Complex-variable matrix optimization problems are fundamental in signal processing and related engineering applications, yet existing methods often become computationally inefficient when handling high-dimensional variables and nonlinear constraints. In this paper, we propose a novel prediction-correction method for complex-variable optimization based on a variational inequality framework. This method directly operates in the complex domain and achieves an ergodic convergence rate of$\mathcal {O}(1/t)$under mild conditions. Numerical results on a practical beamforming design problem demonstrate that the proposed approach achieves comparable solution accuracy to conventional methods while reducing the computational time by more than three orders of magnitude.

To facilitate the development of industrial Internet of Things applications, thewireless networked control system(WNCS) is envisioned to support real-time control and communication interactions performed in finite-time manner. A WNCS comprising multiple wirelessly interconnectedsub-systems(SSs) is considered, wherein the sensed state information in each SS is transmitted to the controller via wireless links, thereby enabling timely decision-making processes. Following multiple operation periods of state sensing and transmission, thesystem identification(SI) is performed, leading to the formulation of optimal control policy. To improve the energy efficiency while guaranteeing the SI performance requirement within the allowed decision-making time, the communication and control co-design for WNCS is investigated, where the transmit power, transmission interval length, number of operation periods, coding block-length, and required transmission reliability are jointly optimized. Our investigation demonstrates the interrelationships among effective capacity, energy consumption, and communication parameters. Furthermore, it is found that the optimal communication parameters, such as transmit power and transmission interval length, should be determined by both communication and control requirements. Consequently, it is found that minimizing energy consumption is equivalent to minimize the number of operation periods while guaranteeing the SI performance with defined confidence, which can be effectively addressed by leveraging the non-decreasing property of controllability Gramian. Moreover, the co-design framework is extended to accommodate the scenarios involving link interruptions and overlapping time slots. Simulation results validate the necessity and effectiveness of exploring optimal system operational configurations from the perspective of the proposed co-design. It is also observed that although a 44.2% surge in energy consumption is associated with the proposed relay scheme in the link interruption case, the proposed time scheduling scheme brings a 23.4% reduction in the extra energy expenditure (from 44.2% to 20.8%).

Extremely large-scale multiple-input multiple-output (XL-MIMO) enables the formation of narrow beams, effectively mitigating path loss in high-frequency communications. This capability makes the integration of wideband high-frequency communications and XL-MIMO a key enabler for future 6G networks. Realizing the full potential of such wideband XL-MIMO systems depends critically on acquiring accurate channel state information. However, channel estimation is significantly challenging due to inherent wideband XL-MIMO channel characteristics, including near-field propagation, beam split, and spatial non-stationarity. To effectively capture these channel characteristics, we formulate channel estimation as a maximum a posteriori problem, which facilitates the use of prior channel knowledge. We then propose an unrolled proximal gradient descent algorithm with learnable step sizes, which employs a dedicated neural network for proximal mapping. This design empowers the proposed algorithm to implicitly learn prior channel knowledge directly from data, thereby eliminating the need for explicit regularization functions. To improve the convergence, we introduce a monotonic descent constraint on the layer-wise estimation error and provide theoretical analyses to characterize the algorithm’s convergence behavior. Simulation results show that the proposed unrolling-based algorithm outperforms the traditional and deep learning-based methods.

In this paper, we study efficientmulti-beamtraining design fornear-fieldcommunications to reduce the beam training overhead of conventional single-beam training methods. In particular, the array-division-based multi-beam training method, which is widely used in far-field communications, cannot be directly applied in the near-field scenario, since different sub-arrays may observe different user angles and there exist coverage holes in the angular domain. To address these issues, we first devise a new near-field multi-beam codebook by sparsely activating a portion of antennas to form an effectivesparse linear array(SLA), hence generating multiple beams simultaneously by exploiting the near-fieldgrating lobes. Next, atwo-stagenear-field beam training method is proposed. In the first stage, several candidate user locations are identified based on multi-beam sweeping over time, followed by the second stage to determine the true user location with a small number of pilots for single-beam sweeping. Finally, numerical results show that our proposed multi-beam training method significantly reduces the beam training overhead as compared to conventional single-beam training methods, while achieving comparable rate performance in data transmissions.

In this paper, we propose to employ a modular-based movableextremely large-scale array(XL-array) at Alice for enhancing covert communication performance. Compared with existing work that mostly considered either far-field or near-field covert communications, we consider in this paper a more general and practicalmixed-fieldscenario, where multiple Bobs are located in either the near-field or far-field of Alice, in the presence of multiple near-field Willies. Specifically, we first consider a two-Bob-one-Willie system and show that conventional fixed-position XL-arrays suffer degraded sum-rate performance due to theenergy-spread effectin mixed-field systems, which, however, can be greatly improved by subarray movement. On the other hand, for transmission covertness, it is revealed that sufficient angle difference between far-field Bob and Willie as well as adequate range difference between near-field Bob and Willie are necessary for ensuring covertness in fixed-position XL-array systems, while this requirement can be relaxed in movable XL-array systems thanks to flexible channel correlation control between Bobs and Willie. Next, for general system setups, we formulate an optimization problem to maximize the achievable weighted sum-rate under covertness constraint. To solve this non-convex optimization problem, we first decompose it into two subproblems, corresponding to an inner problem for beamforming optimization given positions of subarrays and an outer problem for subarray movement optimization. Although these two subproblems are still non-convex, we obtain their high-quality solutions by using the successive convex approximation technique and devising a customized differential evolution algorithm, respectively. Last, numerical results demonstrate the effectiveness of proposed movable XL-array in balancing sum-rate and covert communication requirements, as compared to various benchmark schemes.

Driven by the rapid development of industrial Internet of Things applications, the wireless networked control system (WNCS) is expected to support real-time control-communication interaction performed in finite-time, which is task-oriented. A WNCS composed of multiple wirelessly inter-connected subsystems (SSs) is considered in this paper. The sensed state information in each SS is transmitted to the controller via wireless links for decision-and-control tasks. After multiple operation periods of state sensing and trans-mission, the system identification (SI) is executed and the optimal control (OC) policy is made. The SI requirement for OC is analyzed via system-level synthesis (SLS) based on robust control theory. A communication and control co-design is investigated, aiming to improve the energy efficiency while guaranteeing the SI performance requirement within the allowed decision-making time. The transmit powers at each sensor and controller, transmission interval length as well as the number of operation periods are jointly optimized. Simulations are conducted to validate the performance of the proposed co-design.

The rapid advancement of Internet of Things (IoT) networks has positioned unmanned aerial vehicles (UAV s) as critical enablers of next-generation wireless communication technologies. This paper focuses on orthogonal time frequency space (OTFS) modulation-assisted wireless control in UAV networks with finite blocklength (FBL) transmission. In particular, we in-vestigate the optimal power allocation that maximizes the fairness of control performance in terms of linear quadratic regulator (LQR) cost, subject to rate-LQR cost bounds and maximum available power budget constraints. To address the optimization problem, we first analyze the concave-convex property of the FBL rate function, followed by developing an efficient successive convex approximation (SCA)-based algorithm to obtain a sub-optimal solution. The convergence and computational complexity of the proposed algorithm are thoroughly analyzed. Simulation results validate the effectiveness of the proposed approach, offering promising insights for UAV-enabled wireless control systems.

The vision of 6G mobile networks aims to connect intelligent machines to humans to provide the latter with cooperation, care, and assistance. The mainstream approach for human-to-machine (H2M) semantic communication is to map words into (word) embedding vectors which are clustered according to their semantic similarity to facilitate machines’ interpretation of human languages. The computation-intensive tasks of text-to-embedding mapping are usually delegated to an edge server that senses human commands, maps them into embedding vectors, and then transmits the vectors to a machine over a wireless link. In this work, we propose a quantization framework customized for embedding vectors, called semantic-topology preserving VQ (SemTop-VQ), to overcome the communication bottleneck due to the vectors’ high dimensionality. While traditional VQ focuses on minimizing the distortion of individual vectors, SemTop-VQ aims to minimize the distortion of the topology of embedding matrix, referring to the vectors’ relative positions that represent semantics. To this end, we adopt a topology-distortion metric, termed pointwise-inner-product (PIP) loss, a hierarchical VQ architecture targeting high-dimensional VQ. In this architecture, an embedding vector is decomposed into blocks; the norm and shape (normalized vector) are quantized separately using a scalar and a Grassmannian quantizers, respectively. The main feature of SemTop-VQ lies in deriving from the PIP loss a set of so-called semantic-importance indicators, which reflect the level of influences of individual blocks’ quantization errors on the topology distortion. Then the indicators are applied to optimize quantization-bit allocation for decomposed vector blocks under the criterion of PIP-loss minimization. In practice, the usage probabilities of embedding vectors for a specific machine task are highly skewed and the task is time-varying. We exploit this fact to further develop SemTop-VQ to feature task adaptation that can attain a higher communication efficiency. The task-adaptive VQ is realized via the use of a frequently used (quantization) codebook that is much smaller in size than the original codebook and continuously updated via estimation of embedding-usage distribution. Our experiments using real embedding datasets, namely Word2Vec and Glove, demonstrate the effectiveness of SemTop-VQ as a goal-oriented technique for efficient H2M communications.

3D scene stylization refers to generating stylized images of the scene at arbitrary novel view angles following a given set of style images while ensuring consistency when rendered from different views. Recently, several 3D style transfer methods leveraging the scene reconstruction capabilities of pre-trained neural radiance fields (NeRF) have been proposed. To successfully stylize a scene this way, one must first reconstruct a photo-realistic radiance field from collected images of the scene. However, when only sparse input views are available, pre-trained few-shot NeRFs often suffer from high-frequency artifacts, which are generated as a by-product of high-frequency details for improving reconstruction quality. Is it possible to generate more faithful stylized scenes from sparse inputs by directly optimizing encoding-based scene representation with target style? In this paper, we consider the stylization of sparse-view scenes in terms of disentangling content semantics and style textures. We propose a coarse-to-fine sparse-view scene stylization framework, where a novel hierarchical encoding-based neural representation is designed to generate high-quality stylized scenes directly from implicit scene representations. We also propose a new optimization strategy with content strength annealing to achieve realistic stylization and better content preservation. Extensive experiments demonstrate that our method can achieve high-quality stylization of sparse-view scenes and outperforms fine-tuning-based baselines in terms of stylization quality and efficiency.

Neural radiance fields (NeRFs) have achieved impressive view synthesis results by learning an implicit volumetric representation from multi-view images. To project the implicit representation into an image, NeRF employs volume rendering that approximates the continuous integrals of rays as an accumulation of the colors and densities of the sampled points. Although this approximation enables efficient rendering, it ignores the direction information in point intervals, resulting in ambiguous features and limited reconstruction quality. In this paper, we propose a learning method that utilizes learnable view-dependent features to improve scene representation and reconstruction. We model the volume rendering integral with a piecewise constant volume density and spherical harmonic-guided view-dependent features, facilitating ambiguity elimination while preserving the rendering efficiency. In addition, we introduce a regularization term that restricts the anisotropic representation effect to be local, with negligible effect on geometry representations, and that encourages recovering the correct geometry. Our method is flexible and can be plugged into NeRF-based frameworks. Extensive experiments show that the proposed representation can boost the rendering quality of various NeRFs and achieve state-of-the-art rendering performance on both synthetic and real-world scenes.

With the access to tens of gigahertz of bandwidth, terahertz (THz) wideband communication emerges as a promising technology for the upcoming next generation mobile networks. To deal with the severe path loss and blockage of THz signals, massive multiple-input multiple-output and intelligent reflecting surface (IRS) can be jointly employed. Due to the extremely large signal bandwidth, the beams generated by the transmit hybrid beamforming may point to different directions around the target direction at different frequencies, which results in the beam splitting effect (BSE). In this paper, a new system design namelyTHzCondenseris introduced to mitigate the BSE, where the signals generated by each transmit radio frequency (RF) chain are reflected by one of the distributed IRSs in a one-to-one manner via the joint transmit and IRS beamforming design, thus creating adjustable multi-path components to achieve both high spatial multiplexing gain and array gain. Moreover, for practical scenarios when the number of transmit RF chains is more than that of IRSs, each IRS may need to reflect the signals generated by multiple RF chains in a one-to-many manner. For the above two cases, the joint beamforming design problems are efficiently solved to maximize the achievable rate. Simulations are conducted to verify the effectiveness of the proposed algorithms for mitigating the BSE and improving the achievable rate in IRS-aided THz wideband communications.

6G mobile networks aim to realize ubiquitous intelligence at the network edge via distributed learning, sensing, and data analytics. Their common operation is to aggregate high-dimensional data, which causes a communication bottleneck that cannot be resolved using traditional orthogonal multi-access schemes. A promising solution, called over-the-air computation (AirComp), exploits channels’ waveform superposition property to enable simultaneous access, thereby overcoming the bottleneck. Nevertheless, its reliance on uncoded linear analog modulation exposes data to perturbation by noise and interference. Hence, the traditional analog AirComp falls short of meeting the high-reliability requirement for 6G. Overcoming the limitation of analog AirComp motivates this work, which focuses on developing a framework for digital AirComp. The proposed framework features digital modulation of each data value, integrated with the bit-slicing technique to allocate its bits to multiple symbols, thereby increasing the AirComp reliability. To optimally detect the aggregated digital symbols, we derive the optimal maximum a posteriori detector that is shown to outperform the traditional maximum likelihood detector. Furthermore, a comparative performance analysis of digital AirComp with respect to its analog counterpart with repetition coding is conducted to quantify the practical signal-to-noise ratio (SNR) regime favoring the proposed scheme. On the other hand, digital AirComp is enhanced by further development to feature awareness of heterogeneous bit importance levels and its exploitation in channel adaptation. Lastly, simulation results demonstrate the achivability of substantial reliability improvement of digital AirComp over its analog counterpart given the same channel uses.

