Channel estimation in a fading environment can be regarded as a typical statistical estimation problem. Its optimal performance relies on the prior distribution of the channel coefficients, which are environment-specific. However, the conventional channel estimators, such as the least square (LS) and linear minimum mean square error (LMMSE) estimators, do not fully exploit the prior channel distribution law. To address this limitation, we propose to use the conditional diffusion model (DM) to achieve environment-aware channel estimation, referred to as EACE-DM. In this framework, the environment information is incorporated as the condition to guide the EACE-DM in learning the hidden features of channels from various environments. The trained EACE-DM is functionally decomposed into two components, i.e., the environment identification module and the channel estimation module. The environment identification module first uses the DM’s forward process to diffuse LS channel estimation into a noisy sample. Then it executes the DM’s reverse denoising process conditioned on candidate environments to recover the LS estimation from the noisy channel. The environment is then identified via maximum a posteriori (MAP) estimation by comparing these recovered estimations with the ground-truth LS estimation. Finally, the identified environment is utilized to guide the channel estimation module, denoising the LS estimation. Numerical simulations demonstrate that the proposed EACE-DM significantly decreases normalized mean square errors (NMSEs) of channel estimation across diverse environments while incurring a moderate increase in computational complexity compared to conventional estimators and existing DM-based approaches.

In this paper, we investigate the uplink joint signal detection for low-altitude aerial cell-free (CF) networks, where each flying access point (AP) locally processes the received signals and then forwards these information to a central processing unit (CPU) for the final detection. However, unlike terrestrial CF networks that typically adopt optic fiber fronthaul links, wireless fronthaul in aerial CF networks connecting flying APs with the CPU will significantly affect the communication performance, due to the practically limited fronthaul capacity. Therefore, we adopt a realistic channel model for wireless fronthaul links, which experience Rician fading and are shared among the flying APs through a combination of frequency division multiple access (FDMA) and space division multiple access (SDMA) protocol. Taking into account the imperfect and capacity-limited wireless fronthaul, we propose a joint uplink signal detection framework, where the local processing matrix at APs and the central detector at the CPU are designed based on the long-term statistical channel state information (CSI) by leveraging the operator-valued free probability theory. This approach significantly reduces the need for frequent, high-capacity signaling exchanges between APs and the CPU. Numerical results demonstrate the accuracy and effectiveness of the proposed joint signal detection framework.

With the rapid development of the Internet of Things (IoT), the number of connected devices has increased exponentially, bringing significant convenience to various aspects of daily life and business operations. However, communication between IoT devices requires a significant amount of bandwidth, putting a strain on the communication system. To address this challenge, we introduce a classification-oriented semantic communication approach that transmits only essential information. We present a novel end-to-end task-oriented semantic communication model, which efficiently serves the classification task at the receiver. In particular, the proposed model first utilizes a neural network-based semantic encoder to extract classification-related semantic features. A transformer-based semantic decoder is used at the receiver to retrieve semantic features and generate classification results. We further introduce a channel encoder and decoder module to improve the ability of a single model to deal with various channel conditions. Simulation results show that, compared with the traditional method, the proposed scheme achieves higher classification accuracy on the ESC-50 dataset and UrbanSound8K dataset and has better performance for various channel conditions.

The identification of channel scenarios in wireless systems plays a crucial role in channel modeling, radio fingerprint positioning, and transceiver design. Traditional methods to classify channel scenarios are based on typical statistical characteristics of channels, such as K-factor, path loss, delay spread, etc. However, statistic-based channel identification methods cannot accurately differentiate implicit features induced by dynamic scatterers, thus performing very poorly in identifying similar channel scenarios. In this paper, we propose a novel channel scenario identification method, formulating the identification task as a maximum a posteriori (MAP) estimation. Furthermore, the MAP estimation is reformulated by a maximum likelihood estimation (MLE), which is then approximated and solved by the conditional generative diffusion model. Specifically, we leverage a transformer network to capture hidden channel features in multiple latent noise spaces within the reverse process of the conditional generative diffusion model. These detailed features, which directly affect likelihood functions in MLE, enable highly accurate scenario identification. Experimental results show that the proposed method outperforms traditional methods, including convolutional neural networks (CNNs), back-propagation neural networks (BPNNs), and random forest-based classifiers, improving the identification accuracy by more than 10%.

