this paper. CDDM can be applied as a new physical layer module after the channel equalization to learn the distribution of the channel input signal, and then utilizes this learned knowledge to remove the channel noise. We design corresponding training and sampling algorithms for the forward diffusion process and the reverse sampling process of CDDM. Moreover, we apply CDDM to a semantic communications system based on joint source-channel coding (JSCC). Experimental results demonstrate that CDDM can further reduce the mean square error (MSE) after minimum mean square error (MMSE) equalizer, and the joint CDDM and JSCC system achieves better performance than the JSCC system and the traditional JPEG2000 with low-density parity-check (LDPC) code approach.

Degraded broadcast channels (DBC) are a typical multiuser communication scenario. There exist classic transmission methods, such as superposition coding with successive interference cancellation, to achieve the DBC capacity region. However, semantic communication method over DBC remains lack of in-depth research. To address this, we design a semantic communications system for wireless image transmission over DBC in this paper. The proposed architecture supports a transmitter extracting semantic features for two users separately, and learns to dynamically fuse these semantic features into a joint latent representation for broadcasting. The key here is to design a flexible image semantic fusion (FISF) module to fuse the semantic features of two users, and to use a multi-layer perceptron (MLP) based neural network to adjust the weights of different user semantic features for flexible adaptability to different users channels. Experiments present the semantic performance region based on the peak signal-to-noise ratio (PSNR) of both users, and show that the proposed system dominates the traditional methods.

This paper addresses the filter design and user selection issues for the downlink of a multi-user MIMO-relay system. To eliminate interuser interference, the filter design is based on the singular value decomposition (SVD) of the first hop link and block-diagonalization (BD) for the second hop link. Then the problem is converted to the power allocation problem at the relay station (RS). It can be determined in a closed- form by the water-filling policy. In a practical system with a large number of users, the RS need to select a subset of best users to serve. The exhaustive search for the optimal userset is, however, computationally prohibitive. Therefore, we propose a low-complexity algorithm that is based on the MIMO-relay channel capacity. Simulation results show that the proposed linear filter design scheme achieves significant system performance improvement compared with the equal power allocation scheme and the user selection scheme allows for a reasonable tradeoff between the complexity and performance.

In this paper, heterogeneous services in multiuser multiple-input multiple-output (MIMO)-Relay systems are investigated, where the relays work in amplify-and-forward mode. A utility based adaptive scheduling algorithm is proposed, which aims to maximize user satisfaction as well as system spectral efficiency. Joint optimal carriers and spatial subchannels allocation is considered based on the proposed utility function, and two factors ratio and w are introduced in order to differentiate QoS and users. Moreover, a suboptimal solution is also presented for the sake of decreasing computational complexity and processing delay. Simulation results show that the proposed strategy can guarantee QoS of multiple services with a tolerable decline in terms of spectral efficiency compared with traditional maximum carrier/interference (MCI) algorithm. Besides, the complexity can be reduced obviously by utilizing the suboptimal scheme, which achieves nearly the same performance compared with the optimal solution.

This paper investigates proportional fairness-oriented scheduling issues for multihop OFDMA systems with multiple relays. Based on the idealized L-hop linear network model, three adaptive proportional fair scheduling (PFS) algorithms, namely optimal PFS, iterative user pairing (IUP) PFS and successive distributed (SD) PFS, are proposed for multihop OFDMA systems. Different with the existing scheme presented in former work, these three algorithms could be applied to OFDMA based multihop (more than two-hop) systems. The optimal PFS is presented as an upper bound in terms of proportional fairness, which involves exponential times of calculations. Thus we propose other two simpler algorithms for practical issue. Given the feature of multihop networks, they try to balance the aggregate data rates of each hop. Simulation results show that both IUP PFS and SD PFS achieves a good tradeoff between performance and complexity.

This paper investigates joint power and subcarrier allocation issues for cellular OFDM-relay networks. Two novel decentralized schemes, namely semi-distributed method and distributed method based on multihop equilibrium are proposed, which aims to exploit the radio resource management (RRM) function for relay nodes (RNs) and decrease the amount of feedback information for RN-MS links. In decentralized mechanism, the base station (BS) distributes resources to direct users and RNs roughly first with partial CSI feedback or without CSI feedback for RN-MS link, and then the RNs allocate resources for each relay user by striking an efficient balance for the multihop transmission. Simulation results show that the proposed decentralized methods can achieve good performances in terms of average throughput and fraction of satisfied users, especially for relay users with multihop equilibrium mechanism. Moreover, the semi-distributed method is better choice for future LTE-A system due to excellent tradeoff between performance and complexity.

This paper addresses the filter-design issues for multi-user non-regenerative MIMO-relay systems. Based on the perfect channel state information (CSI), optimal joint linear filter schemes at the base station and the relay are derived, aiming to minimize the mean squared error (MSE). We first propose the joint optimal filter scheme in the downlink scenario along with a more practical suboptimal scheme, and then a closed-form optimal solution in the uplink scenario is exploited. Numerical results show that the proposed joint schemes can reduce the bit error rate (BER) significantly, especially for the high SNR case.