Wireless resource allocation is a critical component in modern communication systems, and deep neural networks (DNNs) have shown great promise in addressing this challenge. However, the conventional DNNs assume that testing data follows the same distribution as that of the training data, which is incongruent with the dynamic nature of real-world wireless environments. This paper introduces a new training algorithm designed specifically for dynamic wireless environments where channel distribution exhibits variability. This method helps DNNs adapt to new environments while preserving previously learned information. The proposed approach distinguishes itself by updating the DNN parameters in the null space of the low-rank covariance of previous data, which reduces memory needs and boosts training efficiency. Additionally, to counter the problem of DNNs hitting their model capacity during continuous adaptation, a selective forgetting mechanism is proposed. This mechanism allows DNNs to discard the unimportant knowledge over time, freeing up model capacity for more effective adaptation. The effectiveness of the algorithm is validated by integrating it with graph neural networks and multilayer perceptrons for weighted sum-rate maximization. Through a comprehensive evaluation that includes synthetic and ray-tracing-based datasets, superior performance is demonstrated compared to existing methods.

Extremely large reconfigurable intelligent surface (XL-RIS) is emerging as a promising key technology for 6G systems. To exploit XL-RIS’s full potential, accurate channel estimation is essential. This paper investigates channel estimation in XL-RIS-aided massive MIMO systems under hybrid-field scenarios where far-field and near-field channels coexist. To handle the high-dimensional nature of XL-RIS channels, a convolutional dictionary learning (CDL) problem is formulated, which is cast as a bilevel optimization problem. To compute the gradient of the upper-level objective, we introduce an unrolled optimization method based on proximal gradient descent (PGD) and its special case, the iterative soft-thresholding algorithm (ISTA). We propose two neural network architectures, Convolutional ISTA-Net (CISTA-Net) and its enhanced version CISTA-Net+, for end-to-end optimization of the CDL. To overcome the limitations of linear convolutional dictionary in capturing complex hybrid-field channel structures, we further replace linear convolution dictionary with convolutional neural network blocks in the gradient descent step, while employing a learnable proximal mapping module and incorporating cross-layer feature integration. Simulation results demonstrate the effectiveness of the proposed channel estimation algorithms for hybrid-field XL-RIS massive MIMO systems.

In continual learning, networks confront a trade-off between stability and plasticity when trained on a sequence of tasks. To bolster plasticity without sacrificing stability, we propose a novel training algorithm called LRFR. This approach optimizes network parameters in the null space of the past tasks’ feature representation matrix to guarantee the stability. Concurrently, we judiciously select only a subset of neurons in each layer of the network while training individual tasks to learn the past tasks’ feature representation matrix in low-rank. This increases the null space dimension when designing network parameters for subsequent tasks, thereby enhancing the plasticity. Using CIFAR-100 and TinyImageNet as benchmark datasets for continual learning, the proposed approach consistently outperforms state-of-the-art methods.

Recent advances in Neural radiance fields (NeRF) have enabled high-fidelity scene reconstruction for novel view synthesis. However, NeRF requires hundreds of network evaluations per pixel to approximate a volume rendering integral, making it slow to train. Caching NeRFs into explicit data structures can effectively enhance rendering speed but at the cost of higher memory usage. To address these issues, we present Hyb-NeRF, a novel neural radiance field with a multi-resolution hybrid encoding that achieves efficient neural modeling and fast rendering, which also allows for high-quality novel view synthesis. The key idea of Hyb-NeRF is to represent the scene using different encoding strategies from coarse-to-fine resolution levels. Hyb-NeRF exploits memory-efficiency learnable positional features at coarse resolutions and the fast optimization speed and local details of hash-based feature grids at fine resolutions. In addition, to further boost performance, we embed cone tracing-based features in our learnable positional encoding that eliminates encoding ambiguity and reduces aliasing artifacts. Extensive experiments on both synthetic and real-world datasets show that Hyb-NeRF achieves faster rendering speed with better rending quality and even a lower memory footprint in comparison to previous state-of-the-art methods.

To meet the stringent requirements of future communication systems, ultra-massive multiple-input and multiple-output (UM-MIMO) technology has garnered significant attention as a key enabling technology for 6G. However, the deployment of UM-MIMO introduces new challenges, particularly the near-field effect. In this paper, by leveraging the unique characteristics of near-field channels, we propose a novel near-field channel estimation algorithm based on the Newton's method. We also design a near-field codebook that meets the requirements for convergence guarantee. Our algorithm overcomes the limitations of existing approaches by offering a low-complexity, tuning-free, and convergence-guaranteed solution. Simulation results show that our proposed algorithm outperforms state-of-the-art baselines in terms of estimation accuracy, establishing its effectiveness in near-field channel estimation for UM-MIMO systems.

Semantic communication (SemCom) has emerged as a promising technology to improve the spectrum efficiency of next-generation wireless networks, by extracting meaningful content from the data and transmitting relevant semantic information only. However, the existing research usually overlooks the limited computing and storage resources on the mobile devices, which may make it unaffordable to implement resource-demanding deep learning (DL)-based semantic encoders/decoders. Moreover, besides the end-to-end SemCom framework, cooperative SemCom has not been well studied in the existing works, which can further enhance the communication performance. To address these issues, we propose a new architecture in this article, called semantic relay (SemRelay), which acts as an edge server to provide DL-enabled SemCom (DeepSC) services for two categories of edge users, called semantic users (SemUsers) with rich computing resources and conventional users (ConUsers) with limited resources. Two new transmission protocols are proposed for enabling text transmissions from the base station to the SemUsers and ConUsers, respectively, via the SemRelay (edge server). Moreover, an optimization problem is formulated to jointly design the SemRelay transmit power allocation and system bandwidth allocation to maximize the weighted sum-rate of all the users. Although this problem is nonconvex and hence difficult to solve, we propose an efficient algorithm to obtain a high-quality suboptimal solution by applying the block coordinate descent and successive convex approximation techniques. Finally, the numerical results demonstrate the effectiveness of our proposed algorithm and the superior performance of the proposed SemRelay as compared to the traditional decode-and-forward relays, especially in the small bandwidth regime.

In the future sixth-generation wireless communications, wireless sensor networks (WSNs) are expected to support target sensing, information communication, and computational tasks concurrently over the same spectrum. Integrated sensing and communication (ISAC) and over-the-air computation (AirComp) arise as two promising techniques. In this article, we design an integrated sensing, communication, and computing framework to improve spectrum efficiency and quality of service in WSNs. We investigate omnidirectional and directional beampattern designs to minimize AirComp error. Leveraging the derived directional patterns, we further consider tradeoff beampatterns to balance sensing and AirComp performance under power constraints. A mismatch-based design is formulated to minimize AirComp errors. To solve these nonconvex problems, we propose an alternating optimization approach using singular value decomposition and projection techniques to jointly optimize sensor and access point beampatterns. In particular, we propose a low-complexity two-step projection-based gradient descent method to solve the convex problems with two norm-ball constraints. In addition, convergence and complexity analysis of the proposed methods is provided. Simulations demonstrate the performance of the proposed beampattern designs.

Wireless power transfer (WPT) is recognized as a promising technique to alleviate the energy limitation of wireless devices (WDs) under mobile-edge computing (MEC) scenario in the upcoming Internet of Things (IoT) era. In wireless-powered MEC networks, WDs can utilize the harvested energy to handle the computation tasks. Furthermore, reconfigurable intelligent surface (RIS) can also play a significant role in MEC systems due to its capability to enhance the channel quality. In this article, we investigate an RIS-assisted wireless-powered MEC system where each WD follows the binary offloading policy. The objective is to minimize the total energy consumption of WDs by jointly optimizing the WPT time, the RIS phase shifts for WPT, the binary mode selection, the CPU frequencies for local computation, the RIS phase shifts for offloading, and the offloading times and powers. A gradient ascent-based algorithm with linear complexity with respect to the number of RIS reflecting elements is proposed to optimize the transmit power and RIS phases shifts design. On the other hand, a penalty-based algorithm with linear complexity with respect to the number of WDs is proposed to solve the offloading decision and time allocation. Numerical results are presented to demonstrate the effectiveness of the proposed system and algorithms.

To support emerging applications ranging from holographic communications to extended reality, next-generation mobile wireless communication systems require ultrafast and energy-efficient baseband processors. Traditional complementary metal-oxide-semiconductor (CMOS)-based baseband processors face two challenges in transistor scaling and the von Neumann bottleneck. To address these challenges, in-memory computing-based baseband processors using resistive random-access memory (RRAM) present an attractive solution. In this article, we propose and demonstrate RRAM-implemented in-memory baseband processing for the widely adopted multiple-input–multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) air interface. Its key feature is to execute the key operations, including discrete Fourier transform (DFT) and MIMO detection, using linear minimum mean square error (L-MMSE) and zero forcing (ZF), in one-step. In addition, RRAM-based channel estimation module is proposed and discussed. By prototyping and simulations, we demonstrate the feasibility of RRAM-based full-fledged communication system in hardware, and reveal it can outperform state-of-the-art baseband processors with a gain of$91.2\times $in latency and$671\times $in energy efficiency by large-scale simulations. Our results pave a potential pathway for RRAM-based in-memory computing to be implemented in the era of the sixth generation (6G) mobile communications.

The unprecedented growth of industrial Internet of Things applications requires the evolution of wireless networked control system (WNCS). WNCSs are becoming the fundamental infrastructure technologies for critical wireless control applications due to the main benefits of the reduced deployment and maintenance cost, as well as the enhanced flexibility and safety. However, independent designs between communication and control without considering their tight interaction in conventional WNCS lead to poor overall system performance and efficiency. Co-designs are expected to achieve the target control performance while improving the wireless resource efficiency. In this paper, by considering how to allocate wireless resource while guaranteeing control performance, a co-design framework is established based on the finite-time wireless system identification (WSI) - a fundamental problem in systems theory and intelligent control. To this end, two design problems are investigated aiming at maximizing the communication throughput or minimizing the power consumption while guaranteeing the WSI performance. In the former design, the joint optimization of power and channel allocations leads to a non-convex integer combinatorial problem, which is iteratively solved by optimizing the power allocation via Lagrangian method and obtaining the optimal channel allocation via Hungarian algorithm. The minimum number of data samples for guaranteeing the WSI accuracy under confidence level is further derived by exploiting the relationship between WSI accuracy and the number of state sampling processes, which leads to the maximum throughput with respect to both the communication and control processes. In the latter design for energy-efficient WSI, by exploiting the relationship between the power consumption and channel allocation given the WSI performance requirement, the optimization problem can be simplified and solved by Hungarian algorithm. Simulations are conducted to verify the performance of the proposed solutions.

In this paper, to stabilize the users’ quality of service (QoS), the symbol detection mean squared error (MSE) constrained hybrid analog and digital beamforming is proposed in millimeter wave (mmWave) system, and the reconfigurable intelligent surface (RIS) is proposed to assist the mmWave system. The inner majorization-minimization (iMM) method is proposed to obtain analog transmitter, RIS and analog receivers, and the alternating direction method of multipliers (ADMM) method is proposed to obtain digital transmitter. The proposed iMM and ADMM methods are faster than the semidefinite relaxation (SDR) and interior point methods, respectively. In order to counter against the changing large-scale path loss, the iMM method is helped by channel normalization to reduce the computational complexity, while the ADMM method is helped by the adaptive parameterizations to robust against the changing large-scale path loss. Simulation results show that the computational times of the proposed method are much faster than other methods, the proposed method is robust against the changing large-scale path loss, and the user’s bit error rate (BER) is stable under small to medium channel estimation errors.

Recently, channel state information (CSI) extracted from Wi-Fi signals has enabled a variety of Wi-Fi sensing applications beyond communications. However, the extraction of CSI relies on specific Wi-Fi devices, which severely limits the CSI-based sensing applications. In this paper, we exploit the beamforming matrix, a compressed version of CSI that is fed back from the beamformee to the beamformer without encryption, for Wi-Fi sensing. In order to recover more channel information from the beamforming matrix for sensing, a QR decomposition (QRD)-based beamforming feedback scheme is proposed in this paper. We analyze the effectiveness of the proposed QRD-based scheme in terms of sensing and communication performance. Simulation results show that the Doppler frequency shift, time of flight, angle of departure, as well as the amplitude information can be recovered using the proposed scheme and at the same time, the spectral efficiency of the proposed QRD-based scheme is comparable to that of the existing SVD-based scheme. We implement the proposed scheme on IEEE 802.11ac/ax based Wi-Fi devices. The experimental results on human respiration and finger tracking demonstrate that the proposed scheme works well for practical Wi-Fi sensing.

In mobile edge computing (MEC) systems, the wireless channel condition is a critical factor affecting both the communication power consumption and computation rate of the offloading tasks. This paper exploits the idea of cooperative transmission and employing reconfigurable intelligent surface (RIS) in MEC to improve the channel condition and maximize computation efficiency (CE). The resulting problem couples various wireless resources in both uplink and downlink, which calls for the joint design of the user association, receive/downlink beamforming vectors, transmit power of users, task partition strategies for local computing and offloading, and uplink/downlink phase shifts at the RIS. To tackle the challenges brought by the combinatorial optimization problem, the group sparsity structure of the beamforming vectors determined by user association is exploited. Furthermore, while the CE does not explicitly depend on the downlink phase shifts, instead of simply finding a feasible solution, we exploit the hidden relationship between them and convert this relationship into an explicit form for optimization. Then the resulting problem is solved via the alternating maximization framework, and the nonconvexity of each subproblem is handled individually. Simulation results show that cooperative transmission and RIS deployment can significantly improve the CE and demonstrate the importance of optimizing the downlink phase shifts with an explicit form.