Rate splitting multiple access (RSMA) is a powerful multiple access technology that enables communication systems to achieve both reliable and fair data transmission by splitting and encoding user messages into common and private streams. This capability is particularly critical for space-air-ground-sea (SAGS) integrated networks, where heterogeneous nodes (e.g., satellites, UAVs, and underwater sensors) coexist with significant channel quality disparities. The common stream is formed by consolidating the diverse common messages that all users can decode, allowing the system to balance resource allocation and maintain reliable connectivity even for users with weaker channel conditions. Nevertheless, the performance of the common stream is often limited by the user with the weakest channel strength, a prevalent challenge in SAGS integrated networks with mixed near-far field communications and dynamic topology. To address this issue, this study proposes a generalized RSMA strategy to mitigate the rate limitation of common streams in RSMA systems, thereby enhancing system fairness and overall performance. The solution holds potential for crossdomain applications where strong and weak maritime/aerial users share spectrum resources. Furthermore, an algorithm is designed to optimize Max-min fairness (MMF) rate among all users. This is formulated as a non-convex optimization problem, which poses significant challenges for direct solution. To tackle this challenge, we design a low-complexity suboptimal iterative algorithm employing the successive convex approximation (SCA) method. Simulations demonstrate that the proposed generalized RSMA system outperforms traditional one-layer RSMA system and other existing counterparts, particularly in systems with weak users, by effectively enhancing the MMF rate and overall system fairness. This improvement suggests broader applicability for future integrated networks requiring unified management of heterogeneous links.

Frequency shift keying (FSK) modulation can avoid the direct path interference problem in backscatter communication (BackCom). However, due to the limited number of reflection impedances of the tag, unwanted high-order harmonics are introduced, thereby degrading bit error rate (BER) performance. In this work, we investigate the harmonic elimination strategy for FSK-based BackCom. In particular, we first propose a novel multi-stage incident signal and the corresponding reflection strategy, which allows the tag to modulate the incident signal through absolute value operations, effectively suppressing harmonic generation. Then, we develop a USRP-based platform to experimentally verify the feasibility of our harmonic elimination strategy. Finally, the results show that our design can achieve a 12 dB attenuation in the third harmonic and up to 34 dB attenuation in high-order harmonics compared to conventional FSK-based BackCom without harmonic elimination, which consequently leads to improved BER performance.

Digital semantic communication has attracted considerable attention attributed to its potential for integration with modern digital communication systems, which has demonstrated significant performance gains. However, despite its ability to save transmission bandwidth, digital semantic communication can degrade the performance of tasks at the receiver, particularly in low-bitrate scenarios. In this article, we propose a novel low-bitrate digital semantic communication method based on a generative model for speech transmission to achieve high-quality reconstructed speech at low-bitrate transmission. In particular, we first investigate a multiscale semantic codec based on residual vector quantization with a generative adversary network (RVQGAN) model for extracting semantic information and obtaining high speech reconstruction quality while transmitting at a low bitrate. We then, design a channel noise suppression (CNS) module based on U-Net to alleviate the channel effect at low signal-to-noise ratio (SNR) by restoring high-quality semantic features, which is capable of improving the performance of the proposed method under challenging channel conditions. Moreover, a Transformer-based code predictor is utilized to further improve the robustness of the proposed method by accounting for both the channel impact and reconstruction quality. Finally, a three-stage training strategy is also presented in this article to ensure the effective operation of the proposed multiscale semantic codec, CNS module, and code predictor module. Experimental results demonstrate that the proposed method operating at 3 kb/s can save at least 50% of bandwidth while achieving higher speech restoration quality than the baseline method.

Recently, reconfigurable intelligent surfaces (RISs) are gaining increasing attention in communication systems due to their ability to adapt themself according to the wireless environment. Therefore, the communication systems with massive RIS deployments are able to extend high-quality coverage ubiquitously. This is especially beneficial for Internet of Things (IoT) systems as the IoT devices can be located in hard-to-reach area. On the other hand, current methods to analyze and optimize the performance of communication systems with multiple RISs deployment are ad hoc, which depends on the specific system configurations. There lacks a unified theoretical framework that provides general analytical treatment for both passive and active RIS-assisting systems, possibly having multiple concatenated reflections via RISs, which is typical in IoT communication scenarios. Thus, we investigate general uplink RIS-assisted multi-user multiple-input multiple-output (MU-MIMO) communication systems under general Rician fading channels, where the number of cascaded RIS panels are arbitrary and the RIS elements can be active or passive. By utilizing the linearization trick and the operator-valued free probability theory, a unified analytic expression of the ergodic sum rate for the considered MU-MIMO systems is derived. Then, we further propose a low-complexity optimization approach to design the RISs’ phase shifts, thus enhancing the ergodic sum rate. Numerical results verify the accuracy of the analytical results for RIS-assisted systems. In addition, the convergence and effectiveness of the proposed optimization algorithm are demonstrated.

