This paper addresses the challenges of image transmission in satellite communication networks, where bandwidth constraints, high interference, and latency issues significantly limit conventional transmission methods. We propose a novel semantic communication framework that adapts to various computational capabilities of receiving terminals in Internet of Everything (IoE). Our approach leverages the Swin Transformer V2 architecture to extract and transmit task-relevant semantic features rather than raw image data, significantly reducing bandwidth requirements while maintaining high reconstruction quality. The proposed system dynamically adjusts its encoding and decoding processes based on receiver computational capacities, enabling efficient image transmission to heterogeneous terminals ranging from high-performance stations to resource-constrained devices. Extensive experiments on various datasets demonstrate that our framework outperforms conventional JPEG+LDPC schemes and state-of-the-art deep learning-based approaches in terms of both PSNR performance and semantic communication utility across various signal-to-noise ratios. The framework shows particular robustness in low-SNR and low-CBR environments, addressing the “efficiency-compatibility” dilemma in resource-constrained satellite communications.

Unmanned aerial vehicles (UAVs) can use the collected data to perform machine-learning tasks and enhance their intelligence level. The distributed framework of federated learning (FL) is suitable for resource-constrained UAV swarms. However, the dynamic channel conditions caused by the mobility of the UAVs impact the wireless FL performance. Additionally, data insufficiency and heterogeneity also affect the performance of the trained models. Thus, in this paper, we propose a joint training, trajectory, and beamforming optimization for an energy-efficient FL scheme that leverages the mobility of UAVs to enhance the model training efficiency, namely TTBO-FL. We adopt a two-level FL for the UAV network, where a high-level UAV (H-UAV) is for model aggregation and a set of low-level UAVs (L-UAVs) is for model training. Considering the dynamic channel conditions, we design a three-dimensional uniform linear array (3D ULA) and implement 3D analog beamforming to increase communication efficiency among UAVs. Moreover, we introduce transfer learning techniques and regularization constraints to mitigate the problem of data insufficiency and heterogeneity. Then, we formulate an optimization problem for UAV trajectory planning, local epoch adjustment, and beamforming, and adopt a soft actor-critic (SAC)-based algorithm to solve it. The simulation results show that, compared to the baseline schemes, the proposed schemes achieve higher model accuracy and lower energy consumption for the UAV swarm.

The advance of vehicle-to-everything (V2X) networks has led to many emerging data-intensive applications at the network edge. To meet the soaring data rate requirements of these applications, numerous coding schemes has been developed. However, the high heterogeneity of edge users bring challenges to these methods, including adaptation to performance requirements, coping with unknown or varying channels, as well as inefficient multicasting. In this paper, we address those problems by developing aratelessdeep joint source-channel coding scheme featuring fine-grained control over rate and informativeness at the user. Towards this end, we first design a novel class of variational information bottleneck (VIB) by employing the multinomial-Gaussian (MG) distribution, to achieve rateless transmission over an erasure channel. We derived important results on the statistical properties of this latent distribution to facilitate efficient training of MG-VIB. Then, we apply this framework to multicasting, proposing MG-VIB-M to enhance adaptability and scalability. Simulations show that our proposed method is more flexible regarding rate-relevance tradeoffs, has greater robustness against channel imperfections, and reduces bandwidth requirements for task-oriented multicasting.

Federated learning (FL) is suitable for unmanned aerial vehicles (UAVs) and ground devices to exchange model parameters periodically and learn a shared model without transmitting raw data. However, the existing UAV-assisted FL systems consider all learning phases to be equally important, which is inconsistent with the critical learning period (CLP) that exists in the FL training process, leading to significant overheads and inefficient resource utilization. Besides, data heterogeneity and insufficiency among devices also affect the performance of the trained models. To address the above issues, in this paper, we propose a CLP-aware FL framework that identifies unequally important learning stages and implements corresponding FL training strategies. Given the significant differences between global and local model parameter distributions in the early training epochs, we introduce a Federated Kullback-Leibler divergence (KLD) Norm (FKN) metric that measures the KLD between these distributions for efficient CLP detection. To capture the data drift caused by environmental shift in UAV swarms, we also develop a computationally efficient Federated Drift Norm (FDN) metric to enable online detection of CLP. We formulate an optimization problem for CLP-based participating device selection, UAV visit frequencies to different devices, and model aggregation period, then adopt a deep reinforcement learning (DRL)-based algorithm to solve it. Simulation results show that our strategy reduces energy consumption while maintaining model accuracy compared to baselines, i.e., CriticalFL, pFedBayes, and FedExp.