Radio frequency (RF) fingerprinting is a promising device authentication technique for securing the Internet of Things. It exploits the intrinsic and unique hardware impairments of the transmitters for device identification. Recently, due to the superior performance of deep learning (DL)-based classification models on real-world datasets, DL networks have been explored for RF fingerprinting. Most existing DL-based RF fingerprinting models use a single representation of radio signals as the input, while the multi-channel input model can leverage information from different representations of radio signals and improve the identification accuracy of RF fingerprints. In this work, we propose a multi-channel attentive feature fusion (McAFF) method for RF fingerprinting. It utilizes multi-channel neural features extracted from multiple representations of radio signals, including in-phase and quadrature samples, carrier frequency offsets, fast Fourier transform coefficients and short-time Fourier transform coefficients. The features extracted from different channels are fused adaptively using a shared attention module, where the weights of neural features are learned during the model training. In addition, we design a signal identification module using a convolution-based ResNeXt block to map the fused features to device identities. To evaluate the identification performance of the proposed method, we construct a Wi-Fi dataset using commercial Wi-Fi end-devices as the transmitters and a Universal Software Radio Peripheral platform as the receiver. Experimental results show that the proposed McAFF method significantly outperforms the single-channel-based as well as the existing DL-based RF fingerprinting methods in terms of identification accuracy and robustness.

In this paper, we propose a fast-convergence federated edge learning by jointly optimizing the number of epochs and batch size. Specifically, we formulate a bilevel optimization problem. The upper level problem aims to trade off between aggregating time and loss error. The lower level problem is designed to eliminate synchronization waiting time. To solve this, we develop an approximate projection method. First, we obtain the optimal solution to the upper level problem using convex optimization. Based on the optimality conditions derived for the lower level problem, we formulate a projection optimization to minimize the distance between the projected points and upper level optimal solution. Our results demonstrate that the proposed method significantly outperforms other benchmark solvers on convergence speed for federated edge learning.

Our paper presents a MapReduce-based wireless distributed computing framework designed to handle data-intensive computing on edge devices with limited storage. The framework involves three stages: Map, Shuffle, and Reduce. However, shuffling large data during the second stage can lead to performance degradation over wireless interference networks with limited spectrum bandwidth. To address these issues, we propose using over-the-air computation (AirComp) technology, which leverages interference in the multiple-access channel to compute multiple target functions reliably. This approach achieves higher computation efficiency than traditional orthogonal multi-access schemes and is more effective in combating interference. Furthermore, we employ cell-free massive MIMO technology to improve coverage and reduce the system power overhead. This technology is essential for the upcoming sixth-generation (6G) networks. We optimize the transmitting-receiving (Tx-Rx) policy to minimize the averaged computation mean squared error (MSE) while adhering to each device’s power constraint. Our simulation results demonstrate that our proposed algorithm is effective and our computation framework has advantages over state-of-the-art baselines.

To facilitate the data collection process, simultaneous wireless information and power transfer utilizes the same signal for powering the devices and delivering the information, while the integrated sensing and communication utilizes the same signal for data transmission and radar sensing. In next generation networks, the sensing, communication, and power transfer functionalities are expected to be integrated together to enhance the radio resource efficiency and enable the data collection by massive low-power devices, which leads to the new research direction namely integrating sensing, communication, and power transfer (ISCPT). The ISCPT beamforming design for multiple users is investigated in this paper to improve the sensing performance while guaranteeing the communication and power transfer requirements. The resultant non-convex optimization problem is solved by the approach based on semidefinite relaxation and rank reduction methods. Simulations are further conducted to verify the effectiveness of the proposed design.

Mobile-edge computing arises as a promising technology to allow mobile devices (MDs) to offload delay-sensitive and computation-intensive tasks to the nearby edge servers. However, overloaded tasks from MDs lead to a large latency due to limited computing and channel resources. To mitigate this situation, a collaborative edge–cloud computing network is considered in this article. Based on power control and task offloading, we formulate a mixed-integer nonlinear programming (MINLP) problem to minimize the weighted sum of the energy consumption and the latency. This NP-hard problem is decomposed into a real variable problem and an integer linear programming problem. By leveraging the proposed extreme-value descent (EVD) method, the optimal power strategy is obtained. The simplex method and branch-and-bound method are adopted to find the optimal offloading strategy. Although these two subproblems are well solved, the solutions may be unsatisfactory for the original problem. To find a high-quality solution, we use an alternating optimization (AO) method to jointly optimize the decomposed problems. Theoretical analysis demonstrates that the EVD method converges at the rate of$O(1/s)$and the AO method can converge to a suboptimal solution if not the optimal solution. Simulation results show that the proposed algorithms significantly outperform other benchmark schemes.

Distributed optimization finds a wide range of applications ranging from machine learning to vehicle platooning. To overcome the bottleneck caused by the required extensive message exchange, we propose in this work the framework of distributed over-the-air computing (AirComp) to realize a one-step aggregation for distributed optimization. Equivalently, the technique superimposes multiple instances of conventional AirComp processes, giving rise to the challenge of jointly designing multicast beamforming at devices to rein in errors due to interference and channel distortion. We consider two design criteria. One is to minimize the sum AirComp error (i.e., sum mean-squared error (MSE)) with respect to the desired average-functional values. An efficient solution approach is proposed by transforming the non-convex beamforming problem into an equivalent concave-convex fractional program and solving it by nesting convex programming into a bisection search. The other one, called zero-forcing (ZF) multicast beamforming, is to force the received over-the-air aggregated signals at devices to be equal to the desired functional values, where the optimal beamforming admits closed form. Last, the convergence of a classic distributed optimization algorithm is analyzed. The distributed AirComp is found experimentally to accelerate convergence by dramatically reducing communication latency.

In this paper, simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) is investigated in the multi-user mobile edge computing (MEC) system to improve the computation rate. Compared with traditional RIS-aided MEC, STAR-RIS extends the service coverage from half-space to full-space and provides new flexibility for improving the computation rate for end users. However, the STAR-RIS-aided MEC system design is a challenging problem due to the non-smooth and non-convex binary amplitude coefficients with coupled phase shifters. To fill this gap, this paper formulates a computation rate maximization problem via the joint design of the STAR-RIS phase shifts, reflection and transmission amplitude coefficients, the receive beamforming vectors, and energy partition strategies for local computing and offloading. To tackle the discontinuity caused by binary variables, we propose an efficient smoothing-based method to decrease convergence error, in contrast to the conventional penalty-based method, which brings many undesired stationary points and local optima. Furthermore, a fast iterative algorithm is proposed to obtain a stationary point for the joint optimization problem, with each subproblem solved by a low-complexity algorithm, making the proposed design scalable to a massive number of users and STAR-RIS elements. Simulation results validate the strength of the proposed smoothing-based method and show that the proposed fast iterative algorithm achieves a higher computation rate than the conventional method while saving the computation time by at least an order of magnitude. Moreover, the resultant STAR-RIS-aided MEC system significantly improves the computation rate compared to other baseline schemes with conventional reflect-only/transmit-only RIS.

3D object detection from LiDAR point cloud is a challenging task in autonomous driving systems. Collaborative perception can incorporate information from spatially diverse sensors and provide significant benefits for accurate 3D object detection from point clouds. In this work, we consider that the autonomous vehicle uses local point cloud data and combines information from neighboring infrastructures through wireless links for cooperative 3D object detection. However, information sharing among vehicles and infrastructures in predefined communication schemes may result in communication congestion and/or bring limited performance improvement. To this end, we propose a novel collaborative 3D object detection framework using an encoder-decoder network architecture and an attention-based learnable communications scheme. It consists of three components: a feature encoder network that maps point clouds into feature maps; an attention-based communication module that propagates compact and fine-grained query feature maps from the vehicle to support infrastructures, and optimizes attention weights between query and key to refine support feature maps; a region proposal network that fuses local feature maps and weighted support feature maps for 3D object detection. We evaluate the performance of the proposed framework on CARLA-3D, a new dataset that we synthesized using CARLA for 3D cooperative object detection. Experimental results and bandwidth consumption analysis show that the proposed collaborative 3D object detection framework achieves a better detection performance and communication bandwidth trade-off than five baseline 3D object detection models under different detection difficulties.

To support the unprecedented growth of the Internet of Things (IoT) applications, tremendous data need to be collected by the IoT devices and delivered to the server for further computation. By utilizing the same signals for both radar sensing and data transmission, theintegrated sensing and communication(ISAC) technique enables simultaneous data collection and delivery in the physical layer. By exploiting the analog-wave addition property in a multi-access channel,over-the-air computation(AirComp) has been proposed as a communication approach that also enables function computation. The promising performances of ISAC and AirComp motivate the current work on developing a framework calledintegrated sensing, communication, and computation over-the-air(ISCCO). Two schemes are designed to supportmultiple-input-multiple-output(MIMO) ISCCO simultaneously, namely theseparated and sharedschemes. The separated scheme splits antenna array for radar sensing and AirComp, while all the antennas transmit a joint waveform for both radar sensing and AirComp in the shared scheme. The performance of radar sensing is evaluated by themean squared error(MSE) of the estimated target response matrix, while the MSE of the estimated function is adopted as the metric to evaluate the performance of the coupled communication and computation in AirComp. The design challenge of MIMO ISCCO lies in the joint optimization of beamformers at both the IoT devices and the server, which results in a non-convex problem. To solve this problem, an algorithmic solution based on the technique of semidefinite relaxation is proposed. The results reveal that the beamformer at each sensor needs to account for supporting dual-functional signals in the shared scheme, while dedicated beamformers for sensing and AirComp are needed to mitigate the mutual interference between the two functionalities in the separated scheme. The application of ISCCO on target location estimation is further demonstrated via simulation.

The unprecedented growth of mobile data traffic has fueled the deployment of artificial intelligence (AI) at the network edge, while distilling the intelligence from raw data by machine learning requires tremendous labelling effort. To overcome this challenge, wireless crowd labelling (WCL) is proposed for efficient data labelling by exploiting billions of available mobile annotators and the multicasting property of wireless channels. A WCL system is considered in this paper where unlabelled data (objects) are multicast via fading channels to different clusters of annotators for repetition labelling to improve the accuracy. Given the desired labelling accuracy, the superposition coding technique together with the repetition labelling scheme give rise to a new tradeoff between radio-and-annotator resource consumption. Building on such tradeoff, the annotator clustering and transmit power control are jointly optimized to maximize the labelling throughput (i.e., the number of labelled objects) or minimize the power consumption, resulting in NP-hard integer programming problems. To solve these problems, the optimal structure of annotator clustering is derived by exploiting the property that the power allocation for multicasting objects tends to compensate for the worst channel among the annotators in each cluster. Based on such structure, the throughput maximization problem can be recognized as a longest-path problem and solved by means of branch-and-bound, while the power minimization problem can be recasted to a shortest-path problem and solved by means of forward dynamic programming. The solution approaches can be further simplified when the channels are symmetric by merging the same nodes and cutting the identical paths in the path graph. In addition, exact polices are derived for the special cases where either the annotators or power are constrained. Last, simulation results are presented to demonstrate the performance of our proposed joint designs.

Driven by the rapid growth of Internet of Things applications, tremendous data need to be collected by sensors and uploaded to the servers for further process. As a promising solution, mobile crowd sensing (MCS) enables controllable sensing and transmission processes of multiple types of data in a single device. Despite the appealing advantages, existing works on MCS have mostly simplified two design issues, namely joint control of sensing and transmission processes and corresponding energy consumption. To address the above issues, a single-user MCS system is considered with a typical MCS device sensing and transmitting data to a server in a given time duration. In particular, there exists a busy time interval when the device is incapable of sensing. To minimize the sensing-and-transmission energy consumption of the device, an optimization problem is formulated, where the sensing and transmission rates are jointly optimized over time subjecting to the constraints on the sensing data sizes, transmission data sizes, data casualty, and busy time of sensing. This problem is highly challenging due to the coupling between the rates as well as the existence of the busy time. To deal with this problem, we first show that it can be equivalently decomposed into two subproblems, corresponding to a search for the amount of data size that needs to be sensed before the busy time (referred to as the height), as well as the control of sensing and transmission rates given the height. Next, we show that the latter problem can be efficiently solved by using the classical string-pulling method, while an efficient algorithm is proposed to progressively find the optimal height without the exhaustive search. Moreover, the solution approach is extended to a more complex scenario where there is a finite-size buffer at the server for receiving data. Last, simulations are conducted to evaluate the performance of the proposed designs.

The rapid development of artificial intelligence together with the powerful computation capabilities of the advanced edge servers make it possible to deploy learning tasks at the wireless network edge, which is dubbed as edge intelligence (EI). The communication bottleneck between the data resource and the server results in deteriorated learning performance as well as tremendous energy consumption. To tackle this challenge, we explore a new paradigm called learning-and-energy-efficient (LEE) EI, which simultaneously maximizes the learning accuracies and energy efficiencies of multiple tasks via data partition and rate control. Mathematically, this results in a multi-objective optimization problem. Moreover, the continuous varying rates introduce infinite variables, which further complicates the problem. To solve this complex problem, the number of variables is reduced to a finite level by exploiting the optimality of constant-rate transmission in each epoch, based on which a string-pulling (SP) algorithm is proposed to obtain the numerical values. The performance of the proposed joint data partition and rate control design is examined by experiments based on public datasets.

Motivated by increasing powerful edge devices with data-intensive computing and limited storage size, we study a MapReduce-based wireless distributed computing framework by allocating a portion of files in the remote data center to the network edge and utilizing computation and memory resources at the edge. Our framework is composed of three step phases: 1)Map; 2)Shuffle; and 3)Reduce. However, in the data shuffling stage, shuffling many data accounts for a large amount of the total running time over wireless interference networks will degrade its performance. Moreover, data shuffling between pervasive edge devices with limited spectrum bandwidth is very challenging. Today, many devices focus on computing functions rather than collecting all the individual wireless data centers. Therefore, we can use over-the-air computation (AirComp) technology to reliably compute multiple target functions by harnessing interference in the multiple-access channel with a higher computation efficiency than the traditional orthogonal multiaccess scheme that combats interference. We study a mixed-timescale optimization of the transmitting–receiving (Tx-Rx) policy and file allocation to minimize the averaged computation mean-squared error (MSE) under the power constraint of each device. File allocation control is adaptive to the long-term statistical channel state information (CSI), while the Tx-Rx policy is adaptive to the CSI and file allocation strategy. We decompose the problem into a short-term Tx-Rx policy and a long-term file allocation control problem to tackle the joint nonconvex optimization. Simulation results indicate the effectiveness of our proposed two-timescale algorithm and the advantages of our computation framework over the state-of-the-art baselines.