Autonomous aerial vehicle (AAV) can be threatening to the information security of wireless communications. By launching the pilot spoofing attack (PSA), a AAV, operating as the active aerial-eavesdropper (A-Eve), is able to intercept the confidential messages sent over the air. On one hand, it is difficult to distinguish the channel state information (CSI) of the ground users (GUs) and the CSI of A-Eve in the contaminated pilots. On the other hand, due to the high-mobility of A-Eve, the CSI of A-Eve is rapidly changing, making the design of secure transmissions challenging. To address these issues, we first propose a location-based minimum mean square error (MMSE) channel estimation algorithm to separate the CSI of GUs and the CSI of A-Eve, where the location of A-Eve is obtained by designing a cooperative localization neural network (CLNet), leveraging its angular-domain channel fingerprint (CF) of A-Eve. Furthermore, we propose an artificial noise (AN) injected MMSE precoding scheme to maximize the worst-case secrecy rate of the multi-user communications, where the power allocation between signal and AN is optimized via a long short-term memory (LSTM)-based secure predictive beamforming neural network (SPBNet). Numerical results verify the secrecy performance gain of the proposed scheme achieved by utilizing the localization ability via the CLNet and the channel tracking ability via the SPBNet, compared to the canonical nullspace AN injection scheme without prior knowledge of A-Eve’s location.

Among the key enabling 6G techniques, multiple-input multiple-output (MIMO) and non-orthogonal multiple-access (NOMA) play an important role in enhancing the spectral efficiency of the wireless communication systems. To further extend the coverage and the capacity, the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has recently emerged out as a cost-effective technology. To exploit the benefit of STAR-RIS in the MIMO-NOMA systems, in this paper, we investigate the analysis and optimization of the downlink dual-user MIMO-NOMA systems assisted by multiple STAR-RISs under the generalized singular value decomposition (GSVD) precoding scheme, in which the channel is assumed to be Rician faded with the Weichselberger’s correlation structure. To analyze the asymptotic information rate of the users, we apply the operator-valued free probability theory to obtain the Cauchy transform of the generalized singular values (GSVs) of the MIMO-NOMA channel matrices, which can be used to obtain the information rate by Riemann integral. Then, considering the special case when the channels between the BS and the STAR-RISs are deterministic, we obtain the closed-form expression for the asymptotic information rates of the users. Furthermore, a projected gradient ascent method (PGAM) is proposed with the derived closed-form expression to design the STAR-RISs thereby maximizing the sum rate based on the statistical channel state information. The numerical results show the accuracy of the asymptotic expression compared to the Monte Carlo simulations and the superiority of the proposed PGAM algorithm.

Orthogonal Frequency Division Multiplexing (OFDM) underpins modern wireless communication due to its resilience against multi-path fading and computationally efficient implementations. However, on one hand, in scenarios with time-frequency doubly selective fading channels, OFDM systems face significant challenges, as channel variation induces inter-carrier interference (ICI) that disrupts subcarrier orthogonality. On the other hand, the limited length of the cyclic prefix (CP), often constrained to a fraction of the symbol duration, may be insufficient to fully mitigate inter-symbol interference (ISI) in scenarios with large time spreads. Extending CP length would reduce spectral efficiency and increase latency, making it incompatible with the demands of high-efficiency, low-latency systems. In this paper, we propose an autoencoder based OFDM architecture integrating adaptive pulse shaping with an equalization neural network (PS-EQNet) that jointly addresses ISI caused by insufficient CP and ICI due to channel dynamics through learnable time-frequency filters. Additionally, we introduce a detection network tailored for simplified channels, which reduces computational complexity compared to existing schemes while maintaining robust performance. Simulation results confirm that the proposed PS-EQNet OFDM system significantly relaxes CP requirements, enhances spectral efficiency (SE), and achieves reliable bit error rate (BER) performance in time-frequency selective channels, establishing a flexible trade-off among SE, BER, and computational complexity.

In this paper, we consider cell-free communication systems with several access points (APs) serving terrestrial users (UEs) simultaneously. To enhance the uplink multi-user multiple-input multiple-output communications, we adopt a hybrid-CSI-based two-layer distributed multi-user detection scheme comprising the local minimum mean-squared error (MMSE) detection at APs and the one-shot weighted combining at the central processing unit (CPU). Furthermore, to improve the propagation environment, we introduce multiple reconfigurable intelligent surfaces (RISs) to assist the transmissions from UEs to APs. Aiming to maximize the weighted sum rate, we formulate the weighted sum-MMSE (WMMSE) problem, where the UEs’ beamforming matrices, the CPU’s weighted combining matrix, and the RISs’ phase-shifting matrices are alternately optimized. Considering the limited fronthaul capacity constraint in cell-free networks, we resort to the operator-valued free probability theory to derive the asymptotic alternating optimization (AO) algorithm to solve the WMMSE problem, which only depends on long-term channel statistics and thus reduces the interaction overhead. Numerical results demonstrate that the asymptotic AO algorithm can achieve a high communication rate as well as reduce the interaction overhead.

