This paper presents Multimodal-Wireless, a large-scale open-source dataset for multimodal sensing and communication research. The dataset is generated through an integrated and customizable data pipeline built upon the CARLA simulator and Sionna framework, and features high-resolution communication channel state information (CSI) fully synchronized with five other sensor modalities, namely LiDAR, RGB and depth camera, inertial measurement unit (IMU) and radar, all sampled at 100 Hz. It contains approximately 160,000 frames collected across four virtual towns, sixteen communication scenarios, and three weather conditions. This paper provides a comprehensive overview of the dataset, outlining its key features, overall framework, and technical implementation details. In addition, it explores potential research applications concerning communication and collaborative perception, exemplified by beam prediction using a multimodal large language model. The dataset is open in https://le-liang.github.io/mmw/.

Decentralized federated learning (DFL) is an emerging paradigm to enable edge devices collaboratively training a learning model using a device-to-device (D2D) communication manner without the coordination of a parameter server (PS). Aggregation weights, also known as mixing weights, are crucial in DFL process, and impact the learning efficiency and accuracy. Conventional design relies on a so-called central entity to collect all local information and conduct system optimization to obtain appropriate weights. In this paper, we develop a distributed aggregation weight optimization algorithm to align with the decentralized nature of DFL. We analyze convergence by quantitatively capturing the impact of the aggregation weights over decentralized communication networks. Based on the analysis, we then formulate a learning performance optimization problem by designing the aggregation weights to minimize the derived convergence bound. The optimization problem is further transformed as an eigenvalue optimization problem and solved by our proposed subgradient-based algorithm in a distributed fashion. In our algorithm, edge devices only need local information to obtain the optimal aggregation weights through local (D2D) communications, just like the learning itself. Therefore, the optimization, communication, and learning process can be all conducted in a distributed fashion, which leads to a genuinely distributed DFL system. Numerical results demonstrate the superiority of the proposed algorithm in practical DFL deployment.

For millimeter wave communications, beams used to transmit synchronization signal blocks (SSBs) affect both base station coverage and beam training overhead. We therefore consider fair SSB codebook design, formulated as an optimization problem, aiming to maximize the minimum average signal-to-interference-plus-noise ratio (SINR) across user clusters. This problem is challenging due to the non-smoothness of the objective function, arising from the optimal beam-pair selection function and the minimum operator. To address this, we propose a double-loop framework, where the outer loop constructs approximations for the selection function with iteratively reduced error, and the inner loop solves the resulting approximate problems. In each inner-loop iteration, the objective function of the approximate problem is smoothed with iteratively reduced smoothness, enabling gradient derivation. This gradient is then estimated using variance-reduced estimators based on samples from users, and the result is used to update codebooks. Following this framework, we develop both first-order (FO) and zeroth-order (ZO) oracle schemes. The FO scheme requires full channel state information samples for gradient estimation while the ZO scheme only requires SINR samples. Simulation results show that in given scenarios, both schemes achieve SINR fairness comparable to or better than that of discrete Fourier transform codebooks, but with fewer beams.

In this paper, we investigate receiver design for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications. We first establish a modified beam based channel model (BBCM) by performing uniform sampling for directional cosine with deterministic sampling interval, where the beam matrix is constructed using a phase-shifted discrete Fourier transform (DFT) matrix. Based on the modified BBCM, we propose a beam structured turbo receiver (BSTR) involving low-dimensional beam domain signal detection for grouped user terminals (UTs), which is proved to be asymptotically optimal in terms of minimizing mean-squared error (MSE). Moreover, we extend it to windowed BSTR by introducing a windowing approach for interference suppression and complexity reduction, and propose a well-designed energy-focusing window. We also present an efficient implementation of the windowed BSTR by exploiting the structure properties of the beam matrix and the beam domain channel sparsity. Simulation results validate the superior performance of the proposed receivers but with remarkably low complexity.

In this paper, we propose a low complexity turbo receiver for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) systems. We first introduce the beam based channel model (BBCM) with uniform sampling for directional cosine. By leveraging the BBCM, we reveal the interference sparsity of HF skywave massive MIMO systems, which is defined as the asymptotic sparsity of the channel Gram matrix. Exploiting the interference sparsity, we provide a condition of extracting sufficient observation for signal detection. Motivated by this condition, we construct the interference user terminal (UT) set (IUS) and extract the observation vector from the received signal after matched filtering (MF) for each UT. Then, a low-dimensional interference sparsity-aware detector (ISD) is separately designed for each UT by minimizing the mean-squared error (MSE), and the interference sparsity-aware turbo receiver (ISTR) is subsequently formulated using ISDs. Under a relaxed version of the condition for sufficient observation selection, we prove the optimality of the ISTR. Further, we develop an efficient implementation of the ISTR, involving approximate computation of the ISD, the signal reconstructed by ISD and the channel Gram matrix. Moreover, an efficient construction of IUS using the statistical channel state information (CSI) is also proposed. Simulation results confirm that the proposed ISTR achieves excellent performance with relatively low complexity.

In this paper, we propose a robust semantic communication system for speech transmission, named Ross-S2T, by delivering the essential semantic information. Specifically, we consider the speech-to-text translation (S2TT) as the transmission goal. First, a new deep semantic encoder is developed to convert speech in the source language to textual features associated with the target language, facilitating the end-to-end semantic exchange to perform the S2TT task and reducing the transmission data without performance degradation. To mitigate semantic impairments inherent in the corrupted speech, a novel generative adversarial network (GAN)-enabled deep semantic compensator is established to estimate the lost semantic information within the speech and extract deep semantic features simultaneously, which enables robust semantic transmission for corrupted speech. Furthermore, a semantic probe-aided compensator is devised to enhance the semantic fidelity of recovered semantic features and improve the understandability of the target text. According to simulation results, the proposed Ross-S2T exhibits superior S2TT performance compared to conventional approaches and high robustness against semantic impairments.

In this paper, we investigate receiver design for high frequency (HF) skywave massive multiple-input multipleoutput (MIMO) communications. We first establish a modified beam based channel model by performing uniform sampling for directional cosine with deterministic sampling interval, where the beam matrix is constructed as discrete Fourier transform (DFT)based structure. Rooted in the modified beam based channel model, we propose a beam structured turbo receiver (BSTR) involving low-dimensional beam structured signal detection for grouped user terminals (UTs). Then we present efficient implementation of the BSTR by exploiting the structure properties of the beam matrix and the beam domain channel sparsity. Simulation results validate the superior performance of the proposed BSTR with low complexity.

In this paper, we introduce a novel framework consisting of hybrid bit-level and generative semantic communications for efficient downlink image transmission within space-air-ground integrated networks (SAGINs). The proposed model comprises multiple low Earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and ground users. Considering the limitations in signal coverage and receiver antennas that make the direct communication between satellites and ground users unfeasible in many scenarios, thus UAVs serve as relays and forward images from satellites to the ground users. Our hybrid communication framework effectively combines bit-level transmission with several semantic-level image generation modes, optimizing bandwidth usage to meet stringent satellite link budget constraints and ensure communication reliability and low latency under low signal-to-noise ratio (SNR) conditions. To reduce the transmission delay while ensuring reconstruction quality for the ground user, we propose a novel metric to measure delay and reconstruction quality in the proposed system, and employ a deep reinforcement learning (DRL)-based strategy to optimize resource allocation in the proposed network. Simulation results demonstrate the superiority of the proposed framework in terms of communication resource conservation, reduced latency, and maintaining high image quality, significantly outperforming traditional solutions. Therefore, the proposed framework can ensure the real-time image transmission requirements in SAGINs, even under dynamic network conditions and user demand.

Satellite communications can provide massive connections and seamless coverage, but they also face several challenges, such as rain attenuation, long propagation delays, and co-channel interference. To improve transmission efficiency and address severe scenarios, semantic communication has become a popular choice, particularly when equipped with foundation models (FMs). In this study, we introduce an FM-based semantic satellite communication framework, termed FMSAT. This framework leverages FM-based segmentation and reconstruction to significantly reduce bandwidth requirements and accurately recover semantic features under high noise and interference. Considering the high speed of satellites, an adaptive encoder-decoder is proposed to protect important features and avoid frequent retransmissions. Meanwhile, a well-received image can provide a reference for repairing damaged images under sudden attenuation. Since acknowledgment feedback is subject to long propagation delays when retransmission is unavoidable, a novel error detection method is proposed to roughly detect semantic errors at the regenerative satellite. With the proposed detectors at both the satellite and the gateway, the quality of the received images can be ensured. The simulation results demonstrate that the proposed method can significantly reduce bandwidth requirements, adapt to complex satellite scenarios, and protect semantic information with an acceptable transmission delay.

As a promising paradigm for the sixth-generation (6G) networks, task-oriented semantic communication significantly enhances transmission efficiency. However, it faces complex security challenges, particularly the risk of eavesdropping due to the open nature of wireless channels. To address this issue, we propose a secure semantic communication framework that integrates physical-layer secure beamforming (SBF) to safeguard semantic information from eavesdropping. Specifically, we design an SBF network to generate SBF vectors that focus signal beams on legitimate users to enhance signal power while directing designed artificial noise toward potential eavesdroppers to strengthen jamming. To further improve system adaptability across varying channel conditions, we introduce attention-based channel-aware modules that dynamically optimize the encoding, decoding, and beamforming processes based on perceived channel state information. Furthermore, task-oriented artificial noise is employed to degrade the task performance of eavesdroppers more effectively. Finally, a stepwise training strategy with task-specific loss functions is employed to jointly optimize the SBF and semantic modules, maximizing the performance gap in downstream tasks between legitimate users and eavesdroppers. The simulation results demonstrate that the proposed approach effectively maintains task performance for legitimate users while significantly suppressing eavesdroppers, outperforming conventional methods.

With the development of computer vision, 3D object detection has become increasingly important in many real-world applications. Limited by the computing power of sensor-side hardware, the detection task is sometimes deployed on remote computing devices or the cloud to execute complex algorithms, which brings massive data transmission overhead. In response, this paper proposes an optical flow-driven semantic communication framework for the stereo-vision 3D object detection task. The proposed framework fully exploits the dependence of stereo-vision 3D detection on semantic information in images and prioritizes the transmission of this semantic information to reduce total transmission data sizes while ensuring the detection accuracy. Specifically, we develop an optical flow-driven module to jointly extract and recover semantics from the left and right images to reduce the loss of the left-right photometric alignment semantic information and improve the accuracy of depth inference. Then, we design a 2D semantic extraction module to identify and extract semantic meaning around the objects to enhance the transmission of semantic information in the key areas. Finally, a fusion network is used to fuse the recovered semantics, and reconstruct the stereo-vision images for 3D detection. Simulation results show that the proposed method improves the detection accuracy by nearly 70% and outperforms the traditional method, especially for the low signal-to-noise ratio regime.

In the domain of reconfigurable intelligent surface (RIS)-assisted wireless communications, efficient channel state information (CSI) feedback is crucial. This paper proposes RIS-CoCsiNet, a novel deep learning-based framework aimed at significantly enhancing feedback efficiency. The proposed method leverages the inherent correlation among neighboring user equipments (UEs) by categorizing RIS-UE CSI information into two parts: shared information among nearby UEs and unique information specific to each individual UE. By exploiting the correlation in RIS-UE CSI, redundant transmission of shared information can be substantially reduced, thereby minimizing the overhead associated with repeatedly feeding back this shared data. Unlike conventional autoencoder-based CSI feedback frameworks, our approach incorporates an additional decoder and a combination neural network (NN) at the base station. These components recover the shared information from the feedback CSI of two neighboring UEs and combine it with the individual information, respectively, without requiring any modifications at the UEs. Through end-to-end learning, the encoders at neighboring UEs are trained to collaboratively feedback shared information while independently feeding back the unique information. For UEs equipped with multiple antennas, a baseline NN architecture with long short-term memory (LSTM) modules is introduced to capture the correlation among nearby antennas. Additionally, since the RIS-UE CSI phase is not sparse, we propose magnitude-dependent phase feedback strategies that incorporate statistical or instantaneous CSI magnitude information into the phase feedback process. Extensive simulations across two diverse channel datasets validate the effectiveness of RIS-CoCsiNet.

In this paper, we consider a radio resource management (RRM) problem in the dynamic wireless networks, comprising multiple communication links that share the same spectrum resource. To achieve high network throughput while ensuring fairness across all links, we formulate a resilient power optimization problem with per-user minimum-rate constraints. We obtain the corresponding Lagrangian dual problem and parameterize all variables with neural networks, which can be trained in an unsupervised manner due to the provably acceptable duality gap. We develop a meta-learning approach with graph neural networks (GNNs) as parameterization that exhibits fast adaptation and scalability to varying network configurations. We formulate the objective of meta-learning by amalgamating the Lagrangian functions of different network configurations and utilize a first-order meta-learning algorithm, called Reptile, to obtain the meta-parameters. Numerical results verify that our method can efficiently improve the overall throughput and ensure the minimum rate performance. We further demonstrate that using the meta-parameters as initialization, our method can achieve fast adaptation to new wireless network configurations and reduce the number of required training data samples.

In this paper, we investigate precoder design for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. We first reveal the effect of the precoder on the effective channel at receivers and formulate the precoder design for a group of subcarriers as a sum-rate maximization problem, where the delay spread of the effective channel is constrained to maintain its smoothness. Then with the beam based channel model and beam domain channel sparsity, the design of space domain precoders for a group of subcarriers are transformed into that of a space-frequency (SF) beam domain vector and the resulting space domain precoder at each subcarrier is beam structured. Efficient calculation for design and implementation of the beam structured precoder (BSP) is proposed. Moreover, effective channel estimation with the BSP is discussed. Simulation results show that the proposed BSP can enhance the effective channel estimation performance and significantly improve the system performance.

Integrated sensing and communication (ISAC) networks leverage extremely large-scale antenna arrays and high frequencies. This inevitably extends the Rayleigh distance, making near-field (NF) spherical wave propagation dominant. This unlocks numerous spatial degrees of freedom, raising the challenge of optimizing them for communication and sensing tradeoffs. To this end, we propose a rate-splitting multiple access (RSMA)-based NF-ISAC transmit scheme utilizing hybrid analog-digital antennas. RSMA enhances interference management, while a variable number of dedicated sensing beams adds beamforming flexibility. The objective is to maximize the minimum communication rate while ensuring multi-target sensing performance by jointly optimizing receive filters, analog and digital beamformers, common rate allocation, and the sensing beam count. To address uncertainty in sensing beam allocation, a rank-zero solution reconstruction method demonstrates that dedicated sensing beams are unnecessary for NF multi-target detection. A penalty dual decomposition (PDD)-based double-loop algorithm is introduced, employing weighted minimum mean-squared error (WMMSE) and quadratic transforms to reformulate communication and sensing rates. Simulations reveal that the proposed scheme: 1) achieves performance comparable to fully digital beamforming with fewer RF chains, 2) maintains NF multi-target detection without compromising communication rates, and 3) significantly outperforms conventional multiple access schemes and far-field ISAC systems.

In this brief, we propose a deep learning-based performance testing framework to minimize the number of required test modules while guaranteeing the accuracy requirement, where a test module corresponds to a combination of one circuit and one stimulus. First, we apply a deep neural network (DNN) to establish the mapping from the response of the circuit under test (CUT) in each module to all specifications to be tested. Then, the required test modules are selected by solving a 0–1 integer programming problem. Finally, the predictions from the selected test modules are combined by a DNN to form the specification estimations. The simulation results validate the proposed approach in terms of testing accuracy and cost.

For high-speed train (HST) millimeter wave (mmWave) communications, the use of narrow beams with small beam coverage needs frequent beam switching, while wider beams with small beam gain leads to weaker mmWave signal strength. In this paper, we consider beam switching based beam design, which is formulated as an optimization problem aiming to minimize the number of switched beams within a predetermined railway range subject to that the receiving signal-to-noise ratio (RSNR) at the HST is no lower than a predetermined threshold. To solve this problem, we propose two sequential beam design schemes, both including two alternately-performed stages. In the first stage, given an updated beam coverage according to the railway range, we transform the problem into a feasibility problem and further convert it into a min-max optimization problem by relaxing the RSNR constraints into a penalty of the objective function. In the second stage, we evaluate the feasibility of the beamformer obtained from solving the min-max problem and determine the beam coverage accordingly. Simulation results show that compared to the first scheme, the second scheme can achieve 96.20% reduction in computational complexity at the cost of only 0.0657% performance degradation.

Cell-free integrated sensing and communication (CF-ISAC) systems are just emerging as an interesting technique for future communications. Such a system comprises several multiple-antenna access points (APs), serving multiple single-antenna communication users and sensing targets. However, efficient beamforming designs that achieve high precision and robust performance in densely populated networks are lacking. This paper proposes a new beamforming algorithm by exploiting the inherent Riemannian manifold structure. The aim is to maximize the communication sum rate while satisfying sensing beampattern gains and per AP transmit power constraints. To address this constrained optimization problem, a highly efficient augmented Lagrangian model-based iterative manifold optimization for the CF-ISAC (ALMCI) algorithm is developed. This algorithm exploits the geometry of the proposed problem and uses a complex oblique manifold. Conventional convex-concave procedure (CCPA) and multidimensional complex quadratic transform (MCQT)-SCA algorithms are also developed as comparative benchmarks. The ALMCI algorithm significantly outperforms both of these. For example, with 16 APs having 12 antennas and 30 dBm transmit power each, our proposed ALMCI algorithm yields 22.7 % and 6.7 % sum rate gains over the CCPA and MCQT-SCA algorithms, respectively. In addition to improvement in communication capacity, the ALMCI algorithm achieves superior beamforming gains and reduced complexity.

Recently, end-to-end ISAC networks have attracted wide attention for their potential in significant performance improvement through joint optimisation. As an important component in ISAC networks, a fast and accurate multi-target detection network is critical. In this paper, we propose a spectral and spatial transformer to exploit the angle information by spectral-wise attention and the range/Doppler information by spatial-wise attention. Regarding multi-target detection as a set prediction problem, we propose a first-match-first-out loss to stabilize the training process. Experiments show our method performs better than two-stage methods and obtain comparable performance with joint estimation methods.

Resource allocation significantly impacts the performance of vehicle-to-everything (V2X) networks. Most existing algorithms for resource allocation are based on optimization or machine learning (e.g., reinforcement learning). In this paper, we explore resource allocation in a V2X network under the framework of federated reinforcement learning (FRL). On one hand, the usage of RL overcomes many challenges from the model-based optimization schemes. On the other hand, federated learning (FL) enables agents to deal with a number of practical issues, such as privacy, communication overhead, and exploration efficiency. The framework of FRL is then implemented by the in-exact alternative direction method of multipliers (ADMM), where subproblems are solved approximately using policy gradients and their second moments. The developed algorithm, FRLPGiA, has a nice numerical performance compared with some baseline methods for solving the resource allocation problem in a V2X network.

Decentralized federated learning (DFL) has gained popularity due to its practicality across various applications. Compared to the centralized version, training a shared model among a large number of nodes in DFL is more challenging, as there is no central server to coordinate the training process. Especially when distributed nodes suffer from limitations in communication or computational resources, DFL will experience extremely inefficient and unstable training. Motivated by these challenges, in this paper, we develop a novel algorithm based on the framework of the inexact alternating direction method (iADM). On one hand, our goal is to train a shared model with a sparsity constraint. This constraint enables us to leverage one-bit compressive sensing (1BCS), allowing transmission of one-bit information among neighbour nodes. On the other hand, communication between neighbour nodes occurs only at certain steps, reducing the number of communication rounds. Therefore, the algorithm exhibits notable communication efficiency. Additionally, as each node selects only a subset of neighbours to participate in the training, the algorithm is robust against stragglers. Additionally, complex items are computed only once for several consecutive steps and subproblems are solved inexactly using closed-form solutions, resulting in high computational efficiency. Finally, numerical experiments showcase the algorithm's effectiveness in both communication and computation.

The metaverse is expected to provide immersive entertainment, education, and business applications. However, virtual reality (VR) transmission over wireless networks is data- and computation-intensive, making it critical to introduce novel solutions that meet stringent quality-of-service requirements. With recent advances in edge intelligence and deep learning, we have developed a novel multi-view synthesizing framework that can efficiently provide computation, storage, and communication resources for wireless content delivery in the metaverse. We propose a three-dimensional (3D)-aware generative model that uses collections of single-view images. These single-view images are transmitted to a group of users with overlapping fields of view, which avoids massive content transmission compared to transmitting tiles or whole 3D models. We then present a federated learning approach to guarantee an efficient learning process. The training performance can be improved by characterizing the vertical and horizontal data samples with a large latent feature space, while low-latency communication can be achieved with a reduced number of transmitted parameters during federated learning. We also propose a federated transfer learning framework to enable fast domain adaptation to different target domains. Simulation results have demonstrated the effectiveness of our proposed federated multi-view synthesizing framework for VR content delivery.

The combination of terahertz and massive multiple-input multiple-output (MIMO) is promising for meeting the increasing data rate demand of future wireless communication systems thanks to the significant bandwidth and spatial degrees of freedom. However, unique channel features, such as the near-field beam split effect, make channel estimation particularly challenging in terahertz massive MIMO systems. On one hand, adopting the conventional angular domain transformation dictionary designed for low-frequency far-field channels will result in degraded channel sparsity and destroyed sparsity structure in the transformed domain. On the other hand, most existing compressive sensing based channel estimation algorithms cannot achieve high performance and low complexity simultaneously. To alleviate these issues, in this study, we first adopt frequency-dependent near-field dictionaries to maintain good channel sparsity and sparsity structure in the transformed domain under the near-field beam split effect. Then, a deep unfolding based wideband terahertz massive MIMO channel estimation algorithm is proposed. In each iteration of the approximate message passing-sparse Bayesian learning algorithm, the optimal update rule is learned by a deep neural network (DNN), whose architecture is customized to effectively exploit the inherent channel patterns. Furthermore, a mixed training method based on novel designs of the DNN architecture and the loss function is developed to effectively train data from different system configurations. Simulation results validate the superiority of the proposed algorithm in terms of performance, complexity, and robustness.

Human-oriented image communication should take the quality of experience (QoE) as an optimization goal, which requires effective image perceptual quality metrics. However, traditional user-based assessment metrics are limited by the deviation caused by human high-level cognitive activities. To tackle this issue, in this paper, we construct a brain response-based image perceptual quality metric and develop a brain-inspired network to assess the image perceptual quality based on it. Our method aims to establish the relationship between image quality changes and underlying brain responses in image compression scenarios using the electroencephalography (EEG) approach. We first establish EEG datasets by collecting the corresponding EEG signals when subjects watch distorted images. Then, we design a measurement model to extract EEG features that reflect human perception to establish a new image perceptual quality metric: EEG perceptual score (EPS). To use this metric in practical scenarios, we embed the brain perception process into a prediction model to generate the EPS directly from the input images. Experimental results show that our proposed measurement model and prediction model can achieve better performance. The proposed brain response-based image perceptual quality metric can measure the human brain's perceptual state more accurately, thus performing a better assessment of image perceptual quality.

Artificial intelligence (AI) and machine learning (ML) have shown tremendous potential in reshaping the landscape of wireless communications and are, therefore, widely expected to be an indispensable part of the next-generation wireless network. This article presents an overview of how AI/ML and wireless communications interact synergistically to improve system performance and provides useful tips and tricks on realizing such performance gains when training AI/ML models. In particular, we discuss in detail the use of AI/ML to revolutionize key physical layer and lower medium access control (MAC) layer functionalities in traditional wireless communication systems. In addition, we provide a comprehensive overview of the AI/ML-enabled semantic communication systems, including key techniques from data generation to transmission. We also investigate the role of AI/ML as an optimization tool to facilitate the design of efficient resource allocation algorithms in wireless communication networks at both bit and semantic levels. Finally, we analyze major challenges and roadblocks in applying AI/ML in practical wireless system design and share our thoughts and insights on potential solutions.

The data heterogeneity across clients and the limited communication resources, e.g., bandwidth and energy, are two of the main bottlenecks for wireless federated learning (FL). To tackle these challenges, we first devise a novel FL framework with partial model aggregation (PMA). This approach aggregates the lower layers of neural networks, responsible for feature extraction, at the parameter server while keeping the upper layers, responsible for complex pattern recognition, at clients for personalization. The proposed PMA-FL is able to address the data heterogeneity and reduce the transmitted information in wireless channels. Then, we derive a convergence bound of the framework under a non-convex loss function setting to reveal the role of unbalanced data size in the learning performance. On this basis, we maximize the scheduled data size to minimize the global loss function through jointly optimize the client selection, bandwidth allocation, computation and communication time division policies with the assistance of Lyapunov optimization. Our analysis reveals that the optimal time division is achieved when the communication and computation parts of PMA-FL have the same power. We also develop a bisection method to solve the optimal bandwidth allocation policy and use the set expansion algorithm to address the client scheduling policy. Compared with the benchmark schemes, the proposed PMA-FL improves 3.13% and 11.8% absolute accuracy on two typical datasets with heterogeneous data distribution settings, i.e., MINIST and CIFAR-10, respectively. In addition, the proposed joint dynamic client selection and resource management approach achieve slightly higher accuracy than the considered benchmarks, but they provide a satisfactory energy and time reduction: 29% energy or 20% time reduction on the MNIST; and 25% energy or 12.5% time reduction on the CIFAR-10.

Semantic communication, through the interpretation of the semantic meaning of transmitted data, effectively reduces the required bandwidth. However, current deep learning-based methods face limitations due to their reliance on joint source-channel coding and end-to-end training, hindering adaptability to new channels and user demands. In this study, we introduce the Reconfigurable Intelligent Surface-Semantic Communication (RIS-SC) framework as a solution. This framework dynamically allocates semantic content, leveraging varying degrees of RIS assistance to cater to the evolving needs of users. It takes into account factors such as user mobility and obstacles in the line of sight, enabling the RIS resource to preserve essential semantics even in challenging channel conditions. While this ensures the preservation of core semantics in difficult channel conditions, it may also lead to the loss of some non-essential semantic details under extreme conditions. To counteract this, we have incorporated a reconstruction method that deduces the missing semantic elements, thereby enhancing visual understanding. The RIS-SC framework stands out for its adaptability, ensuring optimal resource distribution for users under favorable conditions and maintaining visual clarity in challenging scenarios. Simulations validate the effectiveness and adaptability of our approach in diverse channel conditions and user demands.

Scalability is a major concern in implementing deep learning (DL) based methods in wireless communication systems. Given various channel reconstruction tasks, applying one DL model for one specific task is costly in both model training and model storage. In this paper, we propose a novel unsupervised deep plug-and-play prior method for three channel reconstruction tasks in the downlink of massive multiple-input multiple-output (MIMO) systems, including channel estimation, antenna extrapolation and channel state information (CSI) feedback. The proposed method corresponding to these three channel reconstruction tasks employs a common DL model, which greatly reduces the overhead of model training and storage. Unlike general multi-task learning, the DL model of the proposed method does not require further fine-tuning for specific channel reconstruction tasks. Extensive experiments are conducted on the DeepMIMO dataset to demonstrate the convergence, performance, and storage overhead of the proposed method for the three channel reconstruction tasks.

This paper investigates an integrated sensing and communication (ISAC) system with reconfigurable intelligent surface (RIS). Our simultaneous beam training and target sensing (SBTTS) scheme enables the base station to perform beam training with the user terminals (UTs) and the RIS, and simultaneously to sense the targets. Based on our findings, the energy of the echoes from the RIS is accumulated in the angle-delay domain while that from the targets is accumulated in the Doppler-delay domain. The SBTTS scheme can distinguish the RIS from the targets with the mixed echoes from the RIS and the targets. Then we propose a positioning and array orientation estimation (PAOE) scheme for both the line-of-sight channels and the non-line-of-sight channels based on the beam training results of SBTTS by developing a low-complexity two-dimensional fast search algorithm. Based on the SBTTS and PAOE schemes, we further compute the angle-of-arrival and angle-of-departure for the channels between the RIS and the UTs by exploiting the geometry relationship to accomplish the beam alignment of the ISAC system. Simulation results verify the effectiveness of the proposed schemes.

This paper investigates beam training for extremely large-scale multiple-input multiple-output systems. By considering both the near field and far field, a triple-refined hybrid-field beam training scheme is proposed, where high-accuracy estimates of channel parameters are obtained through three steps of progressive beam refinement. First, the hybrid-field beam gain (HFBG)-based first refinement method is developed. Based on the analysis of the HFBG, the first-refinement codebook is designed and the beam training is performed accordingly to narrow down the potential region of the channel path. Then, the maximum likelihood (ML)-based and principle of stationary phase (PSP)-based second refinement methods are developed. By exploiting the measurements of the beam training, the ML is used to estimate the channel parameters. To avoid the high computational complexity of ML, closed-form estimates of the channel parameters are derived according to the PSP. Moreover, the Gaussian approximation (GA)-based third refinement method is developed. The hybrid-field neighboring search is first performed to identify the potential region of the main lobe of the channel steering vector. Afterwards, by applying the GA, a least-squares estimator is developed to obtain the high-accuracy channel parameter estimation. Simulation results verify the effectiveness of the proposed scheme.

In this paper, beam training and beam tracking are investigated for extremely large-scale multiple-input-multiple-output communication systems with partially-connected hybrid combining structures. Firstly, we propose a two-stage hybrid-field beam training scheme for both the near field and the far field. In the first stage, each subarray independently uses multiple far-field channel steering vectors to approximate near-field ones for analog combining. To find the codeword best fitting for the channel, digital combiners in the second stage are designed to combine the outputs of the analog combiners from the first stage. Then, based on the principle of stationary phase and the time-frequency duality, the expressions of subarray signals after analog combining are analytically derived and a beam refinement based on phase shifts of subarrays (BRPSS) scheme with closed-form solutions is proposed for high-resolution channel parameter estimation. Moreover, a low-complexity near-field beam tracking scheme is developed, where the kinematic model is adopted to characterize the channel variations and the extended Kalman filter is exploited for beam tracking. Simulation results verify the effectiveness of the proposed schemes.

To reduce multiuser interference and maximize the spectrum efficiency in orthogonal frequency division duplexing massive multiple-input multiple-output (MIMO) systems, the downlink channel state information (CSI) estimated at the user equipment (UE) is required at the base station (BS). This paper presents a novel method for massive MIMO CSI feedback via a one-sided one-for-all deep learning framework. The CSI is compressed via linear projections at the UE, and is recovered at the BS using deep learning (DL) with plug-and-play priors (PPP). Instead of using handcrafted regularizers for the wireless channel responses, the proposed approach, namely CSI-PPPNet, exploits a DL based denoisor in place of the proximal operator of the prior in an alternating optimization scheme. In this way, a DL model trained once for denoising can be repurposed for CSI recovery tasks with arbitrary compression ratio. The one-sided one-for-all framework reduces model storage space, relieves the burden of joint model training and model delivery, and could be applied at UEs with limited device memories and computation power. Extensive experiments over the open indoor and urban macro scenarios show the effectiveness and advantages of the proposed method.

In terahertz (THz) massive multiple-input multiple-output (MIMO) systems, the combination of huge bandwidth and massive antennas results in severe beam split, thus making the conventional phase-shifter based hybrid precoding architecture ineffective. With the incorporation of true-time-delay (TTD) lines in the hardware implementation of the analog precoders, delay-phase precoding (DPP) emerges as a promising architecture to effectively overcome beam split. However, existing DPP approaches suffer from poor performance, high complexity, and weak robustness in practical THz channels. In this paper, we propose a novel DPP approach in wideband THz massive MIMO systems. First, the matrix decomposition optimization problem is converted into a compressive sensing (CS) form, which can be solved by the proposed extended spatially sparse precoding (SSP) algorithm. To compensate for beam split, frequency-dependent measurement matrices are designed, which can be approximately realized by feasible phase and delay codebooks. Furthermore, several efficient atom selection techniques are developed to further reduce the complexity of the extended SSP algorithm. In simulation, the proposed DPP approach achieves superior performance, complexity, and robustness by using it alone or in combination with existing DPP approaches under various settings.

In this paper, we propose a deep learning based breadth-first sphere decoding (SD) scheme to reduce the detection complexity for multiple-input multiple-output (MIMO) communication systems. Specifically, we first design the DenseNet-based deep neural network (DN-DNN) to provide the pruning threshold for SD at each layer. Then, we develop modified number-based SD (MNSD) to reduce the complexity of SD by constraining the number of visited nodes at each layer with the output of DN-DNN. We use a distance-based SD (DSD) to further reduce the complexity of MNSD by constraining the accumulated distance at each layer with the output of DN-DNN. Compared with the traditional M-best SD withM= 16, the proposed MNSD achieves similar performance but reduces about 25% complexity for QPSK modulation; the proposed DSD has better performance with up to 75% complexity reduction at the high SNR region for 16QAM.

Integrated sensing and communications (ISAC) has been deemed as a key technology for the sixth generation (6G) wireless communications systems. In this paper, we explore the inherent clustered nature of wireless users and design a multi-user based environment reconstruction scheme. Specifically, we first select users based on the estimation precision of channel’s multipath, including the line-of-sight (LOS) and the non-line-of-sight (NLOS) paths, to enhance the accuracy of environment reconstruction. Then, we develop a fusion strategy that merges communications signalling with camera image to increase the accuracy and robustness of environment reconstruction. The simulation results demonstrate that the proposed algorithm can achieve a remarkable sensing accuracy of centimeter level, which is about 17 times better than the scheme without user selection. Meanwhile, the fusion of communications data and vision data leads to a threefold accuracy improvement over the image only method, especially under challenging weather conditions like raining and snowing.

The spiking neural network (SNN) is distinguished by its ultra-low power consumption, making it attractive for resource-limited edge intelligence. This paper investigates an energy-efficient (EE) distributed SNN, where multiple edge nodes, each containing a subset of spiking neurons, collaborate to gather and process information through wireless channels. To leverage the benefits of the joint design of neuromorphic computing and wireless communications, we develop quantitative system models and formulate the problem of minimizing the energy consumption of edge devices under constraints of limited bandwidth and spike loss probability. Particularly, a simplified homogeneous SNN is first explored, where the system is proved to have stationary states with a constant firing rate and an alternating optimization based algorithm is proposed for jointly allocating the computation and communication resources. The algorithms are further extended to heterogeneous SNNs by exploiting the statistics of spikes. Extensive simulation results on neuromorphic datasets demonstrate that the developed algorithms can significantly reduce the power consumption of edge systems while ensuring inference accuracy. Moreover, SNNs achieve comparable performance with state-of-the-art recurrent neural networks (RNNs) but are much more bandwidth-efficient and energy-saving.