Mobile edge computing (MEC) is envisioned as a promising technique to support computation-intensive and time-critical applications in future Internet of Things (IoT) era. However, the uplink transmission performance will be highly impacted by the hostile wireless channel, the low bandwidth, and the low transmission power of IoT devices. Recently, intelligent reflecting surface (IRS) has drawn much attention because of its capability to control the wireless environments so as to enhance the spectrum and energy efficiencies of wireless communications. In this article, we consider an IRS-aided multidevice MEC system where each IoT device follows the binary offloading policy, i.e., a task has to be computed as a whole either locally or remotely at the edge server. We aim to minimize the total energy consumption of devices by jointly optimizing the binary offloading modes, the CPU frequencies, the offloading powers, the offloading times, and the IRS phase shifts for all devices. Two algorithms, which are greedy based and penalty based, are proposed to solve the challenging nonconvex and discontinuous problem. It is found that the penalty-based method has only linear complexity with respect to the number of devices, but it performs close to the greedy-based method with cubic complexity with respect to the number of devices. Furthermore, binary offloading via IRS indeed saves more energy compared to the case without IRS.

With the maneuverability and mobility control of unmanned aerial vehicle (UAV), carrying mobile edge computing (MEC) servers on UAVs is able to effectively alleviate the explosive growth of data traffic pressure. However, UAV adopts line-of-sight transmission which has broadcasting characteristics. Malicious eavesdroppers can easily take advantage of the characteristics to eavesdrop information during the UAV edge computing. Therefore, the security of the UAV-MEC systems is a challenging problem. This article proposes a secure communication scheme for the dual-UAV-MEC system. In the proposed scheme, UAV server assists ground users in calculating the offloading tasks. In order to reduce the eavesdropping of offloading information by UAV eavesdropper, jammer sends interference signals on the ground. We aim to maximize the user's minimum secure calculation capacity by optimizing resources and trajectory of the UAV server. We first transform the optimization problem into a tractable form through mathematical methods and use successive convex approximation and block coordinate descent algorithms to solve it in an iterative manner. The final numerical results show that, compared with the benchmark schemes, the method proposed in this article effectively increases the secure calculation capacity of the system.

The rapid development of artificial intelligence together with the powerful computation capabilities of the advanced edge servers make it possible to deploy learning tasks at the wireless network edge, which is dubbed as edge intelligence (EI). The communication bottleneck between the data resource and the server results in deteriorated learning performance as well as tremendous energy consumption. To tackle this challenge, we explore a new paradigm called learning-and-energy-efficient (LEE) EI, which simultaneously maximizes the learning accuracies and energy efficiencies of multiple tasks via data partition and rate control. Mathematically, this results in a multi-objective optimization problem. Moreover, the continuously varying communication rates introduce infinite variables, which further complicates the problem. To solve this complex problem, we consider the case with infinite server buffer capacity and one-shot data arrival at sensor. First, the number of variables is reduced to a finite level by exploiting the optimality of constant-rate transmission in each epoch. Second, the optimal solution of the multi-objective problem is found by applying the stratified sequencing or merging of objectives. By assuming higher priority of learning efficiency in stratified sequencing, the optimal data partition is derived in closed form by the Lagrange method, while the optimal rate control is proved to have the structure of directional water filling (DWF), based on which a string-pulling (SP) algorithm is proposed to obtain the numerical values. The DWF structure of rate control is also proved to be optimal in merging of objectives, which combines different objectives in a weighted manner. By exploiting the optimal rate changing properties, the SP algorithm is further extended to tackle the more challenging cases with limited server buffer capacity or bursty data arrival at sensor. The performance of the proposed joint data partition and rate control design is examined by extensive experiments based on public datasets.

The deployment of federated learning in a wireless network, calledfederated edge learning(FEEL), exploits low-latency access to distributed mobile data to efficiently train an AI model while preserving data privacy. In this work, we study the spatial (i.e., spatially averaged) learning performance of FEEL deployed in a large-scale cellular network with spatially random distributed devices. Both the schemes of digital and analog transmission are considered, providing support of error-free uploading and over-the-air aggregation of local model updates by devices. The derived spatial convergence rate for digital transmission is found to be constrained by a limited number of active devices regardless of device density and converges to the ground-true rate exponentially fast as the number grows. The population of active devices depends on network parameters such as processing gain and signal-to-interference threshold for decoding. On the other hand, the limit does not exist for uncoded analog transmission. In this case, the spatial convergence rate is slowed down due to the direct exposure of signals to the perturbation of inter-cell interference. Nevertheless, the effect diminishes when devices are dense as interference is averaged out by aggressive over-the-air aggregation. In terms of learning latency (in second), analog transmission is preferred to the digital scheme as the former dramatically reduces multi-access latency by enabling simultaneous access.

Robustness against noise is critical for modulation recognition (MR) approaches deployed in real-world communication systems. In MR systems, a corrupted signal is normally enhanced using low-level signal enhancement (SE) before signal classification (SC). Many existing approaches address signal distortion problems by compartmentalizing SE from SC. While those approaches allow for efficient development, they also dictate compartmentalized performance metrics, without feedback from the SC module. For example, SE modules are designed using perceptual signal quality metrics but not with SC in mind. To improve the effectiveness of SE on MR, this paper proposes a joint learning framework consisting of three cascaded modules: dual-channel spectrum fusion, SE, and SC. Instead of separately processing SE and SC, these three modules are integrated into one framework and jointly trained with a single recognition loss. In contrast to estimating clean signals, the SE module in the proposed joint learning framework is trained to predict a ratio mask and find important time-frequency bins for the SC module. We integrate a multistage attention mechanism into the framework to further increase the robustness. The multistage attention mechanism is deployed to strengthen the recognition-related features learned from context information in channel, time, and frequency domains. We evaluate the recognition performance of the proposed framework and its modules on two benchmark datasets: RadioML2016.10a and RadioML2016.10b. The experiment results show that the proposed joint learning framework outperforms the separate learning framework. Moreover, comparisons are performed with several existing learning-based MR methods in the literature. The proposed joint learning framework leads to significant performance improvement, especially for modulated signals corrupted by channel noise.

Driven by the new era of big data and artificial intelligence (AI), as well as the increasing demands for the privacy protection, how to deployment the AI on wireless networks is drawing increasing attention. In this paper, we investigate the distributed learning mechanism of hosting AI over intelligent reflecting surface (IRS)-assisted wireless networks, where IRS is utilized to enhance communication in a cost-effective and energy-efficient manner. Firstly, a distributed learning framework is formulated based on the alternating direction method of multipliers (ADMM) to achieve the parallel processing of the objective function. Specifically, in the proposed architecture each user updates the learning model with its own data and uploads it to the global model through wireless networks. Thence, a joint passive phase shift of IRS and user scheduling scheme based on a metric of efficiency-efficacy weighted sum (EEWS) is formulated to explore both the learning efficiency and efficacy. In addition, aiming at improving the one-round learning efficiency, a grouping-based suboptimal solution about IRS’s phase is adopted to realize the max-min fair transmission. Simulation results demonstrate the relationship among the number of users involved, the scale of IRS and the learning performance.

Internet of Things (IoT) is able to provide various physical objects to exchange their information through the 6G wireless communication network. However, with the large increasing number of the IoT devices (IoDs), the deployment of IoDs faces two basic challenges, i.e., spectrum scarcity and energy limitation. Cooperative spectrum sharing and simultaneous wireless information and power transfer (SWIPT) provide effective ways to improve the spectrum and energy efficiency. In this article, two SWIPT cooperative spectrum sharing methods are proposed to improve the energy and spectrum efficiency for 6G-enabled cognitive IoT network, in which IoDs access to the primary spectrum by serving as orthogonal frequency-division multiplexing (OFDM) relay with the energy harvested from the received radio-frequency (RF) signal. Specifically, in phase1, the IoDs transmitter (DT) in the cognitive IoT network performs information decoding and energy harvesting with the received RF signal. In phase2, DT transmits the signals of the primary system and itself to the corresponding receiver by utilizing orthogonal subcarriers with the harvested energy to avoid the interference. Achievable rates of the cognitive IoT system with amplify-and-forward (AF) and decode-and-forward (DF) relaying mode are maximized through joint power and subcarrier optimization, while ensuring the target rate of the primary system. Simulation results are performed to illustrate the improvement of the spectrum and energy efficiency.

The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), while tremendous labels are required for AI model training via supervised learning. To tackle this challenge, a novel framework of wireless crowd labelling is proposed that downloads data to many imperfect mobile annotators for repetition labelling by exploiting multicasting in wireless networks. The integration of the rate-distortion theory and the principle of repetition labelling gives rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy. Aiming at maximizing the labelling throughput, this work focuses on optimizing the joint annotator-and-spectrum allocation (JASA). To develop an efficient solution approach, an optimal sequential annotator-clustering scheme is derived. Thereby, the optimal JASA policy can be found by an efficient tree search.

The scheduling of downlink video streaming in a massive multiple-input-multiple-output (MIMO) network is considered in this paper, where active users arrive randomly to request video contents of a finite playback duration via their service base stations. Each video consists of a sequence of segments, which can be transmitted to the requesting users with variable video bitrates. To facilitate adaptive video streaming, a number of physical-layer frames are grouped as a super frame. We formulate the adaptation of transmitted segment number, frame allocation and segment bitrate in all the super frames as an infinite-horizon Markov decision process (MDP), whose objective is a discounted measurement of the average Quality-of-Experience (QoE). A novel approximate MDP method is proposed to obtain a low-complexity scheduling policy. Specifically, a baseline policy is introduced and its asymptotic value function is derived analytically. The low-complexity scheduling policy will be obtained from one-step iteration based on the analytical expression, which becomes a performance lower bound on the derived policy. It is shown by simulations that the proposed low-complexity scheduling policy has significant performance gain over the baseline policy.

In this paper, we present a deep learning based trainable approximate message passing algorithm (TAMP) for signal detection in massive multiple-input multiple-output (MIMO) systems. The TAMP network consists of a preprocessing layer and a fixed number of detection layers, where the preprocessing layer is designed by using a standard fully connected layer, and the structure of each detection layer is derived by unfolding each iteration of the iterative GAMP algorithm. In addition, the proposed TAMP includes trainable parameters controlling prior mean and variance of minimum mean squared error (MMSE) denoiser. The parameters are trained by standard deep learning techniques. We evaluate the signal detection performance of the proposed TAMP under Rayleigh-fading and spatial correlated MIMO channels. Furthermore, we compare TAMP with existing state-of-the-art iterative message passing-based detection algorithms and deep learning based detection algorithms. Computer experiments show that TAMP is applicable under both Rayleigh-fading and spatial correlated channel in massive MIMO systems. Moreover, comparison results demonstrate that TAMP can achieve better detection accuracy and faster convergence.

Wireless sensor networks (WSNs) have the disadvantages of short lifetime due to the limited energy of the energy storage batteries of the sensor nodes and scarcity of spectrum resources as the number of sensor nodes increasing. Simultaneous wireless information and power transfer (SWIPT) can make WSNs solve the problem of short lifetime through sensor nodes harvest energy from radio-frequency (RF) signals. Cognitive radio(CR) can make WSNs solve the problem of the scarcity of spectrum resources through sensor nodes sense and access free licensed spectrum. This paper mainly investigates the performance of an underlay cognitive sensor network (CSN). The sensor nodes in the underlay CSN can communicate with each other through the help of energy harvesting (EH) relay sensor node (RSN) by using amplify-and-forward (AF) relaying protocol. To maximize the throughput of CSN, we propose a algorithm through optimizing the transmit power of sensor nodes. Simulation results show the algorithm is correct and has good performance.

Wireless power transfer (WPT) and mobile edge computing (MEC) are two prospective technologies to enhance the computing power and endurance of mobile devices. Integrating unmanned aerial vehicle (UAV) into wireless powered MEC system, the energy collection efficiency can be effectively improved with the short‐distance line‐of‐sight path power transfer. However, WPT is susceptible to the ‘double near‐far’ effect. Therefore, in this study, the authors study power optimisation in UAV‐assisted wireless powered cooperative MEC system, which utilises the user cooperation to make the mobile device which is closer to the UAV acting as a relay for offloading. They aim to minimise the total transmission energy of the UAV through the joint power optimisation while satisfying the delay and size of the computational task. Simulation results demonstrate the performance of the proposed scheme.

In this paper, the scheduling of downlink file transmission in one cell with the assistance of cache nodes with finite cache space is studied. Specifically, requesting users arrive randomly and the base station (BS) reactively multicasts files to the requesting users and selected cache nodes. The latter can offload the traffic in their coverage areas from the BS. We consider the joint optimization of the abovementioned file placement and delivery within a finite lifetime subject to the cache space constraint. Within the lifetime, the allocation of multicast power and symbol number for each file transmission at the BS is formulated as a dynamic programming problem with a random stage number. Note that there are no existing solutions to this problem. We develop an asymptotically optimal solution framework by transforming the original problem to an equivalent finite-horizon Markov decision process (MDP) with a fixed stage number. A novel approximation approach is then proposed to address the curse of dimensionality, where the analytical expressions of approximate value functions are provided. We also derive analytical bounds on the exact value function and approximation error. The approximate value functions depend on some system statistics, e.g., requesting users’ distribution. One reinforcement learning algorithm is proposed for the scenario where these statistics are unknown.