To meet the capacity requirements of future communications systems, multiple-input multiple-output (MIMO) and non-orthogonal multiple-access (NOMA) are promising techniques to improve the link and system capacities. In addition, the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) can be well integrated with the communication systems to effectively extend the wireless coverage in a cost-efficient manner. Therefore, in this paper, we investigate the performance of the downlink dual-user MIMO-NOMA systems assisted by STAR-RIS under the generalized singular value decomposition (GSVD) precoding scheme. Specifically, we first apply the operator-valued free probability theory to obtain the Cauchy transform of the generalized singular values (GSVs) of the MIMO-NOMA channel matrices under Rician fading with the Weichselberger’s correlation structure, in which a linearization trick for the matrix-valued rational functions is applied to simplify the derivations. Then, based on the Cauchy transform of GSVs, we obtain the closed-form expression for the asymptotic information rates of the users under the considered system. Furthermore, we propose a projected gradient ascent method (PGAM) to enhance the sum rate of the system by designing the phase shifts of the STAR-RIS based on the derived closed-form expressions. The numerical results show the accuracy of the asymptotic expression compared to the Monte Carlo simulations and the effectiveness of the proposed PGAM algorithm.

Recent years have witnessed an increasing interest in leveraging reconfigurable intelligent surfaces (RISs) to enhance the capabilities of integrated sensing and communication (ISAC) systems. RISs are advantageous in improving detection and communication performance, especially in challenging environments characterized by nonLine of Sight (NLOS) conditions and dense urban settings. In this article, a hybrid RIS, comprising passive reflecting elements and active sensors, and multiple fully passive RISs are deployed to enhance an ISAC system, where the direct paths between the base station (BS) and users/targets are blocked. The signal sent from the BS and reflected by RISs is received by the communication user, and simultaneously scattered by the target toward the sensors of the hybrid RIS. A joint optimization of the transmit covariance matrix at the BS and phase-shifting matrices at RISs is formulated, which considers the tradeoff between the communication and sensing performance. The optimization is based on the derived closed-form communication achievable rate by leveraging the free probability theory and positioning error bound (PEB) via the Cramér-Rao lower bound (CRLB) analysis. The block coordinate descent (BCD) algorithm is utilized to tackle the nonconvex problem, where the transmit covariance matrix and phase-shifting matrices are optimized iteratively. Therein, the Riemannian gradient descent algorithm is exploited for optimizing the phase-shifting matrices. Numerical results verify the effectiveness of the proposed algorithm, and both communication and sensing performance gains increase with the number of RIS panels and RIS elements.

In this paper, we investigate the uplink signal detection in cell-free massive MIMO systems with unmanned aerial vehicles (UAVs) serving as aerial access points (APs). The ground users are equipped with multiple antennas and the ground-to-air propagation channels are subject to correlated Rician fading. To overcome huge signaling overhead in the fully-centralized detection in cell-free systems, we propose a two-layer distributed uplink detection scheme, where the uplink signals are first detected in AP-UAVs by using the minimum mean-squared error (MMSE) detector based on local channel state information (CSI), and then collected and weighted combined at the CPU-UAV to obtain the refined detection. By using the operator-valued free probability theory, the asymptotic expressions of the combining weights are obtained, which only depend on the statistical CSI and show excellent accuracy compared to the exact but intractable expressions. Based on the proposed scheme, we further investigate the impacts of different deployment scenarios on the spectral efficiency (SE). Numerical results show that in urban and dense urban environments, it is more beneficial to deploy more AP-UAVs to increase SE. Nonetheless, in suburban environment, an optimal combination of the number of AP-UAVs and the number of antennas per AP-UAV exists to maximize SE.

Reconfigurable intelligent surface (RIS) has recently been widely investigated in wireless communication systems due to its low deployment cost and high-performance gain. In this work, we study the multiple-RIS-assisted uplink multiple-user multiple-input multiple-output (MU-MIMO) communication systems, where each user’s signal is sent to the base station via both the direct and the reflected links. To obtain informative insight into the considered system with the statistical channel information, we first derive the closed-form expression for the ergodic sum rate of the MU-MIMO systems by applying the operator-valued free probability theory. Then, the covariance matrices of the transmit signals and the phase shifts of the RIS elements are jointly optimized to maximize the derived asymptotic ergodic sum rate via alternating optimization (AO). Specifically, the AO procedure is composed of a water-filling algorithm and a gradient descent algorithm over the Riemannian manifold and the two algorithms iterate until convergence. The numerical results show the accuracy of the asymptotic expression compared to the Monte Carlo simulation and the superiority of the proposed AO algorithm compared to the benchmark. Furthermore, the rank deficiency of the MIMO channel can be significantly improved by the deployment of multiple RISs and the proposed AO algorithm.