In this paper, we investigate signal detection for HF skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. We first introduce beam based channel models (BBCM) in the space domain at each subcarrier and in the space-frequency domain for all subcarriers. Based on the BBCM in the space domain, we propose a beam structured detector (BSD) for each subcarrier. Specifically, we prove that the space domain detector design can be transformed into that of a beam domain detector without sacrificing optimality, and the asymptotically optimal space domain detector is beam structured with a low-dimensional beam domain detector, thus significantly reducing the design and implementation complexities. Furthermore, we extend the BSD to the space-frequency domain based on the BBCM jointly for all subcarriers. The design of space-frequency domain detector is also converted to that of a low-dimensional beam domain detector, which enables a very efficient design and implementation of BSD. Simulation results demonstrate the low complexity and satisfactory performance of the proposed detectors.

In this paper, we investigate high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. Based on the triple-beam (TB) based channel model and the channel sparsity in the TB domain, we propose a beam structured channel estimation (BSCE) approach. Specifically, we show that the space-frequency-time (SFT) domain estimator design for each TB domain channel element can be transformed into that of a low-dimensional TB domain estimator and the resulting SFT domain estimator is beam structured. We also present a method to select the TBs used for BSCE. Then we generalize the proposed BSCE by introducing window functions and a turbo principle to achieve a superior trade-off between complexity and performance. Furthermore, we present a low-complexity design and implementation of BSCE by exploiting the characteristics of the TB matrix. Simulation results validate the proposed theory and methods.

Cell-free massive MIMO is emerging as a promising technology for future wireless communication systems, which is expected to offer uniform coverage and high spectral efficiency compared to classical cellular systems. We study in this paper how cell-free massive MIMO can support federated edge learning. Taking advantage of the additive nature of the wireless multiple access channel, over-the-air computation is exploited, where the clients send their local updates simultaneously over the same communication resource. Such an approach, known as over-the-air federated learning (OTA-FL), is proven to alleviate the communication overhead of federated learning over wireless networks. Considering channel correlation and only imperfect channel state information available at the central server, we propose a practical implementation of OTA-FL over cell-free massive MIMO. The convergence of the proposed implementation is studied analytically and experimentally, confirming the benefits of cell-free massive MIMO for OTA-FL.

The recently-developed orthogonal time frequency space (OTFS) modulation is capable of transforming the time-varying fading of the time-frequency (TF) domain into the time-invariant fading representations of the delay-Doppler (DD) domain. The OTFS system using orthogonal frequency-division multiplexing (OFDM) as inner core naturally requires the subcarrier spacing (SCS) Δfto be larger than the maximum Doppler frequency ϑmax, i.e. Δf> ϑmax, when perfect channel state information (CSI) knowledge is assumed. However, for the first time in literature, we explicitly demonstrate that the practical OFDM-based OTFS systems have to double their SCS in order to facilitate CSI estimation, requiring Δf′ = 2Δf> 2ϑmax. In order to mitigate this loss, we propose a novel noncoherent OTFS system, which is capable of operating at Δf> ϑmax. The major challenge in this context is the mitigation of the DD-domain interference without CSI. Against this background, we draw an analogy between the input-output model of OTFS and that of V-BLAST, where V-BLAST’s blind inter-antenna interference mitigation technique is invoked. Moreover, we propose to partition the DD-domain modulated symbols into groups, where space-time block coding is invoked in order to eliminate the DD-domain interference within each group. Our simulation results demonstrate that the proposed noncoherent OTFS is capable of substantially outperforming its coherent counterparts relying on CSI estimation.

Semantic communication can significantly reduce transmission payload by sending only semantic information related to the task. However, existing end-to-end trained semantic studies degrade under extreme channel environments, while reconfigurable intelligent surface (RIS) technology offers a potential solution for realizing channel customization. In this work, we propose a reconfigurable RIS-enhanced semantic communication framework called RIS-SC. This framework allows for customization of the channel environment based on the user's requirements for different semantic parts, rather than relying solely on the conventional bit error rate requirement. Using reinforcement learning, the RIS controller interacts with varying channels to meet the user's different requirements. The RIS controller adaptively protects important semantic parts by adjusting the channel conditions. Simulation results demonstrate that the proposed RIS-SC framework can adapt to different channel environments and improve task performance under varying requirements, such as vertical semantic or true image reconstruction.

Few-shot learning (FSL) allows effective adaptation to a new environment with limited labeled data. In wireless communications, where environments may vary significantly, FSL has the potential to enhance the performance of communication systems. This paper introduces a FSL approach for wireless communication. The meta-learner is employed to leverage experiences from multiple known environments, enabling adaptation to a new environment with few-shot samples without overfitting. The experimental results validate the effectiveness and superiority of our approach over conventional transfer learning (TL). Our approach offers an effective solution for FSL in wireless communications.

This work aims to address two of the main challenges for federated learning (FL), i.e., the limited communication resources and the data heterogeneity across devices. To this end, we first devise a novel FL framework with partial model aggregation (PMA), which only aggregates the lower layers of neural networks responsible for feature extraction while the upper layers corresponding to complex pattern recognition remain at devices for personalization. This design is able to address the data heterogeneity and reduce the transmitted information in wireless channels. Then, we maximize the scheduled data sample volume by joint optimizing the device scheduling, bandwidth allocation, computation and communication time division. Specifically, our analysis reveals that the optimal time division is achieved when the communication and computation parts of PMA-FL have the same power. We also develop a bisection method to solve the optimal bandwidth allocation policy and use the set expansion algorithm to address the optimal device scheduling. Experimental results on the CIFAR-10 dataset show that the proposed PMA-FL improves 11.6% accuracy compared with the state-of-art benchmarks, and the proposed joint dynamic device scheduling and resource optimization approach achieves slightly higher accuracy than the considered benchmarks but reduced 25% energy or 12.5% time budgets.

Intelligent wireless networks have long been expected to have self-configuration and self-optimization capabilities to adapt to various environments and demands. In this paper, we develop a novel distributed hierarchical deep reinforcement learning (DHDRL) framework with two-tier control networks in different times cales to optimize the long-term spectrum efficiency (SE) of the downlink cell-free multiple-input single-output (MISO) network, consisting of multiple distributed access points (AP) and user terminals (UT). To realize the proposed two-tier control strategy, we decompose the optimization problem into two sub-problems, AP-UT association (AUA) as well as beamforming and power allocation (BPA), resulting in a Markov decision process (MDP) and Partially Observable MDP (POMDP). The proposed method consists of two neural networks. At the system level, a distributed high-level neural network is introduced to optimize wireless network structure on a large timescale. While at the link level, a distributed low-level neural network is proposed to mitigate inter-AP interference and improve the transmission performance on a small timescale. Numerical results show that our method is effective for high-dimensional problems, in terms of spectrum efficiency, signaling overhead as well as satisfaction probability, and generalize well to diverse multi-object problems.

In this paper, we investigate signal detection for HF skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. We first introduce beam based channel model (BBCM) in the space domain and reveal the sparsity of the channel in the space-beam domain. Based on the BBCM in the space domain, we propose a beam structured detector (BSD) for each subcarrier. Specifically, we prove that the space domain detector design can be transformed to that of a beam domain detector without sacrificing optimality, and the asymptotically optimal space domain detector is beam structured with a low-dimensional beam domain detector, thus significantly reducing the design and implementation complexities. Furthermore, we provide a beam selection criterion to choose the beams that are used for the BSD. Simulation results demonstrate the low complexity and satisfactory performance of the proposed detector.

In this article, we investigate sequential power allocation over fast varying channels for mission-critical applications, aiming to minimize the expected sum power while guaranteeing the transmission success probability. In particular, a reinforcement learning framework is constructed with appropriate reward design so that the optimal policy maximizes the Lagrangian of the primal problem, where the maximizer of the Lagrangian is shown to have several good properties. For the model-based case, a fast converging algorithm is proposed to find the optimal Lagrange multiplier and thus the corresponding optimal policy. For the model-free case, we develop a three-stage strategy, composed in order of online sampling, offline learning, and online operation, where a backward$Q$-learning with full exploitation of sampled channel realizations is designed to accelerate the learning process. According to our simulation, the proposed reinforcement learning framework can solve the primal optimization problem from the dual perspective. Moreover, the model-free strategy achieves a performance close to that of the optimal model-based algorithm.

Video conferencing has become a popular mode of meeting despite consuming considerable communication resources. Conventional video compression causes resolution reduction under a limited bandwidth. Semantic video conferencing (SVC) maintains a high resolution by transmitting some keypoints to represent the motions because the background is almost static, and the speakers do not change often. However, the study on the influence of transmission errors on keypoints is limited. In this paper, an SVC network based on keypoint transmission is established, which dramatically reduces transmission resources while only losing detailed expressions. Transmission errors in SVC only lead to a changed expression, whereas those in the conventional methods directly destroy pixels. However, the conventional error detector, such as cyclic redundancy check, cannot reflect the degree of expression changes. To overcome this issue, an incremental redundancy hybrid automatic repeat-request framework for varying channels (SVC-HARQ) incorporating a novel semantic error detector is developed. SVC-HARQ has flexibility in bit consumption and achieves a good performance. In addition, SVC-channel state information (CSI) is designed for CSI feedback to allocate the keypoint transmission and enhance the performance dramatically. Simulation shows that the proposed wireless semantic communication system can remarkably improve transmission efficiency.

While semantic communication succeeds in efficiently transmitting due to the strong capability to extract the essential semantic information, it is still far from the intelligent or human-like communications. In this paper, we introduce an essential component, memory, into semantic communications to mimic human communications. Particularly, we investigate a deep learning (DL) based semantic communication system with memory, named Mem-DeepSC, by considering the scenario question answer task. We exploit the universal Transformer based transceiver to extract the semantic information and introduce the memory module to process the context information. Moreover, we derive the relationship between the length of semantic signal and the channel noise to validate the possibility of dynamic transmission. Specially, we propose two dynamic transmission methods to enhance the transmission reliability as well as to reduce the communication overheads by masking some unessential elements, which are recognized through training the model with mutual information. Numerical results show that the proposed Mem-DeepSC is superior to benchmarks in terms of answer accuracy and transmission efficiency, i.e., number of transmitted symbols.

One of the crucial issues in federated learning is how to develop efficient optimization algorithms. Most of the current ones require full device participation and/or impose strong assumptions for convergence. Different from the widely-used gradient descent-based algorithms, in this article, we develop an inexact alternating direction method of multipliers (ADMM), which is both computation- and communication-efficient, capable of combating the stragglers' effect, and convergent under mild conditions. Furthermore, it has high numerical performance compared with several state-of-the-art algorithms for federated learning.

Hybrid analog-digital (HAD) architecture is widely adopted in practical millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems to reduce hardware cost and energy consumption. However, channel estimation in the context of HAD is challenging due to only limited radio frequency (RF) chains at transceivers. Although various compressive sensing (CS) algorithms have been developed to solve this problem by exploiting inherent channel sparsity and sparsity structures, practical effects, such as power leakage and beam squint, can still make the real channel features deviate from the assumed models and result in performance degradation. Besides, the high complexity of CS algorithms caused by a large number of iterations hinders their applications in practice. To tackle these issues, we develop a deep learning (DL)-based channel estimation approach where the sparse Bayesian learning (SBL) algorithm is unfolded into a deep neural network (DNN). In each SBL layer, Gaussian variance parameters of the sparse angular domain channel are updated by a tailored DNN, which is able to capture complicated channel sparsity structures in various domains effectively and efficiently. The measurement matrix is jointly optimized for performance improvement. Then, the proposed approach is extended to the multi-block case where channel correlation in time is further exploited to adaptively predict the measurement matrix and facilitate the update of variance parameters. Simulation results show that the proposed approaches outperform existing approaches in terms of both performance and complexity.

In this paper, we investigate the downlink robust precoding with imperfect channel state information (CSI) for massive multiple-input-multiple-output (MIMO) communications. With the estimated channel and channel error statistics, the general design of the robust precoder is to maximize the ergodic sum rate subject to the total transmit power constraint. To make the problem more tractable, we find a lower bound of the ergodic sum rate and propose the robust weighted minimum mean-squared-error (WMMSE) precoder to maximize the bound. We characterize the structure of the precoding vectors by low-dimensional parameters, which are learned directly from the available CSI through a neural network. As such, the precoding vectors can be immediately computed without iterations. To extend the deep learning design to multi-antennas users, we present a flexible approach that allows the various antenna configurations at the user side to be handled. Simulation results show that the deep learning design can significantly reduce the computational complexity compared with the existing precoder designs while achieving near optimal performance.

In heterogeneous networks (HetNets), the overlap of small cells and the macro cell causes severe cross-tier interference. Although there exist some approaches to address this problem, they usually require global channel state information, which is hard to obtain in practice, and get the sub-optimal power allocation policy with high computational complexity. To overcome these limitations, we propose a multi-agent deep reinforcement learning (MADRL) based power control scheme for the HetNet, where each access point makes power control decisions independently based on local information. To promote cooperation among agents, we develop a penalty-based Q learning (PQL) algorithm for MADRL systems. By introducing regularization terms in the loss function, each agent tends to choose an experienced action with high reward when revisiting a state, and thus the policy updating speed slows down. In this way, an agent’s policy can be learned by other agents more easily, resulting in a more efficient collaboration process. We then implement the proposed PQL in the considered HetNet and compare it with other distributed-training-and-execution (DTE) algorithms. Simulation results show that our proposed PQL can learn the desired power control policy from a dynamic environment where the locations of users change episodically and outperform existing DTE MADRL algorithms.

Millimeter-wave (mmWave) communications have been one of the promising technologies for future wireless networks that integrate a wide range of data-demanding applications. To compensate for the large channel attenuation in mmWave band and avoid high hardware cost, a lens-based beamspace massive multiple-input multiple-output (MIMO) system is considered. However, the spatial-wideband effect in wideband mmWave systems makes channel estimation very challenging, especially when the receiver is equipped with a limited number of radio-frequency (RF) chains. Furthermore, the real channel data cannot be obtained before the mmWave system is used in a new environment, which makes it impossible to train a deep learning (DL)-based channel estimator using real data set beforehand. To solve the problem, we propose a model-driven unsupervised learning network, named learned denoising-based generalized expectation consistent (LDGEC) signal recovery network. By utilizing the Stein’s unbiased risk estimator loss, the LDGEC network can be trained only with limited measurements corresponding to the pilot symbols, instead of the real channel data. Even if designed for unsupervised learning, the LDGEC network can be supervisingly trained with the real channel via the denoiser-by-denoiser way. The numerical results demonstrate that the LDGEC-based channel estimator significantly outperforms state-of-the-art compressive sensing-based algorithms when the receiver is equipped with a small number of RF chains and low-resolution ADCs.

Incorporating deep learning (DL) into multiple-input multiple-output (MIMO) detection has been deemed as a promising technique for future wireless communications. However, most of the DL-based MIMO detection algorithms are lack of interpretation on internal mechanisms. In this paper, we analyze the performance of the DL-based MIMO detection to better understand its strengths and weaknesses. We investigate and compare two different models: data-driven DL detector with neural networks activated by rectifier linear unit (ReLU) function and model-driven DL detector based on traditional detection algorithms. We show that the data-driven DL detector asymptotically approaches to the maximum a posterior (MAP) detector in various scenarios but requires a large amount of training samples to converge in time-varying channels. On the other hand, the model-driven DL detector utilizes the expert knowledge to alleviate the impact of channels and achieves relatively high detection accuracy with a small set of training data. Simulation results confirm our analytical results and demonstrate the effectiveness of the DL-based MIMO detection for both linear and nonlinear signal systems.

Although semantic communications have exhibited satisfactory performance on a large number of tasks, the impact of semantic noise and the robustness of the systems have not been well investigated. Semantic noise refers to the misleading between the intended semantic symbols and received ones, thus causes the failure of tasks. In this paper, we first propose a framework for the robust end-to-end semantic communication systems to combat the semantic noise. In particular, we analyze sample-dependent and sample-independent semantic noise. To combat the semantic noise, the adversarial training with weight perturbation is developed to incorporate the samples with semantic noise in the training dataset. Then, we propose to mask a portion of the input, where the semantic noise appears frequently, and design the masked vector quantized-variational autoencoder (VQ-VAE) with the noise-related masking strategy. We use a discrete codebook shared by the transmitter and the receiver for encoded feature representation. To further improve the system robustness, we develop a feature importance module (FIM) to suppress the noise-related and task-unrelated features. Thus, the transmitter simply needs to transmit the indices of these important task-related features in the codebook. Simulation results show that the proposed method can be applied in many downstream tasks and significantly improve the robustness against semantic noise with remarkable reduction on the transmission overhead.

In this paper, we investigate channel acquisition for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. We first introduce the concept of triple beams (TBs) in the space-frequency-time (SFT) domain and establish a TB based channel model using sampled triple steering vectors. With the established channel model, we then investigate the optimal channel estimation and pilot design for pilot segments. Specifically, we find the conditions that allow pilot reuse among multiple user terminals (UTs), which significantly reduces pilot overhead and increases the number of UTs that can be served. Moreover, we propose a channel prediction method for data segments based on the estimated TB domain channel. To reduce the complexity, we formulate the channel estimation as a statistical inference problem and then obtain the channel by the proposed constrained Bethe free energy minimization (CBFEM) based channel estimation algorithm, which can be implemented with low complexity by exploiting the structure of the TB matrix together with the chirp z-transform (CZT). Simulation results demonstrate the superior performance of the proposed channel acquisition approach.

In this paper, we develop a deep learning based semantic communication system for speech transmission, named DeepSC-ST. We take the speech recognition and speech synthesis as the transmission tasks of the communication system, respectively. First, the speech recognition-related semantic features are extracted for transmission by a joint semantic-channel encoder and the text is recovered at the receiver based on the received semantic features, which significantly reduces the required amount of data transmission without performance degradation. Then, we perform speech synthesis at the receiver, which dedicates to re-generate the speech signals by feeding the recognized text and the speaker information into a neural network module. To enable the DeepSC-ST adaptive to dynamic channel environments, we identify a robust model to cope with different channel conditions. According to the simulation results, the proposed DeepSC-ST significantly outperforms conventional communication systems and existing DL-enabled communication systems, especially in the low signal-to-noise ratio (SNR) regime. A software demonstration is further developed as a proof-of-concept of the DeepSC-ST.

In this paper, we investigate the robust precoding with imperfect channel state information (CSI) for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications. Starting with a sparse beam based a posteriori channel model for the available imperfect CSI at the base station (BS), we prove that the robust precoder for ergodic sum-rate maximization can be designed by optimizing the beam domain robust precoder (BDRP) without any loss of optimality. Furthermore, the asymptotic optimal precoder is beam structured for a sufficiently large number of antennas at the BS, involving a low-dimensional BDRP. As a result, the beam structured robust precoding is asymptotic optimal and can be efficiently implemented based on chirp z-transform. We then derive an iterative algorithm to design the BDRP using majorization-minimization (MM). Furthermore, we develop a low-complexity BDRP design with an ergodic sum-rate upper bound, simplifying the MM based design algorithm. Based on our simulation results, the proposed beam structured robust precoding can achieve a near-optimal performance with significantly reduced complexity in various scenarios.

Deep learning has been widely adopted for channel state information (CSI)-fingerprinting indoor localization systems. These systems usually consist of two main parts,${i}$.${e}$., a positioning network that learns the mapping from high-dimensional CSI to physical locations and a tracking system that utilizes historical CSI to reduce the positioning error. This paper presents a new localization system with high accuracy and generality. On the one hand, the receptive field of the existing convolutional neural network (CNN)-fingerprinting positioning networks is limited, restricting their performance as useful information in CSI is not explored thoroughly. As a solution, we propose a novel attention-augmented residual CNN to utilize the local information and global context in CSI exhaustively. On the other hand, considering the generality of a tracking system, we decouple the tracking system from the CSI environments so that one tracking system for all environments becomes possible. Specifically, we remodel the tracking problem as a denoising task and solve it with deep trajectory prior. Furthermore, we investigate how the precision difference of inertial measurement units will adversely affect the tracking performance and adopt plug-and-play to solve the precision difference problem. Experiments show the superiority of our methods over existing approaches in performance and generality improvement.

This paper investigates joint channel estimation and device activity detection in the LEO satellite-enabled grant-free random access systems with large differential delay and Doppler shift. In addition, the multiple-input multiple-output (MIMO) with orthogonal time-frequency space modulation (OTFS) is utilized to combat the dynamics of the terrestrial-satellite link. To simplify the computation process, we estimate the channel tensor in parallel along the delay dimension. Then, the deep learning and expectation-maximization approach are integrated into the generalized approximate message passing with cross-correlation-based Gaussian prior to capture the channel sparsity in the delay-Doppler-angle domain and learn the hyperparameters. Finally, active devices are detected by computing energy of the estimated channel. Simulation results demonstrate that the proposed algorithms outperform conventional methods.

In this paper, we investigate channel estimation for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. We first introduce the concept of triple beams (TBs) in the space-frequency-time (SFT) domain and establish a TB based channel model using sampled triple steering vectors. With the established channel model, we then investigate the optimal channel estimation and pilot design for pilot segments. Specifically, we find the conditions that allow pilot reuse among multiple user terminals (UTs), which significantly reduces pilot overhead. To reduce the complexity, we are able to formulate the channel estimation as a sparse signal recovery problem due to the channel sparsity in the TB domain and then obtain the channel by the proposed constrained Bethe free energy minimization (CBFEM) based channel estimation algorithm. Simulation results demonstrate the superior performance of the proposed channel estimation approach.

With the aim of accomplishing intelligence tasks, semantic communications transmit task-related information only, yielding significant performance gains over conventional communications. To guarantee user requirements for different tasks, we study the semantic-aware resource allocation in a multi-cell multi-task network in this paper. Specifically, an approximate measure of semantic entropy is first developed to quantify the semantic information for different tasks, based on which a novel quality-of-experience (QoE) model is proposed. We formulate the QoE-aware resource allocation in terms of the number of transmitted semantic symbols, channel assignment, and power allocation. To solve this problem, we first decouple it into two independent subproblems. The first one is to optimize the number of transmitted semantic symbols with given channel assignment and power allocation, which is solved by the exhaustive search method. The second one is the channel assignment and power allocation subproblem, which is modeled as a many-to-one matching game and solved by the proposed low-complexity matching algorithm. Simulation results demonstrate the effectiveness and superiority of the proposed method on the overall QoE.

In this paper, we investigate the robust precoding for high frequency skywave massive multiple-input multiple-output communications with imperfect channel state information (CSI). Starting with a sparse beam based a posteriori channel model for the available imperfect CSI at the base station (BS), we prove that the robust precoder for ergodic sum-rate maximization can be designed by optimizing the beam domain robust pre-coder (BDRP) without any loss of optimality. Furthermore, the asymptotic optimal precoder is beam structured for a sufficiently large number of antennas at the BS, involving a low-dimensional BDRP. As a result, the beam structured robust precoding is asymptotic optimal and can be efficiently implemented based on chirp z-transform. We then derive an iterative algorithm to design the BDRP using majorization-minimization. Based on our simulation results, the proposed beam structured robust precoding can achieve a near-optimal performance with significantly reduced complexity in various scenarios.

Although the semantic communications have exhibited satisfactory performance in a large number of tasks, the impact of semantic noise and the robustness of the systems have not been well investigated. Semantic noise is a particular kind of noise in semantic communication systems, which refers to the misleading between the intended semantic symbols and received ones. In this paper, we first propose a framework for the robust end-to-end semantic communication systems to combat the semantic noise. Particularly, we analyze the causes of semantic noise and propose a practical method to generate it. To remove the effect of semantic noise, adversarial training is proposed to incorporate the samples with semantic noise in the training dataset. Then, the masked autoencoder (MAE) is designed as the architecture of a robust semantic communication system, where a portion of the input is masked. To further improve the robustness of semantic communication systems, we firstly employ the vector quantization-variational autoencoder (VQ-VAE) to design a discrete codebook shared by the transmitter and the receiver for encoded feature representation. Thus, the transmitter simply needs to transmit the indices of these features in the codebook. Simulation results show that our proposed method significantly improves the robustness of semantic communication systems against semantic noise with significant reduction on the transmission overhead.

Reconfigurable intelligent surface (RIS) is a promising technology for energy efficient wireless communications and has drawn significant attention recently. Combining unmanned aerial vehicle with RIS (UAV-RIS) can provide on-demand deployment services in dynamic scenarios. However, reaping the benefits of UAV-RIS will be limited by the energy of the battery-powered UAV. To enhance the endurance of UAV-RISs, we develop a novel energy harvesting scheme for simultaneous wireless information and power transfer (SWIPT), resource allocation, and energy harvest from impinging radio-frequency (RF) signals. Different from the exist works, the proposed scheme creatively splits the passive reflected arrays on geometric space for transporting information and harvesting energy simultaneously. Furthermore, a deep deterministic policy gradient (DDPG) scheme is designed to continuously allocate UAV-RIS’s resources on both the time and space domains to maximize the total harvested energy, while guaranteeing the communication quality for each user. As shown by our simulation results, the proposed UAV-RIS SWIPT system improves performance significantly over the benchmark.

Employing unmanned surface vehicles (USVs) as marine data collectors is promising for large-scale environment sensing in remote ocean monitoring network. In this article, we consider a USV-aided marine data collection network, where a USV collects data from multiple monitoring terminals while avoiding collisions with monitoring terminals and obstacles. Aiming at minimizing energy consumption and data loss, we formulate a trajectory optimization problem with practical constraints, including collision avoidance, steering angle, and velocity limitation. The problem is intractable due to the stochastic arrived data and the random emergence and movement of dynamic obstacles. To efficiently solve it, we transform it as a constrained Markov decision process (MDP) problem and address it using a target-oriented double deep${Q}$-learning network (D2QN)-based collision avoidance and trajectory planning algorithm. In the proposed algorithm, the USV acts as an agent to explore and learn its trajectory planning policy by utilizing the causal knowledge. Numerical results demonstrate that the performance of the proposed algorithm is superior in terms of successful probability, energy consumption, and data loss.

In this paper, an integrated communication, radar sensing, and mobile-edge computing (CRMEC) architecture is developed, where user terminals (UTs) perform radar sensing and computation offloading simultaneously at the same spectrum by using multiple-input and multiple-output (MIMO) arrays and dual-function radar-communication techniques. We formulate a multi-objective optimization problem to jointly consider the performance of multi-UT MIMO radar beampattern design and computation offloading energy consumption while jointly optimizing individual transmit precoding for radar and communication and computation resource allocation. To address the optimization problem, we first decompose the it into three subproblems and adopt an iterative optimization algorithm. Specifically, quadratic transform based fractional programming methods are used to minimize the offloading energy consumption. The design objective of MIMO radar beampattern is handled by the first-order Taylor expansion. Transmit precoding is designed to optimize radar sensing and computation task offloading. The local and edge computation resource allocation are obtained in closed-form. Numerical results verify the effectiveness of the proposed algorithms. The proposed CRMEC architecture can generate the desired multi-UT MIMO radar beampattern and perform computation offloading simultaneously.

The fourth call for papers of the Series on Machine Learning in Communications and Networks has continued to receive a great number of high-quality papers covering various aspects of intelligent communications, from which we have included 16 original contributions in this issue. In the following, we provide a brief review of these papers according to their topics.

The third call for papers of the Series on Machine Learning in Communications and Networks has continued to receive a great number of high-quality papers covering various aspects of intelligent communications, from which we have included 26 original contributions in this issue. In the following, we provide a brief review of key contributions of papers in this issue according to their topics.

The fourth (and final) call for papers of the Series on Machine Learning in Communications and Networks has continued to receive a great number of high-quality papers covering various aspects of intelligent communications. In addition to those published in the August issue, we include in this issue 16 articles submitted to the call. In the following, we provide a brief review of these articles according to their topics.

Facial images are the main focused contents in video conferences and many other applications. Therefore, it turns to a critical issue to measure and maintain the perceptual quality of facial images if transmitted over a bandwidth-limited communication system. In this letter, we propose a regional distortion perceptual threshold measurement model based on electroencephalography (EEG) to establish the relationship between image quality and human perception. Then, a facial image compression method is presented based on the model to improve the perceptual quality. Specifically, we construct a facial image dataset with regional distortion using the better portable graphics (BPG) compression. With the dataset, we design an EEG experiment and collect the brain responses to measure the human perception on regional distortions. By this method, a regional distortion perceptual threshold map (RDPTM) is constructed to guide the data rate allocation process for different regions of facial images. The experimental results show that our method can measure the human perception of regional distortion using EEG and improve the image perceptual quality by data rate allocation based on the RDPTM.

Massive multiple-input multiple-output can obtain more performance gain by exploiting the downlink channel state information (CSI) at the base station (BS). Therefore, studying CSI feedback with limited communication resources in frequency-division duplexing systems is of great importance. Recently, deep learning (DL)-based CSI feedback has shown considerable potential. However, the existing DL-based explicit feedback schemes are difficult to deploy because current fifth-generation mobile communication protocols and systems are designed based on an implicit feedback mechanism. In this paper, we propose a DL-based implicit feedback architecture to inherit the low-overhead characteristic, which uses neural networks (NNs) to replace the precoding matrix indicator (PMI) encoding and decoding modules. By using environment information, the NNs can achieve a more refined mapping between the precoding matrix and the PMI compared with codebooks. The correlation between subbands is also used to further improve the feedback performance. Simulation results show that, for a single resource block (RB), the proposed architecture can save 25.0% – 40.0% of overhead compared with the Type I codebook under different antenna configurations. For a wideband system with 52 RBs, overhead can be saved by 30.7% and 48.0% compared with the Type II codebook when ignoring and considering extracting subband correlation, respectively.

Many performance gains achieved by massive multiple-input and multiple-output depend on the accuracy of the downlink channel state information (CSI) at the transmitter (base station), which is usually obtained by estimating at the receiver (user equipment) and feeding back to the transmitter. The overhead of CSI feedback occupies substantial uplink bandwidth resources, especially when the number of transmit antennas is large. Deep learning (DL)-based CSI feedback refers to CSI compression and reconstruction by a DL-based autoencoder and can greatly reduce feedback overhead. In this paper, a comprehensive overview of state-of-the-art research on this topic is provided, beginning with basic DL concepts widely used in CSI feedback and then categorizing and describing some existing DL-based feedback works. The focus is on novel neural network architectures and utilization of communication expert knowledge to improve CSI feedback accuracy. Works on joint design of CSI feedback with other communication modules are also introduced, and some practical issues, including bitstream generation, multirate feedback, imperfect feedback, NN complexity, training dataset collection, online training, and standardization effect, are discussed. At the end of the paper, some challenges and potential research directions associated with DL-based CSI feedback in future wireless communication systems are identified.

Recently, semantic communication has been brought to the forefront because deep learning (DL)-based methods, such as Transformer, have achieved great success in semantic extraction. Although semantic communication has been successfully applied in sentence transmission to reduce semantic errors, the existing architecture is usually fixed in terms of codeword length and inefficient and inflexible for varying sentence lengths. In this study, we exploit hybrid automatic repeat request (HARQ) to reduce the semantic transmission error further. We combine semantic coding (SC) with Reed-Solomon (RS) channel coding and HARQ (called SC-RS-HARQ). SC-RS-HARQ exploits the superiority of SC and the reliability of conventional methods successfully. Although SC-RS-HARQ can be easily applied in existing HARQ systems, we also develop an end-to-end architecture called SCHARQ to pursue enhanced performance. Numerical results demonstrate that SCHARQ significantly reduces the required number of bits for semantic sentence transmission and the sentence error rate. We also attempt to replace error detection from cyclic redundancy check to a similarity detection network called Sim32 to allow the receiver to reserve wrong sentences with similar semantic information and conserve transmission resources.

In this paper, we propose a deep learning based sphere decoding (SD) scheme to reduce the detection complexity for the multiple-input multiple-output (MIMO) communication systems. Specifically, we first design the sparsely connected deep neural network (SC-DNN) to find a moderate radius for the SD algorithm. Then, we develop the SC-SD algorithm to reduce the computational complexity by deciding the detection order from the output of the SC-DNN, the zero-forcing (ZF) detector, and the transmit power. We further reduce the complexity of the SC-SD by defining partial layers without searching. For multi-stream MIMO, where a large number of parameters in neural networks should be trained, we propose a partitioned training procedure to achieve a reasonable computational complexity. Simulation results demonstrate that the SC-SD almost achieves the performance of the maximum likelihood (ML) in MIMO system but is much faster than the classic SD algorithm.

A variable-phase-shifter (VPS) architecture with hybrid precoding for mixture use of phase shifters and switches, is proposed for millimeter wave massive multiple-input multiple-output communications. For the VPS architecture, a hybrid precoding design (HPD) scheme, called VPS-HPD, is proposed to optimize the phases according to the channel state information by alternately optimizing the analog precoder and digital precoder. To reduce the computational complexity of the VPS-HPD scheme, a low-complexity HPD scheme for the VPS architecture (VPS-LC-HPD) including alternating optimization in three stages is then proposed, where each stage has a closed-form solution and can be efficiently implemented. To reduce the hardware complexity introduced by the large number of switches, we consider a group-connected VPS architecture and propose a HPD scheme, where the HPD problem is divided into multiple independent subproblems with each subproblem flexibly solved by the VPS-HPD or VPS-LC-HPD scheme. Simulation results verify the effectiveness of the propose schemes and show that the proposed schemes can achieve satisfactory spectral efficiency performance with reduced computational complexity or hardware complexity.

In this paper, we investigate a satellite-aerial integrated edge computing network (SAIECN) to combine a low-earth-orbit (LEO) satellite and aerial high altitude platforms (HAPs) to provide edge computing services for ground user equipment (GUE). In the SAIECN, GUE’s computing tasks can be offloaded to HAP(s) or LEO satellite. In this paper, we minimize the weighted sum energy consumption of SAIECN via joint GUE association, multi-user multiple input and multiple output (MU-MIMO) transmit precoding, computation task assignment, and resource allocation. To solve the nonconvex problem, we decompose the optimization problem into four subproblems and solve each one iteratively. For the GUE association subproblem, quadratic transform based fractional programming (QTFP) and difference of convex function are utilized. The MU-MIMO transmit precoding subproblem is solved via QTFP and the weighted minimum mean-squared method. The computation task assignment is addressed using the classic interior point method while the computation resource allocation is derived in closed form. The numerical results show that the proposed SAIECN and the corresponding algorithm can solve the satellite based edge computing quite well and the energy cost is maintained at a relative low level.