In unmanned aerial vehicle (UAV) swarm routing algorithms, traditional methods struggle to adapt to rapidly changing environments, while existing reinforcement learning (RL) approaches fail to adequately consider packet priority, leading to increased packet delay and lower packet delivery rate. Furthermore, the reward function of RL often relies on manual tuning based on experience, resulting in limited effectiveness and cumbersome parameter adjustments. In this paper, an improved RL method is proposed, that incorporates packet priority considerations and leverages the powerful capabilities of large language model (LLM) to automatically correct and optimize the reward function, significantly enhancing algorithm performance. Simulation results show that the proposed algorithm significantly reduces transmission delay and effectively improves packet delivery rate.

Space-air-ground integrated networks (SAGIN) are a key technology in 6G, enabling seamless global connectivity through a unified communication framework, while integrated sensing and communication (ISAC) mitigates spectrum congestion by reusing spectrum and transceivers for dual communication and sensing functions. Despite its potential, existing ISAC research has largely focused on terrestrial networks, with limited exploration in SAGIN environments. This paper proposes a novel ISAC framework for SAGIN, incorporating orthogonal time frequency space (OTFS) modulation and non-orthogonal multiple access. OTFS enhances the system's robustness in doubly-dispersive channels and supports precise communication and sensing integration. These channels experience both time dispersion caused by multipath propagation and frequency dispersion caused by Doppler shifts. In this framework, high-altitude platform stations serve as relay nodes, amplifying communication signals and processing echo signals for accurate target parameter estimation. To further enhance system performance, we design a beamforming optimization strategy using successive convex approximation to improve communication energy efficiency. Simulation results demonstrate that the proposed scheme significantly enhances the overall performance of the SAGIN system.

To support the large-scale connectivity of massive intelligent devices in the Internet of Things (IoT) scenario, in this paper, an uplink multi-user semantic communication system based on model division multiple access (MDMA) is investigated. A model resource pool consisting of multiple mutually exclusive semantic models is built as one new kind of access resource, and a global optimization problem is formulated to make the mapping of different semantic models have stronger mutual exclusion. To solve this problem, a joint deep adversarial semantic decomposition (JDASD) algorithm is proposed to enhance the ability of the semantic models to eliminate interference from other devices. Simulation results demonstrate that the proposed JDASD algorithm achieves higher anti-interference capability in the MDMA system than the traditional independent training scheme applied in most works, showing the advantages of the proposed scheme for the IoT scenario with massive devices.

Using linear arrays for compressive sensing-based (CS) three-dimensional (3D) millimeter-wave (mmWave) channel estimation presents significant challenges due to the dimensional limitations of the array aperture, which prevents direct sparse 3D channel representation. Fortunately, the emergence of array motion provides a chance for that. In this paper, by exploring the motion of linear arrays in time-varying channels and identifying their similarities to planar arrays, we propose a method for sparse representation of 3D channels based on linear arrays. Utilizing the sparsity, a two-step sparse channel recovery algorithm is presented. Initially, the on-grid and off-grid approaches are employed to efficiently and accurately obtain the angles of arrival (AoAs). Subsequently, path gains are estimated based on these AoAs. Simulation results show that the proposed method can effectively estimate 3D time-varying mmWave channels using a linear array with only a small number of pilots.