The scheduling of downlink video streaming in a massive multiple-input multiple-output (MIMO) network is considered in this paper, where active users arrive randomly to request video contents of a finite playback duration via their service base stations (BSs). Each video content consisting of a sequence of segments can be transmitted to the requesting users with variable video bitrates. We formulate the joint control of transmitted segment number, frame allocation and segment bitrate in all the super frames (each comprising multiple frames) as an infinite-horizon Markov decision process (MDP). The maximization objective is a discounted measurement of the average Quality of Experience (QoE). Since there is no efficient method for scheduling design with random user arrivals and departures in the existing literature, a novel approximate MDP method is proposed to obtain a low-complexity scheduling policy, where a lower bound on its performance is derived. Specifically, we first introduce a baseline policy and derive its asymptotic value function. One-step policy iteration is then applied to improve this value function, yielding the mentioned low-complexity policy. Finally, we propose a novel and efficient reinforcement learning (RL) algorithm to evaluate the value function when the prior knowledge on user arrival intensity is absent.

The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), automating the operations of our society ranging from transportation to healthcare. The implementation of ubiquitous AI, however, entails labelling of an enormous amount of data prior to the training of AI models via supervised learning. To tackle this challenge, we explore a new direction called wireless crowd labelling, which involves downloading data to many imperfect mobile annotators for repetition labelling with an aim of exploiting multicasting in wireless networks. In this cross-disciplinary area, the rate-distortion theory and the principle of repetition labelling for accuracy improvement together give rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy. Building on the tradeoff and aiming at maximizing the labelling throughput, this work focuses on the joint optimization of encoding rate, annotator clustering, and sub-channel allocation, which results in an NP-hard integer programming problem. To devise an efficient solution approach, we establish an optimal sequential annotator-clustering scheme based on the order of decreasing signal-to-noise ratios, thereby allowing the optimal solution to be found by an efficient tree search. This solution can be further simplified when the channels are symmetric. Alternatively, the optimization problem can be recognized as a knapsack problem, which can be efficiently solved in pseudo-polynomial time by means of dynamic programming. In addition, the optimal polices are derived for the annotator constrained and spectrum constrained cases. Last, simulation results are presented to demonstrate the significant throughput gains based on the optimal solution compared with decoupled allocation of the two types of resources.

A modified nested linear array (MNLA) has been reported recently for a greater potential in increasing the degree-of-freedom. However, there exist some "holes" in the difference co-array, which results in missing "lags" and limited performance of direction-of-arrival (DOA) estimation. In order to tackle this problem, this paper applies a Toeplitz matrix completion technique to MNLA, and investigates the performance of DOA estimation on this basis. Particularly, a semidefinite program with trace minimization is derived to obtain the covariance matrix with Hermitian and Toeplitz structure. The recovered Toeplitz covariance matrix is then utilized to perform DOA estimation. Various numerical examples are provided to verify the effectiveness and superiority of the proposed method.

Considering the limitations on communication capability in the big data era, measurement selection plays an important role in obtaining the desired information by collecting only a part of data from the sensors. In this paper, we study the large-scale measurement selection problem, and propose a distributed algorithm exploiting the sparsity property extracted from the on-line data processing. Different to the existing works, we propose a mission-oriented framework to analyze the performance improvements for the specific mission of collecting new data. Specifically, a Bayesian hierarchical prior is adopted in order to quantify the importance of uncollected data by the on-line inference from the collected data. Based on the sparsity property obtained by on-line data processing, the sensors with important uncollected data will have high priority to access. Due to the massive number of sensors, the measurement selection algorithm is executed distributively at each device according to the common information broadcast by the fusion center. Simulation results demonstrate the performance gain of our proposed measurement selection method compared to the conventional schemes.

Mobile edge computing (MEC) is an innovative computing paradigm to enhance the computing capacity of mobile devices (MDs) by offloading computation‐intensive tasks to MEC servers. With the widespread deployment of wireless local area networks, each MD can offload computation task to server via multiple wireless access points (WAPs). However, computation offloading can bring a higher system cost if all users select the same access points to offload their tasks. This study proposes a computation offloading strategy and resource allocation optimisation scheme in a multiple wireless access points network with MEC, which aims to minimise the system cost by providing the optimal computation offloading strategy, transmission power allocation, bandwidth assignment, and computation resource scheduling. The proposed scheme decouples the optimisation problem into subproblems of offloading strategy and resource allocation since the problem is NP‐hard. The offloading strategy involves the optimal access point selection, which is analysed by the potential game. The resource allocation is obtained using Lagrange multiplier. The authors' analysis and simulation results verify the convergence performance of the proposed scheme, and the proposed scheme outperforms the simple resource allocation scheme and the offloading strategy optimisation scheme in terms of the system cost.

Channel estimation in time division duplexing (TDD)‐based massive multiple‐input multiple‐output (MIMO) systems is heavily hampered by the pilot contamination, which constitutes a major bottleneck on the overall system performance. This study considers the pilot contamination problem in multi‐cell TDD‐based massive MIMO systems, and analytical expressions are presented on the normalised mean square error (NMSE) of the minimum mean square error channel estimation algorithm. Based on the obtained NMSE, this study proposes an optimal pilot assignment strategy to minimise the effect of pilot contamination. In order to further improve the system performance, a pilot design‐based channel estimation scheme is proposed, where Chu sequences with perfect auto‐correlation property are employed to design the optimal pilot sequences aiming at acquiring the accurate channel state information. Simulation results show that the proposed pilot assignment strategy outperforms the random pilot assignment method, and approaches to the performance of the exhaustive search method which requires high computational complexity. Moreover, the performance gain of the pilot design‐based channel estimation scheme is verified in massive MIMO systems.

In Internet of Things, with large amounts of sensor data gathered in fusion center, it is important to detect a public hypothesis, but at the same time it is crucial to prevent a private hypothesis being detected. In order to achieve this goal, a multilayer nonlinear processing procedure is proposed to distort the sensor's data before it is sent to the fusion center. In particular, each sensor applies linear and nonlinear distortions to balance the public hypothesis test and the privacy distortion. Mirror descent methodology is reformulated to optimize the distortion matrices so as to ensure that the regularized empirical risk of detecting the private hypothesis is above a given privacy threshold, while minimizing the regularized empirical risk of detecting the public hypothesis. Experiments on empirical datasets demonstrate that the proposed approach achieves a good tradeoff between the error rates of the public and private hypotheses.

Leveraging massive numbers of sensors in user equipment as well as opportunistic human mobility, mobile crowd sensing (MCS) has emerged as a powerful paradigm, where prolonging battery life of constrained devices and motivating human involvement are two key design challenges. To address these, we envision a novel framework, named wirelessly powered crowd sensing (WPCS), which integrates MCS with wireless power transfer for supplying the involved devices with extra energy and thus facilitating user incentivization. This paper considers a multiuser WPCS system where an access point (AP) transfers energy to multiple mobile sensors (MSs), each of which performing data sensing, compression, and transmission. Assuming lossless (data) compression, an optimization problem is formulated to simultaneously maximize data utility and minimize energy consumption at the operator side, by jointly controlling wireless-power allocation at the AP as well as sensing-data sizes, compression ratios, and sensor-transmission durations at the MSs. Given fixed compression ratios, the proposed optimal power allocation policy has the threshold-based structure with respect to a defined crowd-sensing priority function for each MS depending on both the operator configuration and the MS information. Further, for fixed sensing-data sizes, the optimal compression policy suggests that compression can reduce the total energy consumption at each MS only if the sensing-data size is sufficiently large. Our solution is also extended to the case of lossy compression, while extensive simulations are offered to confirm the efficiency of the contributed mechanisms.

As a revolution in networking, the Internet of Things (IoT) aims at automating the operations of our societies by connecting and leveraging an enormous number of distributed devices (e.g., sensors and actuators). One design challenge is efficient wireless data aggregation (WDA) over the dense IoT devices. This can enable a series of the IoT applications ranging from latency-sensitive high-mobility sensing to data-intensive distributed machine learning. Over-the-air (function) computation (AirComp) has emerged to be a promising solution that merges computing and communication by exploiting analog-wave addition in the air. Another IoT design challenge is battery recharging for dense sensors which can be tackled by wireless power transfer (WPT). The coexisting of AirComp and WPT in the IoT system calls for their integration to enhance the performance and efficiency of WDA. This motivates the current work on developing the wirelessly powered AirComp (WP-AirComp) framework by jointly optimizing wireless power control, energy and (data) aggregation beamforming to minimize the AirComp error. To derive a practical solution, we recast the non-convex joint optimization problem into the equivalent outer and inner sub-problems for (inner) wireless power control and energy beamforming, and (outer) the efficient aggregation beamforming, respectively. The former is solved in closed form while the latter is efficiently solved using the semidefinite relaxation technique. The results reveal that the optimal energy beams point to the dominant Eigen-directions of the WPT channels, and the optimal power allocation tends to equalize the close-loop (down-link WPT and up-link AirComp) effective channels of different sensors. The simulation demonstrates that the controlling WPT provides additional design dimensions for substantially reducing the AirComp error.

Since urban rail unmanned train is considered as one of the core technologies of Intelligent Transportation System (ITS) , how to shorten the train headway as the driven interval between unmanned trains properly is still a challenge in Communications Based Train Control (CBTC) system. In this paper, we focus on analyzing the main factors which affect the train headway in inter-station barrier tracking mode, interstation stop tracking mode and station tracking mode and present an optimization scheme for Automatic Train Control (ATC) system with mobile block technology. Simulation results show that the proposed optimization scheme can reduce train headway to improve operational efficiency and reduce costs of urban rail transit system.

To compensate the imprecise modeling method using a Poisson point process (PPP) in a cellular network, especially in urban areas, we adopt a more suitable modeling method using a Poisson cluster process (PCP) and analyze the coverage probability of millimeter-wave (mmWave) cellular networks. We apply a distribution function of the shortest distance between the typical user and its serving base station (BS) to derive the probability density function (PDF) of the distance. Then, accurate formulas for the Laplace transform of interference are derived under Rayleigh fading and lognormal fading. Furthermore, we compute the expressions of signal to interference-plus-noise ratio (SINR) and rate coverage probability under these two fading, respectively. Our analysis and simulations show that the PCP-based modeling method of mmWave networks outperforms the PPP scheme in terms of coverage probabilities in low SINR threshold, high SINR threshold and high rate threshold. The results also confirm the accuracy of the formulas derived in this paper and guide the deployment of the mmWave cellular networks.

We propose a downlink cell-edge-aware zero forcing (CEA-ZF) and block diagonalization (BD) cooperative precoding scheme to reduce the downlink interference caused by the small cell range expansion in a heterogeneous network (HetNet). The CEA-ZF precoding algorithm adopted in the downlink transmission of macro base station (MBS) exploits the spatial degrees of freedom from large antenna array to suppress the inter-cell interference, and the BD precoding algorithm is introduced in the small access point (SAP) to eliminate the multi-user interference. Simulation results demonstrate the benefits of the proposed downlink precoding scheme over the alternative approach, and verify the proposed scheme as a more effective interference mitigation scheme for the downlink massive multiple-input multiple-output (MIMO) HetNet. Moreover, the optimal range expansion bias (REB) of small cell range expansion is obtained, and we also give a lower bound for the UE sum-rate of the proposed precoding scheme.

As an effective approach to alleviate the spectrum scarcity problem, cognitive radio (CR) has recently attracted an increasing amount of attention. In this paper, an optimization algorithm that joints bandwidth and power allocation of hybrid spectrum sharing is proposed in CR. According to the location variation of cognitive user (CU) that adopt the random waypoint based mobility models, the state of CU can switch between Underlay spectrum sharing model and Overlay spectrum one. Simultaneously, this algorithm can maximize the channel capacity through jointly optimizing power and bandwidth of CU when the Primary User's (PU's) interference temperature and the CU's transmission can be satisfied. Simulation results show that the proposed algorithm can more effectively improve the channel capacity than the single Underlay system and traditional solutions that the bandwidth is evenly allocated.

Predictive green streaming have recently gained attention in wireless network literature due to its significant energy-savings and quality of experience (QoE) gains. In this paper, we investigate how predicted user rates can be exploited for mobile video streaming with the popular Hypertext Transfer Protocol (HTTP) [e.g., HTTP adaptive streaming (HAS)]. To this end, we develop a stochastic predictive HTTP Adaptive streaming (PHAS) optimization framework to achieve the following objectives: 1) an edge-cloud assisted framework for prediction based HAS and identify its key functional entities and their interactions; 2) modelling uncertainty in predicted user rates and propose a robust two-stage QoE optimization approach which dynamically allocate the risks and optimize system efficiency over a time horizon; 3) an efficient heuristic algorithm allocating time slot ratio for multi-users to improve the network efficiency, fairness and overall QoE under different prediction error variances, wireless link conditions and buffer length constraints; Simulation studies and analytical results show that our method has a better performance than traditional methods in terms of average QoE, fairness and energy efficiency.

Smart city is able to make the city source and infrastructure more efficiently utilized, which improves the quality of life for citizens. In this framework, wireless sensor networks (WSNs) play an important role to collect, process, and analyze the corresponding information. However, the massive deployment of WSNs consumes a significant energy consumption, which has raised the growing demand for green WSNs for smart cities. Exploiting the recent advance in collaborative energy and information transfer to power the WSNs and transmit the data has been considered a promising approach to realize the green WSNs for smart cities. We propose an architecture design of the green WSNs for smart cities, by exploiting the collaborative energy and information transfer protocol, and illustrate the challenging issues in this design. To achieve a green system design, the sensor nodes in WSNs harvest the energy simultaneously with the information decoding (ID) from the received radio frequency signals. Specifically, the energy-constrained sensor nodes partition the received signals into two independent groups to perform energy harvesting (EH) and ID. The sensor nodes then use the harvested energy to amplify and forward the information signals. We study the joint optimization of subcarrier grouping, subcarrier pairing, and power allocation such that the transmission rate performance is maximized with the EH constraint. The joint optimization problem is solved via dual decomposition after transforming it into an equivalent convex optimization problem. Simulation results tested with the real WSNs system data indicate that the performance of our proposed protocol can be significantly improved.