The massive multi-input multi-output (mMIMO) systems are vulnerable to both pilot contamination and pilot spoofing attack (PSA), which jeopardize the uplink channel estimation and cause information leakage in the downlink transmissions. Motivated by the canonical large-scale fading precoding (LSFP) [1], a secure LSFP is proposed to eliminate the impact of the coexistence of pilot contamination and PSA. The proposed framework enables multi-cell coordinated transmissions leveraging the large-scale fading coefficients, thus is suitable to be implemented in mMIMO systems with limited fronthaul. Specifically, the presence of the eavesdropper is first identified by designing a distributed mixture-of-experts neural network (D-MoENN)-based PSA detector, which combines the detected results of distributed nodes to improve the detection accuracy. Subsequently, an optimal jamming base station (BS) is selected by designing a D-MoENN-based localizer, which estimates the locating cell of the eavesdropper and selects the nearest jamming BS to the eavesdropper. Numerical results show that the proposed D-MoENN-based PSA detector outperforms the existing detectors in the low SNR regime. Moreover, the average secrecy rate achieved by the secure LSFP with the jamming BS selected by the D-MoENN-based localizer is close to the upper bound achieved by the genie-aided selector that perfectly knows the location of the eavesdropper.

Dual-functional radar-communication (DFRC) has been widely concerned in future communication systems. By aggregating communication and detection resources, DFRC enables improved communication data rate as well as continual real-time sensing capability. However, the spectral efficiency of the communication channel will be inevitably compromised since the transceiver design has to take into account both of the communication and radar oriented beampatterns. In this paper, the reconfigurable intelligent surface (RIS) panels are deployed to assist the design of multi-antenna DFRC transceivers, where the RIS panels introduce additional degree-of-freedom to accommodate the dual-functional beampattern. First, applying the operator-valued free probability theory, we derive the closed-form expression of the asymptotic achievable rate of the multi-RIS-assisted MIMO DFRC systems in presence of general Rician fading. Then, we propose an alternating optimization (AO) algorithm to jointly optimize the transmit signals of the DFRC transmitter and the phase shifts of the reflecting elements of the RIS panels, which achieves an optimal tradeoff between the achievable rate and the desired radar beampattern. Simulation results verify the accuracy of the asymptotic expression of the achievable rate. In addition, the deployment of RISs and the proposed AO algorithm are proven to improve both the detection and communication performance.

In this paper, we investigate the multiple access technique for the aerial networks, where the multi-antenna base stations are carried by unmanned aerial vehicles (UAVs) to serve ground users. The uplink signals are transmitted by the users simultaneously, then detected and recovered by the aerial networks. On one hand, compared to the case that each UAV individually detects the signals of serving users, we aim to improve the quality of the recovered signals by using cooperative techniques that leverage the signals from multiple UAVs. On the other hand, the fully-centralized cooperation requires signaling exchange between UAVs, which incurs huge signaling overhead and latency, and is infeasible for aerial networks. Therefore, we propose a two-stage distributed minimum mean squared error (MMSE) detection with one-shot signal combining. Specifically, the users' signals are first locally detected by each UAV via the MMSE detector, and then weighted combined at the central UAV in a one-shot manner, where the combining weights are designed to minimize the MSE between the combined signals and the original signals. When only imperfect channel state information is locally available at each UAV, by using the random matrix theory, these combining weights are shown to depend on the long-term channel statistics and thus, greatly reduce the interaction overhead and latency. Numerical results show that the proposed scheme outperforms the non-cooperative detection in terms of the achievable spectral efficiency. Meanwhile, with a much smaller interaction overhead, the proposed scheme achieves comparable spectral efficiency as the fully centralized signal detection.