Distributed path planning for multiple unmanned aerial vehicles (UAVs) plays a significant role in data collection systems. However, insufficient collaboration among multiple UAVs and inadequate emergency response capability in the case of UAV failure will lead to longer collection time. To address these challenges, we put forward a set of novel distributed path planning schemes in plural data collection scenarios, aimed at minimizing the task completion time and enhancing robustness. Specifically, a distributed framework of multiple UAVs collaboration data collection system (MUC-DCS) is designed, which can effectively avoid repeated data collection and UAV collision through inter-UAV communication. In order to optimize the task completion time in the MUC-DCS, a distributed path planning algorithm based on multi-agent deep Q-networks (DPP-MDQN) is proposed. In addition, so as to improve the algorithm efficiency, the pheromone is set to represent the state information and reward function. Further, we develop an emergency response strategy and propose a distributed path planning algorithm for emergency response (DPP-ER), enabling response to sudden UAV failure as well as ensuring the robustness and timeliness of MUC-DCS. The simulation results show that DPP-MDQN is superior to existing distributed algorithms and reduces the task completion time, as well as DPP-ER can effectively handle the sudden emergency of UAV failure.

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.

Mutual coupling (MC) has a severe effect on the direction-of-arrival (DOA) estimation and even leads to estimation failures. The emerging array motion techniques, which construct flexible virtual arrays with larger apertures by moving an initial array and combining phase-corrected signals sampled at designed time delays, have shown potential in mitigating the effect of MC by constructing virtual sensors. However, the virtual array remains susceptible to MC. To handle this problem, in this letter, a general synthetic signal model with MC is derived and an iterative method on the array configuration is proposed to form a virtual array with less MC for the circular motion-based DOA estimation. Specifically, a virtual uniform concentric circular array (VUCCA) is constructed by synthesizing all the observations at different time delays with uniform spacing, and the equation between MC matrix of the VUCCA and the initial array is derived, proving that the MC of VUCCA is solely related to the configuration of initial array. Thus, an iterative method is proposed to yield an optimal configuration with less MC by minimizing the norm close with MC. A higher degree-of-freedom and better estimation accuracy are achieved effectively.

The unmanned aerial vehicle (UAV) aided mobile edge computing (MEC) network has attracted significant attention due to its enhanced computing power, reliable network connectivity, dynamic environment adaptability, which however still suffer from non-instantaneous channel reconstruction and wireless security threats. Therefore, this paper considers a reconfigurable intelligent surface (RIS) assisted UAV-MEC network, aiming to minimize UAV energy consumption by jointly optimizing task offloading rate, user scheduling coefficient, RIS phase and UAV trajectory with the constraints of secure offloading rate. Given the multi-variable coupling and non-convex nature of this optimization problem, we decouple it into three subproblems, which include the user scheduling and offloading ratio optimization, the RIS phase optimization, and the UAV trajectory optimization. For the fractional programming problem involving RIS phase optimization, the Dinkelbach algorithm is used to transform it into a parametric subtraction problem, thus obtaining a closed-form solution for the RIS phases. Furthermore, the successive convex approximation (SCA) algorithm is employed for the UAV trajectory optimization subproblem. Ultimately, a double-layer iterative optimization algorithm based on block coordinate descent (BCD) is proposed to solve the original non-convex optimization problem. Simulation results confirm its superior performance in saving UAV energy compared to other baseline schemes.

In order to meet the requirements of 6G communication for environmental adaptability and interference resistance, obtaining accurate Channel State Information (CSI) is of paramount importance. However, traditional communication methods struggle to fulfill these demands, leading to a growing interest in deep learning-based channel estimation solutions among researchers. This paper introduces a solution to the channel estimation problem in OFDM systems, employing a deep learning approach based on the MLP-Mixer block, referred to as CENet. CENet's channel-mixing and token-mixing structures enable better capturing of both temporal and spectral channel characteristics. The proposed channel estimation method consists of two parts: firstly, preliminary channel estimation results are generated using the LS algorithm, and then CENet is employed to further refine these preliminary results. Simulation results demonstrate the superiority of the proposed approach over other deep learning methods. Additionally, this paper extends the method to MIMO scenarios and introduces pruning techniques to reduce redundant parameters in the MLP layers, thereby reducing computational complexity.