The cooperative cellular relaying network is expected to achieve the higher capacity and enlarge the coverage. In this paper, a novel cost-aware handover decision algorithm (CHDA) for cooperative cellular relaying networks is proposed. Two cost functions, namely the triggering and priority decision cost functions are exploited, which involves the signal transmission quality, the handover signaling cost, the handover latency and the interference estimation. Simulation results show that the signaling overhead and the handover delay decrease significantly by utilizing the CHDA scheme. It is also proved that the CHDA strategy is an efficient method to achieve the tradeoff among the QoS requirements and the system overheads, which can remarkably enhance the system performance.

In this paper, three centralized packet scheduling schemes with fairness-oriented feature are proposed for OFDMA multihop relaying networks, namely Greedy polling with novel starvation restrained (SR-GP), enhanced proportional fairness with SR (SR-EPF) and novel subcarrier pairing with hop balance (HB-SP) scheduling algorithms. SR-GP can bring better fairness than traditional throughput-oriented algorithms by sacrificing a little complexity. SR-EPF with novel priority function can guarantee the quality-of-service (QoS) of the cell edge users. HB-SP can enhance the system throughput significantly by utilizing the relay link efficiently with multihop equilibrium. Simulation results of the throughput's deviation of different users indicate that HB-SP and SR-EPF are applicable to realtime services and SR-GP is suitable for non-realtime services. Generally, HB-SP algorithm is a good achievement for both capacity enhancement and resource fairness.

This paper presents a distributed power allocation (PA) algorithm for the downlink of relay enhanced cellular networks. The PA problem is built into a non-cooperative game where a utility function is formulated and maximized. The utility function is comprised of two parts considering the interests of the node B and the relay node respectively, which facilitates the distributed PA on the nodes. Since the relay user's data rate is constrained by the minimum capacity of the two hops, a novel pricing function is developed to avoid allocating excess power to the second hop when the capacity is inferior in the first hop. The proposed game theoretic approach is compared with the uniform power allocation and the pure iterative water-filling method. Simulation results show that within a few steps of iteration, the proposed scheme can not only achieve the highest system capacity, but also efficiently balance the capacities of relay users in the two hops.

This paper investigates the antenna selection and relay node selection issues for multi-antenna relay system. Based on the channel statistics, optimal selection criteria for antenna and relay node are derived respectively, aiming to maximize the ergodic capacity. We first discuss the statistical antenna selection in the single-relay scenario, and then the joint antenna and node selection in the multi-relay scenario is exploited. Simulation results show that the proposed statistical optimal selection provides significant selection gains. For the antenna selection in high signal-to-noise ratio regime, the achieved capacity based on the derived statistical criterion is close to that based on the instantaneous channel knowledge. In addition, the system can benefit from a moderate increase of relay number with node selection, compared to the transmission with all nodes used.

In this paper, a QoS differentiation adaptive retransmission limits ARQ (QDARL-ARQ) is proposed to improve the efficiency of retransmission in conventional SR-ARQ for the IEEE 802.16e BWA systems. With a simple algorithm implemented based on the conventional SR-ARQ, QDARL-ARQ scheme is able to dynamically adjust the retransmission limits for services with different characteristics by considering their QoS requirements as well as the current system states simultaneously. This scheme aims to achieve lower packet error rate with restrained end-to-end delay in the time-variable and error prone wireless environment in comparison with conventional SR-ARQ. Several performance metrics of QDARL-ARQ are compared with conventional SR-ARQ in both single service scenarios and multiple services scenarios. The performance improvement due to QDARL-ARQ is evaluated through the IEEE 802.16e system level simulation, and the results clearly show that it can improve the performance of mean end-to-end delay, packet error rate and throughput, especially the retransmission efficiency. It can also be found that the conventional SR-ARQ is in fact a special instance of the QDARL-ARQ designed here.

Relaying transmission is a candidate way to combat wireless channel fading and enlarge the coverage, and efficient radio resource allocation is essential to provide quality-of-service (QoS) for wireless networks. In this paper, an adaptive multiuser radio resource allocation model is proposed for the downlink of OFDM-relay system, which exploits performance gain in both frequency domain and time domain. According to the different transmission modes, two QoS-oriented scheduling algorithms based on the feedback of the channel state information (CSI) of two hops are investigated. One is enhanced proportional fairness (EPF) algorithm, and the other is improved priority (IPRI) algorithm. Both of them can achieve high system throughput and better resource fairness due to the adaptive allocation, especially in the QoS-guarantee aspect for cell edgy users compared with conventional scheduling schemes. The priority strategy is a novel scheme, because of considering resource fairness with artificial starve (AS) state in IPRI, which yields higher spectral efficiency and achieve better data rate requirements for the users.