In this paper, physical layer security techniques are investigated for cooperative multi-input multi-output (C-MIMO), which operates as an underlaid cognitive radio system that coexists with a primary user (PU). The underlaid secrecy paradigm is enabled by improving the secrecy rate towards the C-MIMO receiver and reducing the interference towards the PU. Such a communication model is especially suitable for implementing Industrial Internet of Things (IIoT) systems in the unlicensed spectrum, which can trade off spectral efficiency and information secrecy. To this end, we propose an eigenspace-adaptive precoding (EAP) method and formulate the secrecy rate optimization problem, which is subject to both the single device power constraint and the interference power constraint. This precoder design is enabled by decomposing the original optimization problem into eigenspace selection and power allocation sub-problems. Herein, the eigenvectors are adaptively selected by the transmitter according to the channel conditions of the underlaid users and the PUs. In addition, a simplified EAP method is proposed for large-dimensional C-MIMO transmission, exploiting the additional spatial degree of freedom for a low-complexity secrecy precoder design. Numerical results show that by transmitting signal and artificial noise in the properly selected eigenspace, C-MIMO can eliminate the secrecy outage and outperforms the fixed eigenspace precoding methods. Moreover, the proposed simplified EAP method for the large-dimensional C-MIMO can significantly improve the secrecy rate.

The reconfigurable intelligent surface (RIS) is useful to effectively improve the coverage and data rate of end-to-end communications. In contrast to the well-studied coverage-extension use case, in this paper, multiple RIS panels are introduced, aiming to enhance the data rate of multi-input multi-output (MIMO) channels in presence of insufficient scattering. Specifically, via the operator-valued free probability theory, the asymptotic mutual information of the large-dimensional RIS-assisted MIMO channel is obtained under the Rician fading with Weichselberger’s correlation structure, in presence of both the direct and the reflected links. Although the mutual information of Rician MIMO channels scales linearly as the number of antennas and the signal-to-noise ratio (SNR) in decibels, numerical results show that it requires sufficiently large SNR, proportional to the Rician factor, in order to obtain the theoretically guaranteed linear improvement. This paper shows that the proposed multi-RIS deployment is especially effective to improve the mutual information of MIMO channels under the large Rician factor conditions. When the reflected links have similar arriving and departing angles across the RIS panels, a small number of RIS panels are sufficient to harness the spatial degree of freedom of the multi-RIS assisted MIMO channels.

In the uplink machine-type communication (MTC) system, a combination of grant-free transmission and non-orthogonal multiple access (NOMA) emerges to reduce the control overhead and transmission latency. In the grant-free scenario, the base station needs to identify the active devices and estimate the channel state information before the data detection. However, due to the lack of a scheduling process, the user activity detection (UAD) and channel estimation (CE) are both challenging, especially when short non-orthogonal preambles are adopted. In this paper, by exploiting the framework of the compressive sensing-based algorithm, we propose a novel deep learning architecture, namely UAD and CE Neural Network (UAD-CE-NN), to effectively solve the joint UAD and CE problem for grant-free NOMA. In the proposed scheme, the user activity and channel state information hidden in the received data signals are also exploited to aid the preamble for higher detection accuracy. Specifically, UAD-CE-NN is composed of two stages: we first build a preamble detection neural network for a tentative UAD-CE; a data detection neural network is then deployed to exploit the data signals. Compared with the conventional schemes, the proposed scheme obtains much higher accuracy for both the UAD and CE, especially when short preamble sequences are employed.

In various location-based Internet of Things (IoT) services, it is required to localize a large number of energy-limited devices simultaneously and accurately. In order to achieve this goal, a reconfigurable intelligent surface (RIS)-assisted positioning method for multiple IoT devices is proposed, where the signals transmitted by the users reach the base station (BS) along the direct path and the reflection path via the RIS. The difference in the propagation delay of the two paths is essential in the proposed triangulation-based localization framework, which is estimated via the cross-correlation function of the received signals. Based on the orthogonality of the transmitted signals, the optimization of the multiantenna BS and the RIS, with the goal of minimizing the total transmission power of the IoT devices, is constructed. For the orthogonal signal case, the nonconvex optimization problem for the RIS is recast into a convex problem via the semidefinite relaxation (SDR). For the nonorthogonal signal case, the zero-forcing (ZF) combining vectors at the BS are adopted to eliminate interferences among multiple users, and the block coordinate descent (BCD) algorithm is used to decouple the combining vectors and the RIS phases. Numerical results show that by using the proposed optimization method, decimeter-level positioning accuracy can be achieved with low-power consumption, and significant power gain can be achieved compared to the unoptimized RIS-assisted localization.

Grant-free Non-Orthogonal Multiple Access (NOMA) techniques are able to reduce the signaling overhead and the transmission latency in multi-user communications system. However, most of the existing code-domain grant-free NOMA schemes reuse the spreading signatures designed for the grant-based scenarios. Considering the sparsity and randomness nature of user activities in the uplink transmissions, we propose a deep learning-based signature design, where the non-equal user activation probabilities are exploited to optimize the code-domain NOMA signature. In addition, the conventional grant-free NOMA signatures are not specifically designed over finite Galois field, which hinders the implementation of the encoder/decoder using practical hardware. To address these challenges, we utilize the quantized deep learning framework for the NOMA signature training, which jointly optimizes the sequence generation and the quantization. The numerical results reveal that the obtained signatures outperform the conventional ones especially when the users has unequal activation probabilities.