To achieve secure communication between legitimate users, a physical layer encryption scheme based on random rotation of the modulated symbol is proposed. By exploiting the random behavior and the reciprocity property of the wireless channel, the channel state information (CSI) shared between transmitter and legitimate receivers is used as a initial seed to generate chaotic sequence. The transmitter uses the chaotic sequence to rotate the modulated symbol to enhance communication security and to reduce eavesdroppers' ability to demodulated symbols correctly. Due to the fact that the eavesdropper does not posses any information about the legitimate channel because the channel response is unique to the location of the transmitter and receiver as well as the environment, the receiver is able to demodulate the random rotated symbols correctly while the eavesdroppers demodulate them erroneously. Simulation results show that bit-error-rate (BER) of the legal user matches theoretical results perfectly while the eavesdroppers' BER stays around 0.5, which means that the proposed scheme keeps data transmission under security.

Exploiting predicted channel information and designing energy efficient content delivery protocols has started to draw attention, which is referred to as predictive, anticipatory, or context-aware resource allocation. In this paper, we investigate how predicted user rates can be exploited for streaming on-demand mobile video with dynamic adaptive streaming over HTTP(DASH). Specifically, we propose an edge-cloud assisted framework for prediction based DASH streaming; For optimal prediction scenario, we propose a lightweight algorithm to solve it; For imperfect prediction scenario, we model uncertainty in predicted user rates and propose a chance constraint programming method to dynamically allocate the risks, optimize QoE and system efficiency; For the multi-user scenario, we propose a quality-level-aware throughput gain maximization method to improve the network efficiency, fairness and QoE for all users under different prediction error variances; Simulation studies show that our method has a better performance than traditional methods in terms of average QoE, fairness and energy efficiency.

This paper considers the problem of tracking a mobile receiver using signals of Low Earth Orbit (LEO) satellites. Based on Time-Difference of Arrival (TDOA) and Frequency-Difference of Arrival (FDOA), we joint the time synchronization and localization together with a static reference anchor, which has unknown position. In this scenario, the satellites is asynchronous. Considering the time- and frequency- offsets as additional unknown parameters, we proposed a Maximum Likelihood estimation approach to get the reference anchor's location and offsets. Then a sequential estimator jointly track the receiver location, velocity using extended Kalman filter (EKF) after revising the TDOA and FDOA measurements. Simulations demonstrate that our measurement model has a good fit, and our proposed estimator can successfully track both the receiver location, velocity with respect to the reference anchor with good accuracy.

An algorithm of power control in two-tier heterogeneous networks is proposed in this paper. We consider femtocell base stations (FBSs) dense deployment in the macrocell base station (MBS) coverage, the MBS dynamically estimates total uplink interference of femtocell user equipments (FUEs). In order to cope with interference issues, the MBS decides the transmit power of macrocell user equipment (MUE) according to the uplink power budget. In the meanwhile, the interference pricing mechanism is introduced. We assume that the MBS protects itself by pricing the interference on each FUEs, so as to achieve the goal of controlling the interference from FUEs. Simulation results show that the proposed algorithm yields a significant performance improvement in terms of the channel capacity.

The performance of multicell massive multiple‐input multiple‐output (MIMO) systems is heavily affected by pilot contamination. This study considers the problem of pilot contamination and analytical expressions are presented on the normalised mean square error (NMSE) of the minimum mean square error channel estimation algorithm. Based on the NMSE of the massive MIMO systems, a pilot design criterion is proposed to design the optimal pilot sequences for mitigating the pilot contamination. Following this criterion, Chu sequence with perfect auto‐correction and cross‐correlation properties are employed to design the optimal pilot sequences. Then the performance of the proposed pilot design‐based scheme is investigated, and the exact NMSE expressions are presented. The excellent performance of this pilot design scheme has been confirmed in the authors’ simulations.

The correlation coefficient of two consecutive Gaussian sequences is modulated by a binary message bit to achieve a secure communication system. The receiver of the proposed random communication system demodulates the received signal by estimating the correlation coefficient of the transmitted two consecutive sequences. Theoretical bit error rate (BER) expressions in frequency‐flat/‐selective fading channels with/without Doppler shift are derived. Simulation results show that the proposed system can achieve reasonably low BERs in an additive white Gaussian noise channel as well as a Rayleigh fading channel. More importantly, the proposed system shows good performance in resisting eavesdropping, since the transmitted sequence appears to be a Gaussian noise which is almost always inevitable in the process of wireless communications.

Signal classification plays an important role in spectrum sensing for cognitive radios to identify and avoid interference from other wireless devices. In this paper, we study a network of cognitive radios that jointly perform signal classification via cooperation. We propose a simple but effective linear cooperation scheme to fuse pre-processed measurements collected from spatially distributed cognitive radios. Our objective is to maximize the probability of successful classification subject to some constraints on the probabilities of misclassification. By applying a divide-and-conquer strategy and new constraint relaxation methods, we are able to derive the closed-form expressions for the optimal weight coefficient for each contributing cognitive radio. The design of such a cooperative signal classification system is further studied through numerical simulation.

Considering the vehicular networks, multi-hop broadcasting is a frequently used method to deliver messages. Connectivity of wireless multi-hop networks is a critical measure for the planning, design, and evaluation of vehicular ad hoc networks. In an urban environment, vehicles can opportunistically exploit infrastructure through open Access Points (APs) and Road Side Units (RSUs) to efficiently communicate with other vehicles. Infrastructures (i.e., Base Stations (BSs), APs) are uniformly deployed along a road, while vehicles are distributed on the road randomly according to a Poisson distribution. For infrastructure-based vehicular networks, connectivity probability is the probability that an arbitrary vehicle access to the infrastructure. This paper proposes an analytical model to improve the connectivity probability of vehicle and infrastructure through multi-hop broadcasting in the infrastructure-based vehicular networks. We also consider the following factors: propagation distance, one hop transmission range, distribution of vehicles, vehicle density, average length of vehicles, and minimum safety distance between vehicles. The analytical model is validated by simulations.

In this paper, a non-stationary geometry-based stochastic model (GBSM) for high-speed train (HST) MIMO channels is proposed. The proposed model employs geometry-based elliptical scattering model, where the received signal is a superposition of line-of-sight (LOS) and single-bounced rays. The time-varying reference system is introduced to more accurately characterize the non-stationarity of HST MIMO channels caused by the high speed factor. Based on the proposed model, the 2D space cross-correlation function (CCF) and the temporal autocorrelation function (ACF) are derived, simulated under both of isotropic and non-isotropic scattering conditions, and discussed in detail.

In this study, the parameter of a symmetric α ‐stable (S α S) noise sequence is modulated by the binary message sequence to achieve a secure communication system. The characteristic exponent ‘ α ’ of an S α S noise sequence carries the binary information. In order to recover the binary message sequence at the receiver, the authors propose a logarithmic moments estimator to estimate the characteristic exponent ‘ α ’ of the transmitted noise sequence. The optimal decision threshold and the minimum theoretical bit error ratio are derived. It is shown that the simulation results of the presented logarithmic moments estimator are consistent with the analytical results. Moreover, this estimator shows better performance and lower computational complexity than the conventional SINC estimator based on the fractional low‐order moment method.

Orthogonal frequency‐division multiplexing (OFDM) is a widely adopted technique in most wireless applications. It has been shown that zero padded OFDM (ZP‐OFDM) with frequency‐domain oversampling (FDO) can exhibit better performance than cyclic prefix OFDM (CP‐OFDM) if the whole OFDM symbol is oversampled in the frequency domain. In this study, the authors propose a novel FDO‐based scheme for CP‐OFDM, which consists of two stages. First, the conventional CP‐OFDM receiver is adopted to estimate the transmitted data and channel information. Second, the received symbol is reconstructed to reduce inter‐symbol interference and oversampled in the frequency domain. The proposed scheme combines FDO and symbol reconstruction together, which enables the minimum mean square error (MMSE) and zero forcing (ZF) equalisers to exploit the channel information in high‐dimensional non‐orthogonal subcarrier domain and thus significantly suppress the interference on multipath channels. Simulation results show that the proposed FDO‐MMSE CP‐OFDM receiver can achieve much better bit error rate performance than the traditional CP‐OFDM receiver and is comparable to the ZP‐OFDM receiver with FDO.

The pilot spoofing attack is one kind of active eavesdropping activities conducted by a malicious user during the channel training phase. By transmitting the identical pilot (training) signals as those of the legal users, such an attack is able to manipulate the channel estimation outcome, which may result in a larger channel rate for the adversary but a smaller channel rate for the legitimate receiver. With the intention of detecting the pilot spoofing attack and minimizing its damages, we design a two-way training-based scheme. The effective detector exploits the intrusive component created by the adversary, followed by a secure beamforming-assisted data transmission. In addition to the solid detection performance, this scheme is also capable of obtaining the estimations of both legitimate and illegitimate channels, which allows the users to achieve secure communication in the presence of pilot spoofing attack. The detection probability is evaluated based on the derived test threshold at a given requirement on the probability of false alarming. The achievable secrecy rate is utilized to measure the security level of the data transmission. Our analysis shows that even without any pre-assumed knowledge of eavesdropper, the proposed scheme is still able to achieve the maximal secrecy rate in certain cases. Numerical results are provided to show that our scheme could achieve a high detection probability as well as secure transmission.

We study a spoofing attack happened in the physical layer of a multiple-antenna system, where an adversary tries to spoof the transmitter by sending the identical pilot (training) signal as that of a legitimate receiver in the uplink channel estimation phase. This attack, named as pilot spoofing attack, could lead to a secrecy information leakage to the adversary and information rate decrease at the legitimate receiver. Due to the serious results caused by the pilot spoofing attack, we propose an energy-ratio detector (ERD) to protect the legitimate components. The ERD makes the decision by exploiting the asymmetry of the received signal strength (RSS) between the transmitter and the legitimate receiver when the system is under the pilot spoofing attack. Numerical results are presented to illustrate the effectiveness of our proposed detector.

In a time-division duplex (TDD) based multi-input single-output (MISO) system, the channel state information (CSI) can be obtained during the channel estimation phase in which the receiver transmits the pilot symbols to the transmitter. This scheme may suffer from a so-called pilot spoofing attack, i.e., an adversary may send identical pilot signals as those of the legitimate receiver to spoof the transmitter. The resultant channel estimate may then contain the CSI of both legitimate and illegitimate receivers, and if such estimated channel is used for transmit beamforming, the information dedicated for legitimate receiver will leak to the illegitimate receiver. In this paper, we study the detection and defending strategies for such pilot spoofing attack. A two-way training based scheme is proposed. The durations of the training periods are also designed to optimize the secrecy rate. Finally, numerical results are presented to show the performance of our proposed method.

Spectrum utility efficiency is key in designing systems that can meet the heavier demands of bandwidth and data rate of future communication technologies. Shared spectrum techniques and collaborative protocols have thus been studied to better utilize already existing spectrum resources. In this paper, we present a spectrum trading approach that allows the licensed user's (LU) resources to be efficiently shared with the secondary user (SU) network in exchange for a monetary cost. The model is based on demand and supply economics, wherein the highest bidder for spectrum resource is awarded with transmission rights over licensed spectrum. The transmission opportunities for the SU consider every state of the licensed link, in the form of dynamic spectrum access (DSA), spectrum sharing, and relaying, each of which has an optimized cost that will maximize the returns for the LU. The numerical results backed by the analytical study show that this spectrum trading scheme allows for significant improvements in data rate and spectrum transmission opportunities than previous work conducted in either DSA or the spectrum sharing fields.

The pilot spoofing attack is one kind of active eavesdropping conducted by a malicious user during the channel estimation phase of the legitimate transmission. In this attack, an intelligent adversary spoofs the transmitter on the estimation of channel state information (CSI) by sending the identical pilot signal as the legitimate receiver, in order to obtain a larger information rate in the data transmission phase. The pilot spoofing attack could also drastically weaken the strength of the received signal at the legitimate receiver if the adversary utilizes large enough power. Motivated by the serious problems the pilot spoofing attack could cause, we propose an efficient detector, named energy ratio detector (ERD), by exploring the asymmetry of received signal power levels at the transmitter and the legitimate receiver when there exists a pilot spoofing attack. Our analysis shows that by setting the ratio of received signal power levels at the transmitter and the legitimate receiver as the test statistic, the detecting threshold is derived without using the knowledge of the CSI of the legitimate channel as well as the illegitimate channel. Furthermore, we study the performance of the proposed ERD in various special cases in order to obtain useful insights. Numerical results are presented to further demonstrate the performance of our proposed ERD.

In this study, the authors propose an improved frequency‐domain fractionally spaced (FDFS) minimum mean square error (MMSE) receiver for zero‐padded multi‐carrier code division multiple access (MC‐CDMA) systems when the guard interval is not enough to avoid the inter‐symbol interference (ISI) caused by the multipath channel. The proposed novel iterative FDFS‐based receivers firstly reconstruct the received symbol to reduce the ISI and then followed by the FDFS‐based equalisers to minimise the effect of ISI and inter‐carrier interference (ICI) caused by carrier frequency offset (CFO) and Doppler shifts. A few iterations are performed to achieve the expected bit error rate (BER) performance. To reduce the receiver complexity, the novel simplified diagonal FDFS‐based receivers with a fixed noise variance are developed with slight performance degradation. The proposed iterative receivers have never been studied in the existing literature. Simulation results show that the proposed iterative FDFS‐based receivers can significantly improve the BER performance of the conventional FDFS‐MMSE receiver in severe multiple interferences environments caused by multipath, CFO and Doppler shift.