Channel estimation is one of the key issues in practical massive multiple-input multiple-output (MIMO) systems. Compared with conventional estimation algorithms, deep learning (DL) based ones have exhibited great potential in terms of performance and complexity. In this paper, an attention mechanism, exploiting the channel distribution characteristics, is proposed to improve the estimation accuracy of highly separable channels with narrow angular spread by realizing the “divide-and-conquer” policy. Specifically, we introduce a novel attention-aided DL channel estimation framework for conventional massive MIMO systems and devise an embedding method to effectively integrate the attention mechanism into the fully connected neural network for the hybrid analog-digital (HAD) architecture. Simulation results show that in both scenarios, the channel estimation performance is significantly improved with the aid of attention at the cost of small complexity overhead. Furthermore, strong robustness under different system and channel parameters can be achieved by the proposed approach, which further strengthens its practical value. We also investigate the distributions of learned attention maps to reveal the role of attention, which endows the proposed approach with a certain degree of interpretability.

Accurate and efficient estimation of the high dimensional channels is one of the critical challenges for practical applications of massive multiple-input multiple-output (MIMO). In the context of hybrid analog-digital (HAD) transceivers, channel estimation becomes even more complicated due to information loss caused by limited radio-frequency chains. The conventional compressive sensing (CS) algorithms usually suffer from unsatisfactory performance and high computational complexity. In this paper, we propose a novel deep learning (DL) based framework for uplink channel estimation in HAD massive MIMO systems. To better exploit the sparsity structure of channels in the angular domain, a novel angular space segmentation method is proposed, where the entire angular space is segmented into many small regions and a dedicated neural network is trained offline for each region. During online testing, the most suitable network is selected based on the information from the global positioning system. Inside each neural network, the region-specific measurement matrix and channel estimator are jointly optimized, which not only improves the signal measurement efficiency, but also enhances the channel estimation capability. Simulation results show that the proposed approach significantly outperforms the state-of-the-art CS algorithms in terms of estimation performance and computational complexity.

In this paper, we investigate an intelligent reflecting surface (IRS) assisted multi-user multiple-input multiple-output (MIMO) full-duplex (FD) system. We jointly optimize the active beamforming matrices at the access point (AP) and uplink users, and the passive beamforming matrix at the IRS to maximize the weighted sum-rate of the system. Since it is practically difficult to acquire the channel state information (CSI) for IRS-related links due to its passive operation and large number of elements, we conceive a mixed-timescale beamforming scheme. Specifically, the high-dimensional passive beamforming matrix at the IRS is updated based on the channel statistics while the active beamforming matrices are optimized relied on the low-dimensional real-time effective CSI at each time slot. We propose an efficient stochastic successive convex approximation (SSCA)-based algorithm for jointly designing the active and passive beamforming matrices. Moreover, due to the high computational complexity caused by the matrix inversion computation in the SSCA-based optimization algorithm, we further develop a deep-unfolding neural network (NN) to address this issue. The proposed deep-unfolding NN maintains the structure of the SSCA-based algorithm but introduces a novel non-linear activation function and some learnable parameters induced by the first-order Taylor expansion to approximate the matrix inversion. In addition, we develop a black-box NN as a benchmark. Simulation results show that the proposed mixed-timescale algorithm outperforms the existing single-timescale algorithm and the proposed deep-unfolding NN approaches the performance of the SSCA-based algorithm with much reduced computational complexity when deployed online.

In this paper, we investigate massive multi-input multi-output (MIMO) high frequency (HF) skywave communications. We first introduce a model for HF skywave massive MIMO channels within the orthogonal frequency division multiplexing transmission framework by using the matrix of sampled steering vectors. Considering the large antenna array aperture and increased signal bandwidth, the effect of the propagation delay across the large-scale antenna array cannot be ignored, and thus the steering vectors vary across different subcarriers. Specifically, we derive a wideband beam based channel model and show that the beam domain statistical channel state information (CSI) is frequency-independent. Then, we consider minimum mean-squared error (MMSE) based uplink receiver and downlink precoder with perfect CSI at the base station (BS). With a large number of antennas at the BS, the sum-rate can be asymptotically increased proportionally to the number of user terminals (UTs) while the transmit power per UT is scaled down inverse-proportionally to the number of antennas. In order to reduce the design complexities of the MMSE receiver and precoder, we derive a polynomial expansion based design using a deterministic equivalent. Simulation results demonstrate very significant performance advantages of the proposed HF skywave massive MIMO system.

Rather than considering only one legitimate user as in the existing works, we investigate multiple legitimate users served by Alice using multicast millimeter wave communications in this paper. Hybrid beamformers for the max-min fairness problem are designed to maximize the minimum covert rate between Alice and the legitimate users subject to the power constraint for confidential signal (CS) and the covertness constraint. In particular, the fully-digital beamformers for the CS and jamming signal are designed by temporarily neglecting the hardware constraints from the constant envelop for phase shifters and the limited number of RF chains, where a semi-definite programming-based method and a successive convex approximation (SCA)-based method are proposed. To approach the fully-digital beamformers, hybrid beamformers are designed subject to the hardware constraints, where an alternating minimization method is proposed to iteratively optimize the analog and digital beamformers. Simulation results show that the proposed methods can achieve better covert communication performance than the existing methods.

Channel estimation is one of the key issues in practical massive multiple-input multiple-output (MIMO) systems. Compared with conventional estimation algorithms, deep learning-based designs have exhibited great potential in terms of both performance and complexity. In this paper, an attention-aided deep neural network is proposed for channel estimation in hybrid analog-digital massive MIMO systems. Specifically, the integrated attention mechanism automatically realizes the “divide-and-conquer” policy to exploit the distribution characteristics of highly separable channels with narrow angular spread. Simulation results show that the channel estimation performance is significantly improved with the aid of attention at the cost of small complexity overhead, and the strong robustness further strengthens the practical value of the proposed approach. Moreover, the distributions of learned attention maps are also investigated to reveal the role of attention and endow the proposed approach with a certain degree of interpretability.

With the explosion of multimedia data, quality of experience (QoE) has become a critical metric in multimedia transmission, and therefore, QoE-oriented image quality assess-ment (IQA) turns more important and urgent. However, the performance of the traditional user-based assessment methods is limited by the deviation caused by human cognitive activities. In this paper, we propose a brain-inspired IQA method based on electroencephalography (EEG) feature learning, which is a psychophysiological method for studying human perception for IQA. We first establish the EEG dataset by collecting the corresponding EEG signals when subjects watch distorted facial images and then design a siamese network to extract the EEG features that can distinguish image quality levels and measure user scores. The siamese network establishes the relationship between image quality and QoE that is reflected by the EEG scores. The relationship is then embedded into a prediction network that directly obtains the EEG scores from images with different qualities. In this way, EEG scores can be predicted through end-to-end learning. Experiment results show that our proposed method can not only better evaluate the perceptual quality of facial images and reflect real human perceptions but also achieve better score prediction performance on the facial image datasets.

Channel estimation in wideband millimeter-wave (mmWave) systems is very challenging due to the beam squint effect. To solve the problem, we propose a learnable iterative shrinkage thresholding algorithm-based channel estimator (LISTA-CE) based on deep learning. The proposed channel estimator can learn to transform the beam-frequency mmWave channel into the domain with sparse features through training data. The transform domain enables us to adopt a simple denoiser with few trainable parameters. We further enhance the adaptivity of the estimator by introducing hypernetwork to automatically generate learnable parameters for LISTA-CE online. Simulation results show that the proposed approach can significantly outperform the state-of-the-art deep learning-based algorithms with lower complexity and fewer parameters and adapt to new scenarios rapidly.

To reduce the number of phase shifters for analog precoding in millimeter wave massive multiple-input multiple-output communications, we investigate the hybrid use of expensive phase shifters and low-cost switches. Different from the existing fixed phase shifter (FPS) architecture where the phases are fixed and independent of the channel state information, we consider variable phase shifter (VPS) whose phases are variable and subject to the hardware constraint. Based on the VPS architecture, a hybrid precoding design (HPD) scheme named VPS-HPD is proposed to optimize the phases according to the channel state information. Specifically, we alternately optimize the analog precoder and the digital precoder, where the former is converted into several subproblems and each subproblem further includes the alternating optimization of the phase matrix and switch matrix. Simulation results show that the spectral efficiency of the VPS-HPD scheme is very close to that of the fully digital precoding, higher than that of the existing MO-AltMin scheme for the fully-connected architecture with much fewer phase shifters, and substantially higher than that of the existing FPS-AltMin scheme for the FPS architecture with the same number of phase shifters.

The traditional communications transmit all the source date represented by bits, regardless of the content of source and the semantic information required by the receiver. However, in some applications, the receiver only needs part of the source data that represents critical semantic information, which prompts to transmit the application-related information, especially when bandwidth resources are limited. In this paper, we consider a semantic communication system for speech recognition by designing the transceiver as an end-to-end (E2E) system. Particularly, a deep learning (DL)-enabled semantic communication system, named DeepSC-SR, is developed to learn and extract text-related semantic features at the transmitter, which motivates the system to transmit much less than the source speech data without performance degradation. Moreover, in order to facilitate the proposed DeepSC-SR for dynamic channel environments, we investigate a robust model to cope with various channel environments without requiring retraining. The simulation results demonstrate that our proposed DeepSC-SR outperforms the traditional communication systems in terms of the speech recognition metrics, such as character-error-rate and word-error-rate, and is more robust to channel variations, especially in the low signal-to-noise (SNR) regime.

In this paper, we investigate massive multi-input multi-output (MIMO) high frequency (HF) skywave communications. We first introduce a model for HF skywave massive MIMO channels within the orthogonal frequency division multiplexing transmission framework by using the matrix of sampled steering vectors. The steering vectors vary across different subcarriers due to the effect of the propagation delay across the largescale antenna array. Specifically, we derive a wideband beam based channel model and show that the beam domain statistical channel state information (CSI) is frequency-independent. Then, we consider minimum mean-squared error based uplink receiver and downlink precoder with perfect CSI at the base station (BS). With a large number of antennas at the BS, the sum-rate can be asymptotically increased proportionally to the number of user terminals (UTs) while the transmit power per UT is scaled down inverse-proportionally to the number of antennas. Simulation results demonstrate very significant performance advantages of the proposed HF skywave massive MIMO system.

In this paper, we consider massive multiple-input multiple-output (MIMO) communication systems with a uniform planar array (UPA) at the base station (BS) and investigate the downlink precoding with imperfect channel state information (CSI). By exploiting both instantaneous and statistical CSI, we aim to design precoding vectors to maximize the ergodic rate subject to a total transmit power constraint. By maximizing an upper bound of the ergodic rate instead, we leverage the corresponding Lagrangian formulation and identify the structural characteristics of the optimal precoder as the solution to a generalized eigenvalue problem. As such, the high-dimensional precoder design problem turns into a low-dimensional power control problem. The Lagrange multipliers play a crucial role in determining both precoder directions and power parameters, yet are challenging to be solved directly. To figure out the Lagrange multipliers, we develop a deep learning approach underpinned by a properly designed neural network that learns directly from CSI. With the offline pre-trained neural network, the online computational complexity of precoding is substantially reduced compared with the existing iterative algorithm while maintaining nearly the same performance.

The large bandwidth at terahertz (THz) and the large number of antennas in massive MIMO result in the non-negligible spatial wideband effect in time domain or the corresponding beam squint issue in frequency domain. For a phased array based hybrid transceiver, beam squint makes the accurate beamforming an enormous challenge since an analog beamformer/combiner cannot generate frequency-dependent phase shift constitutionally. In this paper, we propose a virtual sub-array based wideband hybrid beamforming approach to eliminate the impact of beam squint. By dividing the whole array into several virtual sub-arrays, a wider beam is generated and provides an evenly distributed array gain across the whole operating frequency band. Analytical and numerical results demonstrate the effectiveness of the proposed wideband beamforming approach.

We consider a semantic communication system for speech signals, named DeepSC-S. Motivated by the breakthroughs in deep learning (DL), we make an effort to recover the transmitted speech signals in the semantic communication systems, which minimizes the error at the semantic level rather than the bit level or symbol level as in the traditional communication systems. Particularly, based on an attention mechanism employing squeeze-and-excitation (SE) networks, we design the transceiver as an end-to-end (E2E) system, which learns and extracts the essential speech information. Furthermore, in order to facilitate the proposed DeepSC-S to work well on dynamic practical communication scenarios, we find a model yielding good performance when coping with various channel environments without retraining process. The simulation results demonstrate that our proposed DeepSC-S is more robust to channel variations and outperforms the traditional communication systems, especially in the low signal-to-noise (SNR) regime.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

The combination of large bandwidth at terahertz (THz) and the large number of antennas in massive MIMO results in the non-negligible spatial wideband effect in time domain or the corresponding beam squint issue in frequency domain, which will cause severe performance degradation if not properly treated. In particular, for a phased array based hybrid transceiver, there exists a contradiction between the requirement of mitigating the beam squint issue and the hardware implementation of the analog beamformer/combiner, which makes the accurate beamforming an enormous challenge. In this paper, we propose two wideband hybrid beamforming approaches, based on the virtual sub-array and the true-time-delay (TTD) lines, respectively, to eliminate the impact of beam squint. The former one divides the whole array into several virtual sub-arrays to generate a wider beam and provides an evenly distributed array gain across the whole operating frequency band. To further enhance the beamforming performance and thoroughly address the aforementioned contradiction, the latter one introduces the TTD lines and propose a new hardware implementation of analog beamformer/combiner. This TTD-aided hybrid implementation enables the wideband beamforming and achieves the nearoptimal performance close to full-digital transceivers. Analytical and numerical results demonstrate the effectiveness of two proposed wideband beamforming approaches.

In the era of the new generation of communication systems, data traffic is expected to continuously strain the capacity of future communication networks. Along with the remarkable growth in data traffic, new applications, such as wearable devices, autonomous systems, and the Internet of Things (IoT), continue to emerge and generate even more data traffic with vastly different requirements. This growth in the application domain brings forward an inevitable need for more intelligent processing, operation, and optimization of future communication networks.

The Second Call for Papers of the Series on Machine Learning in Communications and Networks has continued to receive a great number of high-quality papers covering various aspects of intelligent communication systems. In addition to 23 original contributions in response to the first call for papers, we include in this issue 5 articles submitted to the second call for papers. In the following, we provide a brief review of key contributions of papers in this issue according to their topics.

Recently, convolutional neural network (CNN)-based channel estimation (CE) for massive multiple-input multiple-output communication systems has achieved remarkable success. However, complexity even needs to be reduced, and robustness can even be improved. Meanwhile, existing methods do not accurately explain which channel features help the denoising of CNNs. In this paper, we first compare the strengths and weaknesses of CNN-based CE in different domains. When complexity is limited, the channel sparsity in the angle-delay domain improves denoising and robustness whereas large noise power and pilot contamination are handled well in the spatial-frequency domain. Thus, we develop a novel network, called dual CNN, to exploit the advantages in the two domains. Furthermore, we introduce an extra neural network, called HyperNet, which learns to detect scenario changes from the same input as the dual CNN. HyperNet updates several parameters adaptively and combines the existing dual CNNs to improve robustness. Experimental results show improved estimation performance for the time-varying scenarios. To further exploit the correlation in the time domain, a recurrent neural network framework is developed, and training strategies are provided to ensure robustness to the changing of temporal correlation. This design improves channel estimation performance but its complexity is still low.

Many mission-critical and latency-sensitive applications require ultra-reliable and low-latency communications (URLLC), which has been listed as a new service category of 5G New Radio (NR). To guarantee stringent latency and reliability constraints, URLLC services always exclusively occupy the spectrum and have priority over enhanced mobile broadband (eMBB) communications in the current coexistence scenario, which will greatly affect the performance of eMBB services and degrade the utilization efficiency of the spectrum resource. On the other hand, millimeter-wave (mmWave) communications can fulfill the enormous throughput requirements of 5G cellular communications. In this paper, we introduce mmWave communications into URLLC systems to provide a more efficient coexistence for eMBB and URLLC. A novel mmWave URLLC system is first developed, where URLLC users are allowed to share the spectrum resources with eMBB users. Besides, multi-connectivity technology, which enables users to access multiple base stations simultaneously, is introduced to the mmWave URLLC system to enhance the reliability. Then, a resource management problem is formulated, which maximizes the throughput of eMBB users while guaranteeing the latency and reliability requirements of URLLC users. To obtain optimal solutions, we first divide it into three subproblems, i.e., power allocation, resource matching, and user paring, and then solve them respectively. Simulation results demonstrate the data rate improvement compared against the traditional coexistence scenario without reusing strategy. Moreover, the multi-connectivity functionality poses a great effect on guaranteeing the latency and reliability requirements for URLLC users.

In this paper, we consider massive multiple-input-multiple-output (MIMO) communication systems with a uniform planar array (UPA) at the base station (BS) and investigate the downlink precoder design with imperfect channel state information (CSI). By exploiting channel estimates and statistical parameters of channel estimation error, we aim to design precoding vectors to maximize the utility function on the ergodic rates of users subject to a total transmit power constraint. By employing an upper bound of the ergodic rate, we leverage the corresponding Lagrangian formulation and identify the structural characteristics of the optimal precoder as the solution to a generalized eigenvalue problem. The Lagrange multipliers play a crucial role in determining both precoding directions and power parameters, yet are challenging to be solved directly. To figure out the Lagrange multipliers, we develop a general framework underpinned by a properly designed neural network that learns directly from CSI. To further relieve the computational burden, we obtain a low-complexity framework by decomposing the original problem into computationally efficient subproblems with instantaneous and statistical CSI handled separately. With the offline pre-trained neural network, the online computational complexity of precoder is substantially reduced compared with the existing iterative algorithm while maintaining nearly the same performance.

Deep learning (DL) has emerged as an effective tool for channel estimation in wireless communication systems, especially under some imperfect environments. However, even with such unprecedented success, DL methods are often regarded as black boxes and are lack of explanations on their internal mechanisms, which severely limits their further improvement and extension. In this paper, we present preliminary theoretical analysis on DL based channel estimation for single-input multiple-output (SIMO) systems to understand and interpret its internal mechanisms. As deep neural network (DNN) with rectified linear unit (ReLU) activation function is mathematically equivalent to a piecewise linear function, the corresponding DL estimator can achieve universal approximation to a large family of functions by making efficient use of piecewise linearity. We demonstrate that DL based channel estimation does not restrict to any specific signal model and asymptotically approaches to the minimum mean-squared error (MMSE) estimation in various scenarios without requiring any prior knowledge of channel statistics. Therefore, DL based channel estimation outperforms or is at least comparable with traditional channel estimation, depending on the types of channels. Simulation results confirm the accuracy of the proposed interpretation and demonstrate the effectiveness of DL based channel estimation under both linear and nonlinear signal models.

Orthogonal frequency division multiplexing (OFDM) has been widely applied in many wireless communi- cation systems. The artificial intelligence (AI)-aided OFDM receivers are currently brought to the forefront to replace and improve the traditional OFDM receivers. In this paper, we first compare two AI-aided OFDM receivers, namely, data-driven fully connected deep neural network and model-driven ComNet, through extensive simulation and real-time video transmission using a 5G rapid prototyping system for an over-the-air (OTA) test. We find a performance gap between the simulation and the OTA test caused by the discrepancy between the channel model for offline training and the real environment. We develop a novel online training system, which is called SwitchNet receiver, to address this issue. This receiver has a flexible and extendable architecture and can adapt to real channels by training only several parameters online. From the OTA test, the AI-aided OFDM receivers, especially the SwitchNet receiver, are robust to OTA environments and promising for future communication systems. At the end of this paper, we discuss potential challenges and future research inspired by our initial study in this paper.

Link scheduling in device-to-device (D2D) networks is usually formulated as a non-convex combinatorial problem, which is generally NP-hard and difficult to get the optimal solution. Traditional methods to solve this problem are mainly based on mathematical optimization techniques, where accurate channel state information (CSI), usually obtained through channel estimation and feedback, is needed. To overcome the high computational complexity of the traditional methods and eliminate the costly channel estimation stage, machine leaning (ML) has been introduced recently to address the wireless link scheduling problems. In this article, we propose a novel graph embedding based method for link scheduling in D2D networks. We first construct a fully-connected directed graph for the D2D network, where each D2D pair is a node while interference links among D2D pairs are the edges. Then we compute a low-dimensional feature vector for each node in the graph. The graph embedding process is based on the distances of both communication and interference links, therefore without requiring the accurate CSI. By utilizing a multi-layer classifier, a scheduling strategy can be learned in a supervised manner based on the graph embedding results for each node. We also propose an unsupervised manner to train the graph embedding based method to further reinforce the scalability and develop a K-nearest neighbor graph representation method to reduce the computational complexity. Extensive simulation demonstrates that the proposed method is near-optimal compared with the existing state-of-art methods but is with only hundreds of training network layouts. It is also competitive in terms of scalability and generalizability to more complicated scenarios.

In this article, we investigate multi-cell scheduling for massive multiple-input-multiple-output (MIMO) communications with only statistical channel state information (CSI). The objective of multi-cell scheduling is to activate a subset of users so as to maximize the ergodic sum rate subject to per-cell total transmit power constraint. By adopting beam division multiple access based on the statistical CSI, i.e., channel-coupling matrix (CCM), we simplify multi-cell scheduling as a power control problem in the beam domain, by which the ergodic sum rate is maximized. To reduce the computational burden on finding the ergodic sum rate, we propose a learning-to-compute strategy, which directly computes the complex ergodic rate function from CCMs via a deep neural network. Specifically, by modeling the probability density function of the ordered eigenvalues of the Hermitian CCM matrices as exponential family distributions, a properly designed hybrid neural network makes the ergodic rate computation feasible. With the learning-to-compute strategy, the online computational complexity of multi-cell scheduling is substantially reduced compared with the existing Monte Carlo or deterministic equivalent (DE) based methods while maintaining nearly the same performance.

In this article, we investigate spectrum allocation in vehicle-to-everything (V2X) network. We first express the V2X network into a graph, where each vehicle-to-vehicle (V2V) link is a node in the graph. We apply a graph neural network (GNN) to learn the low-dimensional feature of each node based on the graph information. According to the learned feature, multi-agent reinforcement learning (RL) is used to make spectrum allocation. Deep Q-network is utilized to learn to optimize the sum capacity of the V2X network. Simulation results show that the proposed allocation scheme can achieve near-optimal performance.

Different from the existing predefined hierarchical codebook before the beam training, we design a computation-aided adaptive codebook and propose a beam training algorithm based on it. At each layer of the hierarchical codebook, we first estimate the channel angle of arrival (AOA) or angle of departure (AOD) according to the beam training results from the previous layers and then adaptively design a codeword in the current layer to align with the estimated AOA or AOD. Benefiting from the computation resources used for the adaptive codebook design and beam alignment, the proposed algorithm can improve the success rate of beam training as well as reducing the training overhead comparing with the existing algorithms. Simulation results verify the effectiveness of the proposed algorithm.

Recently, deep learned enabled end-to-end (E2E) communication systems have been developed to merge all physical layer blocks in the traditional communication systems, which makes joint transceiver optimization possible. Powered by deep learning, natural language processing (NLP) has achieved great success in analyzing and understanding large amounts of language texts. Inspired by research results in both areas, we aim to provide a new view on communication systems from the semantic level. Particularly, we propose a deep learning based semantic communication system, named DeepSC, for text transmission. Based on the Transformer, the DeepSC aims at maximizing the system capacity and minimizing the semantic errors by recovering the meaning of sentences, rather than bit- or symbol-errors in traditional communications. Compared with the traditional communication system without considering semantic information exchange, the proposed DeepSC is more robust to channel variation and can achieve better performance, especially in the low signal-to-noise ratio (SNR) regime, as demonstrated by the extensive simulation results.

As an efficient data fusion method, over-the-air computation integrates computation and communication by exploiting the superposition property of multiple access channels. In this paper, a framework on deep learning enabled over-the-air computation is proposed, where both the pre-processing and post-processing functions are represented by deep neural networks (DNNs). In this way, the over-the-air computation can approximate any function via learning through the data. The deep over-the-air framework is useful to a variety of machine learning applications on the Internet-of-Things (IoT). The experiments on distribution regression and anomaly detection have shown the effectiveness of the proposed method.

Link scheduling for device-to-device (D2D) communications is usually formulated as an NP-hard non-convex combinatorial problem, which is difficult to get the optimal solution. Traditional methods are mainly based on mathematical optimization techniques with the help of accurate channel state information (CSI), which is costly to obtain. In this paper, we propose a graph embedding based method to achieve link scheduling without CSI for D2D communications. We first construct a fully-connected directed graph for the D2D network, and then compute a low-dimensional feature vector for each node in the graph based on the distances of both communication and interference links. Finally, a scheduling strategy can be learned based on the graph embedding results by utilizing a multi-layer classifier. Extensive simulation demonstrates that the proposed method is near-optimal compared with the existing state-of-art methods and only needs hundreds of training network layouts. It is also competitive in terms of scalability and generalizability to more complicated scenarios.

Recently, deep learning based end-to-end communication systems have been developed, where both the transmitter and the receiver are represented as deep neural networks (DNN) and an end-to-end loss is optimized directly. In this paper, we address the effects of the more general wireless channels to the end-to-end framework. We formulate this problem as training a deep auto-encoder system with an adversarial convolutional layer and propose a training procedure with mini-batches of input samples and channels. Instead of using pilots to explicitly estimate the unknown channel, the auto-encoder learns to address the channel effects without any pilot information. In particular, the receiver contains two modules, designed for channel information extraction and data recovery, respectively. The features obtained from the channel information extraction module are combined with received signals by a bilinear production and then processed by the data recovery module to reconstruct the original input data. The experimental results show a performance improvement compared with the traditional methods in commonly seen wireless channels, including frequency-selective channels and multi-input multi-output (MIMO) channels.

It has been a long-held belief that judicious resource allocation is critical to mitigating interference, improving network efficiency, and ultimately optimizing wireless communication performance. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve the problem to a certain level of optimality. Nonetheless, as wireless networks become increasingly diverse and complex, for example, in the high-mobility vehicular networks, the current design methodologies face significant challenges and thus call for rethinking of the traditional design philosophy. Meanwhile, deep learning, with many success stories in various disciplines, represents a promising alternative due to its remarkable power to leverage data for problem solving. In this article, we discuss the key motivations and roadblocks of using deep learning for wireless resource allocation with application to vehicular networks. We review major recent studies that mobilize the deep-learning philosophy in wireless resource allocation and achieve impressive results. We first discuss deep-learning-assisted optimization for resource allocation. We then highlight the deep reinforcement learning approach to address resource allocation problems that are difficult to handle in the traditional optimization framework. We also identify some research directions that deserve further investigation.

This paper presents a novel model-driven deep learning (DL) architecture, called TurboNet, for turbo decoding that integrates DL into the traditional max-log-maximuma posteriori(MAP) algorithm. The TurboNet inherits the superiority of the max-log-MAP algorithm and DL tools and thus presents excellent error-correction capability with low training cost. To design the TurboNet, the original iterative structure is unfolded as deep neural network (DNN) decoding units, where trainable weights are introduced to the max-log-MAP algorithm and optimized through supervised learning. To efficiently train the TurboNet, a loss function is carefully designed to prevent tricky gradient vanishing issue. To further reduce the computational complexity and training cost of the TurboNet, we can prune it into TurboNet+. Compared with the existing black-box DL approaches, the TurboNet+ has considerable advantage in computational complexity and is conducive to significantly reducing the decoding overhead. Furthermore, we also present a simple training strategy to address the overfitting issue, which enable efficient training of the proposed TurboNet+. Simulation results demonstrate TurboNet+’s superiority in error-correction ability, signal-to-noise ratio generalization, and computational overhead. In addition, an experimental system is established for an over-the-air (OTA) test with the help of a 5G rapid prototyping system and demonstrates TurboNet’s strong learning ability and great robustness to various scenarios.

Millimeter-wave (mmWave) communication has been regarded as one of the most promising means to improve the cellular system capacity in the fifth-generation (5G) era. Compared with the conventional microwave communication networks, mmWave terminals should connect with multiple base stations (BSs) simultaneously to prevent the signal blockage. Meanwhile, the accurate instantaneous channel state information (CSI) is difficult to estimate and collect due to the densification of mmWave BSs. These unique characteristics pose stiff challenges to user association in mmWave networks. To deal with these issues, we develop a novel machine learning based user association approach to support multi-connectivity in mmWave networks. Specifically, we first formulate the mmWave user association problem as a multi-label classification problem, which is then transformed into a series of single-label classification problems through efficient multi-label classification algorithms. To further reduce the requirement on the amount of training samples, we utilize graphical model to represent the user association scenario and adopt novel feature extraction methods to obtain appropriate features from both geographical location information and topological information. With appropriate features, each single-label classification problem can be trained in a supervised manner. Test results show that the proposed approach can achieve a good performance with only a few training samples and without the need of CSI.

Resource allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. In this paper, we develop a hybrid architecture consisting of centralized decision making and distributed resource sharing (the C-Decision scheme) to maximize the long-term sum rate of all vehicles. To reduce the network signaling overhead, each vehicle uses a deep neural network to compress its observed information that is thereafter fed back to the centralized decision making unit. The centralized decision unit employs a deep Q-network to allocate resources and then sends the decision results to all vehicles. We further adopt a quantization layer for each vehicle that learns to quantize the continuous feedback. In addition, we devise a mechanism to balance the transmission of vehicle-to-vehicle (V2V) links and vehicle-to-infrastructure (V2I) links. To further facilitate distributed spectrum sharing, we also propose a distributed decision making and spectrum sharing architecture (the D-Decision scheme) for each V2V link. Through extensive simulation results, we demonstrate that the proposed C-Decision and D-Decision schemes can both achieve near-optimal performance and are robust to feedback interval variations, input noise, and feedback noise.

Massive multiple-input multiple-output (MIMO) is a promising technology to increase link capacity and energy efficiency. However, these benefits are based on available channel state information (CSI) at the base station (BS). Therefore, user equipment (UE) needs to keep on feeding CSI back to the BS, thereby consuming precious bandwidth resource. Large-scale antennas at the BS for massive MIMO seriously increase this overhead. In this paper, we propose a multiple-rate compressive sensing neural network framework to compress and quantize the CSI. This framework not only improves reconstruction accuracy but also decreases storage space at the UE, thus enhancing the system feasibility. Specifically, we establish two network design principles for CSI feedback, propose a new network architecture, CsiNet+, according to these principles, and develop a novel quantization framework and training strategy. Next, we further introduce two different variable-rate approaches, namely, SM-CsiNet+ and PM-CsiNet+, which decrease the parameter number at the UE by 38.0% and 46.7%, respectively. Experimental results show that CsiNet+ outperforms the state-of-the-art network by a margin but only slightly increases the parameter number. We also investigate the compression and reconstruction mechanism behind deep learning-based CSI feedback methods via parameter visualization, which provides a guideline for subsequent research.

Unmanned aerial vehicles (UAVs) have been applied in many different communication scenarios due to their mobility and manipuility. In this paper, we investigate a UAV-aided relay network, where a number of ground users in the urban area with many obstructions need to collect data from a base station (BS), and a UAV could fly around above the users and serve as a decode-and-forward (DF) mobile relay to improve the transmission coverage and performance. In this situation, channel can be represented by the segmented ray-tracing model. To ensure fairness, we aim to maximize the minimum throughput among all the users by jointly optimizing the three-dimensional (3D) UAV trajectory, user scheduling, and bandwidth allocation. To tackle the non-convex objective function and coupling constraints, we first construct surrogate functions, and then approximate the problem into a convex one and develop a constrained successive convex approximation (CSCA) algorithm. In particular, through insightful auxiliary variables and linearly coupled equality (LCE) constraints, we propose a low-complexity algorithm based on the alternating direction method of multipliers (ADMM) to solve the approximated convex problem in the iteration of the proposed CSCA algorithm. Furthermore, we prove the convergence of the proposed algorithm and analyze its complexity. The proposed algorithm can be easily extended to the multi-UAV scenario. Simulation results show that the proposed design significantly outperforms the existing schemes.

In this paper, we develop two high-resolution channel estimation schemes based on the estimating signal parameters via the rotational invariance techniques (ESPRIT) method for frequency-selective millimeter wave (mmWave) massive MIMO systems. The first scheme is based on two-dimensional ESPRIT (TDE), which includes three stages of pilot transmission. This scheme first estimates the angles of arrival (AoA) and angles of departure (AoD) and then pairs the AoA and AoD. The other scheme reduces the pilot transmission from three stages to two stages and therefore reduces the pilot overhead. It is based on one-dimensional ESPRIT and minimum searching (EMS). It first estimates the AoD of each channel path and then searches the minimum from the identified mainlobe. To guarantee the robust channel estimation performance, we also develop a hybrid precoding and combining matrices design method so that the received signal power keeps almost the same for any AoA and AoD. Finally, we demonstrate that the proposed two schemes outperform the existing channel estimation schemes in terms of computational complexity and performance.

In this article, multiuser beam training based on hierarchical codebook for millimeter wave massive multi-input multi-output is investigated, where the base station (BS) simultaneously performs beam training with multiple user equipments (UEs). For the UEs, an alternative minimization method with a closed-form expression (AMCF) is proposed to design the hierarchical codebook under the constant modulus constraint. To speed up the convergence of the AMCF, an initialization method based on Zadoff-Chu sequence is proposed. For the BS, a simultaneous multiuser beam training scheme based on an adaptively designed hierarchical codebook is proposed, where the codewords in the current layer of the codebook are designed according to the beam training results of the previous layer. The codewords at the BS are designed with multiple mainlobes, each covering a spatial region for one or more UEs. Simulation results verify the effectiveness of the proposed hierarchical codebook design schemes and show that the proposed multiuser beam training scheme can approach the performance of the beam sweeping but with significantly reduced beam training overhead.

In this article, we develop an end-to-end wireless communication system using deep neural networks (DNNs), where DNNs are employed to perform several key functions, including encoding, decoding, modulation, and demodulation. However, an accurate estimation of instantaneous channel transfer function, i.e., channel state information (CSI), is needed in order for the transmitter DNN to learn to optimize the receiver gain in decoding. This is very much a challenge since CSI varies with time and location in wireless communications and is hard to obtain when designing transceivers. We propose to use a conditional generative adversarial net (GAN) to represent channel effects and to bridge the transmitter DNN and the receiver DNN so that the gradient of the transmitter DNN can be back-propagated from the receiver DNN. In particular, a conditional GAN is employed to model the channel effects in a data-driven way, where the received signal corresponding to the pilot symbols is added as a part of the conditioning information of the GAN. To address the curse of dimensionality when the transmit symbol sequence is long, convolutional layers are utilized. From the simulation results, the proposed method is effective on additive white Gaussian noise (AWGN) channels, Rayleigh fading channels, and frequency-selective channels, which opens a new door for building data-driven DNNs for end-to-end communication systems.