We propose and study the cooperative multi-input multi-output (MIMO) architecture to enable and improve the secrecy transmissions between clusters of mobile devices in the presence of an eavesdropper with certain location constraint. The cooperative MIMO system in this paper (referred to as reconfigurable distributed MIMO) is formed by temporarily activating clusters of nearby trusted mobile devices, with each cluster being centrally coordinated by its corresponding cluster head. We assume that the transmitters apply a practical eigendirection precoding scheme to transmit the confidential signal and artificial noise, while the eavesdropper can be located in multiple possible locations in the proximity. We first obtain the expression of the secrecy rate, where the required average mutual information between the transmitters and the eavesdropper is characterized by closed-form approximations. The proposed approximations are especially useful in the secrecy rate maximization, where the original non-convex problem can be solved by the successive convex approximations. Numerical results show that the secrecy rate can be significantly improved by leveraging the location constraint of the eavesdropper, compared to the existing result. We also demonstrate that the secrecy rate can be further improved by increasing the cluster size.

In this paper, we propose an analytical framework to derive all positive integer moments of MIMO mutual information in high-SNR regime. The approach is based on efficient use of the underlying matrix integrals of the high-SNR mutual information. As an example, the framework is applied to the study of Jacobi MIMO channel model relevant to fiber optical and interference-limited multiuser MIMO communications. For such a channel model, we obtain explicit expressions for the exact moments of the mutual information in the high-SNR regime. The derived moments are utilized to construct approximations to the corresponding outage probability. Simulation shows the usefulness of the results in a crucial scenario of low outage probability with finite number of antennas.

Multicast routing that meets multiple quality of service constraints is important for supporting multimedia communications in the Internet of Things (IoT). Existing multicast routing technologies for IoT mainly focus on ad hoc sensor networking scenarios; thus, are not responsive and robust enough for supporting multimedia applications in an IoT environment. In order to tackle the challenging problem of multicast routing for multimedia communications in IoT, in this paper, we propose two algorithms for the establishing multicast routing tree for multimedia data transmissions. The proposed algorithms leverage an entropy-based process to aggregate all weights into a comprehensive metric, and then uses it to search a multicast tree on the basis of the spanning tree and shortest path tree algorithms. We conduct theoretical analysis and extensive simulations for evaluating the proposed algorithms. Both analytical and experimental results demonstrate that one of the proposed algorithms is more efficient than a representative multiconstrained multicast routing algorithm in terms of both speed and accuracy; thus, is able to support multimedia communications in an IoT environment. We believe that our results are able to provide in-depth insight into the multicast routing algorithm design for multimedia communications in IoT.

We propose a new distributed Multi-Input Multi-Output (MIMO) architecture for mobile networks, which we refer to as reconfigurable distributed MIMO (RD-MIMO), where the communicating mobile nodes temporarily recruit adjacent nodes to operate as distributed antenna arrays. To best serve the communicating nodes, the node clusters are continuously reconfigured due to the node mobility and varying channel conditions. The frequency of reconfiguration depends on the required system performance, exhibiting a tradeoff between performance and complexity. We propose a practical node selection scheme, which activates only a small subset of transmitters. We evaluate the asymptotic performance of the scheme as a function of the number of recruited nodes, demonstrating that there is an optimal number of such nodes. Compared with the system that blindly activates all available transmitting nodes, our results show that the proposed RD-MIMO architecture with node selection achieves superior performance, especially as evident at low SNR. Furthermore, assuming the Brownian motion model, an analytical expression for the reconfiguration time to select a new cluster of transmitting nodes is obtained. Numerical results show that the obtained expression serves as a good estimation for the order of magnitude of the cluster reconfiguration time for other mobility patterns, such as the random walk model.

The Rayleigh product channel model is useful in capturing the performance degradation due to rank deficiency of MIMO channels. In this paper, such a performance degradation is investigated via the distribution of mutual information assuming the block fading channels and the uniform power transmission scheme. Using techniques of free probability theory, the asymptotic variance of mutual information is derived when the dimensions of the channel matrices approach infinity. In this asymptotic regime, the mutual information is rigorously proven to be Gaussian distributed. Using the obtained results, a fundamental tradeoff between multiplexing gain and diversity gain of Rayleigh product channels under the uniform power transmission can be characterized by the closed-form expression at any finite signal-to-noise ratio. Numerical results are provided to compare the outage performance between the Rayleigh product channels and the conventional Rayleigh MIMO channels.