To support large-scale connectivity of massive machine-type communication (mMTC) devices in Industrial Internet of Things (IIoT) scenario, a grant-free (GF) transmission aided non-orthogonal multiple access (NOMA) system is considered in this paper, where the devices with diverse behavior characteristics coexist. In order to reflect the diversity of IIoT scenario, multiple types of MTC devices are taken into account, and the network environment is divided into stationary mode and overload mode based on the differences in behavior characteristics of devices. Further, the successful access probability maximization problem is established to obtain the priority-aware access strategy. To solve this problem, we propose a novel distributed Q-learning algorithm and an adaptive update (AdaUpdate) aided priority-aware deep Q network (PA-DQN) algorithm for the stationary mode and the overload mode, respectively. Simulation results demonstrate that the proposed algorithms achieve better performance than that of traditional reinforcement learning algorithms and random access algorithm in terms of overall access efficiency and satisfying the access requirements of emergency devices preferentially.

In this letter, we propose a deep learning-based method for the direction of arrival (DOA) estimation in the low signal-to-noise ratio (SNR) scenario. Specifically, the DOA estimation is modeled as a multi-label classification task, and a novel dual class token Vision Transformer (DCT-ViT) is designed to fit it. Different from the classical ViT architecture with a single class token, the DCT-ViT includes two class tokens which are located at the beginning and end of the latent vector sequence, respectively. This architecture enables enhanced information mining and feature extraction from the array signal data in order to improve the accuracy of DOA estimation. Furthermore, a single DCT-ViT model can accommodate different source numbers by leveraging a training dataset with different numbers of sources. Simulation results illustrate that our proposed method outperforms existing methods in the low SNR scenario, including classical model-based and other deep learning-based methods.

Single-user semantic communications have attracted extensive research recently, but multi-user semantic broadcast communication (BC) is still in its infancy. In this paper, we propose a practical robust features-disentangled multi-user semantic BC framework, where the transmitter includes a feature selection module and each user has a feature completion module. Instead of broadcasting all extracted features, the semantic encoder extracts the disentangled semantic features, and then only the users’ intended semantic features are selected for broadcasting, which can further improve the transmission efficiency. Within this framework, we further investigate two information-theoretic metrics, including the ultimate compression rate under both the distortion and perception constraints, and the achievable rate region of the semantic BC. Furthermore, to realize the proposed semantic BC framework, we design a lightweight robust semantic BC network by exploiting a supervised autoencoder (AE), which can controllably disentangle sematic features. Moreover, we design the first hardware proof-of-concept prototype of the semantic BC network, where the proposed semantic BC network can be implemented in real time. Simulations and experiments demonstrate that the proposed robust semantic BC network can significantly improve transmission efficiency.

Cell-free (CF) massive MIMO is considered one of the key technologies for 6G to achieve high spectral efficiency (SE) and ultralow latency. However, as the number of users increases, pilot contamination becomes more serious, and the optimal SE can not be achieved when the number of users exceeds the access points (APs). Therefore, we study the CF massive MIMO-NOMA system. Specifically, we design a user clustering algorithm based on the average Signal to Interference plus Noise Ratio (SINR), using orthogonal pilots between different clusters, and different users in the cluster using the same pilot, thereby reducing pilot contamination. Then we propose a flexible power allocation problem to maximize the system SE while taking into account user fairness. We model the problem as a Markov Decision Process (MDP) and then solve it using the asynchronous advantage actor-critic (A3C) algorithm in deep reinforcement learning. Simulation results show that the proposed A3C based power allocation scheme in CF massive MIMO-NOMA outperforms the baseline schemes in terms of fairness and SE.