This paper studies spatial capacity in a stochastic wireless ad hoc network. We propose a novel signal-to-interference-ratio (SIR) threshold based scheduling scheme with multi-stage probing and data transmission, where each transmitter iteratively decides to further probe or stay idle, depending on whether the estimated SIR in the proceeding probing is no smaller than a predefined threshold. Though the locations of the initial transmitters can be modeled as a homogeneous Poisson Point Process (PPP), the SIR based scheduling makes the PPP model no longer applicable in the subsequent probing and data transmission phases. We first focus on single-stage probing and find that when the SIR threshold is set sufficiently small to assure an acceptable network interference level, the proposed scheme can greatly outperform the reference scheme without any transmission scheduling in terms of spatial capacity. We clearly characterize the spatial capacity with exact/approximate closed-form expressions, by proposing a new approximate approach to deal with the correlated SIR distributions over non-PPPs. Then, we successfully extend to multi-stage probing, by properly designing the multiple SIR thresholds to assure gradual improvement of the spatial capacity. Furthermore, we analyze the impact of multi-stage probing overhead and present a probing-capacity tradeoff in scheduling design. Finally, extensive numerical results are presented to demonstrate the scheduling performance.

Throughput maximization is considered in this paper for a multiple-input multiple-output (MIMO) cognitive radio system when no cross channel state information is available at the secondary transmitter (ST). We study cognitive beamforming subject to interference expectation constraint and interference outage probability constraint at the primary receiver (PR), respectively. We prove that with the former constraint, the beamforming problem can be formulated as a conventional MIMO transmission problem, which can be easily solved via the water-filling method. With the latter constraint, we find that the interference power is related to the eigenvalues of the covariance matrix of the secondary signals, and thus it can be expressed as a linear combination of multiple chi-square distributed variables. By exploiting the impact of the eigenvalues on the interference outage probability and establishing the relationship between the vibrations of secondary covariance matrix and its eigenvalues, we design an algorithm for this beamforming problem. Finally, simulation results are presented to demonstrate the performance of the proposed beamforming solutions subject to both constraints.

This paper studies the spatial capacity of wireless ad hoc networks. We propose a transmission scheme with threshold-based scheduling, where each transmitter decides to transmit in the data transmission phase if the signal-to-interference-ratio (SIR) at its receiver in the preceding pilot phase is no smaller than a predefined threshold. For comparison, we also consider a reference scheme, where all transmitters transmit independently in both the pilot and data transmission phases. For both schemes, we assume a homogeneous Poisson Point Process (PPP) to model the locations of transmitters that have the intention to transmit. However, for the proposed scheme, the point process formed by the retained transmitters in the data transmission phase is generally not a PPP due to the SIR-based scheduling. First, we show how to set the SIR threshold in the proposed scheme to assure that it outperforms the reference scheme in terms of network spatial capacity. Then, we present exact/approximate spatial capacity expressions for the proposed scheme with different SIR-threshold values. Finally, we provide simulation results to validate our analysis.

Physical layer security in wireless networks has received increasing attention in recent years. In this paper, we consider multiple-input single-output (MISO) fading wiretap channels, where the transmitter utilizes artificial noise-aided precoding (ANaP) transmission strategy to maximize the secrecy capacity of the channel. When the channel state information (CSI) of Eavesdropper's (Eve's) channel is known at Alice, we prove that the optimal ANaP strategy reduces to the conventional precoding strategy, i.e., all the transmit power should be allocated to the precoding of information signal. When the CSI of Eve's channel is unknown at Alice, we find that there exists an optimal power allocation ratio between the information signal and the artificial noise and that this power ratio depends on the number of antennas as well as the available transmit power at Alice. In particular, when the available transmit power at Alice increases or the number of antennas at Alice decreases, more power should be allocated to the artificial noise.

Opportunistic spectrum access (OSA) is a key technique enabling the secondary users (SUs) in a cognitive radio (CR) network to transmit over the "spectrum holes" unoccupied by the primary users (PUs). In this paper, we focus on the OSA design in the presence of reactive PUs, where PU's access probability in a given channel is related to SU's past access decisions. We model the channel occupancy of the reactive PU as a 4-state discrete-time Markov chain. We formulate the optimal OSA design for SU throughput maximization as a constrained finite-horizon partially observable Markov decision process (POMDP) problem. We solve this problem by first considering the conventional short-term conditional collision probability (SCCP) constraint. We then adopt a long-term PU throughput (LPUT) constraint to effectively protect the reactive PU transmission. We derive the structure of the optimal OSA policy under the LPUT constraint and propose a suboptimal policy with lower complexity. Numerical results are provided to validate the proposed studies, which reveal some interesting new tradeoffs between SU throughput maximization and PU transmission protection in a practical interaction scenario.

In this paper, we consider multiple‐input multiple‐output (MIMO) multi‐tone code division multiple access (MT‐CDMA) uplink transmission over multipath fading channels. The zero‐forcing vertical Bell Laboratories layered space‐time architecture (ZF V‐BLAST) algorithm and maximum ratio combining scheme are applied at the receiver. The average bit error rate (BER) expression is derived provided that the number of receive antennas is not less than that of transmit antennas. The BER expression is verified by simulations. Numerical results show that the numbers of transmit and receive antennas have significant effects on the BER performance of the considered system. Spatial and path diversity show different capabilities to improve the BER performance. The MIMO MT‐CDMA system based on the ZF V‐BLAST algorithm is capable of achieving a better BER performance and a higher capacity than the conventional MT‐CDMA system. Copyright © 2011 John Wiley & Sons, Ltd.

In this paper, we consider opportunistic spectrum sharing when the primary system experiences unfavorable channel conditions. In the proposed spectrum sharing protocol, the secondary system tries to help the primary system to achieve its target rate by acting as an amplify-and-forward relay and allocating a fraction of its subcarriers to forward the primary signal. As a reward, the secondary system uses the remaining subcarriers to transmit its own signal, and thus gaining spectrum access. We study the joint optimization of the set of subcarriers used for cooperation, subcarrier pairing and secondary subcarrier power allocation such that the transmission rate of the secondary system is maximized, while helping the primary system, as a higher priority, to achieve its target rate. Simulation results demonstrate that both primary and secondary systems benefit from the proposed opportunistic spectrum sharing protocol.

In this paper, we propose an opportunistic spectrum sharing protocol that exploits the situation when the primary system is incapable of supporting its target transmission rate. Specifically, the secondary system tries to help the primary system to achieve its target rate via two-phase cooperative OFDM relaying, where the secondary system acts as an amplify-and-forward relay for the primary system by allocating a fraction of its subcarriers to forward the primary signal. At the same time, the secondary system uses the remaining subcarriers to transmit its own signal, and thus gaining opportunistic spectrum access. As a part of the protocol, if the primary system finds that outage will occur even when the secondary system serves as a pure relay, the primary system will cease transmission and the secondary system will be granted access to the primary spectrum. We study the joint optimization of the set of subcarriers used for cooperation, subcarrier pairing, and subcarrier power allocation such that the transmission rate of the secondary system is maximized, while helping the primary system, as a higher priority, to achieve its target rate. Simulation results demonstrate the performance of the proposed spectrum sharing protocol as well as the win-win solution for the primary and secondary systems.

ABSTRACT We consider joint power allocation (PA) and relay positioning in a dual‐hop regenerative relay system with multiple antennas equipped at the destination. With a fixed relay location, an adaptive PA strategy at the source and relay under a sum power constraint that minimizes the system outage probability is presented. With a fixed PA strategy, the optimal relay location is derived. We then propose to jointly optimize PA and relay location. It is shown that employing more destination antennas and/or choosing an appropriate relay location can significantly save the power needed at the relay. Numerical results are presented to demonstrate the performance of the proposed PA and relay positioning algorithms. Copyright © 2011 John Wiley & Sons, Ltd.

Prior work in implementing spectrum sharing scenarios for cognitive radio networks has relied on the unlikely assumption of full cross-channel knowledge made available to the cognitive transmitter (CT) and/or the target primary receiver (PR). However, estimation of this cross-channel knowledge is not only important from a practical stand-point, but also in limiting the real interference power felt at the PR due to concurrent CT transmissions. We propose a supervised probing and sensing model, which enables the CT to gain a decent estimate of the cross-channel, assess the spectrum opportunities in its region of interference, and according to its throughput needs safely share or access primary user spectrum. With the probing power increment playing an essential part in the probing model, its optimization with respect to cross-channel estimation success and its influence on mean square error of the cross-channel estimation are discussed.

Opportunistic spectrum access (OSA) is a key technique for the secondary user (SU) in a Cognitive Radio network to transmit over the "spectrum holes" unoccupied by the primary user (PU). Most existing work on the design of OSA has assumed a non-reactive (NR) PU model, i.e., the PU transmission on-off status is independent of the SU access policy, which may not be practical. In this paper, we propose a new Reactive Primary User (RPU) model for the study of OSA, where the PU's access probability over a particular channel is related to the SU's past access history. We model the channel occupancy of the RPU as a 4-state memoryless Markov chain, as opposed to the conventional 2-state (on/off) counterpart, where the expanded state space and state transition probabilities are used to model the reactions of the PU subject to the SU transmit collision. Under this model, we formulate the optimal OSA design for the SU's throughput maximization as a finite-horizon partially observable Markov decision process (POMDP) problem, subject to a conditional collision probability constraint for protecting the PU. Because of the high complexity of the proposed problem, we further propose a separation principle to obtain the optimal policy for the SU with implementable complexity. Numerical results show the new tradeoff between the SU's and the PU's throughput under the RPU model, as compared to the conventional NRPU model.

We analyze the achievable diversity-multiplexing tradeoff (DMT) in MIMO fading channels with two-way channel training. We first consider a typical training scenario, where the transmitter transmits training symbols followed by data symbols, and the receiver performs channel estimation using the training symbols and then uses the imperfect channel estimates to decode the data symbols. It turns out that as long as the training power is equal to the data power, the obtained DMT based on imperfect channel state information at receiver (CSIR) is the same as the original DMT result with perfect CSIR given in Zheng and Tse's seminal work. We further extend our analysis to two-way training scenarios, with single and multiple training rounds. Our results show that two-way training together with power control further significantly improves the achievable diversity gain. In particular, we show that if the multiplexing gain is larger than a certain value, no training can help get any diversity and therefore the achievable diversity is zero.

We investigate the impact of imperfect channel state information at the transmitter (CSIT) on the diversity gain in dynamic decode-and-forward (DF) relaying channels. A diversity and multiplexing tradeoff analysis is presented, which reveals that power control based on imperfect CSIT significantly improves the achievable diversity gain. It is found that if the multiplexing gain is higher than 1/2, the achievable diversity gain only depends on the CSIT of the source-destination (S-D) link and the relay-destination (R-D) link; otherwise the CSIT of the source-relay (S-R) link might also contribute. It is also found that the CSIT of the R-D link does not contribute to the overall diversity gain if the source has no CSIT. The presented results show that dynamic DF relaying supports not only a higher multiplexing gain but also a higher diversity gain than conventional DF relaying protocols.

We analyze the impact of imperfect channel state information at the transmitter and receiver (CSITR) on the achievable diversity gain in multi-input multi-output (MIMO) fading channels with joint rate and power control. With rate control only, we consider the system with and without a minimum spatial multiplexing gain constraint, respectively. With joint rate and power control, we show that the achievable diversity gain can be improved significantly. The conducted analysis adopts the diversity-multiplexing tradeoff framework and uses the notions of diversity gain and multiplexing gain to study the impact of noisy CSITR in MIMO fading channels.

The achievable diversity and multiplexing tradeoff (DMT) in MIMO fading channels with channel training is analyzed in this paper. We first consider a typical training scenario, where the transmitter transmits training symbols followed by data symbols, and the receiver performs channel estimation using the training symbols and then uses the imperfect channel estimates to decode the data symbols. From the DMT perspective, our results show that as long as the training power is equal to the data power, the obtained DMT result based on imperfect channel state information at receiver (CSIR) is the same as the original DMT result with perfect CSIR given in Zheng and Tse's seminal work . We extend the analysis to two-way training scenarios, with single and multiple training rounds. Our results show that two-way training together with power control can substantially improve the achievable diversity gain. Specifically, the achievable DMT with multiple training rounds can be described as a single straight line; while in the case of single training round, the achievable DMT is much lower and can be described as a single straight line or a collection of line segments, depending on the underlying training strategy and channel qualities.

Abstract Automatic modulation classification (MC) is beneficial to digital radio receivers. Conventional MC schemes usually assume that perfect synchronization has been accomplished and then rely on certain statistical characteristics to distinguish different modulation formats. In practice, however, this perfect synchronization assumption is not reasonable since modulation classifiers operate in a non‐cooperative manner and therefore, the receiver has little prior knowledge about a transmitted signal and no training is available. In this paper, we first address asynchronous MC for high‐order QAMs through blind time synchronization. A characteristic function (CF) based approach is proposed to improve the performance of the conventional cumulant method. We then move on to consider asynchronous MC in the presence of frequency offset. We propose a hybrid MC scheme based on blind time synchronization, differential processing, and cumulants to solve this difficult problem. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper, we study the impact of imperfect channel state information at the transmitter and receiver (CSITR) on the diversity-multiplexing tradeoff in single-input multi-output (SIMO)/multi- input single-output (MISO) fading channels with two- way training. We consider two two-way training strategies: 1) the destination initiates the training, and 2) the source initiates the training. We show that for time division duplex (TDD) systems, imperfect CSITR significantly improves the optimal diversity gain through two-way training and power control.

In this paper, we investigate the impact of imperfect channel state information at the transmitters (CSIT) on the achievable diversity gain in a cooperative relaying channel with multiple destination antennas, where the CSIT comes from channel estimation at the transmitters. Both decode-and-forward (DF) and amplify-and-forward (AF) relaying protocols are considered. We show that transmit power control based on the imperfect CSIT significantly improves the achievable diversity gain. The diversity and multiplexing tradeoff (DMT) as a function of the CSIT quality of the source-relay link, source-destination link and relay-destination link is derived for each of the considered relaying schemes. An upper bound on the DMT of the relaying channel is also provided.