In this paper, we consider MIMO beamforming in the presence of Rayleigh product channels. Based on a derived largest eigenvalue distribution, the key performance metrics of the beamforming system are obtained, assuming perfect channel knowledge at the transmitter and receiver. Using the closed-form expressions, we gain insights into the behavior of MIMO beamforming systems in scenarios of practical interest.

The performance of MIMO radar has been a subject of intense study in the past decades. For such a system, however, the important phenomenon of phase transition has received little attention in the literature. In this paper, we study the phase transition on the target detection probability of a SNR maximizing detector. Such a detector declares a target to be present when the largest eigenvalue of the observed data matrix exceeds a threshold. In particular, we identify a critical value below and above which the limiting detection performance is described by the Tracy-Widom law and the Gaussian law, respectively. Under both laws, the scaling limits and asymptotic expansions of misdetection probability at the vanishing regime are derived using tools from random matrix theory.

Opportunistic scheduling exploits the multiuser diversity to improve the performance of wireless communications. The scheduling gain is enabled by the channel feedback sent from the receiver to the transmitter. In this paper, we consider the so-called opportunistic cumulative distribution function (CDF) scheduling with threshold-based selective feedback in the orthogonal frequency division multiple access downlink system. Among opportunistic scheduling techniques, the CDF scheduling is known to provide multiuser diversity gain while maintaining fair radio resource sharing among users. We first derive the exact and asymptotic average sum rates assuming antenna selection at transmitters. The expressions are valid for arbitrary number of inter-cell interference and number of transmit antennas. Moreover, a closed-form approximation for the user rate distribution is calculated using the exact moments of the user sum rate. The approximation is shown to provide an accurate estimate for the rate distribution, and the results can be utilized in rate adaptation at the transmitter.

The Diversity-Multiplexing Tradeoff (DMT) is studied for the large Rayleigh product channel at non-asymptotic SNRs. The first result is that, as matrix dimensions growing to infinity, the channel capacity converges to a Gaussian random variable. Based on this, we derive a compact expression for the finite-SNR DMT. From the analytical and numerical results, we gain useful insight into the fundamental tradeoff of the considered channel model in the realistic SNR regime.

The multiple cluster scattering MIMO channel is a useful model for pico-cellular MIMO networks. In this paper, space-time coded transmission over such a channel is considered, where the effective channel corresponds to a product of complex Gaussian matrices. An accurate closed-form approximation to the channel outage capacity using orthogonal space-time block codes has been derived. The result is valid for an arbitrary number of clusters of scatterers and an arbitrary antenna configuration. From the analytical and numerical results, we study the relative outage performance between the multi-cluster MIMO channel and the special case of Rayleigh-fading MIMO channel.

We consider orthogonal space-time block coded transmission over the multiple cluster scattering MIMO channels, where the effective channel equals the product of n complex Gaussian matrices. The considered channel model is typical in modeling pico-cellular MIMO propagations. In this setting, we derived a closed-form approximation to the channel outage capacity. The result is valid for an arbitrary number of clusters n-1 of scatterers and an arbitrary antenna configuration. Numerical results show the usefulness of the proposed approximation in diverse scenarios.

A novel closed-form approximation is derived for the statistics of cascaded Nakagami-m channels with integer and half-integer m. The adopted approximation generalizes the prior results by incorporating cross-correlations between each pair of component channels. The approximative probability density function is constructed by an easily computable log-normal density and its associated orthogonal polynomials. The proposed approximation shows good agreement with numerical simulations under light fading condition and yields improved accuracy compared with the well-known lognormal distribution. To illustrate the usefulness of the derived result, we apply it in performance analysis involving the cascaded Nakagami-m channels.

A blind target detector using the time reversal transmission is proposed in the presence of channel correlation. We calculate the exact moments of the test statistics involved. The derived moments are used to construct an accurate approximative Likelihood Ratio Test (LRT) based on multivariate Edgeworth expansion. Performance gain over an existing detector is observed in scenarios with channel correlation and relatively strong target signal.

In this paper, we propose a simple decentralized frequency-domain resource allocation strategy combined with uplink power optimization for Long Term Evolution (LTE) femtocell base stations (FBSs). We consider femtocells as an underlay network that reuse the existing macrocell spectral bands. The objective of this work is to maximize the uplink throughput of femtocell user equipments (FUEs) without causing performance degradation to macrocell user equipments (MUEs). System-level simulations are carried out to investigate the power optimization problem by extensive search over the power control (PC) parameter space. Based on the parameters selection, the FUEs uplink throughput is evaluated using the resource allocation scheme which roughly separates the macro- and femtocell users in frequency by imposing different allocation probabilities on different parts of the spectrum. Results show a significant FUE throughput improvement compared with an uniform allocation scheme.