In this paper, a simultaneous multiuser hierarchical beam training scheme for multiuser mmWave massive MIMO systems is proposed based on the designed adaptive hierarchical codebook. Different from the existing work sequentially performing the beam training for different users with the same hierarchical codebook, in our work the hierarchical codebook is designed in an adaptive manner, where the codewords in the current layer are designed according to the beam training results of the previous layer. In particular, multi-mainlobe codewords are designed for simultaneously beam training with all the users, where each mainlobe of the multi-mainlobe codeword covers a spatial region that one or more users are probably in. Except for the bottom layer, there are only two codewords at each layer in the designed adaptive hierarchical codebook, which only requires two times of simultaneous beam training for all the users no matter how many users the BS serves. Simulation results verify the effectiveness of our scheme and show that our scheme can approach the performance of the beam scanning but with considerable reduction in training overhead.

In this paper, we examine the use of autoencoders as an optimization tool for the construction of noncoherent space-time MIMO codes. In particular, we consider the quasi-static block fading channel, where the channel state information is not available at either the transmitter or the receiver, and changes independently between transmissions. Different from traditional constructions which aim to maximize an approximation of the minimum pairwise distance of the constellation, we use the autoencoder to directly target minimizing the probability of error. We show that this different optimization goal leads to constellations with more favorable pairwise distances' distribution and better error performance at low to medium signal to noise ratios where the minimum distance is not the limiting factor. Finally, we present simulation results showing that the constructed codes outperform traditional Grassmannian codes up to a signal-to-noise ratio of 20 dB using the traditional generalized likelihood ratio test detector.

Fingerprint technique is a promising enabler for mobile terminals (MTs) localization in rich scattering environments, such as urban areas and indoor corridors. In this paper, we investigate fingerprint-based localization for massive multiple- input multiple-output (MIMO) orthogonal frequency- division multiplexing (OFDM) systems with deep convolutional neural networks (DCNNs). By taking full advantage of the high resolution in the angle domain and the delay domain in massive MIMO-OFDM systems, we first propose an efficient angle-delay channel amplitude matrix (ADCAM) fingerprint extraction method. Then a DCNN enabled localization method is proposed, in which the modeling error for fingerprint similarity calculation can be overcome. Both DCNN classification and DCNN regression are considered. For practical implementation, a hierarchical DCNN architecture is proposed. Numerical simulation results demonstrate that DCNN performs well in achieving high localization accuracy as well as reducing storage overhead and computational complexity.

For millimeter wave (mmWave) systems with large-scale arrays, hybrid processing structure is usually used at both transmitters and receivers to reduce the complexity and cost, which poses a very challenging issue in channel estimation, especially at the low transmit signal-to-noise ratio regime. In this paper, deep convolutional neural network (CNN) is employed to perform wideband channel estimation for mmWave massive multiple-input multiple-output (MIMO) systems. In addition to exploiting spatial correlation, our joint channel estimation approach also exploits the frequency correlation, where the tentatively estimated channel matrices at multiple adjacent subcarriers are input into the CNN simultaneously. The complexity analysis and numerical results show that the proposed CNN based joint channel estimation outperforms the non-ideal minimum mean-squared error (MMSE) estimator with reduced complexity and achieves the performance close to the ideal MMSE estimator. It is also quite robust to different propagation scenarios.

Channel estimation and signal detection are very challenging for an orthogonal frequency division multiplexing (OFDM) system without cyclic prefix (CP). In this article, deep learning based on orthogonal approximate message passing (DL-OAMP) is used to address these problems. The DL-OAMP receiver includes a channel estimation neural network (CE-Net) and a signal detection neural network based on OAM-P, called OAMP-Net. The CE-Net is initialized by the least square channel estimation algorithm and refined by minimum mean-squared error (MMSE) neural network. The OAMP-Net is established by unfolding the iterative OAMP algorithm and adding some trainable parameters to improve the detection performance. The DL-OAMP receiver is with low complexity and can estimate time-varying channels with only a single training. Simulation results demonstrate that the bit-error rate (BER) of the proposed scheme is lower than those of competitive algorithms for high-order modulation.

In this paper, we investigate binary frequency shift keying (BFSK) over ambient OFDM signals. By cycling through a sequence of antenna loads providing different phase shifts at the tag, we are able to unidirectionally shift the ambient spectrum either up or down in frequency allowing the implementation of BFSK. We exploit the guard band and the orthogonality of the OFDM subcarriers to avoid both direct-link and adjacent channel interference. Different from energy detection based techniques which suffer from asymmetric error probabilities, the proposed scheme has symmetric error probabilities. Furthermore, we analyze the error performance of the optimal noncoherent detector and obtain an exact expression for the average probability of error. Finally, simulation results corroborate our analysis and show that the proposed scheme outperforms energy detection based schemes available in the literature by up to 3 dB.

In device-to-device (D2D) enabled caching cellular networks, user terminals (UTs) collaboratively store and share a large volume of popular contents from the base stations (BSs) for traffic offloading and delay reduction. In this paper, the multi-winner auction based caching placement in D2D-enabled caching cellular networks is investigated for UT edge caching incentive and content caching redundancy reduction. Firstly, a multi-winner once auction caching placement (MOAC) algorithm is proposed. The maximum social welfare problem is solved by semidefinite programming (SDP) relaxation to obtain a near optimal caching placement. Moreover, the pricing strategy of the auction is developed as a Nash bargaining game. We further propose a multi-winner repeated auction based caching placement (MRAC) algorithm, which can greatly reduce the computation complexity with tiny performance loss. Simulation results show that the proposed algorithms can reduce the traffic load and the average content access delay effectively compared with the existing caching placement algorithms.

In non-orthogonal multiple access (NOMA) systems, the outage is considered to happen when a user cannot correctly decode the messages for the users with higher decoding order and hence the successive interference cancellation (SIC) is failed in traditional definition. However, in this case, the user may still correctly decode its message by treating the uncancelled signal as interference and the outage is avoided. By considering this behavior, a more accurate alternative outage probability can be defined. In this paper, we investigate user scheduling and power allocation for a downlink NOMA system with imperfect SIC by employing the alternative outage probability as then performance metric. The coupling of user scheduling and power allocation makes the problem complicated. Therefore, we propose a two-phase algorithm, in which the user scheduling is first optimized through a matching theory based algorithm, and then power allocation is performed with the aid of the concave-convex procedure (CCCP) method. Simulation results show that the proposed low- complexity algorithm can achieve near-optimal performance and the algorithm based on the alternative outage probability outperforms the traditional one when the decoding is significantly affected by imperfect SIC.

Resource allocation for device-to-device (D2D) communications is usually formulated as mixed integer nonlinear programming (MINLP) problems, which are generally NP-hard and difficult to solve. Traditional methods are based on mathematical optimization techniques, which suffer from forbidding computational complexity or unsatisfactory optimality. In this paper, we introduce a machine leaning (ML) technique, imitation learning, to address the resource allocation in D2D communications. The key idea is learning a good prune policy to speed up the widely-used globally optimal algorithm for the MINLP problems, the branch- and-bound (B&B) algorithm. With appropriate feature selection, imitation learning can be converted into a binary classification problem, which can be solved by the classical support vector machine (SVM). Extensive simulation demonstrates that the proposed method can achieve good optimality and reduce computational complexity simultaneously. It only needs hundreds of training samples and has a good generalization ability. Our proposed method can be also applied to the MINLP problems in other wireless communication networks.

In this paper, we investigate the latency minimization resource allocation problem in a hierarchical cloud-edge coexistence system by optimally splitting tasks for partial cloud computing and partial edge computing. A joint communication and computation resource allocation problem is first formulated and the structural characteristics are further analyzed. Next, by defining two novel parameters: the normalized backhaul communication capacity and the normalized cloud computation capacity, an optimal task splitting strategy is developed. With the help of these definitions, the joint communication and computation resource allocation policy can be devised in closed-form. Finally, numerical results demonstrate that the proposed collaborative cloud-edge computing scheme performs better than some baseline schemes in terms of minimizing the end-to-end latency of mobile devices.

Unmanned aerial vehicles (UAVs) have been considered in wireless communication systems to provide high-quality services for their low cost and high maneuverability. This paper addresses a UAV-aided mobile edge computing system, where a number of ground users are served by a moving UAV equipped with computing resources. Each user has computing tasks to complete, which can be separated into two parts: one portion is offloaded to the UAV and the remaining part is implemented locally. The UAV moves around above the ground users and provides computing service in an orthogonal multiple access manner over time. For each time period, we aim to minimize the sum of the maximum delay among all the users in each time slot by jointly optimizing the UAV trajectory, the ratio of offloading tasks, and the user scheduling variables, subject to the discrete binary constraints, the energy consumption constraints, and the UAV trajectory constraints. This problem has highly nonconvex objective function and constraints. Therefore, we equivalently convert it into a better tractable form based on introducing the auxiliary variables, and then propose a novel penalty dual decomposition-based algorithm to handle the resulting problem. Furthermore, we develop a simplified l0-norm algorithm with much reduced complexity. Besides, we also extend our algorithm to minimize the average delay. Simulation results illustrate that the proposed algorithms significantly outperform the benchmarks.

As wireless networks evolve toward high mobility and providing better support for connected vehicles, a number of new challenges arise due to the resulting high dynamics in vehicular environments and thus motive rethinking of traditional wireless design methodologies. Future intelligent vehicles, which are at the heart of high mobility networks, are increasingly equipped with multiple advanced onboard sensors and keep generating large volumes of data. Machine learning, as an effective approach to artificial intelligence, can provide a rich set of tools to exploit such data for the benefit of the networks. In this paper, we first identify the distinctive characteristics of high mobility vehicular networks and motivate the use of machine learning to address the resulting challenges. After a brief introduction of the major concepts of machine learning, we discuss its applications to learn the dynamics of vehicular networks and make informed decisions to optimize network performance. In particular, we discuss in greater detail the application of reinforcement learning in managing network resources as an alternative to the prevalent optimization approach. Finally, some open issues worth further investigation are highlighted.

Vehicular communications face a tremendous challenge in guaranteeing low latency for safety-critical information exchange due to fast varying channels caused by high mobility. Focusing on the tail behavior of random latency experienced by packets, latency violation probability (LVP) deserves particular attention. Based on only large-scale channel information, this paper performs spectrum and power allocation to maximize the sum ergodic capacity of vehicle-to-infrastructure (V2I) links while guaranteeing the LVP for vehicle-to-vehicle (V2V) links. Using the effective capacity theory, we explicitly express the latency constraint with introduced latency exponents. Then, the resource allocation problem is decomposed into a pure power allocation subproblem and a pure spectrum allocation subproblem, both of which can be solved with global optimum in polynomial time. Simulation results show that the effective capacity model can accurately characterize the LVP. In addition, the effectiveness of the proposed algorithm is demonstrated from the perspectives of the capacity of the V2I links and the latency of the V2V links.

In this paper, we study energy-efficient resource allocation in distributed antenna system with wireless power transfer, where time-division multiple access is adopted for downlink multiuser information transmission. In particular, when a user is scheduled to receive information, other users harvest energy at the same time using the same radio-frequency signal. We consider two types of energy efficiency (EE) metrics: user-centric EE (UC-EE) and network-centric EE (NC-EE). Our goal is to maximize the UC-EE and NC-EE, respectively, by optimizing the transmission time and power subject to the energy harvesting requirements of the users. For both UC-EE and NC-EE maximization problems, we transform the nonconvex problems into equivalently tractable problems by using suitable mathematical tools and then develop iterative algorithms to find the globally optimal solutions. Simulation results demonstrate the superiority of the proposed methods compared with the benchmark schemes.

This paper investigates the spectrum sharing problem in vehicular networks based on multi-agent reinforcement learning, where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links. Fast channel variations in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. In response, we model the resource sharing as a multi-agent reinforcement learning problem, which is then solved using a fingerprint-based deep Q-network method that is amenable to a distributed implementation. The V2V links, each acting as an agent, collectively interact with the communication environment, receive distinctive observations yet a common reward, and learn to improve spectrum and power allocation through updating Q-networks using the gained experiences. We demonstrate that with a proper reward design and training mechanism, the multiple V2V agents successfully learn to cooperate in a distributed way to simultaneously improve the sum capacity of V2I links and payload delivery rate of V2V links.

In this paper, we investigate joint trajectory design and resource allocation algorithms to maximize the minimum average rate among ground users for unmanned aerial vehicle (UAV) communication systems, where both the orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) modes are considered. We first formulate the problems for UAV communications with the OMA and NOMA modes, respectively, which contain binary variables and highly coupled nonconvex objective functions and constraints. In order to handle the challenging problems, we transform the original problems into more tractable forms and then develop novel algorithms based on penalty dual-decomposition technique to solve them. Simulation results show that the proposed algorithms outperform the benchmarks.

In recent years, ambient backscatter communications have gained a lot of interests as a promising enabling technology for the Internet-of-Things and green communications. In ambient backscatter communication systems, ultra-low power devices are able to transmit information by backscattering ambient radio-frequency signals generated by legacy communication systems such as Wi-Fi and cellular networks. This paper is concerned with ambient backscatter communications over legacy orthogonal frequency division multiplexing (OFDM) signals. We propose a backscatter modulation scheme that allows backscattering devices to take advantage of the spectrum structure of ambient OFDM symbols to transmit information. The proposed modulation scheme allows both binary and higher-order modulation using noncoherent energy detection. We investigate the detector design and analyze the error performance of the proposed scheme. We provide an exact expression for the error probability for the binary case, whereas accurate approximate expressions for the error probability are derived for the M-ary case. We corroborate our analysis using Monte-Carlo simulation and investigate the effects of varying the OFDM symbol size, maximum channel delay spread, and the number of receive antennas on the error performance. Our numerical results show that the proposed technique outperforms other techniques available in this paper for backscatter communication over ambient OFDM signals in different scenarios.

Millimeter-wave (mmWave) heterogeneous network (HetNet) has been regarded as a promising means to improve the cellular system capacity in the 5G era. In this paper, we investigate the joint user association and resource allocation problem in a multi-band mmWave HetNet where different bands have different propagation characteristics. According to whether a user can transmit on multiple mmWave bands simultaneously, two different access schemes are considered: the single-band access scheme and the multi-band access scheme. For the single-band access scheme, we first find a closed-form expression for the optimal time fraction allocation and then develop an iterative algorithm for joint user association and power allocation based on the Lagrangian dual decomposition methods and the Newton-Raphson method. For the multi-band access scheme, we develop a near-optimal solution based on the Markov approximation framework. Our analytical results reveal that different users can only access at most one band simultaneously although the multi-band access scheme allows a user to transmit on multiple bands. Finally, numerical results demonstrate that the multi-band access scheme performs better than the single-band access scheme, especially in the light load scenario.

In this paper, we investigate a multi-cell millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) network with low-precision analog-to-digital converters (ADCs) at the base station. Each cell serves multiple users and each user is equipped with multiple antennas but driven by a single RF chain. We first introduce a channel estimation strategy for the mmWave massive MIMO network and analyze the achievable rate with imperfect channel state information. Then, we derive an insightful lower bound for the achievable rate, which becomes tight with a growing number of users. The bound clearly demonstrates the impacts of the number of antennas and the ADC precision, especially for a single-cell mmWave network at low signal-to-noise ratio. It characterizes the tradeoff among various system parameters. Our analytical results are finally confirmed by extensive computer simulations.

In this paper, we propose a terrestrial-satellite network (TSN) architecture to integrate the ultra-dense low earth orbit (LEO) networks and the terrestrial networks to achieve efficient data offloading. In TSN, each ground user can access the network over C-band via a macro cell, a traditional small cell, or a LEO-backhauled small cell (LSC). Each LSC is then scheduled to upload the received data via multiple satellites over Ka-band. We aim to maximize the sum data rate and the number of accessed users while satisfying the varying backhaul capacity constraints jointly determined by the LEO satellite-based backhaul links. The optimization problem is then decomposed into two closely connected subproblems and solved by our proposed matching algorithms. The simulation results show that the integrated network significantly outperforms the non-integrated ones in terms of the sum data rate. The influence of the traffic load and LEO constellation on the system performance is also discussed.

Proximity-based communications have been considered as a promising candidate for supporting vehicular communications. However, the high mobility in vehicular communications makes it hard to obtain accurate fast varying channel information, which poses significant challenges on meeting the requirements of high reliability and low latency. Based only on slowly varying large-scale fading channel information, this paper performs a reliability and latency aware resource allocation, which maximizes the throughput of vehicular-to-network (V2N) links while satisfying reliability and latency requirements of vehicular-to-vehicular (V2V) links. First, we obtain steady-state reliability and latency expressions based on queueing analysis for each possible spectrum reusing pair of a V2N link and a V2V link. Then, an optimal power allocation algorithm is developed for each possible spectrum reusing pair. Afterward, the spectrum reusing pattern is optimized by addressing a polynomial time solvable bipartite matching problem. The simulation results demonstrate the accuracy of the proposed queueing analysis and confirm the effectiveness of the proposed resource allocation comparing with available strategies.

We investigate beam training and allocation for multiuser millimeter wave massive MIMO systems. An orthogonal pilot-based beam training scheme is first developed to reduce the number of training times, where all users can simultaneously perform the beam training with the base station (BS). As the number of users increase, the same beam from the BS may point to different users, leading to beam conflict and multiuser interference. Therefore, a quality-of-service (QoS) constrained (QC) beam allocation scheme is proposed to maximize the equivalent channel gain of the QoS-satisfied users, under the premise that the number of the QoS-satisfied users without beam conflict is maximized. To reduce the overhead of beam training, two partial beam training schemes, an interlaced scanning (IS)-, and a selection probability (SP)-based schemes, are proposed. The overhead of beam training for the IS-based scheme can be reduced by nearly half, while the overhead for the SP-based scheme is flexible. The simulation results show that the QC-based beam allocation scheme can effectively mitigate the interference caused by the beam conflict and significantly improve the spectral efficiency, while the IS-based and SP-based schemes significantly reduce the overhead of beam training at the cost of sacrificing spectral efficiency, a little.

In this article, we investigate the application of NOMA in mobile unmanned aerial vehicle (UAV) communication networks and propose the algorithm to jointly optimize the UAV trajectory and power allocation. Specifically, we formulate the optimization problem to maximize the minimum average rate among ground users for NOMA based UAV communication systems, which contains complicated and discrete binary constraints, as well as the highly coupled nonconvex objective function. Then, we transform the challenging original problem into a more tractable form with some equality constraints. Finally, we develop a double-loop algorithm to solve it with the aid of penalty dual-decomposition (PDD) technique. From the simulation results, the proposed algorithm outperforms the benchmarks.

In this paper, we propose a terrestrial-satellite network (TSN) architecture to integrate the ultra- dense low earth orbit (LEO) networks and the terrestrial networks for data offloading. In TSN, each user can access the network over C-band via a macro cell, a traditional small cell, or a LEO- backhauled small cell (LSC). Each LSC is scheduled to upload the received data via multiple satellites over Ka-band. We aim to maximize the sum data rate while satisfying the varying backhaul capacity constraints jointly determined by the LEO satellite based backhaul links. The optimization problem is then solved by our proposed matching algorithm. Simulation results show that the integrated network significantly outperforms the non-integrated one in terms of the sum data rate.

In this paper, we propose a simple and efficient approach to reduce the overhead of downlink channel estimation and feedback using linear regression (LR) and support vector regression (SVR) in machine learning. Specifically, we divide the indexes of the antennas at the base station (BS) into two sets. We first use some well estimated channel samples to train a regression model, where the channel state information (CSI) corresponding to one set is used as input while the other is output. In the online channel estimation phase, only the CSI of the antennas in one set needs to be estimated and the CSI of the antennas in the other set can be predicted by inputting the estimated CSI into the trained regression model. Numerical results show the proposed approach can reduce the overhead of both downlink pilot and uplink feedback considerably and thus can improve the downlink achievable rate significantly compared with the existing schemes.

In recent years, ambient backscatter communications have gained a lot of interest as a promising enabling technology for Internet-of-Things and green communications. In ambient backscatter communication systems, battery-less devices are able to transmit information by backscattering ambient RF signals generated by legacy communication systems such as digital TV broadcasting, Wi-Fi, or cellular. This paper is concerned with ambient backscatter communications over legacy cellular OFDM signals. We propose a novel modulation scheme that allows backscattering devices to take advantage of the spectrum structure of ambient OFDM symbols to transmit information. We analyze the error performance of the proposed scheme, provide an exact expression for the error probability, and validate our analysis using Monte-Carlo simulation. We investigate the effects of varying the OFDM symbol size and maximum channel delay spread on the error performance. Our numerical results show that the proposed technique outperforms other techniques available in the literature for backscatter communication over ambient OFDM signals in different scenarios.

Vehicular communications have stringent latency requirements on safety-critical information transmission. However, lack of instantaneous channel state information due to high mobility poses a great challenge to meet these requirements and the situation gets more complicated when packet retransmission is considered. Based on only the obtainable large- scale fading channel information, this paper performs spectrum and power allocation to maximize the ergodic capacity of vehicular-to- infrastructure (V2I) links while guaranteeing the latency requirements of vehicular-to-vehicular (V2V) links. First, for each possible spectrum reusing pair of a V2I link and a V2V link, we obtain the closed- form expression of the packets' average sojourn time (the queueing time plus the service time) for the V2V link. Then, an optimal power allocation is derived for each possible spectrum reusing pair. Afterwards, we optimize the spectrum reusing pattern by addressing a polynomial time solvable bipartite matching problem. Numerical results show that the proposed queueing analysis is accurate in terms of the average packet sojourn time. Moreover, the developed resource allocation always guarantees the V2V links' requirements on latency.

In this paper, the performance of indoor Terahertz (THz) communication systems with one-bit digital-to- analog converters (DACs) is investigated. Array-of- subarrays architecture is assumed for the antennas at the access points, where each RF chain uniquely activates a disjoint subset of antennas, each of which is connected to an exclusive phase shifter. Hybrid precoding, including maximum ratio transmission (MRT) and zero-forcing (ZF) precoding, is considered. The best beamsteering direction for the phase shifter in the large subarray antenna regime is first proved to be the direction of the line-of-sight (LoS) path. Subsequently, the closed-form expression of the lower- bound of the achievable rate in the large subarray antenna regime is derived, which is the same for both MRT and ZF and is independent of the transmit power. Numerical results validating the analysis are provided as well.

In this paper, we study the resource allocation problem for a cooperative device-to-device (D2D)- enabled wireless caching network, where each user randomly caches popular contents to its memory and shares the contents with nearby users through D2D links. In order to enhance the throughput of spectrum-sharing D2D links, which may be severely limited by the interference among D2D links, we enable the cooperation among some of the D2D links to eliminate the interference among them. We formulate a joint link scheduling and power allocation problem to maximize the overall throughput of cooperative D2D links (CDLs) and non- cooperative D2D links (NDLs), which is NP-hard. To solve the problem, we decompose it into two sub- problems, which maximize the sum rates of the CDLs and the NDLs, respectively. For CDL optimization, we propose a semi-orthogonal-based algorithm for joint user scheduling and power allocation. For NDL optimization, we propose a novel low-complexity algorithm to perform link scheduling and develop a Difference of Convex functions (D.C.) programming method to solve the non-convex power allocation problem. Simulation results show that cooperative transmission can significantly improve both the number of served users and the overall system throughput.

A large-scale antenna array introduces the innegligible propagation delay for a received signal across the array aperture in addition to a phase rotation. If the delay is comparable to a symbol period in wideband millimeter-wave (mmWave) communications, then its impact on channel estimation and signal detection needs to be properly treated. In this case, different frequencies actually “see” distinct angles of arrival (AoAs) for the same physical path, which is also known as the beam squint effect. In this article, we propose a new channel estimation scheme with full consideration of beam squint for the mmWave massive multiple-input multiple-output (MIMO) systems. A super-resolution compressed sensing approach is first developed to jointly extract the initial AoA, the time delay, and the complex gain of each physical path, from which channel covariance matrix can be constructed rather than acquired through the long-term average. Then, the least-square (LS) and the linear minimum mean- squared error (LMMSE) channel estimators are designed with a small amount of training. Numerical results demonstrate the superiority of the proposed scheme over the conventional methods.

In the paper, we investigate the energy efficiency (EE) tradeoff between licensed and unlicensed bands for an LTE unlicensed (LTE-U) small cell system, where the small base station (SBS) can use both licensed and unlicensed bands to serve users. The tradeoff between the EE on licensed and unlicensed bands is first analyzed to reveal this interaction when the SBS reuses the licensed bands with a macro base station (MBS) and shares unlicensed bands with multiple Wi-Fi access points (AP)s. Accordingly, an algorithm is proposed to find the complete Pareto optimal solution set for the tradeoff problem via weighted Tchebycheff method. Then, numerical results are presented to validate the analysis and demonstrate the performance of the proposed scheme.

Caching popular files in the storage of edge networks, namely edge caching, is a promising approach for service providers (SPs) to reduce redundant backhaul transmission to edge nodes (ENs). In this paper, an edge network with one SP, a large number of ENs, and mobile users with time-dependent requests is considered. A convergent and scalable Stackelberg game for edge caching is designed. Specifically, the game is decomposed into two types of sub-games, a storage allocation game (SAG) and a number of user allocation games (UAGs). A Stackelberg game-based alternating direction method of multipliers (Stackelberg game-based ADMM) is proposed to solve either the SAG or each UAG in a distributed manner. The convergence speed and the optimum of the entire game are linearly (or sublinearly) related to the network size, which indicates that this framework can potentially cope with large-scale caching problems. It is also seen in the simulation results that this framework requires fewer backhaul resources than existing approaches.

In this article, we address joint optimization of unmanned aerial vehicle (UAV) trajectories and user communication scheduling for a dual-UAV enabled secure communication system, where one UAV moves to serve multiple users on the ground in a time division multiple access (TDMA) mode while the other UAV in the area flies to jam the colluding eavesdroppers on the ground to protect communications of the desired users. Specifically, we maximize the minimum average secrecy rate among the users within each period by jointly optimizing UAV trajectories and user scheduling variables under the maximum UAV speed constraints, the UAV return constraints, and the discrete binary constraints on user scheduling variables. The resulting optimization problem is very challenging due to its highly nonconvex objective function and constraints. We then develop a novel algorithm based on the penalty concave-convex procedure (CCCP) technique to solve it. Based on our simulation results, the proposed joint optimization algorithm achieves significantly better performance than the conventional algorithms.

This paper investigates the resource allocation problem in device-to-device (D2D)-based vehicular communications, based on slow fading statistics of channel state information (CSI), to alleviate signaling overhead for reporting rapidly varying accurate CSI of mobile links. We consider the case when each vehicle-to-infrastructure (V2I) link shares spectrum with multiple vehicle-to-vehicle (V2V) links. Leveraging the slow fading statistical CSI of mobile links, we maximize the sum V2I capacity while guaranteeing the reliability of all V2V links. We propose a graph- based algorithm that uses graph partitioning tools to divide highly interfering V2V links into different clusters before formulating the spectrum sharing problem as a weighted 3-dimensional matching problem, which is then solved through adapting a high-performance approximation algorithm.

In this paper, we investigate mobile association and power allocation in device-to-device (D2D)- enabled heterogeneous networks with non-orthogonal multiple access (NOMA) protocol. We formulate two optimization problems to maximize the minimum rate and the sum rate of the network, respectively. Each problem includes power allocation, access point selection and transmission mode switching. To solve the problems, we develop a two-step method, which can always reach the closed-form solution. Simulation results show that the proposed method can significantly improve the overall throughput of the system, compared with the traditional solutions without D2D communications. In addition, we also investigate the tradeoff between overall throughput and fairness.

In this article, we develop a decentralized resource allocation mechanism for vehicle-to- vehicle (V2V) communications based on deep reinforcement learning. Each V2V link is supported by an autonomous "agent", which makes its decisions to find the optimal sub-band and power level for transmission without requiring or having to wait for global information. Hence, the proposed method is decentralized, with minimum transmission overhead. From the simulation results, each agent can effectively learn how to satisfy the stringent latency constraints on V2V links while minimizing the interference to vehicle-to-infrastructure (V2I) communications.

In this article, we exploit deep reinforcement learning for joint resource allocation and scheduling in vehicle-to-vehicle (V2V) broadcast communications. Each vehicle, considered as an autonomous agent, makes its decisions to find the messages and spectrum for transmission based on its local observations without requiring or having to wait for global information. From the simulation results, each vehicle can effectively learn how to ensure the stringent latency constraints on V2V links while minimizing the interference to vehicle-to-infrastructure (V2I) links.

In this paper, we investigate a novel unmanned aerial vehicle (UAV)-enabled secure communication system. Two UAVs are applied in this system where one UAV moves around to communicate with multiple users on the ground using orthogonal time-division multiple access while the other UAV in the area jams the eavesdroppers on the ground to protect communications of the desired users. Specifically, we maximize the minimum worst-case secrecy rate among the users within each period by jointly adjusting UAV trajectories and user scheduling under the maximum UAV speed constraints, the UAV return constraints, the UAV collision avoidance constraints, and the discrete binary constraints on user scheduling variables. Since the resulting optimization problem is very difficult to solve due to its highly nonlinear objective function and nonconvex constraints, we first equivalently transform it into a more tractable problem. In particular, the binary constraints are equivalently converted to a number of equality constraints. Then, we develop a novel joint optimization algorithm to handle the converted problem. In order to further improve the secrecy rate performance, we also extend the developed algorithm to the case with multiple jamming UAVs. The simulation results show that the proposed joint optimization algorithm achieves significantly better performance than the conventional algorithms.

This paper addresses the design of joint beamforming and jamming for a millimeter wave (mmWave) information surveillance system where a suspicious transmitter in the network sends messages to a suspicious receiver under the supervision of a surveillant controller (SC), which not only carries out the duty of a base station or other access point, but also legitimately monitor the suspicious link. Specifically, we seek to maximize the effective monitoring rate for information surveillance by jointly optimizing the analog transmit and receive beamforming vectors of the suspicious link, the analog jamming and monitoring beamforming vectors at the SC and the jamming signal's power level under transmit power, successful monitoring, and self-interference power constraints at the SC, along with unit modulus constraint on the elements of the radio frequency analog beamforming vectors. The resulting optimization problem is quite challenging due to the tight coupling of the design variables in the objective function and constraints. To solve it, we develop a novel algorithm based on the penalty dual decomposition (PDD) technique, where the exacting constraints are penalized and dualized into the objective function as augmented Lagrangian components. The proposed PDD-based algorithm performs double-loop iterations, i.e., the inner loop resorts to the concave-convex procedure to update the optimization variables; while the outer loop adjusts the Lagrange multipliers and penalty parameter of the augmented Lagrangian cost function. We show that the proposed PDD-based joint beamforming and jamming algorithm converges to a stationary solution of the original problem. Based on our simulation results, the proposed algorithm achieves significantly better performance than the conventional beamforming and jamming algorithms.

This paper investigates the resource allocation problem in device-to-device-based vehicular communications, based on slow fading statistics of channel state information (CSI), to alleviate signaling overhead for reporting rapidly varying accurate CSI of mobile links. We consider the case when each vehicle-to-infrastructure (V2I) link shares spectrum with multiple vehicle-to-vehicle (V2V) links. Leveraging the slow fading statistical CSI of mobile links, we maximize the sum V2I capacity while guaranteeing the reliability of all V2V links. We use graph partitioning tools to divide highly interfering V2V links into different clusters before formulating the spectrum sharing problem as a weighted 3-D matching problem. We propose a suite of algorithms, including a baseline graph-based resource allocation algorithm, a greedy resource allocation algorithm, and a randomized resource allocation algorithm, to address the performance-complexity tradeoffs. We further investigate resource allocation adaption in response to slow fading CSI of all vehicular links and develop a low-complexity randomized algorithm.

In this paper, we leverage multi-antenna transmit beamforming techniques in order to enable spatial reuse for coexisting LTE and Wi-Fi systems in unlicensed spectrum. For the cellular small cell base stations equipped with multiple transmit antennas and operating in the unlicensed spectrum, some spatial degrees of freedom (DoF)s are dedicated to serving small cell user terminals (SUEs) and others are employed to mitigate interference to the co-existing co-channel Wi-Fi users by applying a linear multi-user precoding technique, such as zero-forcing transmit beamforming (ZFBF). Through careful allocation of spatial DoFs, enhanced spatial reuse of unlicensed spectrum resources can be achieved, thereby improving spectrum efficiency on unlicensed bands. However, due to inherent channel state information (CSI) estimation and feedback errors, ZFBF cannot completely alleviate detrimental co-channel interference effects. After analysing the so-called intra radio technology (intra-RAT) interference among SUEs, i.e., the residual interference caused by imperfect CSI used in ZFBF, and the inter-RAT interference experienced by the Wi-Fi users, we derive the throughput of the co-existing LTE and Wi-Fi systems, respectively. Based on the derived throughput, spatial DoF and power can be optimally allocated to balance the throughput between the small cell and Wi-Fi systems in different scenarios. Our theoretical analysis and proposed schemes are further confirmed with exhaustive numerical simulation results.

Caching popular files in the storage of edge networks, namely edge caching, is a promising approach for service providers (SPs) to reduce redundant backhaul transmission to edge nodes (ENs). It is still an open problem to design an efficient incentive mechanism for edge caching in 5G networks with a large number of ENs and mobile users. In this paper, an edge network with one SP, a large number of ENs and mobile users with time-dependent requests is investigated. A convergent and scalable Stackelberg game for edge caching is designed. Specifically, the game is decomposed into two types of sub-games, a storage allocation game (SAG) and a number of user allocation games (UAGs). A Stackelberg game-based alternating direction method of multipliers (Stackelberg game-based ADMM) is proposed to solve either the SAG or each UAG in a distributed manner. Based on both analytical and simulation results, the convergence speed, the optimum of the entire game, and the amount of information exchange are linearly (or sublinearly) related to the network size, which indicates that this framework can potentially cope with large-scale caching problems. The proposed approach also requires less backhaul resource than the existed approaches.

For massive multiple-input multiple-output (MIMO) systems, the furthest distance between two antennas may be very large compared with the carrier wavelength. Therefore, the physical propagation delay of electromagnetic wave across the array aperture cannot be ignored, which will cause spatial-wideband effect and make the system design much different from the conventional one that only considers the frequency-wideband effect. Taking mmWave-band communications as an example, we demonstrate the spatial- and frequency-wideband effects, called the dual-wideband effects, in massive MIMO systems. We first discuss a new dual-wideband channel model. By exploiting the channel sparsity in the angle and the delay domains, we then develop a simple yet effective channel estimation algorithm. Thanks to the angular-delay reciprocity, the proposed channel estimation strategy is suitable for both TDD and FDD communication systems. The subsequent numerical results clarify that the proposed transmission design can well address the dual-wideband effects and significantly outperform the existing designs that only consider the frequency-wideband effect.