In this paper, we investigate the trainable-step-size least mean square (TSS-LMS) algorithm, which combines the LMS algorithm model with deep learning for the direction of arrival (DOA) estimation using adaptive filtering. Although the existing variable-step-size LMS approaches have been proposed to improve the DOA estimation accuracy, they are generally unsuitable for scenarios with the limited number of snapshots. Therefore, we propose a deep learning-based algorithm driven by the LMS model in this work, which incorporates the trainable step size. More specifically, the iterative mathematical model based on the LMS algorithm is unfolded into a deep network, where each iteration corresponds to a layer of this network. Based on this structure, the iteration step size in each layer is set as a trainable variable, allowing for adaptation during the training of the network. Through deep learning with the received signal dataset, the TSS-LMS network improves the adaptability and DOA estimation accuracy of the LMS algorithm for the limited snapshots scenario. Simulation results show that our proposed method is effective and outperforms existing algorithms in terms of DOA estimation performance and computational complexity trade-off.

Federated learning (FL) provides an efficient distributed learning framework for computing-constrained Unmanned aerial vehicles (UAVs) swarms. However, due to the dynamic channel condition and limited resources in UAV swarm, the efficiency of FL requires to be further improved. In this paper, by leveraging the motion characteristics of UAVs, we propose an energy-efficient FL framework based on trajectory planning to train machine learning (ML) models. In the proposed framework, a two-level UAV swarm is established, consisting of a high-level UAV (H-UAV) and a group of low-level UAVs (L-UAVs). Considering the limited energy resources of the UAVs, our primary objective is to minimize the flight energy consumption of the H-UAV as it is much higher than the communication and computation energy consumption. To achieve this goal, we formulate the optimization problem jointly considering the adjustment of FL training parameters and the trajectory planning of H-UAV. Then, we reformulate the problem as a Markov decision process (MDP) and employ soft actor-critic (SAC) and deep deterministic policy gradient (DDPG) algorithms to tackle it. Simulation results show that the proposed approach and the proposed algorithms have good performance.

This paper considers a cell-free (CF) massive multiple-input multiple-output (MIMO) with network-assisted full-duplex (NAFD) system for the ultra-reliable and low-latency (URLLC) communications, jointly optimizing duplex mode selection and transceiver design. To reduce the cross-link interference (CLI) and improve the weighted URLLC sum-rate of downlink and uplink users, we propose an optimization problem to maximize the achievable sum rates for the cell-free massive MIMO with NAFD system under a finite blocklength, limited by data rates and transmission power constraints. Then, a concave-convex procedure (CCCP) algorithm is used to solve this optimization problem. Simulation results show that the proposed algorithm has better performance than the traditional co-frequency co-time full-duplex (CCFD) and half-duplex (HD) schemes.

In this article, we propose the Raptor coded random access (RCRA) scheme to enable reliable transmission in the decentralized unmanned aerial vehicle (UAV) network. The considered network is composed of several overlapped random access systems with interference nodes, and the proposed RCRA scheme reduces bit-error ratio (BER) of the random access systems by three steps. First, we choose the number of slots based on a derived lower bound, which is necessary for reliable random access. Second, error-correcting codes are incorporated as the precode before random access, and then the access probability is optimized to achieve the minimum BER. Third, by correlating two consecutive slots, an idle-slot-filling approach is designed to further improve the efficiency of the random access systems. Numerical results show that the proposed RCRA scheme reduces significantly both block-error ratio (BLER) and BER at moderate- and high-signal-to-noise ratio (SNR) region. With$E_{s}/N_{0}$equal to 0 dB, the RCRA scheme saves 20% slots, compared with the existing frameless ALOHA scheme, to achieve a target BER of 10−4.

In this letter, we explore the problem of DOA estimation using neural networks for two closely spaced sources. Since the traditional high-resolution techniques based on classical algorithms cannot achieve high-accuracy DOA estimation in the presence of two closely spaced sources, especially at low signal-to-noise ratios (SNR), we propose a novel DOA estimation method based on a cascaded neural network to address this problem. Specifically, this network comprises two parts: the SNR classification network and the DOA estimation network. The latter network contains two estimation subnetworks, which are appropriate for different SNRs by training with noisy data and activated by the output of the SNR classification network. Simulation results demonstrate that the estimation performance of our proposed method achieves much better than that of the existing algorithms under various conditions, especially for the scenes with low SNRs or small snapshot number.