Two-way amplify-and-forward relaying is considered in this paper. We propose adaptive power allocation (PA) algorithms to maximize the instantaneous achievable rate and minimize the system outage probability, respectively. Both single- and multi-relay systems are considered. It is shown that the proposed adaptive PA algorithms significantly outperform the uniform PA algorithms. Furthermore, exploiting multiple relays obtains higher diversity order and lower outage probability than using only one relay, at the price of a lower achievable information rate. The relay locations and relay selection are also taken into account to further improve the system performance.

In this paper, we investigate the impact of imperfect channel state information at the transmitter (CSIT) on the fundamental tradeoff of diversity and multiplexing in multi-input multi-output (MIMO) fading channels. We show that through power adaptation, a higher diversity gain as well as a more efficient diversity-multiplexing tradeoff can be achieved than the reported results in literature. Our analysis reveals that imperfect CSIT significantly improves the achievable diversity gain while enjoying the full spatial multiplexing gain.

In this paper, we consider the uplink of a Direct Sequence Code Division Multiple Access (DS-CDMA) system, where the source users cooperate in relaying each other's message to the destination node based on the decode-and-forward relaying protocol. We study the following partner selection strategies: 1. each user helps all other users (All-Cooperate); 2. each user helps all successfully decoded users (Sue-Cooperate); 3. each user helps a user with the maximum observed signal-to-(interference plus noise) ratio (Max-Cooperate). To mitigate the multiple access interference (MAI) due to the usage of non-orthogonal spreading codes, we develop MMSE multiuser detectors at both the cooperative users and the destination node and evaluate the bit-error-rate (BER) performance. Simulation results show that the cooperative relaying significantly outperforms the direct transmission. Among the three partner selection strategies, Max- Cooperate achieves the lowest average BER. It is found that when the number of users increases, the BER performance of direct transmission gets worse, but the BER performance of Max- Cooperate improves significantly. For All-Cooperate and Sue- Cooperate selection strategies, the number of users does not have much impact on the BER performance.

The authors consider a dual-hop multi-relay cooperative relay system in this study. Both decode-and-forward (DF) and amplify-and-forward (AF) protocols are considered. Under different relay selection strategies, the authors derive closed-form outage probability expressions. With the second-order channel statistics, the authors propose to jointly optimise power allocation (PA) and relay positions in order to minimise the system outage probability. Simulation results show that the proposed adaptive allocation algorithms significantly outperform fixed allocation algorithms. With the proposed joint optimisation algorithm, AF relaying outperforms DF relaying when multiple relays are selected to help. When only the best relay is selected to help, DF relaying is shown to have better performance.

Based on the frequency-domain oversampling and minimum mean-square error (MMSE) principles, we propose three linear single-user detectors for downlink multicarrier codedivision multiple-access (MC-CDMA) systems. We begin with an optimal linear MMSE detector, which is computationally demanding. To reduce the complexity, a two-stage MMSE detector and a diagonal one-stage MMSE detector are developed subsequently. Simulation results show that the proposed detectors can efficiently suppress the multiple access interference (MAI) caused by frequency-selective fading, near-far effect, frequency offset, and nonlinear power amplification.

We consider a dual-hop regenerative relay system with multiple antennas at the destination. Subject to a total power constraint, we explore adaptive power allocation between the source and relay to minimize the system outage probability and the average symbol error rate. Simulation results show that the presented adaptive power allocation solutions significantly outperform the uniform power allocation. For a fixed relay location, we show that using two destination antennas brings significant power saving and performance improvement over using one destination antenna, and that using more than two destination antennas does not necessarily bring further performance improvement. It is also found that by using more destination antennas and/or choosing an appropriate relay location, less power will be needed at the relay.

We consider a two-hop relaying system with multiple antennas deployed at the destination node in this paper. The relay node is regenerative and working in half-duplex mode. We propose adaptive power allocation (PA) algorithms under joint transmit power constraint in order to minimize outage probability, instantaneous symbol-error-rate (SER) upper bound and average SER upper bound with the assumption that perfect channel state information (CSI) is known at all nodes. Simulation results show that the proposed adaptive PA algorithms outperform uniform PA and direct transmission. It is also found that the more antennas are deployed at the destination, the less power will be needed to allocate to the relay node in order to satisfy the optimization criteria.

In certain multiple-input multiple-output (MIMO) fading environments, the offered channel capacity can be very low even when the MIMO channels are uncorrelated; this effect has been termed as keyhole or pinhole effect. In this paper, we present rigorous error rate analysis of orthogonal space time block codes in Nakagami-m keyhole channels with arbitrary fading parameters. Using a moment-generation-function based approach, we provide the exact expressions of symbol error rate. Furthermore, the closed-form asymptotic expressions are derived, based on which one can easily quantify the diversity gain of the considered system with arbitrary m-fading parameters while existing methods in literature fail to do so. Simulation results are also provided to verify our analytical results.

Conventional modulation classification (MC) schemes usually assume that perfect synchronization has been accomplished and then rely on certain statistical characteristics to distinguish different modulation formats. In practice, however, this perfect synchronization assumption is not reasonable since modulation classifiers operate in a non-cooperative manner and therefore, the receiver has little prior knowledge about the transmitted signals and no training is available. In this paper, we first address asynchronous MC for high-order QAMs through blind time synchronization. A characteristic function (CF) based approach is proposed to improve the performance of the conventional cumulant method. We then move on to consider asynchronous MC with frequency offset. We propose a hybrid MC scheme based on blind time synchronization, differential processing, and cumulants to solve this difficult problem.

Space-time block codes are known to be a powerful tool to achieve full spatial diversity gain while maintaining a very simple receiver structure. In this paper, the authors analyze the asymptotic error performance of orthogonal space-time block codes over flat Nakagami-m fading channels. Both identical and non-identical m-distributions are considered. Based on the moment generation function (MGF) approach, the closed-form asymptotic symbol error probabilities over Nakagami-m fading channels are derived. Compared to the previous works in literature, our results are more accurate and more general. Simulation results are provided to verify our analytical results.

It has been shown recently that multiple-input multiple-output (MIMO) fading channels could experience a keyhole phenomenon, under which the performance of MIMO systems will be degraded in terms of link quality as well as capacity. In this paper, the performance of orthogonal space-time block codes in MIMO fading channels under keyhole condition is analyzed. Closed-form expressions for the error probability of space-time block codes as well as diversity gains of keyhole channels are derived. We prove that the maximum spatial diversity gain of a keyhole channel with to transmit and n receive antennas is min(m, n) when m ne n. In the case of m = n, the achievable diversity gain is less than n but higher than n - 1. Accordingly, for space-time block codes in keyhole channels, there are two forms of error rate expressions for m ne n and m = n, respectively. Furthermore, coding gains of various space-time block codes in keyhole channels are obtained. Simulation results are also provided to demonstrate the accuracy of our analytical results.

Space-time block coding has recently been proposed as one of most promising transmission techniques over multiple-input multiple-output (MIMO) fading channels. In independent identically distributed Rayleigh fading channels, orthogonal space-time block codes are able to provide full diversity while requiring extremely simple encoding and decoding. However, keyhole effects in realistic MIMO fading channel severely degrade the performance of MIMO systems in terms of link quality as well as channel capacity. In this paper, performance of orthogonal space-time block codes in MIMO fading channels under keyhole condition is analyzed systematically. Achievable diversity gains of keyhole channels are derived as well as closed-form expressions for error rate of space-time block codes. Simulation results are also provided to verify our analytical results.

Based on orthogonal designs, space-time (ST) block codes that enable full diversity as well as a simple maximum likelihood (ML) decoding algorithm at the decoder can be constructed for more than two transmit antennas. For real constellations (such as PAM), ST block codes with transmission rate 1 can be designed from the real Hurwitz-Radon families for any number of transmit antennas. However, for complex constellations (such as M-PSK or M-PAM), ST block codes for more than two transmit antennas can only be constructed with rates less than 1. Previous attempts have been concentrated on complex orthogonal designs that provide ST block codes with full diversity and high transmission rates. In this paper, we present two rate 2/3 complex ST block codes from orthogonal designs for five and six transmit antennas, respectively.

We propose an efficient space-frequency coded orthogonal frequency-division multiplexing (OFDM) system for high-speed transmission over wireless links. The analytical expression for the pairwise probability of the proposed space-frequency coded OFDM system is derived in slow, space- and frequency-selective fading channels. The design criteria of trellis codes used in the proposed system are then developed and discussed. It is shown that the proposed space-frequency coded OFDM can efficiently achieve the full diversity provided by the fading channel with low trellis complexity, while for traditional space-frequency coded OFDM systems, we need to design space-time trellis codes with high trellis complexity to exploit the maximum achievable diversity order. The capacity properties of space-frequency coded OFDM over multipath fading channels are also studied. Numerical results are provided to demonstrate the significant performance improvement obtained by the proposed space-frequency coded OFDM scheme, as well as the excellent outage capacity properties.

In this article, channel estimation for space-time coded orthogonal-frequency division multiplexing (OFDM) systems is considered. By assuming that the channel frequency response is quasi-static over two consecutive OFDM symbols, we develop channel parameter estimators based on the use of space-time block coded (STBC) training blocks. Using an STBC training pattern, a low-rank Wiener filter-based channel estimator with a significant complexity reduction is proposed. A simplified approach for the optimal low-rank estimator is also proposed to further reduce the estimator complexity while retaining an accurate frequency domain channel estimation. Numerical results are provided to demonstrate the performance of the proposed low complexity channel estimators for space-time trellis coded OFDM systems.

A space-frequency coded OFDM system for high-speed transmission over wireless links is proposed. The analytical expression for the error event probability of such space-frequency coded OFDM system is derived in slow, spatial and frequency selective fading channels. The design criteria of TCM codes for the proposed system are then developed and discussed. It is shown that the proposed space-frequency coded OFDM with low trellis complexity can efficiently exploit the available diversity resources of the fading channel, while in conventional space-frequency coded OFDM systems, space-time trellis codes with very high trellis complexity are required to make full use of the diversity resources. Simulation results show that at a FER of 10/sup -2/, the proposed scheme outperforms the traditional space-frequency coded OFDM by 2-4 dB at an equal trellis complexity and spectral efficiency.

Trellis coded modulation (TCM) is a bandwidth efficient transmission scheme that can achieve high coding gain by integrating coding and modulation. This paper presents an analytical expression for the error event probability of concatenated space-time block coding with TCM which reveals some dominant factors affecting the system performance over slow fading channels when perfect interleavers are used. This leads to establishing the design criteria for constructing the optimal trellis codes of such a concatenated system over slow flat fading channels. Through simulation, significant performance improvement is shown to be obtained by concatenating the interleaved streams of these codes with space-time block codes over fading channels. Simulation results also demonstrate that these trellis codes have better error performance than traditional codes designed for single-antenna Gaussian or fading channels. Performance results over quasi-static fading channels without interleaving are also compared in this paper. Furthermore, it is shown that concatenated space-time block coding with TCM (with/without interleaving) outperforms space-time trellis codes under the same spectral efficiency, trellis complexity, and signal constellation.

In this paper, we propose and analyze an efficient space-frequency-time coded OFDM technique for high-speed transmission over wireless links. Joint use of space-time trellis coding and space-time block coding is considered. It is shown that the maximum achievable diversity order increases linearly not only with the number of transmit and receive antennas but also with the number of multiple paths provided that a proper joint coding scheme is employed. Because of the orthogonality of space-time block codes, space-time trellis codes with low trellis complexity can be used to efficiently achieve the full diversity provided by the fading channel. This is in contrast to traditional space-frequency coded OFDM systems, where we need to design space-time trellis codes with very high trellis complexity to exploit the maximum achievable diversity of the fading channel. Numerical results are provided to demonstrate the significant performance improvement achieved by the proposed space-frequency-time coded OFDM technique.

To provide high-speed data transmission for the next generation wireless communication systems employing multiple transmit antennas, a space-time coded orthogonal frequency division multiplexing (OFDM) system has previously been proposed as a promising and efficient approach with both transmit diversity and high coding gains. In this paper, we study the channel estimation for spacetime coded OFDM using least squares (LS) and minimum mean-squared error (MMSE) criteria. In particular, the MSE bounds are given for the temporal channel estimations. By assuming that the channel frequency response is quasi-static over two consecutive OFDM symbols, a low rank Wiener filter based channel estimator is proposed. This significantly reduces the computation complexity while retaining accurate frequency domain channel estimation. Numerical results are provided to demonstrate the performance of the low-rank frequency-domain channel estimation for space-time coded OFDM systems.

Trellis coded modulation (TCM) is a bandwidth efficient transmission scheme that can achieve high coding gain by integrating coding and modulation. This paper presents an analytical expression for the error event probability of TCM with transmit diversity (Realized by space-time block coding) which reveals some dominant factors affecting the system performance over slow fading channels when perfect interleavers are used. This leads to establishing the design criteria for constructing optimal trellis codes for use with space-time block coding over flat fading channels. These results also apply to fast fading channels where the maximum Doppler spread normalized by the symbol rate is of the order of 10/sup -2/. Through simulation, significant performance improvement is shown to be achieved by concatenating the interleaved streams of these codes with space-time block codes over fading channels. Simulation results also demonstrate that these outer codes have better error performance than codes of the same number of states designed for Gaussian or fading channels without the use of transmit diversity.

This paper investigates the use of space-time (ST) coding for high-speed data transmission, as well as studies the effect of time delay spread on such scheme over unequalized fading channels. Using a random variable decomposition technique, we present an analytical model and obtain an approximate bound of the pairwise-error probability for ST coded systems over multipath and time-dispersive fading channels. It is shown that the presence of multipath does not reduce the diversity gain provided by the original design criteria, which is adopted to construct specific ST codes in quasi-static flat fading, but the coding gain diminishes due to the effect of multipath fading.