In this paper, we consider a dense vehicular communication network where each vehicle broadcasts its safety information to its neighborhood in each transmission period. Such applications require low latency and high reliability, and thus, we exploit non-orthogonal multiple access to reduce the latency and to improve the packet reception probability. In the proposed scheme, the BS performs semi-persistent scheduling and allocates time-frequency resources in a non-orthogonal manner while the vehicles autonomously perform distributed power control. We formulate the centralized scheduling and resource allocation problem as a multi-dimensional stable roommate matching problem and develop a novel rotation matching algorithm to solve it. Simulation results show that the proposed scheme outperforms the traditional orthogonal multiple access scheme in terms of the latency and reliability.

Fingerprint techniques are promising localization strategies in rich scattering environments, such as urban areas and indoor corridors. However, most existing approaches rely on multiple base station (BS) cooperation and suffer from multipath propagation. In this paper, we propose a fingerprint based single-site localization method for massive multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. The new angle delay channel power matrix (ADCPM) fingerprint is extracted from instantaneous channel state information (CSI) by taking full advantage of the high resolution in the angle and delay domains for massive MIMO-OFDM systems. The applicable fingerprint similarity criterion，, as well as location estimation method, are proposed to reduce measurement, storage, and matching overheads. Numerical results demonstrate the desirable performance of the proposed localization method.

This paper investigates multiuser full-duplex (FD) multiple-input multiple-output (MIMO) relay systems. We study joint design of the base station (BS) beamforming matrix and the relay station (RS) amplify-and-forward (AF) transformation matrix to maximize the system sum rate with only imperfect channel state information (CSI) at the RS. To deal with highly coupled design variables in the objective function and constraints in the optimization problem, we develop a novel algorithm based on the penalty dual decomposition (PDD) algorithmic framework. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

In this paper, we study how to reuse the unlicensed spectrum in LTE-U systems while guaranteeing harmonious coexistence between the LTE-U and Wi-Fi systems. For a small cell with multiple antennas at the base station (SBS), some spatial degrees of freedom (DoFs) are used to serve small cell users (SUEs) while the rest are employed to mitigate the interference to the Wi-Fi users by applying zero-forcing beamforming (ZFBF). As a result, the LTE-U and Wi-Fi throughput can be balanced by carefully allocating the spatial DoFs. Due to the channel state information (CSI) estimation and feedback errors, ZFBF cannot eliminate the interference completely. We first analyze the residual interference among SUEs, called intra-RAT interference, and the interference to the Wi-Fi users, called inter-RAT interference after ZFBF, due to imperfect CSI. Based on the analysis, we derive the throughputs of the small cell and the Wi-Fi systems, respectively. Accordingly, a spatial DoF allocation scheme is proposed to balance the throughput between the small cell and the Wi-Fi systems. Our theoretical analysis and the proposed scheme are verified by simulation results.

The widely deployed cellular network, assisted with device-to-device (D2D) communications, can provide a promising solution to support efficient and reliable vehicular communications. In this paper, we identify differentiated requirements for different types of vehicular links, i.e., high capacity for vehicle-to-infrastructure (V2I) links and ultra reliability for vehicle-to-vehicle (V2V) links, and attempt to maximize the ergodic capacity of V2I connections while ensuring reliability guarantee for each V2V link. To account for fast channel variations caused by high mobility, we propose to perform spectrum sharing and power allocation based only on slowly varying large-scale fading information of wireless channels. A novel algorithm that yields optimal resource allocation and is robust to channel variations is proposed. Their desirable performance is confirmed by computer simulation.

We investigate the uplink transmission performance of low-power wide-area networks (LPWANs) with regards to coexisting radio modules using LoRa as an example. In doing so we adopt a new topology to model the network where the node locations of the network of focus (LoRa) follow a Poisson cluster process (PCP) while other coexisting interfering radio modules follow a Poisson point process (PPP). To characterize the performance of the proposed model as well as obtain insights, both analytical and closed-form approximated expressions for coverage probability are derived. Based on this, area spectrum efficiency, and energy efficiency are further characterized. These results demonstrate the degree to which the performance, with regard to the aforementioned metrics, is capable of being enhanced through varying the density of the deployment of LoRa nodes around each LoRa receiver. Moreover, simulation results unveil that an optimal value of active LoRa nodes in each cluster exists that maximizes area spectrum efficiency.

Two-stage precoding is a promising transmission strategy for multi-user frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems due to its large multiplexing gain with overhead reduction in both downlink channel estimation and channel state information (CSI) feedback. The performance of existing two-stage precoding schemes mainly depends on appropriate selection and clustering of the users, which is sometimes difficult to realize in a realistic scenario with limited number of users having different covariance eigenspace. In this paper, we propose a new agglomerative clustering method for user grouping which can be easily implemented in realistic scenario. We also develop an average signal-to-leakage-plus-noise ratio (SLNR) based downlink group scheduling method to achieve combination of user groups in a particular time-frequency slot. Numerical results validate the performance improvement of the proposed methods over existing methods.

With device-to-device (D2D) communications, a user terminal (UT) can be used as a relay node to support multi-hop transmission so that cell-edge or deeply faded users can obtain a better connective experience. In this paper, we investigate energy-efficient transmission for D2D-enabled cooperative networks. We aim to maximize the energy efficiency (EE) of the uplink transmission while guaranteeing the minimum data rate requirement via joint D2D relay node (DRN) placement and power allocation. To solve the problem, we first decompose it into four sub-problems depending on the minimum data rate requirement and the power limits at the UT and DRN and then derive a closed-form solution for each problem. Numerical results demonstrate that the maximum EE can be always achieved with the proposed DRN placement and power allocation.

We propose beam division multiple access (BDMA) with per-beam synchronization (PBS) in time and frequency for wideband massive multiple-input multiple-output (MIMO) transmission over millimeter-wave (mmW)/Terahertz (THz) channels. Based on a physically motivated beam domain channel model, we first show that the envelopes of the beam domain channel elements tend to be independent of time and frequency when both the numbers of antennas at base station and user terminals (UTs) tend to infinity. Motivated by this, we then propose PBS for massive MIMO. We show that both the effective delay and Doppler frequency spreads of massive MIMO channels with PBS are reduced by a factor of the number of UT antennas compared with the conventional synchronization approaches. Subsequently, we apply PBS to BDMA and investigate beam scheduling to maximize the achievable ergodic rates for BDMA. Simulation results verify the effectiveness of BDMA with PBS for mmW/THz massive MIMO in typical mobility scenarios.

This paper considers the uplink of large-scale multiple-user multiple-input multiple-output (MU-MIMO) millimeter wave (mmWave) systems, where a number of mobile stations (MSs) communicate with a single base station (BS) equipped with a large-scale antenna array, for application to fifth generation (5G) wireless networks. Within this context, the use of hybrid transceivers along with antenna selection can significantly reduce the implementation cost and energy consumption of analog phase shifters and low-noise amplifiers (LNA). We aim to jointly design the MS beamforming vectors, the hybrid receiving matrices (baseband and analog) and the antenna selection matrix at the BS in order to maximize the achievable system sum-rate. By exploiting the special structure of the problem and linear relaxation, we first convert this problem into three subproblems which are solved via an alternating optimization (AO) method. Specifically, the antenna selection matrix is optimized via the concave-convex procedure (CCCP); the weighted mean-square error minimization (WMMSE) approach is used to find the solution for the transmit beamformer; and the hybrid receiver is obtained via manifold optimization (MO). The convergence of the proposed algorithm is analysed and its effectiveness is verified by simulation.

Active antenna system (AAS) is a promising technology to boost the capacity of next generation wireless communication systems. As a key feature of AAS, vertical sectorization can help form new sub-sectors vertically in a conventional macro cell, facilitates reusing the frequency resources for multiple users, and thus has the potential to improve the system peroformance. In this paper, we investigate the performance of vertical sectorization by optimizing the antenna downtilt and transmit power in LTE-A downlink systems. We first derive the achievable data rate of a downlink wireless communication system considering vertical sectorization and then formulate the problem based on the derived data rate. Finally, antenna downtilt and transmit power are optimized to improve the performance of vertical sectorization. The simulation results demonstrate the effectiveness of the proposed algorithm.

Three-dimensional (3D) beamforming and small cells are two effective techniques to meet the demand of explosive data rate in 5G wireless networks nowadays. The cooperation of these two schemes can help small cell involved heterogeneous networks (HetNets) to achieve high performance. In this paper, we investigate the performance of small cell in-band wireless backhaul in a downlink HetNet considering 3D beamforming. We first analyze the received signals of small cells and users for in-band small cell backhauling and then derive the closed-form achievable data rates for users and the overall system based on gamma distribution. Finally, we formulate the problem based on the derived achievable data rate, followed by analysis of the problem. Simulation results demonstrate that combining 3D beamforming with HetNets can significantly improve the system performance.

In this paper, we consider a multiple dimension resources allocation problem, including user grouping in the spatial domain and resource blocks (RBs) allocation in the time-frequency domain, in a frequency-division-duplexing (FDD) massive MIMO system. We formulate an optimization problem on joint user grouping and resource allocation to maximize the system capacity. Then, we propose two user grouping methods with load balance to fully use the resources in each user group and a corresponding greedy resource allocation algorithm to verify the effectiveness of the user grouping methods. The simulation results demonstrate that the proposed schemes can obtain better performance over the existing ones and the optimal number of scheduled users can be obtained.

Historically, most of the channel encoding and decoding algorithms have been designed to deal with and evaluated under the additive white Gaussian noise (AWGN) channel. However, in the reality, the channel is far more complicated than the AWGN assumption. Traditionally, qualizers are employed to combat the channel effects and frequency-selective fading of wireless channels. In this article, we take the advantage of deep learning approaches to handle the various channel distortions, by proposing an end-to-end approach. To train the model efficiently, the training data is obtained by simulation where the encoding process and the channel effects are viewed as a complete black box. This method can also be applied to time- varying channels for simultaneously channel estimation and symbol detection. Simulation results show that the deep learning based decoders have the ability to learn the complicated encoder function and address various channel effects. Furthermore, the deep learning based method provides an end-to-end approach, leading to a better performance in the channels with various distortions. This article represents the first step for a universal framework of information recovering from a large range of channel codes and channels.

In this paper, we study millimeter-wave (mmWave) and sub-Terahertz (THz) communications with array- of-subarray architecture. To obtain the dominant channel information, two multi-resolution codebooks are designed for beamforming training by exploiting subarray coordination, where beam enhancement and broadening are jointly implemented. Then, based on the proposed codebooks, a hierarchical beamforming training strategy is developed to enable simultaneous training for multiple users. Simulation results are provided to show that the proposed multi- resolution codebooks could significantly increase the beam gain. Also, the effectiveness of the hierarchical beamforming training is verified.

We investigate multi‐user (MU) multiple‐input multiple‐output (MIMO) scheduling under the practical constraints in long‐term evolution advanced (LTE‐A) downlink cellular networks. The authors first derive the received signal model in MU‐MIMO systems when there exist both inter‐stream interference (ISI) and inter‐user interference (IUI) at each user device. Based on this, they formulate the optimisation problem as joint user pairing, precoding matrix indicator (PMI) selection, and resource block (RB) allocation to maximise the total system throughput. The authors then develop a codebook grouping technique for user pairing and PMI selection in MU‐MIMO. With the help of codebook grouping, the authors propose some suboptimal and low‐complexity scheduling algorithms to improve system throughput. From system‐level simulation, the proposed algorithms can improve the network throughput significantly when exploiting only limited feedback designed for single‐user (SU) MIMO in the LTE‐A specification.

In this paper, we consider a dense vehicular communication network where each vehicle broadcasts its safety information to its neighborhood in each transmission period. Such applications require low latency and high reliability, and thus, we exploit non-orthogonal multiple access to reduce the access latency and to improve the packet reception probability. In the proposed two-fold scheme, the BS performs semi-persistent scheduling and allocates time-frequency resources in a nonorthogonal manner while the vehicles autonomously perform distributed power control with iterative signaling control. We formulate the centralized scheduling and resource allocation problem as equivalent to a multi-dimensional stable roommate matching problem, in which the users and time/frequency resources are considered as disjoint sets of objects to be matched with each other. We then develop a novel rotation matching algorithm, which converges to an L-rotation stable matching after a limited number of iterations. Simulation results show that the proposed scheme outperforms the traditional orthogonal multiple access scheme in terms of the access latency and reliability.

This paper presents a new view of multi-user (MU) hybrid massive multiple-input and multiple-output (MIMO) systems from array signal processing perspective. We first show that the instantaneous channel vectors corresponding to different users are asymptotically orthogonal if the angles of arrival of users are different. We then decompose the channel matrix into an angle domain basis matrix and a gain matrix. The former can be formulated by steering vectors and the latter has the same size as the number of RF chains, which perfectly matches the structure of hybrid precoding. A novel hybrid channel estimation is proposed by separately estimating the angle information and the gain matrix, which could significantly save the training overhead and substantially improve the channel estimation accuracy compared with the conventional beamspace approach. Moreover, with the aid of the angle domain matrix, the MU massive MIMO system can be viewed as a type of non-orthogonal angle division multiple access to simultaneously serve multiple users at the same frequency band. Finally, the performance of the proposed scheme is validated by computer simulation results.

Millimeter-wave (mmWave) and sub-Terahertz (THz) communications are compelling as an enabler for next-generation wireless networks. In this paper, we study mmWave and sub-THz systems with array-of-subarray architecture. To accommodate the ultrabroad bandwidth in the mmWave and sub-THz bands, time-delay phase shifters are introduced in system design. Our goal is to investigate beamforming training with hybrid processing to extract the dominant channel information, which would fully exploit channel characteristics while respecting the nature of circuit hardware. In particular, codebooks based on time-delay phase shifters are defined and structured. Then, two multi-resolution time-delay codebooks are designed through subarray coordination. One is built on adaptation of physical beam directions, and the other relies on dynamic approximation of beam patterns. Also, a low-complexity system implementation with modifications on the time-delay codebooks is studied. Furthermore, based on the proposed codebooks, a hierarchical beamforming training strategy with reduced overhead is developed to enable simultaneous training for multiple users. Simulation results show that the proposed multi-resolution time-delay codebooks could provide sufficient beam gains and are robust over large bandwidth. Also, the effectiveness of the hierarchical beamforming training is verified.

We propose beam division multiple access (BDMA) with per-beam synchronization (PBS) in time and frequency for wideband massive multiple-input multiple-output (MIMO) transmission over millimeter-wave (mmW)/Terahertz (THz) bands. We first introduce a physically motivated beam domain channel model for massive MIMO and demonstrate that the envelopes of the beam domain channel elements tend to be independent of time and frequency when both the numbers of antennas at base station and user terminals (UTs) tend to infinity. Motivated by the derived beam domain channel properties, we then propose PBS for mmW/THz massive MIMO. We show that both the effective delay and Doppler frequency spreads of wideband massive MIMO channels with PBS are reduced by a factor of the number of UT antennas compared with the conventional synchronization approaches. Subsequently, we apply PBS to BDMA, investigate beam scheduling to maximize the ergodic achievable rates for both uplink and downlink BDMA, and develop a greedy beam scheduling algorithm. Simulation results verify the effectiveness of BDMA with PBS for mmW/THz wideband massive MIMO systems in typical mobility scenarios.

This paper considers the uplink of large-scale multiple-user multiple-input multiple-output millimeter wave systems, where several mobile stations (MSs) communicate with a single base station (BS) equipped with a large-scale antenna array, for application to fifth generation wireless networks. Within this context, the use of hybrid transceivers along with antenna selection can significantly reduce the implementation cost and energy consumption of analog phase shifters and low-noise amplifiers. We aim to jointly design the MS beamforming vectors, the hybrid receiving matrices (baseband and analog), and the antenna selection matrix at the BS in order to maximize the achievable system sum-rate under a set of constraints. The corresponding optimization problem is nonconvex and difficult to solve, mainly due to the receive antenna selection and constant modulus constraints on the analog receiving matrix. By exploiting the special structure of the problem and linear relaxation, we first convert this problem into three subproblems, which are solved via an alternating optimization method. The latter iteratively updates the antenna selection matrix, the transmit beamforming vectors, and the hybrid receiving matrices by sequentially addressing each subproblem while keeping the other variables fixed. Specifically, the antenna selection matrix is optimized via the concave-convex procedure; the weighted mean-square error minimization approach is used to find the solution for the transmit beamformer; and the hybrid receiver is obtained via manifold optimization. The convergence of the proposed algorithm is analyzed and its effectiveness is verified by simulation.

This paper addresses joint transceiver design for secure downlink communications over a multiple-input multiple-output relay system in the presence of multiple legitimate users and malicious eavesdroppers. Specifically, we jointly optimize the base station (BS) beamforming matrix, the relay station (RS) amplify-and-forward transformation matrix, and the covariance matrix of artificial noise, so as to maximize the system worst-case secrecy rate in the presence of the colluding eavesdroppers under power constraints at the BS and the RS, as well as quality of service constraints for the legitimate users. This problem is very challenging due to the highly coupled design variables in the objective function and constraints. By adopting a series of transformation, we first derive an equivalent problem that is more tractable than the original one. Then, we propose and fully develop a novel algorithm based on the penalty concave-convex procedure (penalty-CCCP) to solve the equivalent problem, where the difficult coupled constraint is penalized into the objective and the resulting nonconvex problem is solved at each iteration by resorting to the CCCP method. It is shown that the proposed joint transceiver design algorithm converges to a stationary solution of the original problem. Finally, our simulation results reveal that the proposed algorithm achieves better performance than other recently proposed transceiver designs.

The widely deployed cellular network, assisted with device-to-device (D2D) communications, can provide a promising solution to support efficient and reliable vehicular communications. Fast channel variations caused by high mobility in a vehicular environment need to be properly accounted for when designing resource allocation schemes for the D2D-enabled vehicular networks. In this paper, we perform spectrum sharing and power allocation based only on slowly varying large-scale fading information of wireless channels. Pursuant to differing requirements for different types of links, i.e., high capacity for vehicle-to-infrastructure (V2I) links and ultrareliability for vehicle-to-vehicle (V2V) links, we attempt to maximize the ergodic capacity of the V2I connections while ensuring reliability guarantee for each V2V link. Sum ergodic capacity of all V2I links is first taken as the optimization objective to maximize the overall V2I link throughput. Minimum ergodic capacity maximization is then considered to provide a more uniform capacity performance across all V2I links. Novel algorithms that yield optimal resource allocation and are robust to channel variations are proposed. Their desirable performance is confirmed by computer simulation.

In the above paper[1], the text discussion of several equations were misrepresented. Below is the corrected text ofSections IIIandIV, in which the errors appear.

In this paper, we investigate the quantization and the feedback of downlink spatial covariance matrix for massive multiple-input multiple-output (MIMO) systems with cascaded precoding. Massive MIMO has gained a lot of attention recently because of its ability to significantly improve the network performance. To reduce the overhead of downlink channel estimation and uplink feedback in frequency-division duplex massive MIMO systems, cascaded precoding has been used to convert high-dimensional physical channels into low-dimensional effective channels. For the cascaded precoding, the inner precoder is determined by the downlink spatial covariance matrix, which is unknown in the base station (BS). To address this issue, we propose a spatial spectrum-based approach for the quantization and the feedback of the spatial covariance matrix. In this manner, the BS can obtain partial information on the downlink spatial covariance matrix. Our result shows that the inner precoder based on the proposed approach can be viewed as modulated discrete prolate spheroidal sequences and thus achieves much smaller spatial leakage than the traditional discrete Fourier transform submatrix-based precoding. Practical issues for the application of the proposed approach are also addressed in this paper.

In this paper, we address the tradeoff between energy efficiency (EE) and spectral efficiency (SE) for cellular networks with full-duplex (FD) communications enabled base stations. To be backward compatible with legacy LTE systems, it is assumed that user devices still work in the conventional half-duplex (HD) mode. There usually exists residual self-interference (RSI) in FD communications after advanced interference suppression techniques are applied. In this paper, we consider two different RSI models: constant RSI model and linear RSI model. First, the necessary conditions for an FD transceiver to achieve better EE-SE tradeoff than an HD one are derived for both the RSI models. Then, for the constant RSI model, a closed-form EE-SE expression is obtained in the scenario of single pair of users. We further extend our result and prove that EE is a quasi-concave function of SE in the scenario of multiple user pairs. Accordingly, an optimal algorithm to achieve the maximum EE based on the Lagrange dual decomposition technique is developed. For the linear RSI model, the EE-SE relation is difficult to deal with and we develop a heuristic algorithm by decoupling the problem into two sub-problems: power control and resource allocation. Our analysis and algorithms are finally verified by comprehensive numerical results.

In this paper, we investigate device-to-device (D2D) overlaying communications with spectrum-power trading, where D2D users (DUs) consume transmit power to relay cell-edge cellular users (CUs) for uplink transmission in exchange for bandwidth from CUs for D2D communications. The proposed spectrum-power trading aims at exploiting individual disparities from both the spectrum and the power perspectives. Recently, energy efficiency (EE) defined by the ratio of the date rate to the power consumption has become increasingly important for devices due to their limited capacity batteries. As such, our goal is to maximize the weighted sum EE (WSEE) of DUs via a joint D2D relay selection, bandwidth allocation, and power allocation while guaranteeing the quality of service of each CU. Specifically, we study WSEE maximization problems for two different cases, i.e., public-interest DUs and self-interest DUs, depending on whether the DUs are willing to share their obtained bandwidth with each other or not. For the case of public-interest DUs, we show that for a given D2D relay selection, the objective function of the WSEE maximization problem in a fractional form can be transformed into a subtractive form that is more tractable based on the fractional programming theory. To perform D2D relay selection, we first reveal a fundamental relationship between the WSEE and two other EE metrics, i.e., system-centric EE and fairness-centric EE, which, to the best of our knowledge, has never been found in the existing works. Based on this insight, the D2D relay selection problem can be cast as a minimum weighted bipartite matching problem. For the case of self-interest DUs, we show that the corresponding problem can also be solved with optimality by the algorithm proposed for the previous case. Simulation results demonstrate the effectiveness of the proposed algorithm.

In this paper, we study resource allocation for full-duplex communications in an orthogonal frequency division multiple access femtocell network. We aim to maximize the throughput of the femtocell while avoiding severe inter-tier interference to the macrocell via joint sub-channel assignment and power allocation. To be more practical, we take channel estimation error into account and use the robust optimization theory to model the uncertainty in interference channels. By using the Lagrangian dual method, we decompose the original optimization problem into a primal problem and a dual problem. We adopt the concave-convex procedure to transform the non-convex primal problem into a tractable form through sequential convex approximations and then utilize the sub-gradient method to solve the dual problem. Simulation results show the effectiveness of the proposed algorithm and demonstrate the impact of channel uncertainty on the system performance.

This paper investigates a cellular network powered by a micro-grid (MG) in the context of green communications, which integrates the conventional generators, energy storage devices, and renewable energy generators, so as to supply electricity to base stations (BSs). Under this model, we study the efficiency aspect of the MG-powered cellular network from the economical perspective. Specifically, the concept of cost efficiency (CE) is employed to measure the sum rate delivered per dollar. Then, our goal is to maximize this CE subject to a series of constraints, including multi-variable coupling and time coupling constraints. Particularly, we assume the zero-forcing beamforming scheme employed by the BSs. To address this established fractional CE optimization problem, we first apply the Dinkelbach method, and then propose a low-complexity algorithm based on the alternating direction method of multipliers approach to jointly schedule power generation in the MG and optimize transmit power for BSs. We introduce a number of auxiliary variables to design a special variable splitting scheme so that the coupling inequality constraints can be separable among two variable sets. Consequently, the proposed algorithm only incorporates simple updates in each step and thus can be implemented in a parallel and completely distributed fashion. Simulation results demonstrate the convergence and energy scheduling performance of the proposed algorithm.

In this paper, we investigate the low-overhead feedback for statistical channel information in massive multiple-input multiple-output (MIMO) systems. As a promising technique for next generation cellular networks, massive MIMO has gained a lot of attention due to its ability to significantly improve the network performance. To reduce the feedback overhead in frequency division duplex systems, cascaded precoding has been proposed for downlink transmission, where the outer precoder with reduced dimension is implemented through traditional limited feedback while the inner precoder is determined by the channel spatial covariance matrix. The size of the covariance matrix in massive MIMO is very large due to the huge number of antennas. In this paper, we propose a spectrum quantization approach to achieve efficient quantization of the covariance matrix with low- overhead feedback. In this approach, the quantization of the covariance matrix is conducted with respect to the corresponding spatial spectrum to avoid the complicated matrix calculation. Simulation results show that our approach can reduce the feedback overhead significantly at the cost of negligible performance degradation.

This paper investigates spectrum-power trading between a small cell (SC) and a macro-cell (MC), where the SC consumes power to serve the macro-cell users (MUs) in exchange for some bandwidth from the MC. Our goal is to maximize the system energy efficiency (EE) of the SC while guaranteeing the quality of service (QoS) of each MU as well as small cell users(SUs). Specifically, given the minimum data rate requirement and the bandwidth provided by the MC, the SC jointly optimizes MU selection, bandwidth allocation, and power allocation while guaranteeing its own minimum required system data rate. The problem is challenging due to the binary MU selection variables and the fractional form objective function. We first show that in order to achieve the maximum system EE, the bandwidth of an MU is shared with at most one SU in the SC. Then, for a given MU selection, the optimal bandwidth and power allocations are obtained by exploiting the fractional programming. To perform MU selection, we first introduce the concept of trading EE. Then, we reveal a sufficient and necessary condition for serving an MU without considering the total power constraint and the minimum data rate constraint. Based on this insight, we propose a low computational complexity MU selection algorithm. Simulation results demonstrate the effectiveness of the proposed scheme.

In this paper, we study resource allocation for secondary users (SUs) in underlay full-duplex cognitive networks, where the channel state information of the links between SUs and primary users (PUs) is uncertain. To protect the transmission of the PUs from interference generated by the SUs, we utilize robust optimization theory to characterize the channel uncertainty and formulate a resource allocation problem by jointly optimizing sub-channel assignment, user pairing, and power allocation. By using the dual method, we decompose the original resource allocation problem into a primal problem and a dual problem. We adopt the concave-convex procedure to transform the primal problem into a tractable form through sequential convex approximations while we utilize the sub-gradient method to solve the dual problem. Simulation results demonstrate the effectiveness of our proposed algorithm.

Large-scale antenna (LSA) has gained a lot of attention recently since it can significantly improve the performance of wireless systems. Similar to multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) or MIMO-OFDM, LSA can be also combined with OFDM to deal with frequency selectivity in wireless channels. However, such combination suffers from substantially increased complexity proportional to the number of antennas in LSA systems. In this paper, we propose a low-complexity recursive convolutional pre-coding to address the issues above. The traditional ZF precoding is implemented through the recursive convolutional precoding in the time domain so that only one IFFT is required for each user and the matrix inversion can be also avoided. Simulation results show that the proposed approach can achieve the same performance as that of ZF but with much lower complexity.

In this paper, we analyze the spectral efficiency (SE) and energy efficiency (EE) of multi-user (MU) multiple-input and multiple-output (MIMO) in multi-cell downlink networks. We first analyze the achievable sum-rate, i.e., the SE, of MU-MIMO systems with maximal ratio transmission (MRT) and zero-forcing (ZF) precoders in downlink cellular networks. Different from the conventional analysis, we derive the achievable sum-rate under the assumption that the number of the BS antennas is huge but limited. Based on the analytical results, we obtain the system EE and further analyze the effect of the number of BS antennas and scheduled users on system EE. The computer simulation results show that the analytical results is accurate and there exists an optimal relationship between the BS antennas and the scheduled users.

In this paper, we study resource allocation in full-duplex OFDMA networks. We explore the joint optimization of subcarrier assignment, uplink-downlink user pairing, and power allocation to maximize the overall throughput with consideration of self-interference and inter-node interference. By using the dual method, we can decompose the original optimization problem into a primal problem and a dual problem. We adopt the concave-convex procedure to transform the primal problem into a tractable form through sequential convex approximations while we utilize the sub-gradient method to solve the dual problem. Simulation results show that the proposed algorithm can always achieve better throughput in comparison with the existing algorithms.

Small cell base stations (SBSs) have been deployed in heterogeneous networks to improve the spectrum efficiency on licensed channels by reusing the spectrum resource of the macro base station (MBS). To relief the shortage on the licensed spectrum resources, licensed-assisted access (LAA) has been introduced to LTE small cell systems to share the unlicensed channel with the Wi-Fi users. In this paper, we investigate the fundamental tradeoff between the collision probability (CP) to the Wi-Fi users and the co-channel interference (CI) power to the MBS in such a heterogeneous LAA system. A multi-objective resource allocation problem is first formulated while guaranteeing the quality-of-service (QoS) of small cell users (SUEs). Then, the double waterfilling-line power allocation on the licensed and unlicensed channels is developed to analyze the CI-CP tradeoff and the weighted Tchebycheff method is applied to convert the multi-objective optimization problem into a single objective optimization problem. To find the complete set of Pareto optimal solutions to the tradeoff problem, a novel feasibility method is proposed. Based on the simulation results, the proposed joint resource allocation algorithm can achieve a flexible CI-CP tradeoff according to the QoS of SUEs in LAA systems.

The employment of Long-Term Evolution (LTE) in unlicensed spectrum, known as LTE-U, can alleviate the spectrum scarcity problem in the 5G networks. With this new technique, the traditional mobile data offloading schemes, which generally offload LTE users to the WiFi network, should be revisited. In this paper, we propose to transfer WiFi users to the LTE-U network and simultaneously allocate some unlicensed spectrum to LTE-U. In this way, a win-win situation could be generated since LTE can achieve better spectrum efficiency than WiFi in the unlicensed spectrum. To facilitate it, three important challenges are addressed in the paper: which WiFi users should be transferred; how many WiFi users need to be transferred; and how much unlicensed resource should be allocated to the LTE network. We utilize the Nash bargaining solution to design fair unlicensed spectrum allocation between WiFi and LTE-U and thereby a win-win strategy is developed, whose performance is demonstrated by numerical simulation.

In this paper, we investigate three-dimensional (3D) multi-user (MU) multiple-input multiple-output (MIMO) transmission for long term evolution advanced (LTE-A) downlink systems. We investigate some key techniques for 3D MU-MIMO to improve the performance of LTE-A systems, including rank and precoding matrix (PM) determination and user pairing. To reduce the complexity caused by a large number of co-scheduled users in 3D MU-MIMO, we develop a simplified and high efficient 3D MU-MIMO scheduling algorithm. The performance improvement of the proposed algorithm is demonstrated by system level simulation.

In this paper, we study path selection and power allocation problems for relay-aided systems with multiple source, relay, and destination nodes. To take fairness among different links into account, we aim at maximizing the minimum source-relay-destination link rate performance. We consider the scenario that the source node can be paired with any destination node. For this scenario, we decouple the original path selection and power allocation problem as two graph-based matching problems and develop algorithms to solve the formulated matching problems with and without sum power constraint, respectively. Numerical results show that our proposed algorithms outperform random matching algorithms in all scenarios and the corresponding performance gains increase with the number of nodes.

To improve system capacity, licensed-assisted access (LAA) has been proposed for long-term evolution (LTE) systems to use unlicensed bands. However, the energy efficiency (EE) of the LTE system may be degraded by LAA since unlicensed bands are generally less energy-efficient than licensed bands. In this paper, we investigate the EE optimization of LAA systems. We first develop a criterion to determine whether unlicensed bands can be leveraged to improve the EE of LAA systems. We prove that unlicensed bands can be used to improve the EE only when the allocated licensed resource blocks (RBs) are not enough. We then investigate joint licensed and unlicensed RB allocation to maximize the EE of each small cell base station (SBS) in a multi-SBS scenario, taking into account fair resource sharing between LTE and WiFi networks. The complete Pareto optimal EE set can be obtained by the weighted Tchebycheff method. We also develop an algorithm to provide fair EE among different SBSs based on the Nash bargaining solution. Numerical results are presented to confirm our analysis and to demonstrate the effectiveness of the proposed algorithms.

In this paper, we investigate spectrum-power trading between a small cell (SC) and a macro cell (MC), where the SC consumes power to serve the MC users (MUs) in exchange for some bandwidth from the MC. Our goal is to maximize the system energy efficiency (EE) of the SC while guaranteeing the quality of service of each MU as well as SC users (SUs). Specifically, given the minimum data rate requirement and the bandwidth provided by the MC, the SC jointly optimizes MU selection, bandwidth allocation, and power allocation while guaranteeing its own minimum required system data rate. The problem is challenging due to the binary MU selection variables and the fractional-form objective function. We first show that the bandwidth of an MU is shared with at most one SU in the SC. Then, for a given MU selection, the optimal bandwidth and power allocation are obtained by exploiting the fractional programming. To perform MU selection, we first introduce the concept of the trading EE to characterize the data rate obtained as well as the power consumed for serving an MU. We then reveal a sufficient and necessary condition for serving an MU without considering the total power constraint and the minimum data rate constraint: the trading EE of the MU should be higher than the system EE of the SC. Based on this insight, we propose a low complexity MU selection method and also investigate the optimality condition. Simulation results verify our theoretical findings and demonstrate that the proposed resource allocation achieves near-optimal performance.

In this paper, we investigate parameter estimation for generalized frequency division multiplexing (GFDM) signals over frequency-selective fading channels. Based on the characteristics of the second-order cyclostationary statistics of GFDM signals, we develop algorithms to blindly estimate pure block duration, overall block duration, symbol duration, and number of subcarriers. The proposed algorithms are robust to timing, phase, and carrier frequency uncertainties and do not require any a priori knowledge of the received signal. Simulation results verify the feasibility of the proposed algorithms.

In this paper, we study cooperative precoder design in two-tier networks, consisting of a macro-cell (MC) and several small-cells (SCs). By exploiting multiuser Vandermonde-subspace frequency division multiplexing (VFDM) transmission, an MC downlink can co-exist with cognitive SCs. In this paper, we first propose a cooperative cross-tier precoder (CTP) among the transmitters in the SCs to increase the transmitted dimension. The cooperative CTP allows us to use more efficient intra-tier precoder (ITP) in SCs to handle intracell interference and improve the throughput of the cognitive system. And then, three ITPs, a block-diagonal zero-forcing (BD-ZF) ITP, a capacity-achieving (CA) ITP, and a generalized MMSE channel inversion (GMI) ITP, are developed. Complexities of all CTPs and ITPs are discussed and compared. The overhead of channel state information (CSI) exchange is analyzed. Numerical results are presented to demonstrate the throughput improvement of the proposed schemes and to discover the impact of the imperfect CSI. From the complexity comparison and the numerical results, the GMI ITP offers a good tradeoff between complexity and throughput.