Spectrum efficiency can be greatly improved through high-accuracy spectrum sensing in cognitive unmanned aerial vehicle (UAV) networks. However, the traditional centralized cooperative spectrum sensing (CCSS) methods are not applicable to the spectrum sensing of cognitive UAV networks, since the mobility of nodes and the dynamicity of network topology make it challenging to gather all the sensing information into a fusion center (FC) quickly enough. To overcome the challenge, this paper proposes a clustering-based distributed cooperative spectrum sensing (c-DCSS) scheme. Specifically, the considered cognitive UAV network is first clustered based on Max-Min distance clustering methods by jointly taking the position, velocity, and moving direction of UAVs in account, and then a two-stage fusion scheme is adopted to execute hierarchical sensing information fusion. Simulation results show that compared to the unclustered DCSS (u-DCSS) scheme, the proposed scheme significantly enhances the spectrum detection performance of cognitive UAV networks, especially when the number of UAV nodes is relatively large.

As the emergence of small cell densification and cache-enabled smart devices, mobile edge caching is regarded as a promising tool to relieve traffic burden of core network and reduce end-to-end delay. To fully utilize the limited caching capacity, cooperative caching has been proposed to further improve user experience by exploiting caching diversity. Under such paradigm, popular contents are prefetched and stored in small base stations (SBSs) or user devices. However, the popularity of a certain content may change over time due to human factors. In this paper, we study the cooperative content caching problem from a reinforcement learning perspective. We investigate a delay minimization problem by jointly considering the spatiotemporal variation of content variation, the cost of content sharing between user devices, and the cost of cooperative caching among BSs. To address this problem, we propose a two-stage multi-armed bandit learning based online cooperative (MAB-LOC) algorithm. In the first stage, we design a MAB based algorithm to estimate the content popularity. In the second stage, we design a semidefinite relaxation based approach to obtain the caching strategy. Through simulation results, we show that the performance of the proposed algorithm is competitive in terms of caching-hit probability and end-to-end delay.

This paper discusses transmission performance and power allocation strategies in an underlay cognitive radio (CR) network that contains relay and massive multi-input multi-output (MIMO). The downlink transmission performance of a relay-aided massive MIMO network without CR is derived. By using the power distribution criteria, the k th user’s asymptotic signal to interference and noise ratio (SINR) is independent of fast fading. When the ratio between the base station (BS) antennas and the relay antennas becomes large enough, the transmission performance of the whole system is independent of BS-to-relay channel parameters and relates only to the relay-to-users stage. Then cognitive transmission performances of primary users (PUs) and secondary users (SUs) in an underlay CR network with massive MIMO are derived under perfect and imperfect channel state information (CSI), including the end-to-end SINR and achievable sum rate. When the numbers of primary base station (PBS) antennas, secondary base station (SBS) antennas, and relay antennas become infinite, the asymptotic SINR of the k th PU and SU is independent of fast fading. The interference between the primary network and secondary network can be canceled asymptotically. Transmission performance does not include the interference temperature. The secondary network can use its peak power to transmit signals without causing any interference to the primary network. Interestingly, when the antenna ratio becomes large enough, the asymptotic sum rate equals half of the rate of a single-hop single-antenna K -user system without fast fading. Next, the PUs’ utility function is defined. The optimal relay power is derived to maximize the utility function. The numerical results verify our analysis. The relationships between the transmission rate and the antenna number, relay power, and antenna ratio are simulated. We show that the massive MIMO with linear pre-coding can mitigate asymptotically the interference in a multi-user underlay CR network. The primary and secondary networks can operate independently.

Millimeter-wave communication in 60 GHz band is considered a promising technology to meet the explosive growth of data demand in Wi-Fi based WLAN. To address potential blockage for 60 GHz signals, multiple APs are proposed for such WLAN. This paper addresses the important problem of AP assignment and transmission scheduling for a multi-AP 60 GHz WLAN. We propose two AP assignment schemes with different complexity and study how to maximize user throughput with joint consideration of AP assignment and transmission scheduling. We advocate to use one-shot AP assignment-based scheduling due to its simplicity for implementation. To address real-time online traffic and human blockage, we propose an online algorithm to implement the one-shot AP assignment scheme without altering the AP assignment for other existing users. Through performance evaluation, we show that the proposed online algorithm is competitive when compared to the offline algorithm.