Traditional mobile data offloading transfers cellular users to WiFi networks to relieve the cellular system from the pressure of the ever-increasing data traffic load. However, the spectrum utilization of the WiFi network is bound to suffer from potential packet collisions due to its contention-based access protocol, especially when the number of competing WiFi users grows large. To tackle this problem, we propose transferring some WiFi users to be served by the LTE system, in contrast to the traditional mobile data offloading which effectively offloads LTE traffic to the WiFi network. Meanwhile, leveraging the emerging LTE in unlicensed spectrum (LTE-U) technology, some unlicensed spectrum resources may be allocated to the LTE system in compensation for handling more WiFi users. In this way, a win-win situation would be generated since LTE can generally achieve better performance than WiFi due to its capability of centralized co-ordination. To facilitate it, three important challenging issues are addressed in the paper: which WiFi users should be transferred; how many WiFi users need to be transferred; and how much unlicensed resources should be relinquished to the LTE-U network. We investigate three different user transfer schemes according to the availability of channel state information (CSI): the random transfer, the distance-based transfer, and the CSI-based transfer. In each scheme, the minimum required amount of unlicensed resources under a given transferred user number is analyzed. Furthermore, we utilize the Nash bargaining solution (NBS) to develop joint user transfer and unlicensed resource allocation strategy to fulfill the win-win situation for both networks, whose performance is demonstrated by numerical simulation.

Traffic offloading and resource sharing are two common methods for delivering cellular data traffic over unlicensed bands. In this paper, we first develop a hybrid method to take full advantages of both traffic offloading and resource sharing methods, where cellular base stations (BSs) offload traffic to WiFi networks and simultaneously occupy certain number of time slots on unlicensed bands. Then, we analytically compare the cellular throughput of the three methods with the guarantee of WiFi per-user throughput in the single-BS scenario. We find that traffic offloading can achieve better performance than resource sharing when existing WiFi user number is below a threshold and the hybrid method achieves the same performance as the resource sharing method when existing WiFi user number is large enough. In the multi-BS scenario where the coverage of small cells and WiFi access points are mutually overlapped, we consider to maximize the minimum average per-user throughput of each small cell and derive a closed-form expression for the throughput upper bound in each method. Meanwhile, practical traffic offloading and resource sharing algorithms are also developed for the three methods, respectively. Numerical results validate our theoretical analysis and demonstrate the effectiveness of the proposed algorithms as well.

Emerging millimeter-wave (mmWave) and Terahertz (THz) systems is a promising revolution for next-generation wireless communications. In this paper, we study an indoor multi-user mmWave and sub-THz system with large antenna arrays, where two different types of architecture, the fully-connected structure and the array-of-subarray structure, are investigated. Specifically, the Doherty power amplifier (PA) is adopted to improve the PA efficiency of the system, and the associated nonlinear system power consumption models with insertion power loss are developed. By capturing the characteristics of the mmWave and sub-THz channels, we design different hybrid beamforming schemes for the two structures with low complexity. We further compare the achievable rates of the two structures and show that, with the insertion loss, the achievable rate of the array-of-subarray structure is generally larger than that of the fully-connected structure. Moreover, we propose the optimal power control strategies for both structures to maximize the energy efficiency of the system and demonstrate that the energy efficiency of the array-of-subarray structure outperforms that of the fully-connected structure. Simulation results are provided to compare and validate the performance of the two structures, where the array-of-subarray structure shows a great advantage over the fully-connected structure in both spectral efficiency and energy efficiency.

Large-scale antenna (LSA) has gained a lot of attention recently since it can significantly improve the performance of wireless systems. Similar to multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) or MIMO-OFDM, LSA can be also combined with OFDM to deal with frequency selectivity in wireless channels. However, such combination suffers from substantially increased complexity proportional to the number of antennas in LSA systems. For the conventional implementation of LSA-OFDM, the number of inverse fast Fourier transforms (IFFTs) increases with the antenna number since each antenna requires an IFFT for OFDM modulation. Furthermore, zero-forcing (ZF) precoding is required in LSA systems to support more users, and the required matrix inversion leads to a huge computational burden. In this paper, we propose a low-complexity recursive convolutional precoding to address the issues above. The traditional ZF precoding can be implemented through the recursive convolutional precoding in the time domain so that only one IFFT is required for each user and the matrix inversion can be also avoided. Simulation results show that the proposed approach can achieve the same performance as that of ZF but with much lower complexity.

With device-to-device (D2D) communications, a user terminal can be used as a relay node to support multi-hop transmission, so that cell-edge or deeply faded users can obtain a better connective experience. In this paper, we investigate energy-efficient mobile association in D2D-enabled heterogeneous networks. We consider joint access point selection, mode switching, D2D relay node (DRN) selection, and power control to maximize the energy efficiency (EE) of uplink transmission while guaranteeing the quality-of-service requirement of users. The optimization problem can be decomposed into three subproblems: access point selection, power control, and joint mode switching and DRN selection. The joint mode switching and DRN selection problem is a 0-1 integer optimization problem, whose optimal solution can be found by the brute-force searching method that is complexity-prohibitive when the number of DRNs is large. To reduce the complexity involved in computation, channel estimation, and feedback, we develop a distance-based mobile association (DMA) algorithm, which only operates based on the location information of users and DRNs. Simulation results demonstrate that the proposed DMA algorithm can achieve a good tradeoff between the EE and the complexity.

Small cell deployment in heterogeneous networks, whereby small cell base stations (SBS) are deployed alongside traditional macro-cell base stations, is a proven solution for enhancing spatial frequency reuse across licensed spectrum in long-term evolution (LTE) networks. In order to mitigate the shortage of licensed spectrum resources, licensed-assisted access (LAA) has been introduced to allow LTE SBSs to share the unlicensed channel with WiFi nodes. As such, a complex yet interesting optimization problem results from the joint utilization of licensed and unlicensed spectrum resources by the SBSs to meet the quality-of-service (QoS) requirements of small cell users (SUEs). In this paper, we highlight the fundamental tradeoff induced by the SBSs between the amount of co-channel interference (CI) resulting from the reuse of licensed spectrum resources and the collision probability (CP) imposed on the co-existing WiFi nodes due to the sharing of unlicensed spectrum resources in such a coexisting LTE LAA-WiFi heterogeneous network deployment. We find that this fundamental tradeoff can be analyzed by developing a power allocation rule with double water-filling lines and the complete set of Pareto optimal solution can be achieved by the weighted Tchebycheff method. Our simulation results show that the proposed joint resource allocation algorithm can achieve a flexible and suitable tradeoff between the licensed spectrum CI and the WiFi CP according to the QoS requirements of SUEs in LTE LAA networks.

Driven by the demand for more radio spectrum resources, mobile operators are looking to exploit the unlicensed spectrum as a complement to the licensed spectrum. LTE-unlicensed (LTE-U), also referred to as licensed-assisted access by the third generation partnership project, is an extension of the LTE standard operating on the unlicensed spectrum. To realize LTE-U, its coexistence with Wi-Fi systems is the main challenge and must be addressed. In this paper, a listen-before-talk access mechanism featuring an adaptive distributed control function protocol is adopted for the small base stations (SBSs), whereby the backoff window size is adaptively adjusted according to the available licensed spectrum bandwidth and the Wi-Fi traffic load to satisfy the quality-of-service requirements of small cell users and minimize the collision probability of Wi-Fi users. Meanwhile, both licensed and unlicensed spectrum bands are jointly allocated to optimize spectrum efficiency. An admission control mechanism is further developed for the SBS to limit collision with Wi-Fi traffic. Extensive simulation results show that the proposed schemes achieve fair and harmonious coexistence between LTE-U small cells and the surrounding Wi-Fi service sets and substantially outperform baseline non-adaptive channel access mechanisms in the unlicensed spectrum.

In this paper, two novel mechanisms are developed for the coexistence of cellular and WiFi systems in unlicensed spectrum. In the opportunistic method, the small cell base station opportunistically selects traffic offloading or resource sharing on each WiFi access point (AP). In the hybrid method, the base station simultaneously offloads users and shares the unlicensed spectrum of each AP. The performances of the proposed methods are analyzed and compared. We find that traffic offloading can achieve better performance than resource sharing when the number of existing WiFi users is below a threshold and the hybrid method achieves the same performance as the resource sharing method when existing WiFi user number is large enough. Numerical results are presented to demonstrate the effectiveness of the proposed methods.

In this paper, we investigate vertical beamforming with antenna downtilt optimization in downlink cellular networks. We first use Gamma distribution to approximate the achievable sum-rates with respect to the antenna downtilts. Then, based on this approximation, we first formulate an optimization to maximize system throughput and then propose a simple heuristic algorithm to find its solution. Simulation result shows that the proposed vertical beamforming with antenna downtilt optimization can significantly improve system sum-rates.

In this paper, we study a multi-user indoor Terahertz (THz) communication system with multiple antenna subarrays. By exploiting the characteristics of the THz channel and the capabilities of large antennas, we design a hybrid beamforming scheme to compensate the severe path loss while keep the system in low complexity. In particular, with the analog beamsteering searching in radio frequency (RF) domain, antenna subarrays are partitioned on a user basis, where data streams are transmitted and steered via a group of subarrays to a specific user. Then, the zero- forcing digital beamformer is implemented at baseband for interference cancellation among different users. Simulation results are provided to verify the effectiveness of the proposed hybrid beamforming scheme for the multi-user THz system, which shows great advantages over the schemes with random subarray assignment and analog-only beamsteering.

Massive multiple-input multiple-output (MIMO) systems have attracted extensive attention recently due to their potentials to provide high system capacity. To obtain the benefits of massive MIMO systems, channel state information (CSI) at the transmitter is essential. The high overhead of traditional channel estimation and feedback scheme for downlink massive MIMO systems makes frequency division duplexing (FDD) impractical. Compressive sensing (CS) is a potential way to alleviate the problem. In this paper, we mainly focus on the CS- based feedback design. To guarantee the performance of the CS-based algorithms, a proper basis to reveal the sparsity of the channel is important. Here, we use the statistical information of the angle-of-departure (AoD) of physical channel paths for the basis design. A l0-norm based basis optimization problem is first formulated. Then, the problem is relaxed by a weighted l1-norm and is solved by an iterative algorithm. The mean-square-error (MSE) performance of the CS-based feedback scheme based on our proposed basis is better than the traditional discrete Fourier transform (DFT) basis.

In this paper, we address the power control problem in an interference network with multiple users transmitting simultaneously on the same channel. We aim at achieving the energy efficiency (EE) balance among difference users. First, a multi-objective optimization problem is formulated, which maximizes the EE of each individual user while guaranteeing their minimum data rate requirements. To find its solution, we adopt two different scalarization methods to combine multiple objectives into a single one, namely, the weighted-sum method and the weighted Tchebycheff method. The problem in the weighted-sum method turns out to be a non-concave sum of- ratios optimization and an effective algorithm is developed based on the concave-convex procedure (CCCP) method. On the other hand, the problem in the weighted Tchebycheff method becomes a generalized fractional programming and we utilize the Dinkelbach method and the CCCP method to solve it. Through numerical simulation, we find that both methods can effectively obtain the Pareto optimal solutions to the multiobjective optimization problem and achieve the EE balance among users as well.

In this paper, an adaptive channel access mechanism is proposed to optimize the performance of licensed-assisted access (LAA) long-term evolution (LTE) small cell networks through joint allocation of licensed and unlicensed spectrum resources all the while ensuring a fair coexistence with Wi-Fi service sets on the unlicensed spectrum. A listen-before- talk (LBT) access mechanism featuring an adaptive distributed control function (DCF) protocol is adopted for the small cell base stations (SBSs), whereby the minimum backoff window size is adaptively adjusted according to the available licensed spectrum bandwidth and Wi-Fi traffic load to satisfy the quality-of-service (QoS) requirements of small cell users (SUs) and minimize the collision probability of Wi-Fi users. Meanwhile, both licensed and unlicensed spectrum bands are jointly allocated to optimize spectrum efficiency. An admission control mechanism is further developed for the SBSs to limit collision with Wi-Fi traffic. Extensive numerical results are presented to demonstrate the effectiveness of the proposed schemes.

Multi-stream aggregation (MSA) allows users to receive data from multiple base stations simultaneously to increase their data rates. In this paper, we propose a joint user association and resource allocation algorithm for MSA systems to achieve energy efficiency (EE) balance among different base stations. The problem is formulated as a non-convex combinatorial sum-of-ratios optimization problem, which is very hard to solve directly. We first relax the combinatorial variables and then transform the problem into a convex optimization problem by the sum-of-ratios algorithm and the successive convex approximation (SCA) method. Based on this, a near-optimal algorithm is developed. Simulation results show that the proposed algorithm can achieve a good performance with a fast convergence speed.

A framework on decentralized interference coordination based on the pricing mechanism is developed for device-to-device (D2D) communication underlying cellular systems to guarantee quality of service (QoS) of both cellular users (CUs) and D2D links. We aim at coordinating two types of interference: inter-layer interference from D2D pairs to CUs and intra-layer interference among D2D pairs. The former is mitigated by the base station through setting a price on the channel being reused by D2D pairs while the latter is solved by a game-theoretic approach, in which the D2D pairs compete for the spectrum until a Nash Equilibrium (NE) is achieved. Finally, numerical results verify that the proposed distributed scheme is effective for the interference coordination and its performance is close to the centralized scheme.

Licensed-assisted access (LAA) has been developed to improve LTE system capacity by using unlicensed bands. However, the energy efficiency (EE) of the LTE system may be degraded by LAA since unlicensed bands are generally less energy-efficient than licensed bands. In this paper, we investigate the EE optimization of LAA systems. We first develop a criterion to determine whether unlicensed bands can be leveraged to improve the EE of LAA systems. We prove that unlicensed bands can be used to improve the EE only when the allocated licensed resource blocks (RBs) are not enough. We then investigate how to jointly allocate licensed and unlicensed RBs to achieve EE fairness among small cell base stations (SBSs), based on the Nash bargaining solution. Numerical results are presented to confirm our analysis and to demonstrate the effectiveness of the proposed algorithm.

In this paper, we investigate three-dimensional (3D) multiple-input multiple-output (MIMO) techniques with dynamic rank selection for long term evolution advanced (LTE-A) downlink cellular networks. To facilitate users to transmit different numbers of data streams, we develop a new structure for designing 3D precoding matrix (PM). Then based on the designed PM, 3D MIMO transmission with rank adaptation is proposed. The performance improvement of the proposed algorithms is demonstrated by system level simulation.

In this paper, we investigate mode selection for coordinated multi-point (CoMP) transmission in downlink LTE-A networks, where inter-cluster interference exists. The proposed scheme selects the single-cell (SC) or coordinated beamforming (CB) transmission mode according to interference level to maximize the weighted sum rate with fairness consideration. To formulate the optimization problem, an approximated closed-form expression for the achievable rate is derived by modeling the probability distributions of signal and interference powers as Gamma distributions. Since the original optimization problem is a combinatorial and non-convex one with high complexity, a low-complexity and sub-optimal algorithm is proposed, which first performs coordinated cell selection and then transmission mode selection. Simulation results show that the closed-form approximation of the achievable rate is very tight and the proposed mode selection scheme can improve the average system throughput by 13% and the 10th percentile user throughput by 10% compared with the existing scheme.

With device-to-device (D2D) communications, a user terminal(UT) can naturally be used as a relay node (RN) and thus inherently support multi-hop transmission. Thus, cell-edge or deeply faded users can obtain a more uniform connectivity experience. In this paper, we investigate mobile association for the UT with the capability of D2D communications in heterogeneous networks (HetNets). We will develop a framework on joint mobile association and transmission mode switching between the direct and the D2D relay modes to improve the system spectrum efficiency (SE) and energy efficiency (EE). We first formulate the optimization problems, and then obtain closed-form solutions. Simulation results show that with the proposed schemes, both SE and EE of the network can be significantly improved compared to the traditional solutions without D2D communications. We also discuss the trade-off between the minimum rate requirement and EE of a network.

In this paper, we investigate robust beamforming to improve the energy efficiency (EE) of wireless networks when only imperfect or partial channel state information (CSI) is available at the transmitter. Due to CSI quantization errors and/or limited feedback information, CSI imperfections can be well modeled by a bounded uncertainty region. We focus on the worst case robust beamforming strategy to optimize the EE of downlink transmission under the deterministic bounded channel model, which merely assumes a maximal channel error magnitude. We start with a single-user single-cell MIMO system and obtain a closed-form design for robust beamforming. For a multicell network, robust beamforming is in a nonconvex fractional form, and the solution cannot be directly extended from the single-cell scenario. To solve this problem efficiently, we resort to a lower bound, instead of the primal problem, and cast it as a semidefinite program (SDP). The robustness and efficiency of the proposed beamforming design are confirmed by computer simulation results.

Heterogeneous network(HetNet) integrated with multipleradio access technologies(RATs) is a promising technique for satisfying the exponentially increasing traffic demand of future cellular systems. In this paper, we investigate energy-efficient resource allocation in a multi-RAT HetNet, aimed at maximizing theenergy efficiency(EE) for each individual user while guaranteeing thequality-of-service(QoS) requirement. Since the EE cannot be simultaneously maximized for every user, amultiple-objective optimization problem(MOOP) is formulated. To find its Pareto optimal solution, we first introduce the concept of Utopia EE, defined as the maximum achievable EE, for each user. Then, using the weighted Tchebycheff method, asingle-objective optimization problem(SOOP) is formulated, which can achieve Pareto optimal solution of the original MOOP. The SOOP is a generalized fractional programming problem that aims to minimize the maximum of several quasiconvex fractional functions. We further transform the problem into an equivalent but better tractable one, and develop an iterative algorithm to effectively solve it. Numerical results demonstrate that the proposed algorithm yields fast convergence, high system EE, and flexible EE tradeoff.

Terahertz (THz) communication is envisioned as a key technology for next-generation ultra-high-speed wireless systems. In this paper, we study an indoor multi-user THz communication system with multiple antenna subarrays. To capture the distance-frequency-dependent characteristics of THz channels, we design a hybrid beamforming scheme with distance-aware multi-carrier transmission, including analog beamforming for user grouping and interference cancellation in radio-frequency (RF) domain and digital beamforming with dynamically selected subarrays at baseband. Specifically, an adaptive power allocation and low-complexity antenna subarray selection policy is developed to serve different users at different distances and reduce the cost of active RF circuits simultaneously, where two greedy subarray selection algorithms are proposed. The effectiveness of the proposed adaptive hybrid beamforming and antenna subarray selection algorithms is verified through simulation results, which achieves significant gains over other nonadaptive and non-distance-aware schemes.

Energy-efficient orthogonal frequency division multiple access (OFDMA) aims to relieve the booming energy consumption in wireless communications networks and has recently received lots of attention. Meanwhile, two-way relay has been extensively studied and proves to be more spectral-efficient than direct transmission and one-way relay in many scenarios. In this paper, we study energy-efficient resource allocation for OFDMA-based two-way relay, maximizing the aggregated energy efficiency (EE) utility while provisioning proportional fairness in EE among different terminal pairs. The energy-efficient joint power and subchannel allocation and active subchannel selection problem is mixed-integer combinatorial and further demonstrates to be nonconvex. To approach the performance limit, we first find an upper-bound solution relying on continuous relaxation and the Lagrange dual method. To reduce the computational complexity, we separate power allocation and subchannel assignment. To this regard, we exploit the hidden concavity and the pseudoconcavity in the subproblems for any fixed subchannel assignment and propose an EE-oriented sequential subchannel assignment policy. Besides, we discover the sufficient condition for early termination of the sequential subchannel assignment without losing the EE optimality. Simulation results demonstrate that the proposed energy-efficient OFDMA-based two-way relay can achieve much larger EE utility while provisioning proportional fairness in EE among different terminal pairs compared to its spectral-efficient counterpart.

This paper investigates energy-efficient device-to-device (D2D) communications in cellular networks. We aim to maximize the overall energy-efficiency (EE) of D2D users and regular cellular users (RCUs) while considering the circuit power consumption and the quality-of-service (QoS) requirements for both types of users as well as power constraints. Three transmission modes, namely, dedicated mode, reusing mode, and cellular mode, are considered for D2D users to share spectrum with RCUs. Parametric Dinkelbach method and concave-convex procedure (CCCP) are adopted to transform the original optimization problems into more tractable forms through sequential convex approximations. Then, interior point method is exploited to obtain the optimal solution. Simulation results show that system EE can be improved significantly with the proposed mode switching algorithm compared with the single mode transmission. Besides, it is also shown that the reusing mode is more preferred in the EE based mode switching while it is the dedicated mode in the spectrum-efficiency (SE) based mode switching in most situations.

Terahertz (THz) communications promise to be the next frontier for wireless networks. Novel solutions should be explored to overcome the hardware constraints and the severe path loss. In this paper, we study a low-complexity indoor THz communication system with antenna subarrays. The Saleh-Valenzuela (S-V) channel model is modified to characterize the THz channel. By exploiting the hybrid beamforming with multiple subarrays, we analyze the ergodic capacity of the system and obtain an upper bound. Furthermore, with the analysis of performance degradation for the uncertainty in THz phase shifters, we provide a guidance on the design of antenna subarray size and number for certain long-term data rate requirements with different distances. Simulation results validate the effectiveness of the ergodic capacity upper bound, and show that the proposed THz system and antenna array structure can efficiently achieve capacity gains and support THz communications.

A pricing-based joint spectrum and power allocation framework is proposed for decentralized interference coordination among device-to-device (D2D) communications and cellular users (CUs), with the quality-of-service guarantee. The interlayer interference from D2D pairs to CUs is controlled by the base station through setting a price for each D2D channel usage. The intralayer interference among D2D pairs is mitigated distributively using a game-theoretic approach, where the D2D pairs compete for the spectrum until a Nash equilibrium is achieved. The effectiveness of the proposed strategy, including a practical scheme with limited signaling overhead, is demonstrated through comparing with a centralized scheme.

In this paper, joint energy-efficient resource allocation for both the base station and users is studied for time division duplex (TDD) systems with carrier aggregation (CA). We aim at balancing the energy efficiency (EE) between downlink and uplink, as well as the EEs among individual users, by joint bandwidth and power allocation on each carrier component (CC). We formulate the optimization problem into maximizing the weighted summation of EEs for the base station and different users, where the weights are used to reflect the levels of importance. The objective function of the problem is a sum of several fractional functions, therefore, nonlinear sum-of-ratios programming needs to be used to solve it, which has not been exploited in resource allocation problems yet. Specifically, a novel transformation is performed to formulate an equivalent but better tractable problem, based on which we develop an iterative algorithm to find the global optimum of the considered problem. Numerical results validate the feasibility, fast convergence, and flexibility of the proposed algorithm in terms of EE balancing.

In this paper, we study antenna arrays for an indoor Terahertz (THz) communication system. The Saleh-Valenzuela (S-V) channel model is modified to characterize the THz channel. We analyze the relationship between the ergodic capacity with hybrid beamforming and the antenna subarray number, and obtain an upper bound. Specifically, the required number of sub-arrays is derived under certain long-term data rate requirement. Simulation results are provided to evaluate the performance of the THz system and verify the analytical results.

In this paper, we develop robust precoding for secondary users (SUs) with quality-of-service (QoS) guarantee based on imperfect channel state information (CSI) to minimize the maximum sum interference power to the primary user (PU). Optimal robust precoder is derived by transforming the original problem into a semidefinite programming (SDP) form. We then propose two suboptimal robust precoding design approaches to simplify the optimization process and to get more structural solutions. Numerical results show the proposed robust precoding methods can provide lower maximal interference powers to the PU compared to the non-robust ones and the performance gaps increase with the channel error region.

In this paper, we propose a novel energy-efficient resource allocation method to simultaneously improve both downlink and uplink energy efficiency (EE) for time division duplex (TDD) systems with carrier aggregation (CA). We aim at EE tradeoff between downlink and uplink by optimizing the power and bandwidth allocation on each carrier component (CC) for each user. The objective function is a sum of several fractional functions, therefore, a novel nonlinear sum-of-ratios programming technique is used to solve it. We first transform the problem into an equivalent and better tractable one and then propose an iterative algorithm to find the global optimum solution. Numerical results show that our method can converge with an acceptable number of iterations and achieve flexible EE tradeoff between downlink and uplink.

In this paper, we investigate robust resource allocation for cognitive radio networks. First, a resource allocation scheme based on stable matching is developed, which takes the preferences of both secondary users and primary users into account. To improve its robustness, we then discuss an ϵ-stable resource allocation scheme. With the help of the properties of ϵ-stable resource allocation, three edge-cutting algorithms are proposed. Numerical results show that the modified algorithms are robust to the channel state information variation.

In this paper, we investigate maximum likelihood equalization (MLE) for a large-scale antenna (LSA) system with single-carrier (SC) modulation over Rician fading channels. Orthogonal frequency division multiplexing (OFDM) is usually used to deal with frequency selectivity of wireless channels. However, for a wireless system with large-scale antennas in a Rayleigh fading channel, by combining the received signals through a matched filter (MF), the frequency selective channel can be converted into a frequency flat channel. As a result, SC modulation can be used directly with a simple one-tap equalizer. In a Rician fading channel, however, the line-of-sight (LOS) path will cause mutliuser-interference (MUI), which cannot be mitigated through MF. As a result, the simple one-tap equalizer leads to an error-floor when the signal-to-noise ratio (SNR) is large. In this paper, MLE is used to improve system performance through multiuser detection. From both theoretical analysis and simulation results, the proposed approach can eliminate the error floor and outperform existing approach.

Energy-efficient design in orthogonal frequency division multiple access (OFDMA) is becoming increasingly important, considering the booming energy consumption in wireless networks and the extensive deployment of OFDMA-based wireless infrastructures. On the other hand, wireless power transfer brings another promising approach for saving energy and prolonging lifetime in wireless networks by letting users scavenge energy from the received radio-frequency (RF) signals all over the wireless environments, including the desired information beams and undesired interference beams. In this paper, we study the downlink and uplink energy efficiency (EE) tradeoff in time-division duplexing (TDD) OFDMA with one access point (AP) and multiple users, where the users are allowed to split the received signals for decoding information and harvesting energy in the downlink, respectively. The established optimization problem turns to be an integer-mixed nonconvex program. Through relaxation and transformation, we develop a near-optimal resource allocation strategy that approaches the Pareto optimal tradeoff performance. Numerical results show that there may exist a tradeoff between the downlink and uplink EE and the AP can help improve the uplink EE by operating the downlink at the suboptimal EE regime.

In this paper, we study a multiuser indoor Terahertz (THz) communication system with antenna array. To capture the distance-frequency dependent peculiarities of THz channels, we design a distance-aware multi-carrier transmission scheme with hybrid beamforming and antenna subarray selection. Specifically, a low complexity greedy subarray selection algorithm is proposed. Simulation results are provided to verify the effectiveness of the transmission policy as well as the antenna subarray selection algorithm for the multiuser THz system.

In this paper, we investigate multiuser MIMO scheduling in LTE downlink cellular networks. We formulate the downlink LTE-A MIMO scheduling as a weighted sum rate maximization problem by allocating the RBs to users or user pairs subject to some constraints in LTE-A. We develop a low-complexity MU-MIMO scheduling and resource allocation algorithm and propose an adaptive switching approach between SU-MIMO and MU-MIMO to improve network performance. System-level simulation results demonstrate significantly performance improvement of the proposed MU-MIMO scheduling algorithm and the adaptive switching algorithm.

In this paper, we investigate multi-user (MU) multiple-input multiple-output (MIMO) scheduling for long term evolution advanced (LTE-A) downlink cellular networks, where only limited feedback designed for single-user (SU) MIMO is available. To help the base station (BS) allocate resource blocks (RBs), we develop codebook grouping and MU-SINR estimation techniques. Two adaptive SU/MU-MIMO scheduling methods are proposed to improve system throughput. Although the proposed adaptive SU/MU- MIMO scheduling algorithms exploit the only limited feedback in the LTE-A specification, they can improve the network throughput by 13% from system level simulation.

In this paper, we discuss three different design criteria for a distributed antenna system (DAS). They are maximizing throughput under the constraint of the overall transmit power, minimizing the overall transmit power while guaranteeing the minimum spectral efficiency (SE) requirements, and maximizing energy efficiency (EE) under the constraints of minimum SE requirements and overall transmit power. We use sub-gradient iteration approach to solve the first two optimization problems and exploit fractional programming method to deal with the third one. Based on these design criteria, three power allocation algorithms are developed for the downlink multi-user DAS. Depending on application enviroments, we can use the first and second criteria to achieve the highest throughput and to save the most energy, respectively, while we can balance throughput and energy consumption using the third criterion.

In this paper, we investigate multi-cell coordinated scheduling and power allocation in downlink long term evolution advanced (LTE-A) systems, where orthogonal frequency division multiple-access (OFDMA) is used. The proposed scheme performs joint scheduling, power allocation, and modulation and coding scheme (MCS) selection to maximize the overall weighted throughput with proportional fairness. Our scheme considers the practical constraints in LTE-A systems. Since the optimization problem is a combinatorial and non- convex one and is with high complexity, low- complexity and suboptimal algorithms are proposed, which separate the scheduling and power allocation into two subproblems. Simulation results show that the proposed scheme can improve the average system throughput by 10% and the 10th percentile throughput by 15% compared with the existing scheme.

Cognitive radio (CR) and energy-efficient design have emerged as two promising techniques to achieve high spectrum efficiency (SE) and energy efficiency (EE), respectively. In this paper, we study energy-efficient opportunistic spectrum access strategies for an orthogonal frequency division multiplexing (OFDM)-based CR network with multiple secondary users (SUs). Both worst EE and average EE are considered and optimized for different emphases and application scenarios. Since the original optimization issues belong to nonconvex integer combinatorial fractional program and are essentially NP-hard for an optimal solution, we use continuous and convex relaxation to modify the problems for somewhat better mathematical tractability. For the relaxed worst-EE-based spectrum access problem, we first demonstrate the joint quasiconcavity of EE on subchannel and power allocation matrices and then develop a framework to find the optimal solution based on efficient root finding and convex optimization. We also develop a low-complexity alternative for suboptimal solution. The relaxed average-EE-based spectrum access problem is still NP-hard and may have many local optima. We first transform the problem into an equivalent form and introduce a general concave envelope based branch-and-bound (B&B) approach to find the global optimal solution. We then exploit the underlying properties of the energy-efficient transmission to speed up the convergence of the B&B approach. Besides, we develop a low-complexity heuristic approach to find a suboptimal solution. Simulation results show that the energy-efficient spectrum access strategies significantly boost EE compared with the conventional spectral-efficient spectrum access ones while the low-complexity suboptimal approaches can well balance the performance and complexity.

Device-to-device (D2D) communications have been recently proposed as an effective way to increase both spectrum and energy efficiency for future cellular systems. In this paper, joint mode selection, channel assignment, and power control in D2D communications are addressed. We aim at maximizing the overall system throughput while guaranteeing the signal-to-noise-and-interference ratio of both D2D and cellular links. Three communication modes are considered for D2D users: cellular mode, dedicated mode, and reuse mode. The optimization problem could be decomposed into two subproblems: power control and joint mode selection and channel assignment. The joint mode selection and channel assignment problem is NP-hard, whose optimal solution can be found by the branch-and-bound method, but is very complicated. Therefore, we develop low-complexity algorithms according to the network load. Through comparing different algorithms under different network loads, proximity gain, hop gain, and reuse gain could be demonstrated in D2D communications.

To support the heterogeneous demands for the network and mobile user equipment (UE), multiple radio access technologies (RATs), operating with different system configurations and resources, have evolved and now coexist. Recently, with data traffic exponentially increasing, there has been a significant expansion in wireless network infrastructure, raising a justifiable concern over the concomitant drastic increase in energy consumption. Thus, energy-efficient design in multi-RAT networks is becoming increasingly important. In this paper, we consider resource allocation that maximizes the energy efficiency (EE) for orthogonal frequency division multiple access (OFDMA) in multi-RAT networks. To this end, we present the optimal resource allocation problem for parallel transmission utilizing multiple RATs. Since the formulated problem is NP-hard, a modified problem is proposed, for which a near-optimal resource allocation algorithm is developed by exploiting the intrinsic quasiconcavity of the problem. To reduce the computational complexity, we develop a low-complexity suboptimal allocation strategy based on joint iterative subcarrier and waterfilling power allocation over multiple RATs. Simulation results show that the proposed algorithms can achieve a higher EE compared to a conventional spectral-efficiency-based approach and can obtain performance comparable with the optimal solution, but with much less complexity.

Iterative channel estimation (ICE) usually exploits soft information of unknown data symbols as references to improve estimation performance. This paper investigates ICE for orthogonal frequency division multiplexing (OFDM) over wireless channels. The optimum ICE is derived in terms of maximum a posteriori (MAP) criterion, which can be solved using fixed-point iteration (FPI). Furthermore, the derived MAP ICE is closely related to the well-known expectation-maximization (EM) estimation. We also demonstrate that the MAP ICE converges within only one step when the signal-to-noise ratio (SNR) is large through analysis and simulation results.

This paper investigates iterative channel estimation (ICE) for orthogonal frequency-division multiplexing (OFDM) with multiple transmit antennas. To improve performance of channel estimation, we exploit the soft information of unknown data symbols on both the expected transmit antenna and the interfering transmit antenna. Maximum a posteriori (MAP)-based ICE is derived and is implemented using the fixed-point iteration (FPI). For an OFDM system with multiple transmit antennas, the proposed MAP-based ICE suggests a harmonic-average-based soft symbol on the expected transmit antenna while an arithmetic-average-based soft symbol on the interfering transmit antennas. Similar to an OFDM system with a single transmit antenna, MAP-based ICE can achieve the Cramer-Rao bound (CRB) within only one iteration, when the signal-to-noise ratio (SNR) is large enough.

In this paper, we investigate optimal resource allocation for device-to-device (D2D) communication underlaying cellular network in fading channels. We consider a scenario that the instantaneous channel power gain of interference links from regular cellular users (CUs) to D2D users are unknown at base station (BS) since obtaining the channel-state-information (CSI) in this case is difficult and requires high overhead. We assume that BS provides guaranteed quality-of-service (QoS) in terms of signal-to-interference-plus-noise-ratio (SINR) for CUs and outage probability for D2D pairs, respectively. Based on the assumptions, we first propose a probabilistic access control for D2D pairs to satisfy all the QoS requirements and power constraints. We then derive joint power and channel allocation to maximize the overall throughput of the CUs and admissible D2D pairs. Through simulation, we show the effectiveness of the proposed probabilistic strategy and there exists an optimal threshold of the targeted outage probability with respect to D2D access rate and overall network throughput.

In this paper, we investigate resource allocation for underlay cognitive radio (CR) networks, where the CR users coexist but do not cause unacceptable interference with licensed users. We focus on the energy efficiency (EE) performance of the system, where both throughput and energy consumption will be considered. We first formulate a sum-EE maximization problem and solve it by the Hungarian algorithm, called sum- EE-based scheme. Then, we take into account the preferences of CR users and licensed users and motivate a resource allocation scheme based on stable matching, called preference-based scheme. Numerical results demonstrate that the preference-based scheme has up to 50% performance gain on interference over the sum-EE-based scheme, while the former scheme has around 5% performance loss on EE performance compared to the latter one.

Cognitive radio (CR) technology has been developed to solve the spectrum-underutilization problem. In CR networks, the CR users have opportunities to access the licensed spectrum bands assigned to the primary users (PUs). Since the PUs have priorities to use the bands, the CR users are not allowed to generate unacceptable interference to them. In this paper, we investigate power allocation schemes for CR networks with both direct and relay-aided transmissions. We formulate an overall rate optimization problem with interference constraints to the PU and peak power constraints at each node and obtain solutions by theoretical analysis. Numerical results are provided to show the impact of the relay node and the PU locations on power allocation.

In device-to-device (D2D) communications underlaying uplink (UP) cellular networks, the channel state information (CSI) of interference links between regular cellular users (CUs) and D2D receivers is necessary to provide guaranteed quality-of-service (QoS) to D2D users. However, getting the CSI is very difficult and requires high overhead. In this paper, we propose a selected-K maximum distance ratio (MDR) feedback scheme (KMDR) to reduce feedback overhead, in which each D2D receivers only needs to feedback CSI of K CUs with the largest MDR metric. Simulation results show that up to 80% feedback can be reduced at D2D receivers by KMDR while still providing a near optimal performance. We also study the effect of side information at the D2D receivers. It is shown that it is possible to further reduce the feedback information when full side information is known at the D2D receivers.

Multi-cell coordinated beamforming (CB) can mitigate inter-cell interference. However, previous study on CB focuses on systems with only one receive antenna. This paper considers CB for systems with multiple receive antennas. To take fairness among scheduled users into account, CB is designed to maximize the harmonic sum of signal-to-interference-plus-noise ratio (SINR). We develop an iterative algorithm that can guarantee convergence. Simulation shows that the proposed algorithm have 70% and 47% throughput gains over single cell beamforming for 10th percentile user throughput and median user throughput, respectively.

In LTE uplink systems, single-carrier frequency-division multiple access (SC-FDMA) has been employed. In SC-FDMA, orthogonal frequency resources are assigned to different users to avoid intra-cell interference. However, inter-cell interference (ICI) caused by the users in neighboring cells significantly deteriorates the performance. Cooperation among base stations must be used to deal with ICI for multi-cell systems. In this paper, we investigate multi-cell scheduling in SC-FDMA for LTE uplink. We propose a novel cooperative scheduling algorithm that takes inter-cluster and intra-cluster interference into account. We first perform coordinated scheduling and then link adaptation to select modulation and coding scheme (MCS). Simulation results show that the proposed algorithm has significant gains over the single-cell proportional fair (PF) scheduling algorithm on both cell-edge and average throughput. It also outperforms the existing cooperative algorithm under full path loss compensation and fractional open-loop power control (OLPC).

Cognitive radio (CR) and energy-efficient design have emerged as two promising technologies to achieve high spectrum efficiency (SE) and energy efficiency (EE), respectively. In this paper, we study energy-efficient opportunistic spectrum access strategies for multiple secondary users (SUs) in an orthogonal frequency division multiplexing (OFDM)-based CR network. The worst EE is considered and optimized to achieve the max-min fairness. Since the original optimization issue belongs to nonconvex integer combinatorial fractional program and are essentially NP-hard for optimal solution, we use continuous and convex relaxation to modify the problem for somewhat better mathematical tractability. For the modified problem, we first develop a framework to find the optimal solution based on efficient root finding and convex optimization. We also develop a low-complexity alternative for suboptimal solution to further reduce complexity. Simulation results show that the energy-efficient spectrum access strategies significantly boost EE compared with the conventional spectral-efficient spectrum access ones. And the low-complexity suboptimal approaches can well balance the performance and complexity.

In this paper, we propose an optimal scheme for a distributed antenna system (DAS) to maximize energy efficiency (EE) under a constraint of overall transmit power of each remote access unit (RAU). We exploit the multicriteria optimization method to systematically investigate the relationship between EE and spectral efficiency (SE). Using the weighted sum method, we first convert the multicriteria optimization function, which is extremely complex, into a simpler single objective optimization function. Then an optimal algorithm is developed to allocate the available power to tradeoff EE and SE effectively. Furthermore, we also illustrate the effectiveness of the proposed method and demonstrate there is a tradeoff between energy-efficient and spectral-efficient transmission through computer simulation of a downlink multiuser DAS.

In this paper, we study interference-free uplink transmission for two-cell multiple-antenna cognitive radio (CR) networks. We consider a scenario that the primary user (PU) transmission is based on orthogonal frequency-division multiplexing (OFDM) and the nullspace generated by the cyclic-prefix (CP) can be used for CR transmission using Vandermonde-subspace frequency division multiplexing (VFDM). Besides interference to the PU system, we will take intra- and inter-cell interference within CR networks into consideration. A signal alignment scheme will be developed to protect the PU transmission while aligning inter-cell interference into a lower dimensional-subspace. When the interference signals are perfectly aligned, the number of interference-free symbols that can be transmitted by the whole CR network will increase with the number of CR users. Numerical results show significant performance gain can be obtained by using the proposed scheme.

In this paper, we investigate an energy-efficient coordinated multiple point (CoMP) transmission strategy for downlink heterogeneous cellular networks. We combine CoMP joint processing (CoMP-JP) and coordinated beamforming (CoMP-CB), two special cases of CoMP, in a time division manner to improve both energy efficiency (EE) and spectral efficiency (SE). We formulate the problem as minimizing the total transmit power consumed by both the macro- and pico-base stations (BSs) under the constraints on the data rate requirements from the macro- and pico-users, and on the maximum transmit powers of the macro- and pico-BSs. Both the transmit time and the transmit powers allocated to the CoMP-JP and CoMP-CB transmissions are optimized. Simulation results show that the hybrid CoMP-JP and CoMP-CB strategy provides a larger capacity region than the CoMP-JP-only or CoMP-CB-only transmission. The time proportion of the CoMP-JP in the hybrid strategy decreases with the data rate requirement of the macro-user and increases with the maximum transmit power of the pico-BS and the average channel gain from the macro-BS to the macro-user. Increasing the transmit power of the pico-BS can improve the EE in the high SE region of the macro-user.

Energy efficiency(EE) has caught more and more attention in future wireless communications due to steadily rising energy costs and environmental concerns. In this paper, we propose an EE scheme with proportional fairness for the downlink multiuser distributed antenna systems (DAS). Our aim is to maximize EE, subject to constraints on overall transmit power of each remote access unit (RAU), bit-error rate (BER), and proportional data rates. We exploit multi-criteria optimization method to systematically investigate the relationship between EE and spectral efficiency (SE). Using the weighted sum method, we first convert the multi-criteria optimization problem, which is extremely complex, into a simpler single objective optimization problem. Then an optimal algorithm is developed to allocate the available power to balance the tradeoff between EE and SE. We also demonstrate the effectiveness of the proposed scheme and illustrate the fundamental tradeoff between energy- and spectral-efficient transmission through computer simulation.

Orthogonal frequency-division multiplexing (OFDM) is an ideal transmission technique for cognitive radio (CR) systems because of its flexible nature to support dynamic spectrum access. However, the out-of-band (OOB) radiation of OFDM signals from different CR users must be strictly controlled to protect licensed users operating in the adjacent frequency bands. In this paper, we propose a spectral precoding approach for multiple OFDM-based CR users to reduce OOB leakage and enhance spectrum compactness. By constructing individual precoders to render selected spectrum nulls, our approach suppresses the overall OOB radiation without sacrificing bit-error rate performance of CR users. The proposed approach also ensures user independence thus with low encoding and decoding complexities. Furthermore, our approach can improve bandwidth efficiency by carefully selecting notched frequencies. As a comprehensive application of the proposed approach, two simplified multiuser spectral precoding schemes are provided to reduce the computational complexity. Simulation results demonstrate that our spectral precoding schemes effectively limit OOB radiation and enable efficient spectrum sharing.

In cellular networks, proximity users may communicate directly without going through the base station, which is called Device-to-device (D2D) communications and it can improve spectral efficiency. However, D2D communications may generate interference to the existing cellular networks if not designed properly. In this paper, we study a resource allocation problem to maximize the overall network throughput while guaranteeing the quality-of-service (QoS) requirements for both D2D users and regular cellular users (CUs). A three-step scheme is proposed. It first performs admission control and then allocates powers for each admissible D2D pair and its potential CU partners. Next, a maximum weight bipartite matching based scheme is developed to select a suitable CU partner for each admissible D2D pair to maximize the overall network throughput. Numerical results show that the proposed scheme can significantly improve the performance of the hybrid system in terms of D2D access rate and the overall network throughput. The performance of D2D communications depends on D2D user locations, cell radius, the numbers of active CUs and D2D pairs, and the maximum power constraint for the D2D pairs.

In this paper, we investigate adaptive configuration of spatial and frequency resources to maximize energy efficiency (EE) and reveal the relationship between the EE and the spectral efficiency (SE) in downlink multiple-input-multiple-output (MIMO) orthogonal frequency division multiple access (OFDMA) systems. We formulate the problem as minimizing the total power consumed at the base station under constraints on the ergodic capacities from multiple users, the total number of subcarriers, and the number of radio frequency (RF) chains. A three-step searching algorithm is developed to solve this problem. We then analyze the impact of spatial-frequency resources, overall SE requirement and user fairness on the SE-EE relationship. Analytical and simulation results show that increasing frequency resource is more efficient than increasing spatial resource to improve the SE-EE relationship as a whole. The EE increases with the SE when the frequency resource is not constrained to the maximum value, otherwise a tradeoff between the SE and the EE exists. Sacrificing the fairness among users in terms of ergodic capacities can enhance the SE-EE relationship. In general, the adaptive configuration of spatial and frequency resources outperforms the adaptive configuration of only spatial or frequency resource.

Cognitive radio (CR) technology has been developed to solve the spectrum-underutilization problem. In CR networks, the CR users have opportunities to access the licensed spectrum bands assigned to the primary users (PUs). Since the PUs have priorities to use the bands, the CR users are not allowed to generate unacceptable interference to them. In this paper, we investigate power allocation schemes for CR networks with both direct and relay-aided transmissions. We first formulate an overall rate optimization problem with interference constraints to the PU and peak power constraints at each node and obtain solutions by theoretical analysis. To take the fairness among CR users into consideration, we further investigate the overall rate optimization problem with an additional sum power constraint and achieve fairness between two CR users by adjusting the sum power threshold. Numerical results are provided to show the impact of the relay node and the PU locations on power allocation solutions.

Scheduling can obtain multi-user diversity if channel state information (CSI) is known, such as for low-mobility users and can exploit frequency diversity if CSI is not available at the transmitter, such as for high-mobility users. In this paper, we investigate resource allocation exploiting frequency and multiuser diversity for LTE downlink systems with users of different mobilities. To facilitate resource allocation, we first develop a user classification algorithm to identify high- and low-mobility users. Based on user mobility classification, we then propose a scheduling algorithm to simultaneously obtain multi-user diversity for those low-mobility users and frequency diversity for those high-mobility users. It is demonstrated by computer simulation that the performance of the proposed scheduling algorithm provides 6% and 23% gain of overall cell throughput, and 5.6% and 18% gain of 10th percentile throughput over proportional fairness based frequency-selective and frequency-diversity scheduling algorithms, respectively. Furthermore, the proposed scheduling algorithm has the same order of computational complexity as the frequency-selective scheduling algorithm.

In LTE systems with heterogeneous user mobilities, low-mobility users favor frequency selective scheduling while high-mobility users benefit from frequency diversity scheduling. To benefit both low- and high-mobility users simultaneously, scheduling exploiting frequency selectivity and diversity is desired. To enable the scheduling, low-complexity user mobility classification to distinguish these two types of users is required. In this paper, we first propose a user mobility classification algorithm, which is robust to different channel delay profiles (CDPs), for single-transmit-antenna systems. Then, we extend it to multiple-input multiple-output (MIMO) systems. A low-complexity scheduling algorithm, exploiting both frequency-selectivity and diversity for low- and high-mobility users simultaneously, is also developed. As demonstrated by the simulation results, the proposed user classification algorithm is robust to different CDPs and the proposed scheduling algorithm is effective.

In this paper, a frequency-domain cross-layer collision-tolerant (CT) media access control (MAC) scheme is proposed for the up-links of broadband wireless networks with asynchronous users. The collision tolerance is achieved with a frequency-domain on-off accumulative transmission (FD-OOAT) scheme, where the spectrum is divided into a large number of orthogonal sub-channels, and each symbol is transmitted over a small subset of the sub-channels to reduce collisions. Such a radio resource management scheme renders a special signal structure that enables multi-user detection (MUD) in the physical layer to resolve the collisions at the MAC layer. Most existing MUDs require precise symbol level synchronization among users. The proposed scheme, however, can operate with asynchronous users. A new theoretical framework is provided to study the impacts of time-domain user delays on system performance. Both analytical and simulation results demonstrate that the proposed FD-OOAT structure with time-domain oversampling is robust to user delays and the timing phase offset caused by the sampling clock difference between the transmitter and the receiver. It is shown that the proposed scheme can achieve significant performance gains, in terms of both the number of users supported and the normalized throughput.

In this paper, a cross-layer collision-tolerant (CT) media access control (MAC) scheme is proposed for wireless networks. Unlike conventional MAC schemes that discard and retransmit signals colliding at a receiver, the CT-MAC extracts the salient information from the colliding signals with a new on-off accumulative transmission (OOAT) scheme in the physical layer. Users employing OOAT deliver information to the base station (BS) through uncoordinated on-off transmissions of multiple identical sub-symbols (accumulative transmission). Silence periods are inserted between sub-symbols inside a frame to reduce collision probability and render a special signal structure for physical layer detection. Algebraic properties of the on-off transmission patterns, which are represented as cyclic-shifted binary vectors, are analyzed, and the results provide guidelines on the design of OOAT systems and other systems that rely on cyclic-shifted binary vectors. Then, we demonstrate that the structure of the on-off transmission patterns enables a sub-optimum iterative detection method, which improves performance by iteratively exchanging extrinsic soft information between a forward and a backward soft interference cancellation (SIC). Both analytical and simulation results show that the new CT-MAC with OOAT scheme significantly outperforms many existing cross-layer MAC schemes in terms of the number of users supported and the normalized throughput.

The tremendous popularity of smart phones and electronic tablets has spurred the explosive growth of high-rate multimedia services and promptly boomed energy consumption in wireless networks. Therefore, energy-efficient design in wireless networks is very important and is attracting more and more attention, just like the conventional spectral-efficient design. In this paper, we study energy-efficient design in downlink orthogonal frequency division multiple access (OFDMA) networks with effective capacity-based delay provisioning for delay-sensitive traffic. By integrating information theory with the concept of effective capacity, we formulate an energy efficiency (EE) optimization problem with statistical delay provisioning, which is a complicated nonconvex combinatorial fractional programming problem. To solve the problem, we first relax it with an upper bound on the original one and then prove and exploit the quasiconcave property of the EE-versus-transmit power curve, which facilitates the optimal algorithm development. Then, we demonstrate that the resultant solution is quite close to the true optimal value when the number of subcarriers is larger than that of the users. We also analyze the tradeoff between EE and delay, the relationship between spectral-efficient and energy-efficient designs, and the impact of system parameters, including circuit power and delay exponents, on the overall performance. Numerical results show that the proposed energy-efficient design scheme greatly improves EE while maintaining the delay requirement.

In this paper, we investigate multiuser pairing and resource allocation with interference avoidance in LTE uplink cellular networks. We first derive the received signal-to-interference-plus-noise ratio (SINR) in spatial multiuser SC-FDMA systems with frequency-domain minimum mean-square error (MMSE) equalization. Based on it, we formulate an optimization multiuser pairing and resource allocation problem to maximize system weighted throughput. With the help of the exchange of high interference indicators among multiple base stations, we develop a distributed joint optimal algorithm and a distributed low-complexity algorithm. Simulation results show that the proposed algorithm with interference avoidance significantly outperforms the algorithm without interference avoidance in [12].

The wide spread usage of mobile smart phones has put an emphasis on high efficiency of wireless networks. Inter-cell interference coordination (ICIC) techniques not only help improve cellular data coverage but also allow more efficient use of the valuable wireless spectrum. This paper discusses soft frequency reuse (SFR), a form of ICIC, for LTE systems. We develop a SFR approach that takes both throughput and fairness among multi-users into consideration. Computer simulation demonstrates that the cell average throughput can be increased as large as 17% while maintaining the same cell edge user throughput or the cell edge throughput can be increased by 11% while maintaining the same cell average throughput compared to traditional non-ICIC wireless networks.

The ubiquitous applications of high-data-rate realtime wireless services have promptly boomed energy consumption in wireless networks. Therefore, energy-efficient design in wireless networks is very important and is attracting more and more research attention. In this paper, we study the quality-of-service (QoS) driven energy-efficient design in the downlink orthogonal frequency division multiple access (OFDMA) network. By integrating information theory with the concept of effective capacity, we formulate an energy efficiency (EE) optimization problem with statistical QoS provisioning. To solve the problem, we first modify it with a tight upper bound on the original EE and solve the modified problem. Then, we demonstrate that the resultant solution is quite close to the true optimal value when the number of subcarriers is large than that of the users. We also find out the tradeoff relation between EE and delay. Numerical results show that the proposed energy-efficient design scheme greatly improves EE whiling maintaining QoS requirements.

We develop cognitive resource allocation scheme to mitigate co-tier and cross-tier interference in overlay femtocell networks. By exploiting statistical models for characterizing multitier interference, our scheme avoids prohibitive exchange of realtime interference statistics in the network. The proposed scheme is independently implemented at each femtocell and allocates resources distributedly in response to the probabilistic interference conditions in the network. This “self-organizing” framework can be useful to address interference management in dense, ad-hoc, and consumer-deployed femtocell networks. Simulation results show that the proposed scheme can improve the throughput of femtocell links while simultaneously reducing cross-tier interference.

In this paper, we propose a new cross-layer technique that utilizes frequency-domain on-off accumulative transmission (OOAT) in the physical layer to achieve collision-tolerance in the media access control (MAC) layer. The frequency-domain OOAT is developed for wideband systems operating in frequency-selective fading channels. The available spectrum is divided into a large number of orthogonal non-overlapping sub-channels. To achieve collision tolerance, each symbol is transmitted over a set of randomly chosen sub-channels to reduce the probability of collision. Spreading the signal over the frequency-domain also enables frequency diversity, and further improves system performance. Performance of the proposed scheme is analyzed and a performance bound, matched filter bound, is derived. Simulation results show that the proposed scheme can support more active users simultaneously than sub-channels, and it achieves a higher spectral efficiency compared to conventional MAC schemes.

In this paper, we investigate adaptive configuration of spatial and frequency resources to maximize energy efficiency (EE) and reveal the relationship between the EE and the spectral efficiency (SE) in downlink multiple-input-multiple-output (MIMO) orthogonal frequency division multiple access (OFDMA) systems. We formulate the problem as minimizing the total power consumed at the base station under constraints on the average data rates from multiple users, the total number of subcarriers, and the number of radio frequency (RF) chains. We develop a two-step searching algorithm to solve this problem, which first finds the near-optimal numbers of subcarriers for multiple users based on Karush-Kuhn-Tucker (KKT) conditions and then optimize the number of active RF chains. Simulation results demonstrate that increasing frequency resource improves both the SE and the EE, and is more efficient than increasing spatial resource. Consequently, there exists tradeoff between the SE and the EE only when the frequency resource is limited. In general, the adaptive configuration of spatial and frequency resources outperforms the adaptive configuration of only spatial resource and that of only frequency resource.

In this paper, we develop a scheduling algorithm to obtain multi-user diversity for those low-mobility users and frequency diversity for those high-mobility users. Computer simulation demonstrates that the proposed scheduling algorithm provides 10% and 16% overall cell throughput gain over proportional fairness based frequency-selective and frequency-diversity scheduling algorithm, respectively. In addition, the proposed scheduling algorithm is shown to have the same order of computational complexity as the frequency-selective scheduling algorithm, and can be easily implemented in the LTE downlink systems.

Widespread application of multimedia wireless services and requirement of ubiquitous access have triggered rapidly booming energy consumption at both transmitter and the receiver sides. Hence, energy-efficient design in wireless networks is very important and is becoming an inevitable trend. In this paper, we take decoding power into consideration when studying joint transmitter and receiver design for achieving high energy efficiency (EE). Based on a new function between transmit power and data rate that is derived by minimizing a lower bound on the overall transmit and receiver power, we investigate when and how should the decoding power be considered for optimizing EE. We find that the decoding power cannot be ignored for short-range wireless communications with a large bandwidth where the transmit power is usually low and give an analytical expression for quantifying the impact of decoding power on EE.

Cognitive radio (CR) can successfully deal with the growing demand and the scarcity of the wireless spectrum. To increase the spectrum usage, CR technology allows secondary users to access licensed spectrum bands. Since licensed users have priorities to use the bands, the secondary users need to continuously monitor the activities of the licensed users to avoid interference and collisions. In this paper, we design sequential detection to maximize the throughput of secondary users for a given detection probability of the licensed users. We further investigate the impact of different system parameters on the performance. Numerical results are given to verify the theoretical analysis and demonstrate the performance.

The explosively increasing demand of high-data-rate multimedia wireless services and ubiquitous access has triggered rapidly booming energy consumption at the wireless network operator side. Therefore, energy-efficient design is becoming a mainstream for future wireless networks. In this paper, we study energy-efficient resource allocation in downlink OFDMA networks with partial channel state information at the transmitter (CSIT). To reduce the channel state information (CSI) feedback overhead while maintaining relatively high achievable energy efficiency (EE), we propose a novel CSI feedback scheme, which leads to higher EE with lower feedback overhead compared with the conventional selective feedback (SF) and bit-map based feedback (BF) schemes. Simulation results show that the energy-efficient design greatly improves EE compared with that of the conventional spectral-efficient design and our CSI feedback scheme outperforms the conventional CSI feedback schemes in EE and spectral efficiency (SE) with almost the same feedback overhead.

Orthogonal frequency-division multiplexing (OFDM) is an ideal transmission technique for dynamic spectrum access in cognitive radio (CR) systems. In this letter, we propose low-complexity spectrum shaping to enable fast decaying of power spectral sidelobes and enhance spectral compactness for OFDM-based CR systems. Based on a basic scheme mapping antipodal symbol pairs onto adjacent subcarriers, we present two modified schemes to further balance sidelobe suppression and system throughput. Compared with existing spectrum shaping schemes, ours exhibit their advantage of both simplicity and flexibility.

Energy-efficient wireless communication is very important for battery-constrained mobile devices. For mobile devices in a cellular system, uplink power consumption dominates the wireless power budget because of RF power requirements for reliable transmission over long distances. Our previous work in this area focused on optimizing energy efficiency by maximizing the instantaneous bits-per-Joule metric through iterative approaches, which resulted in significant energy savings for uplink cellular OFDMA transmissions. In this paper, we develop energy efficient schemes with significantly lower complexity when compared to iterative approaches, by considering time-averaged bits-per-Joule metrics. We consider an uplink OFDMA system where multiple users communicate to a central scheduler over frequency-selective channels with high energy efficiency. The scheduler allocates the system bandwidth among all users to optimize energy efficiency across the whole network. Using time-averaged metrics, we derive energy optimal techniques in "closed forms" for per-user link adaptation and resource scheduling across users. Simulation results show that the proposed schemes not only have low complexity but also perform close to the globally optimum solutions obtained through exhaustive search.

The widespread application of multimedia wireless services and requirements of ubiquitous access have triggered rapidly booming energy consumption at both the base station side and the user equipment (UE) side. Hence, energy-efficient design in wireless networks is very important and is becoming an inevitable trend. In this paper, we study the energy-efficient resource allocation in both downlink and uplink cellular networks with orthogonal frequency division multiple access (OFDMA). For the downlink transmission, the generalized energy efficiency (EE) is maximized while for the uplink case the minimum individual EE is maximized, both under certain prescribed per-UE quality-of-service (QoS) requirements. For both transmission scenarios, we first provide the optimal solution and then develop a suboptimal but low-complexity approach by exploring the inherent structure and property of the energy-efficient design. For the downlink case, by modifying the original problem, we also find a computationally efficient and numerically tractable upper bound on the EE, which indicates the performance limit and is demonstrated to be quite tight if the number of subcarriers is larger than that of UEs and motivates us to find a near-optimal approach relying on the quasiconcave relation between the modified EE and transmit power. Simulation results show that the energy-efficient design greatly improves EE compared with the conventional spectral-efficient design and the low-complexity suboptimal approaches can achieve a promising tradeoff between performance and complexity.

In this paper, power allocation between pilots and data symbols is investigated to maximize energy efficiency (EE) for downlink orthogonal frequency division multiple access (OFDMA) networks. We first derive an EE function considering channel estimation error, which depends on large-scale channel gains of multiple users, allocated power to pilots and data symbols, and circuit power consumption. Then an optimization problem is formulated to maximize the EE under overall transmit power constraint. Exploiting the quasiconcavity property of the EE function, we propose an alternating optimization method in the low transmit power region and reformulate a joint quasiconcave problem in the high transmit power region. Analysis and simulation results show that the power ratio for pilots decreases with the circuit power. When the circuit power is small, the optimal overall transmit power increases with the circuit power. Otherwise, the optimal transmit power does not depend on it. Transmitting more data symbols to the users with higher channel gains improves the EE but at a cost of sacrificing the fairness among multiple users. Simulation results also demonstrate that compared with spectral efficiency (SE)-oriented design, the EE-oriented design can improve the EE performance significantly with a relatively small SE loss.

In this paper, we solve a fundamental problem: how to use distributed random access to achieve the performance of centralized schedulers. We consider wireless networks with arbitrary topologies and spatial traffic distributions, where users can receive traffic from or send traffic to different users and different communication links may interfere with each other. The channels are assumed heterogeneous, and the random channel gains of different links may have different distributions. To resolve the network contention in a distributed way, each frame is divided into contention and transmission periods. The contention period is used to resolve conflicts, while the transmission period is used to send payload in collision-free scenarios. We design a multistage channel-aware Aloha scheme for the contention period to enable users with relatively better channel states to have higher probabilities of contention success while assuring fairness among all users. We show analytically that the proposed scheme completely resolves network contention and achieves throughput close to that of centralized schedulers. Furthermore, the proposed scheme is robust to any uncertainty in channel estimation. Simulation results demonstrate that it significantly improves network performance while maintaining fairness among different users. The proposed random access approach can be applied to different wireless networks, such as cellular, sensor, and mobile ad hoc networks, to improve quality of service.

In this paper, we investigate joint user pairing and resource allocation under the practical constraints in single-carrier frequency-division multiple access (SC-FDMA) LTE uplink systems. We first introduce a joint optimal algorithm based on branch-and-bound search as a benchmark. To reduce complexity, we divide the joint optimization problem into two subproblems: user pairing and resource allocation. For both these subproblems, we develop several low-complexity algorithms by exploiting the properties of the application of the optimization problem itself. It is shown by the simulation results that the proposed algorithms have better throughput and fairness than the conventional algorithms in and in for LTE uplink no matter whether power control is perfect or not.

In this paper, we develop a general reduced-rate orthogonal frequency division multiplexing (OFDM) transmission scheme for inter-subchannel interference (ICI) self-cancellation over high-mobility fading channels. Via transmit and receive processing, we transform the original OFDM system into an equivalent one with fewer subcarriers. By reducing transmission rate, we are able to design a transmitted signal structure with inherent ICI self-cancellation capability without requiring the instantaneous channel state information. We develop a general structure of transmit and receive processing matrices so that all equivalent subchannels in the transformed OFDM system have the same average signal-to-interference ratio (SIR). For the developed structure, we further optimize the transmit and the receive processing coefficients to maximize the SIR based on channel statistics. Numerical and simulation results demonstrate that the developed reduced-rate OFDM transmission achieves an SIR gain of around 5 dB over the existing ICI self-cancellation schemes and significantly reduces the error floor at the receiver.

Fractional frequency reuse (FFR) is an efficient way to mitigate inter-cell interference (ICI) in multi-cell orthogonal frequency division multiple access (OFDMA) networks. In this paper, we investigate the throughput and the optimal threshold for the FFR scheme. The average cell throughputs are derived for both round robin (RR) and maximum SINR (MSINR) scheduling strategies when users are uniformly distributed in the cell region. It is shown from the analysis and simulation results that the throughput increases and the optimal distance threshold decreases with the number of users for both scheduling strategies. The optimal distance threshold approaches the minimum distance that users can be away from the base station when the number of users goes to infinity. The optimal distance threshold increases with the frequency reuse factor of the cell-edge region when the MSINR scheduling is used. The impact of the RR scheduling strategy on the optimal threshold of the FFR scheme is negligible. Simulation also demonstrates that the FFR scheme with the optimal threshold significantly outperforms that with the existing fixed threshold.

In this paper, we investigate joint user pairing and resource allocation under the practical constraints in single carrier frequency-division multiple access (SC-FDMA) LTE uplink systems. We first introduce a joint optimal algorithm based on branch-and-bound search as a benchmark. To reduce complexity, we divide the joint optimization problem into two subproblems: user pairing and resource allocation. For these subproblems, we develop several suboptimal but low-complexity algorithms. The simulation results show that the proposed algorithms outperform the conventional one.

Random on-off accumulative transmission (R-OOAT) is a cross-layer technique that can achieve collision-tolerance in the media access control (MAC) layer by leveraging on the signal processing capability in the physical (PHY) layer. In this paper, a new PHY/MAC cross-layer design is proposed for the R-OOAT framework. In the PHY layer, we propose an iterative method for the detection of R-OOAT signals colliding at the receiver, such that the transmitted information can be recovered with a low complexity in the presence of severe signal collisions. The iterative detection is enabled by the unique signal structure of the R-OOAT, and it can operate in both coded and uncoded systems. In the MAC layer, the R-OOAT scheme uses silence periods inside a frame to achieve collision-tolerance, which is different from most conventional MAC schemes that rely on random intervals between frames to reduce collision. The theoretical spectral efficiency of R-OOAT is analyzed with the PHY/MAC operations. Analytical and simulation results show that the proposed cross-layer design can support more users and achieve a much higher spectral efficiency compared to conventional MAC schemes.

The widespread application of multimedia wireless services and requirement of ubiquitous access have triggered rapidly booming energy consumption at both the base station side. Hence, energy-efficient design in wireless networks is very important and is becoming an inevitable trend. In this paper, we study energy-efficient resource allocation in downlink cellular OFDMA networks. For the downlink transmission, the weighted energy efficiency (EE) is maximized under certain prescribed per-user quality- of-service (QoS) requirements. We first obtain the optimal solution then propose a suboptimal approach by exploring the inherent structure and property of the energy-efficient design to reduce complexity. Simulation results show that the energy-efficient design greatly improves EE compared with that of the conventional spectral-efficient design and our low- complexity suboptimal approaches can achieve promising tradeoff between performance and complexity.

In this paper, power allocation between pilots and data symbols is investigated aiming at maximizing energy efficiency(EE) for downlink orthogonal frequency division multiple access (OFDMA) networks. We first derive an EE function when the channel estimation error is considered, which depends on the large-scale channel gains of multiple users, the allocated power to pilots and data symbols, and the circuit power consumption. Then an optimization problem is formulated to maximize the EE under overall transmit power constraint. The relationship between the power for pilots and data symbols is analyzed based on Karush-Kuhn-Tucker (KKT) conditions and the impacts of channel gains on both power allocation and the EE are studied. Exploiting the quasiconcavity property of the EE function, a bisection searching algorithm is developed to find the optimal power allocation. Simulation results demonstrate the performance gain of the proposed optimal power allocation scheme in terms of the EE and the required overall transmit power.

In this paper, we propose a cognitive interference management approach, called fractional frequency donation, to alleviate co-channel interference among selfish femtocells. In such networks, our approach allows each femtocell to access all available bands but requires good femtocells with high throughput to "donate" some bands to poor ones. When the donors and the corresponding donated bands are properly selected, both good performance on average- and 5% edge-throughputs can be achieved. Simulation results show that in femtocell networks, the proposed fractional frequency donation approach is more suitable than the conventional fractional frequency reuse ones.

Orthogonal frequency-division multiplexing (OFDM) is a candidate transmission technique for cognitive radio (CR) because of its flexible nature to support spectrum sharing. However, the out-of-band (OOB) radiation of OFDM signal from CR users must be strictly controlled to protect licensed users in adjacent bands. In this paper, we propose a spectral precoding scheme for multiple OFDM-based CR users to reduce OOB emission and enhance spectrum compactness. By constructing individual precoders to render selected spectrum nulls, our scheme suppresses the overall OOB radiation without sacrificing the bit-error rate performance of CR users. The proposed scheme ensures user independence with low encoding and decoding complexity. We also study the selection of notched frequencies to further increase the bandwidth efficiency and implementation flexibility. Simulation results demonstrate that our spectral precoding scheme effectively limits OOB radiation and enables efficient spectrum sharing.

In this paper, we develop a general reduced-rate orthogonal frequency division multiplexing (OFDM) transmission scheme for inter-subchannel interference (ICI) mitigation in a high-mobility environment. By transmit and receive preprocessing, we transform the original N-subcarrier OFDM system into an equivalent K-subcarrier one with significantly reduced ICI. In particular, we develop a general structure of transmit and receive preprocessing matrices so that the K subchannels in the equivalent OFDM system share a common average signal-to-interference ratio (SIR). Without requiring the instantaneous channel state information, we optimize the preprocessing coefficients to maximize the SIR based on channel statistics. Numerical and simulation results demonstrate that the developed reduced-rate OFDM transmission achieves significant performance improvements over the existing ICI self-cancellation schemes.