We propose a novel channel model for massive multiple-input multiple-out (MIMO) communication systems that incorporate the spherical wave-front assumption and non-stationary properties of clusters on both the array and time axes. Because of the large dimension of the antenna array in massive MIMO systems, the spherical wave-front is assumed to characterize near-field effects resulting in angle of arrival (AoA) shifts and Doppler frequency variations on the antenna array. Additionally, a novel visibility region method is proposed to capture the non-stationary properties of clusters at the receiver side. Combined with the birth-death process, a novel cluster evolution algorithm is proposed. The impacts of cluster evolution and the spherical wave-front assumption on the statistical properties of the channel model are investigated. Meanwhile, corresponding to the theoretical model, a simulation model with a finite number of rays that capture channel characteristics as accurately as possible is proposed. Finally, numerical analysis shows that our proposed non-stationary channel model is effective in capturing the characteristics of a massive MIMO channel.

In this paper, we focus on a new small cell discovery signal which improves the detection probability in ultra-dense small cell scenario. Due to the severe inter-cell interference, the detection performance of primary synchronization signal (PSS) and secondary synchronization signal (SSS) is deteriorated in future ultra-dense small cell scenario in the 3rd Generation Partnership Project (3GPP) long term evolution (LTE) system. Thus the current supported 504 cell IDs may not be sufficient. To mitigate the impact of such inter-cell interference problems and improve the cell search performance with increasing number of detect target cells, we propose a novel defined auxiliary secondary synchronization signal (A-SSS) in cell search procedure. Our simulation results show that the detection probability of cell search can be improved effectively with the proposed method.

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) is the promising technology for next generation communication systems due to high throughput. Due to the coherent receiving and demodulation at the receiver, accurate channel state information (CSI) is indispensable. Conventional rich assumption-based channel estimators have been proposed at the cost of enough training resource which leads to extra spectrum waste. However, physical measurements have verified that the wireless channels tend to exhibit sparse structure in high-dimensional space, e.g., delay spread, Doppler spread and space spread. Some sparse channel estimation methods for the MIMO-OFDM have been proposed. These estimation methods utilize either greedy algorithm or convex optimization. In this paper, we propose a novel sparse channel estimation method using sparse cognitive matching pursuit (SCMP) algorithm. Compared to other compressive algorithms in the state of art, the major innovation of the SCMP sparse channel estimation method (SCMP-SCE) is the ability of obtaining the accurate CSI without prior information of sparsity. Simulation results confirm that the proposed method has better estimation performance and lower estimation complexity.

The traditional channel estimation for orthogonal frequency division multiplexing (OFDM) systems over fast-varying fading channels is usually carried out in two steps. Firstly obtain the least-square (LS) estimate over the pilot sub-carriers, and then interpolate it over the entire frequency-domain. In this paper, we propose a high precision channel estimation method by adding an intermediate step, which is based on the strong correlation in each path of the channel during the coherent time and can distinguish valid path taps and noise taps effectively, to improve the accuracy of the preliminary estimate over the pilot sub-carriers. The simulation results in the frequency band of 2.4 GHz show that the proposed method can obtain refined channel functions more efficiently and achieve a good bit error rate (BER) performance close to the theoretical bound of ideal channel estimation.

Low-density parity-check (LDPC) codes have been a focus in the research of error-correcting coding in recent years. Moreover, it may be desirable to combine these marvelous codes, which have exhibited an excellent performance, with spectral efficient modulations to improve the transmission capacity in bandwidth limited channels. In this paper we present a new coding and modulation scheme in this way, using LDPC codes, and in the bit-interleaved coded modulation (BICM) style. Several simulation results show that this scheme can provide a substantial coding gain both on Gaussian channels and Rayleigh channels.