Conventional design of wireless networks mainly focuses on system capacity and spectral efficiency (SE). As green radio (GR) becomes an inevitable trend, energy-efficient design in wireless networks is becoming more and more important. In this paper, the fundamental tradeoff relation between energy efficiency (EE) and SE in downlink orthogonal frequency division multiple access (OFDMA) networks is addressed. We obtain a tight upper bound and lower bound on the optimal EE-SE tradeoff relation for general scenarios based on Lagrange dual decomposition, which accurately reflects the optimal EE-SE tradeoff relation. We then focus on a special case that priority and fairness are considered and derive an alternative upper bound, which is even proved to be achievable for flat fading channels. We also develop a low-complexity but near-optimal resource allocation algorithm for practical application of EE-SE tradeoff. Numerical results demonstrate that the optimal EE-SE tradeoff relation is a bell shape curve and can be well approached with our resource allocation algorithm.

Fractional frequency reuse (FFR) is an efficient method to suppress inter-cell interference (ICI) in multi-cell OFDMA networks. In this paper, the optimal threshold is designed for FFR schemes to identify cell central and edge users. We first derive throughput of the FFR scheme considering small-scale fading channels and different scheduling strategies. Then we conclude from the analysis and simulation results that the throughput of maximum normalized SINR (MNSINR) scheduling is larger than that of round robin scheduling, and the optimal thresholds for both scheduling strategies decrease with the increase of cell user number. Simulation results also show that the performance of FFR scheme with the optimal threshold outperforms that with the conventional predefined threshold.

In this paper, we investigate adaptive resource allocation in downlink transmission of orthogonal frequency division multiple access (OFDMA) networks. A block-level resource allocation scheme is developed to maximize the overall throughput of the networks. We focus on application in long term evolution (LTE) systems where all resource blocks (RB's) allocated to the same user must use the same modulation and coding scheme (MCS). The complexity of such optimization problem is usually high. In order to reduce the complexity, we divide the original optimization problem into two suboptimal ones. We first allocate appropriate RB's to users with the best channel quality and then perform power allocation. Finally, a more effective MCS is selected, which not only ensures block-error rate (BLER) performance of RB with the poor channel condition but also makes full use of the RB's with good channel condition. Simulation results show that the proposed resource allocation scheme can improve the overall throughput of the network by 20% compared with the existing scheme when the number of users is 4 and SINR = 10 dB.

In this paper, we investigate resource block (RB) assignment and modulation-and-coding scheme (MCS) selection to maximize downlink throughput of long-term evolution (LTE) systems, where all RB's assigned to the same user in any given transmission time interval (TTI) must use the same MCS. We develop several effective MCS selection schemes by using the effective packet-level SINR based on exponential effective SINR mapping (EESM), arithmetic mean, geometric mean, and harmonic mean. From both analysis and simulation results, we show that the system throughput of all the proposed schemes are better than that of the scheme in. Furthermore, the MCS selection scheme using harmonic mean based effective packet-level SINR almost reaches the optimal performance and significantly outperforms the other proposed schemes.

In this paper, we develop a downlink dynamic soft frequency reuse (SFR) scheme with inter-cell coordination in orthogonal frequency division multiple access (OFDMA) cellular networks. A clustered base station (BS) coordination strategy is employed to facilitate frequency resource allocation among coordinated sectors. Compared to the traditional static SFR scheme, the proposed scheme can exploit frequency selectivity of wireless channels and multi-user diversity through inter-cell coordination and achieve better performance. Since the proposed approach performs resource allocation in a distributed way, only limited information needs to be shared or exchanged even within a cluster. As shown by computer simulation, compared with the traditional scheme, the proposed one can improve the throughput for those users at the edge of a sector by 41.9% while maintaining the same throughput for those users at the center of a sector.

In this paper, both configuration of active radio frequency (RF) chains and resource allocation are investigated for improving energy efficiency of downlink multiple-input-multiple-output (MIMO) orthogonal frequency division multiple access (OFDMA) systems. We first formulate an optimization problem to minimize the total power consumed at the base station with the maximum transmit power constraint and ergodic capacity constraints from multiple users. Then a two-step suboptimal algorithm is proposed. Specifically, the continuous variable optimization problem is first solved, and then a discretization algorithm is presented to obtain the number of active RF chains and the number of subcarriers allocated to each user. Simulation results demonstrate that the proposed algorithm can provide significant power-saving gain over the all-on RF chain scheme and the adaptive subcarrier allocation helps to save more power.

Cognitive radio (CR) technology provides great flexibility in spectrum utilization with orthogonal frequency-division multiplexing (OFDM) as its candidate transmission technique. In this paper, we propose a simple spectrum shaping scheme for OFDM-based CRs to enhance spectral compactness and ensure bandwidth efficiency. By mapping antipodal symbol pairs onto adjacent subcarriers at the edges of the utilized spectrum band, our scheme enables fast power spectral sidelobe decaying without bringing much extra complexity to the transmitter or receiver. Sidelobe suppression and system throughput can be well balanced by adjusting the coding rate while power control on different sets of subcarriers will further deepen the sidelobes. The proposed scheme is also validated by our simulation.

We investigate the secrecy performance of a codebook based transmission beamforming for a sensitive data link against passive eavesdropping. We characterize the secrecy outage probability of a communication link that is being eavesdropped. We consider a limited feedback scenario where the transmitter is using a predefined codebook known to both transmitter and receiver for beamforming, and analyze the secrecy outage probability of the link when it being eavesdropped. Our analysis also provides the secrecy outage probability of the beamforming transmission in the direction of the intended receiver. We characterize how the secrecy outage probability improves as the number of transmit antennas increases. We further analyze the effect of codebook design on secrecy enhancement and provide bounds on the secrecy outage probability of codebook beamforming.

In this paper, we investigate adaptive resource allocation in downlink transmission of orthogonal frequency division multiple access (OFDMA) networks. A block-level resource allocation scheme is developed to maximize the overall throughput of the networks by appropriately allocating resource blocks (RB's) and power to different users. We focus on application in long term evolution (LTE) systems where all RB's allocated to the same user must use the same modulation and coding scheme (MCS). Unfortunately, the complexity of solving such an optimization problem is prohibitively high in general. In order to reduce the complexity, we divide the original joint optimization problem into two subproblems. We first allocate appropriate RB's to users with the best channel condition and then perform power allocation. Then, a more effective MCS is selected in our scheme, which not only ensures the block-error rate (BLER) of the RB with the worst channel condition but also makes full use of the RB's with better channel conditions. Simulation results show that the proposed resource allocation scheme can improve the overall throughput of the network by 20% compared with the existing scheme when the number of users is 4 and the signal-to-interference-plus-noise ratio (SINR) is 10 dB.

In this paper, we investigate joint relay selection and power allocation to maximize system throughput with limited interference to licensed (primary) users in cognitive radio (CR) systems. As these two problems are coupled together, we first develop an optimal approach based on the dual method and then propose a suboptimal approach to reduce complexity while maintaining reasonable performance. From our simulation results, the proposed approaches can increase the system throughput by over 50%.

In this paper, we consider a two-hop multi-input-multi-output (MIMO) amplify-and-forward (AF) relay system consisting of a source node (SN), a destination node (DN), and a relay node (RN) that simply amplifies and forwards its received signal to the DN without any further processing. In this system, the overall channel from the SN to the DN is a cascade of the backward relay channel over the SN-RN hop, the amplifying matrix at the RN, and the forward relay channel over the RN-DN hop. We investigate the estimation of the two cascaded relay channels at the DN based on the predefined amplifying matrix applied at the RN and the corresponding overall channel obtained through the conventional channel estimation algorithms with the help of pilots transmitted by the SN. In particular, we find necessary and sufficient conditions on the pilot amplifying matrix sequence at the RN to ensure feasible relay channel estimation at the DN. Based on these conditions, we present rules to design diagonal or quasi-diagonal pilot amplifying matrices so that the cascaded relay channels can be estimated with minimum complexity at the RN. In the presence of imperfect overall channel state information at the DN, we further develop the approximate linear least-square estimation of the relay channels based on the designed pilot matrix sequence and demonstrate its performance by simulation results.

Statistics-based inter-subchannel interference (ICI) mitigation schemes are developed for orthogonal frequency division multiplexing (OFDM) transmission over high-mobility channels with line-of-sight components. By utilizing the channel statistics, we develop the Wiener filtering in the downlink and the transmit preprocessing in the uplink for ICI mitigation. Numerical and simulation results demonstrate that the proposed schemes effectively mitigate ICI and lower the error floor, especially in the presence of a strong line-of-sight path.

Power optimization techniques are becoming increasingly important in wireless system design since battery technology has not kept up with the demand of mobile devices. They are also critical to interference management in wireless systems because interference usually results from both aggressive spectral reuse and high power transmission and severely limits system performance. In this paper, we develop an energy-efficient power optimization scheme for interference-limited wireless communications. We consider both circuit and transmission powers and focus on energy efficiency over throughput. We first investigate a non-cooperative game for energy-efficient power optimization in frequency-selective channels and reveal the conditions of the existence and uniqueness of the equilibrium for this game. Most importantly, we discover a sufficient condition for generic multi-channel power control to have a unique equilibrium in frequency-selective channels. Then we study the tradeoff between energy efficiency and spectral efficiency and show by simulation results that the proposed scheme improves both energy efficiency and spectral efficiency in an interference-limited multi-cell cellular network.

Conventional design of wireless networks mainly focuses on system capacity and spectral efficiency (SE). As green radio (GR) becomes an inevitable trend, energy-efficient design is becoming more and more important. In this paper, the fundamental tradeoff between energy efficiency (EE) and SE in downlink orthogonal frequency division multiple access (OFDMA) networks is addressed. We first set up a general EE-SE tradeoff framework, where the overall EE, SE and per-user quality-of-service (QoS) are all considered, and prove that under this framework, EE is strictly quasiconcave in SE. We then discuss some basic properties, such as the impact of channel power gain and circuit power on the EE-SE relation. We also find a tight upper bound and a tight lower bound on the EE-SE curve for general scenarios, which reflect the actual EE-SE relation. We then focus on a special case that priority and fairness are considered and suggest an alternative upper bound, which is proved to be achievable for flat fading channels. We also develop a low-complexity but near-optimal resource allocation algorithm for practical application of the EE-SE tradeoff. Numerical results confirm the theoretical findings and demonstrate the effectiveness of the proposed resource allocation scheme for achieving a flexible and desirable tradeoff between EE and SE.

Cognitive radio (CR) networks are designed to utilize the licensed spectrum when it is not used by the primary (licensed) users. In this paper, we investigate how a CR user senses multiple channels and determine the optimal transmission duration and power allocation. When performing optimization, we take energy efficiency, throughput, and interference with the primary users into consideration and find a closed-form solution for transmission duration for chosen channels. It is shown that the proposed optimization approach significantly improves energy efficiency and throughput of CR networks.

In this paper, a random on-off accumulative transmission (R-OOAT) scheme is proposed to achieve collision-tolerant (CT) media access control (MAC) for asynchronous wireless sensor networks. Unlike conventional MAC schemes that discard packages with collisions at receivers, the CT-MAC extracts the salient information from the colliding signals by using the R-OOAT scheme in the physical layer. Nodes employing the R-OOAT deliver information to a base station through asynchronous on-off transmission of multiple identical sub-symbols at random positions. The R-OOAT improves the deterministic OOAT (D-OOAT) proposed in [1], where sub-symbols are transmitted at deterministic positions. Compared to the D-OOAT, the R-OOAT scheme achieves a smaller collision probability and supports more simultaneous users, while it inherits all the advantages of the D-OOAT. Design guidelines of the R-OOAT system are presented for given parameters, such as the probability of collisions and the maximum number of supported users. It is demonstrated by simulation results that the new CT-MAC with R-OOAT scheme can operate at the presence of severe signal collision.

In this paper, we investigate channel allocation for cooperative relays in cognitive radio networks. Different from conventional cooperative relay channels, cognitive radio relay channels are actually a combination of three kinds of channels: direct, dual-hop, and relay channels, which belong to different spectrum bands and provide parallel end-to-end transmission. In order to maximize the achievable end-to-end throughput, we propose two channel allocation approaches with different complexities to assign all the channels cooperatively. Numerical results illustrate the performance improvement in different number of available channels. In particular, it has about 40% improvement in throughput when the average SNR is 15 dB and eight available channels are used.

In this paper, we investigate bandwidth efficient combination of spectrum sensing information in cooperative cognitive radio (CR) networks. We propose a general approach in which CR users are allowed to simultaneously send local sensing data to a combining node through a common control channel, based on which we discuss bandwidth efficient combination schemes under two different cases. In the proposed schemes, the bandwidth required for reporting is fixed regardless of the number of cooperative users. With proper preprocessing at individual users, the proposed schemes maintain reasonable performance with the superposition of sensing data at the combining node. Simulation results also demonstrate the effectiveness of the proposed approach.

In this paper, a cross-layer collision-tolerant (CT) media access control (MAC) scheme is proposed to achieve reliable low power communications for one-hop asynchronous wireless sensor networks (WSNs). Unlike conventional MAC schemes that discard and retransmit signals colliding at a receiver, the CT-MAC extracts the salient information from the colliding signals by leveraging on the signal processing capability in the physical layer with a new on-off accumulative transmission (OOAT) scheme. Nodes employing OOAT delivers information to a base station (BS) through asynchronous duty-cycled transmission (on-off transmission) of multiple identical sub-symbols (accumulative transmission). The on-off transmission reduces collision probability at the BS, and the accumulative transmission in the time-domain enables the simultaneous detection of colliding signals in the spatial-domain. An optimum maximum likelihood sequence estimation detector with time-varying trellis structure is employed by the BS to perform detection over the colliding signals.

Spectrum sensing in cognitive radio (CR) typically assumes that the primary user appears only at the beginning of the sensing block. In this paper, we first establish a probability model regarding the appearance of the primary user at any sample of a CR user frame by utilizing the statistical characteristics of the licensed channel occupancy. While conventional spectrum sensing schemes allocate the same weight to each sample, we vary the weight for each sample based on the probability of the presence of the primary user at the corresponding sample and show that such a probability-based spectrum sensing scheme has nearly optimal performance. Based on the assumption that the idle duration of the licensed channel is exponentially distributed, we further investigate how the probability model on the primary user appearance varies from frame to frame in periodic spectrum sensing and show that both the conventional fixed weight and the probability-based dynamic weight energy detection schemes converge to their respective stable detection probability.

Energy efficiency is becoming increasingly important for small form factor mobile devices, as battery technology has not kept up with the growing requirements stemming from ubiquitous multimedia applications. This paper addresses link adaptive transmission for maximizing energy efficiency, as measured by the "throughput per Joule" metric. In contrast to the existing water-filling power allocation schemes that maximize throughput subject to a fixed overall transmit power constraint, our scheme maximizes energy efficiency by adapting both overall transmit power and its allocation, according to the channel states and the circuit power consumed. We demonstrate the existence of a unique globally optimal link adaptation solution and develop iterative algorithms to obtain it. We further consider the special case of flat-fading channels to develop an upper bound on energy efficiency and to characterize its variation with bandwidth, channel gain and circuit power. Our results for OFDM systems demonstrate improved energy savings with energy optimal link adaptation as well as illustrate the fundamental tradeoff between energy-efficient and spectrum-efficient transmission.

This letter proposes a probability-based scheme for combination of spectrum sensing information collected from cooperative cognitive radio users. The proposed scheme enables combination of both synchronous and asynchronous sensing information by utilizing the statistics of licensed band occupancy and is superior to conventional schemes in terms of detection performance.

Opportunistic spectrum access (OSA) in cognitive radio (CR) networks significantly improves spectrum efficiency by allowing secondary usage of licensed spectrum. In this paper, we propose a probabilistic resource allocation approach to further exploit the flexibility of OSA. Based on the probabilities of channel availability obtained from spectrum sensing, the proposed approach optimizes channel and power allocation in a multi-channel environment. The given algorithm maximizes the overall utility of a CR network and ensures sufficient protection of licensed users from unacceptable interference, which also supports diverse quality-of-service requirements and enables a distributed implementation in multi-user networks. Both analytical and simulation results demonstrate the effectiveness of this approach as well as its advantage over conventional approaches that rely upon the hard decisions on channel availability.

In wireless MIMO ad-hoc networks, channel reciprocity is assumed to optimize transmit and receive beamformers in conventional beamforming algorithms. But, when channels between nodes are not reciprocal, it is a huge burden to the feedback channel to inform a transmitting node of the channel state information. In this paper, we propose a distributed beamforming scheme with limited feedback for non-reciprocal channels in MIMO ad-hoc networks. In the proposed scheme, all transmitting and receiving node pairs working at the same resource perform an iterative sequential beamformer update in a distributed way. Numerical results are also presented to verify the performance of the proposed scheme.

In this paper, we are concerned with cross-talk interference from the transmit to the receive antenna of a wireless channel-reuse-relay-station (CRRS) that forwards signals over the same channel as it receives signals. By estimating the coupling channel from the transmit to the receive antenna, the proposed scheme performs cross-talk reconstruction and cancellation at the RS. Different from the conventional coupling channel estimation schemes that require the RS to transmit dedicated pilots, the proposed scheme utilizes the random forwarded signals of the RS as pilots for coupling channel estimation, thus avoiding changing the structure of the RS's transmitted signal. For a general RS with any relay mechanism, we propose a least square coupling channel estimator; for an RS with the decode-and-forward mechanism, we further propose a minimum mean-square error coupling channel estimator. Also, we investigate the performance of the proposed cross-talk cancellation scheme with both analytical and numerical results.

We investigate distributed channel-aware random access for networks with arbitrary topologies and traffic distributions, where users can receive traffic from or send traffic to different users and different communication links may interfere with others. We consider heterogeneous channels, where the random channel gains of different links may have different distributions. To resolve the network contention in a distributed way, each frame is divided into contention and transmission periods. The contention period is used to resolve conflicts near optimally and to schedule users with better channel states with higher probabilities while assuring fairness among all users. The proposed scheme completely resolves contention of networks with arbitrary topologies and is robust to any channel uncertainty. Besides, it performs close to central schedulers.

While cyclic prefix (CP) is no larger than the delay span of wireless channels in an orthogonal frequency division multiplexing (OFDM) system, inter-block interference (IBI) and inter-carrier interference (ICI) will occur and the performance of the system will be deteriorated. If OFDM is designed with long enough CP, the efficiency of OFDM modulation is significantly reduced. To effectively mitigate interference over channel with large delay spreads, pre-processing approaches at transmitters based on full channel state information (CSI) have been proposed. However, feeding full CSI back will occupy significant bandwidth and is impractical sometimes. Therefore, pre-processing optimization exploiting limited CSI feedback is investigated in this paper. An pre-processing optimization approaches for IBI and ICI mitigation has been developed and tested. Computer simulation result shows that the proposed optimization algorithm can effectively cancel IBI and ICI over channels with large delay spreads and significantly improve the performance for OFDM systems.

In this paper, we propose probability-based resource allocation in cognitive radio (CR) networks to exploit the flexibility of opportunistic spectrum access (OSA). Assisted by the statistical information acquired from spectrum sensing, the proposed approach maximizes the overall utility of CR users and ensures sufficient protection of licensed users from unacceptable interference. It also supports diverse quality-of-service (QoS) requirements of multiple CR users and allows distributed implementation. Simulation results demonstrate the effectiveness of the approach as well as its advantage over any conventional approach that ignores the statistical information.

In this paper, we are concerned with a two-hop multi-input-multi-output (MIMO) amplify-and-forward (AF) relay system consisting of a source node (SN), a relay node (RN), and a destination node (DN). Since the simple RN in this system is unaware of the structure of its received signal and incapable of performing complicated signal processing, the interim channels over the SN-RN and the RN-DN hops can not be estimated directly. Therefore, we develop a novel interim channel estimation approach in this paper. Furthermore, we find necessary and sufficient conditions for the pilot amplifying matrix sequence at the RN to ensure successful interim channel estimation at the DN, and present rules to design low-complexity pilot amplifying matrices meeting these conditions.

While the demand for battery capacity on mobile devices has grown with the increase in high-bandwidth multimedia rich applications, battery technology has not kept up with this demand. Therefore power optimization techniques are becoming increasingly important in wireless system design. Power optimization schemes are also important for interference management in wireless systems as interference resulting from aggressive spectral reuse and high power transmission severely limits system performance. Although power optimization plays a pivotal role in both interference management and energy utilization, little research addresses their joint interaction. In this paper, we develop energy-efficient power optimization schemes for interference-limited communications. Both circuit and transmit powers are considered and energy efficiency is emphasized over throughput. We note that the general power optimization problem in the presence of interference is intractable even when ideal user cooperation is assumed. We first study this problem for a simple two-user network with ideal user cooperation and then develop a practical non-cooperative power optimization scheme. Simulation results show that the proposed scheme improves not only energy efficiency but also spectral efficiency in an interference-limited cellular network.

Energy efficient communications in wireless communications is very important as mobile devices are battery- constrained. For mobile devices in a cellular system, uplink power consumption dominates the wireless power budget, due to the RF power requirements for reliable communications over long distances. Our previous work in this area demonstrated significant energy savings in uplink cellular OFDMA transmissions, with iterative approaches maximizing the instantaneous bits-per- Joule energy efficiency. In this paper, we use a time-averaged bits-per-Joule metric to develop low-complexity schemes. Specifically, we obtain closed-form solutions for energy-efficient link adaptation in frequency-selective channels. We also derive closed- form approaches for the maximum arithmetic and geometric mean energy-efficient schedulers. Simulation results show that the proposed schemes not only have low complexity but also perform close to the globally optimum solutions.

Multi-carrier (MC) code division multiple access (CDMA) is able to take the advantages of OFDM and CDMA and is a potential technique for future wireless communications. For an uplink MC-CDMA system, the symbols of different users are spread in the frequency domain. However, the frequency-selective fading of wireless channels destroys the othogonality of the spreading codes for different users and causes multiple access interference (MAI), especially for a network with full load. To reduce the impact of MAI, we adaptively assign spreading codes according to channel state information and MAI environments. Since it requires high computational complexity to find an optimal set of spreading codes for all active users, we develop several simplified approaches to search the suboptimal spreading code sets. It is demonstrated by the computer simulation that the adaptive spreading code assignment, even though suboptimal, can significantly improve the performance of MC-CDMA systems.

Most of existing works on spectrum sensing detect primary transmitters while the purpose of spectrum sensing is to avoid interfering with primary receivers (PRs). Therefore, it is more important to detect PRs. In this paper, we propose a proactive spectrum sensing method that detects whether a PR is within the coverage or the interference range of a CR transmitter by exploiting the close-loop power control policy in primary systems. With the proposed scheme, the CR user may still access the spectrum band even though a primary signal is present as long as its transmission does not interfere with the PR. Simulation results show the advantages of the proposed method.

To take advantage of time-varying spectrum opportunities, acognitiveradio(CR) network monitors the dynamic usage of the licensed band through cooperative spectrum sensing. In this paper, we propose a probability-based scheme for combination of spectrum sensing information collected from several CR users. Different from conventional cooperative spectrum sensing schemes that assume synchronous local sensing information, our scheme enables combination of both synchronous and asynchronous sensing information by utilizing the statistics of licensed band occupancy. In our scheme, the amount of information from each CR user is flexible and a simplified implementation is also feasible under a symmetrical case. Simulation results demonstrate that our scheme is robust and superior in terms of detection performance.

In this paper, we propose a static power allocation algorithm for a two-hop multi-input-multi-output (MIMO) amplify-and-forward (AF) relay system in which the interim channel state information over the first and the second hops is unavailable. Based on the path losses over the first and the second hops, this algorithm performs static power allocation between the source and relay nodes to maximize the equivalent received SNR of the system. We further investigate the optimal location of the relay node when the conventional fixed and the proposed optimal static power allocation schemes are applied. Our comparison between direct transmission and relay-based two-hop transmission indicates that whether the latter outperforms the former depends on a tradeoff between the received SNR gain and the multiplexing loss in the relay-based two-hop transmission scheme.

In this paper, we propose a relay-assisted transmission scheme in cognitive radio (CR) to exploit spatial spectrum holes, which are generated by relay techniques. The proposed scheme enables CR users to coexist with primary users at the same time in the same geographic area and spectrum band. Compared to conventional schemes, a higher spectrum efficiency is achieved by our method. We further analyze the successful communication probability and present numerical results to show advantages of our method.

Cognitive radio allows for usage of licensed frequency bands by unlicensed users. However, these unlicensed (cognitive) users need to monitor the spectrum continuously to avoid possible interference with the licensed (primary) users. Apart from this, cognitive radio is expected to learn from its surroundings and perform functions that best serve its users. Such an adaptive technology naturally presents unique signal-processing challenges. In this paper, we describe the fundamental signal-processing aspects involved in developing a fully functional cognitive radio network, including spectrum sensing and spectrum sculpting.

Severe cochannel interference in wireless cellular networks significantly affects users at cell edges. We propose a cost-effective cochannel interference avoidance (CIA) medium access control (MAC) scheme to improve network performance. For CIA-MAC, base stations judged as severe interferers transmit randomly and the transmission is controlled by wireless channel states to optimize the overall network performance while maintaining proportional fairness among users. Conditions for triggering CIA-MAC are derived and two simple trigger mechanisms are obtained. The CIA-MAC scheme requires low signaling overhead and only minor changes to the existing mobile systems. Simulation results show that the proposed scheme, CIAMAC, significantly outperforms traditional approaches through the avoidance of severe cochannel interference as well as the exploitation of multiuser diversity through cross-layer design.

We consider schemes for decentralized cross-layer optimization of multichannel random access by exploiting local channel state and traffic information. In the network we are considering, users are not necessarily within the transmission ranges of all others; therefore, when a user is transmitting, it may only interfere with some users, which is different from most existing channel aware Aloha schemes. Besides, we also consider complicated traffic distribution, e.g. each user may choose to send packets to or receive packets from different users simultaneously. We develop decentralized optimization for multichannel random access (DOMRA). DOMRA consists of three steps: neighborhood information collection, transmission control of the MAC layer based on the instantaneous channel state information, and power allocation for each traffic flow on each subchannel. Simulation results demonstrated that DOMRA significantly outperforms existing channel aware Aloha schemes due to its exploitation of both multiuser diversity through cross-layer design and the inhomogeneous characteristics of traffic spatial distribution in the network. Besides, DOMRA performs closely to the globally optimum solution, which requires full network knowledge to be obtained. DOMRA can be applied to different types of wireless networks, such as wireless sensor networks and mobile ad hoc networks, to improve quality of service.

In this paper, packet scheduling in an orthogonal frequency division multiplex access downlink is investigated based on cross-layer design and optimization. We first develop max-delay-utility scheduling with the help of channel and queue state information to exploit multiuser diversity and guarantee quality of service. The stability property of a scheduling policy is characterized by the stability region, which is the largest region on arrival rates for which the queueing system can be stabilized by the scheduling policy. It is shown in this paper that under very loose conditions, the max-delay-utility scheduling has the maximum stability region. In environments with insufficient scattering or strong line-of-sight components, delay transmit diversity can increase the fluctuation in the frequency domain to improve the performance. The simulation results show that the max-delay-utility scheduling with a frugality constraint is highly advantageous to data transmission with a low latency requirement over shared multiple channels, and that joint scheduling and power allocation can substantially boost the performance.

Spectrum sensing finds spectrum opportunities for cognitive radio (CR) and enables CR users to work without harmful interference to primary users. Most of existing contributions on spectrum sensing detect whether a primary signal is present or absent. Since the ultimate goal of spectrum sensing is to avoid interfering with primary receivers (PRs), it is more efficient to detect PRs directly. In this paper, we propose a proactive spectrum sensing scheme to detect whether a PR is within the coverage or the interference range of a CR transmitter by exploiting the close-loop power control that has been widely used in wireless systems. With the proposed scheme, the CR user may still be able to access the licensed spectrum band even though a primary signal is detected as long as its transmission does not interfere with the PR. As a result, more spectrum opportunities can be exploited compared to conventional spectrum sensing methods.

Spectrum hole (SH) is defined as a spectrum band that can be utilized by unlicensed users, which is a basic resource for cognitive radio (CR) systems. Most of existing contributions detect SHs by sensing whether a primary signal is present or absent and then try to access them so that the CR and primary users use the spectrum band either at different time slots or in different geographic regions. In this paper, we propose a novel scheme with relays or directional relays for CR users to exploit new spectrum opportunity, called spatial SH. It can provide higher spectrum efficiency by coexistence of primary and CR users at the same region, time, and spectrum band. In particular, when the spectrum opportunity of a direct link from a CR transmitter to a CR receiver does not appear, our scheme may still establish the communication through indirect links, i.e., other CR users act as relay stations to assist the communication by using other spatial domains. Furthermore, we analyze the successful communication probabilities of CR users and demonstrate that the spectrum efficiency can be considerably improved by our scheme.

Probability-based strategies are proposed in this letter to determine the optimal inter-sensing duration and power control for cognitive radio (CR). With utilization of the statistics of licensed band occupancy, appropriate inter-sensing duration is determined to capture the recurrence of spectrum opportunity in time when the licensed signal is detected, or to achieve the maximum spectrum efficiency under a certain level of interference with licensed communication when the licensed signal is declared absent. Transmit power is varied dynamically according to the non-interfering probability at each sample so as to increase the transmission rate and decrease the interference power.

Abstract Since battery technology has not progressed as rapidly as semiconductor technology, power efficiency has become increasingly important in wireless networking, in addition to the traditional quality and performance measures, such as bandwidth, throughput, and fairness. Energy‐efficient design requires a cross layer approach as power consumption is affected by all aspects of system design, ranging from silicon to applications. This article presents a comprehensive overview of recent advances in cross‐layer design for energy‐efficient wireless communications. We particularly focus on a system‐based approaches toward energy optimal transmission and resource management across time, frequency, and spatial domains. Details related to energy‐efficient hardware implementations are also covered. Copyright © 2008 John Wiley & Sons, Ltd.

Energy efficiency is becoming increasingly important for small form factor mobile devices, as battery technology has not kept up with the growing requirements stemming from ubiquitous multimedia applications. This paper addresses link adaptive transmission for maximization of energy efficiency rather than throughput. We extend our previous results for flat fading OFDMA to frequency-selective channels. Different from existing water-filling power allocation schemes that maximize throughput subject to overall transmit power constraints, our scheme adapts both overall transmit power and its allocation according to the states of all subchannels and circuit power consumption to maximize energy efficiency. We demonstrate the existence of a unique globally optimal link adaptation solution and provide iterative algorithms to obtain this optimum. Simulation results show at least a 15% improvement in energy utilization when frequency selectivity is exploited.

In this paper, we propose a cognitive radio (CR) transmission scheme, which enables secondary users to coexist with primary users by exploiting spatial spectrum holes (SSHs) through directional antennas or antenna arrays with beamforming. To ensure reliable CR links and avoid interference to primary users, some CR users may act as relays . We investigate successful communication probability of CR users when this scheme is applied. We further demonstrate that the spectrum efficiency can be greatly improved by multiplexing CR links with directional transmission.

In this paper, detection timing and channel selection for periodic spectrum sensing are investigated to improve the performance of cognitive radio (CR) users. Designed to maximize the channel efficiency of CR users under a given level of interference with licensed users, the detection timing scheme utilizes the statistics of the licensed channel occupancy to determine the optimal starting point of each sensing action. A channel selection scheme in the multichannel multiuser environment is also proposed to specify which channel to detect for the upcoming sensing action based on detection timing. Numerical results demonstrate that our schemes considerably improve the overall channel efficiency while protecting the communication among licensed users.

Spectrum sensing in cognitive radio (CR) typically assumes that the primary signal appears before the sensing block. In this paper, we develop a spectrum sensing technique for CR systems where the primary signal may appear at anytime within a sensing block. We first establish the probability model regarding the appearance of the primary signal at each sampling point of a CR user frame. While the conventional spectrum sensing scheme allocates the same weight to each sample, we propose a probability-based energy detection scheme, in which the weight for each sample is based on the probability of the presence of the primary signal at the corresponding sampling point. Numerical results indicate that the probability-based scheme exhibits better performance than the conventional scheme.

Energy-efficient transmission is an important aspect of wireless system design due to limited battery power in mobile devices. We consider uplink energy-efficient transmission in OFDMA systems since mobile stations are battery powered. We account for both circuit and transmit power when designing energy-efficient communication mechanisms and emphasize energy efficiency over peak rates or throughput. Both link adaptation and resource allocation schemes are developed to optimize the overall bits transmitted per Joule of energy, which allows for maximum energy savings in a network. Our simulation results show that the proposed schemes significantly improve energy efficiency.

A transmission scheme of orthogonally multiplexing pilots in frequency domain with single carrier data signal is proposed in this paper. In this scheme, information-bearing data symbols are modulated in time domain using a simple precode linear transformation, and multiplexed with pilot symbols in frequency domain. The proposed single carrier transmitter has lower complexity and the transmitted signal has lower peak to average power ratio (PAPR) than that of OFDM. In addition, same channel estimation algorithms for OFDM can be applied to the proposed single carrier transmission system due to the similar orthogonal multiplexing of pilot and data symbols in frequency domain. Equalizers based on MMSE and zero-forcing criteria are also presented. Receiver performance is analyzed and compared with OFDM using numerical examples.

This paper considers decentralized cross-layer optimization for multichannel random access with partial-local knowledge. In the network scenario considered, users are not necessarily within the transmission ranges of all other users, and each user may choose to send packets to or receive packets from different users simultaneously. A criterion for cross-layer optimization is provided leading to a decentralized optimization for multichannel random access (DOMRA) scheme. DOMRA can be applied to different types of wireless networks, such as wireless sensor networks and mobile ad hoc networks, to improve quality of service. Furthermore, DOMRA can be applied to optimize downlink transmissions in cellular networks for proposed cochannel interference avoidance (CIA) medium access control (MAC). CIA-MAC requires low signalling overhead and minor changes to existing mobile systems. The conditions for triggering CIA-MAC are investigated and a simple trigger mechanism is obtained. Simulation results confirm the significant performance improvement by the proposed schemes.

Cognitive radio networks need to continuously monitor spectrum to detect the presence of the licensed users. In this paper, we have exploited spatial diversity in multiuser networks to improve the spectrum sensing capabilities of centralized cognitive radio (CR) networks. We develop a fixed and a variable relay sensing scheme. The fixed relay scheme employs a relay that has a fixed location to help the cognitive network base station detect the presence of the primary user. The variable relay sensing scheme employs cognitive users distributed at various locations as relays to sense data and to improve the detection capabilities. This effectively reduces the average detection time by exploiting spatial diversity inherent in multiuser networks. Finally, we study the network outage probabilities to compare the performances of the fixed and variable relay schemes.

In this paper, we apply recently developed energy spreading transform (EST) technique to down-link MC-CDMA systems for frequency and spatial diversity. The performance of the proposed EST based MC-CDMA systems is independent of the number of active users, which is different from all current MC-CDMA systems. It is demonstrated by computer simulation that the BER for the EST based system is 1.1 times 110-4while it is 1.6 times 10-3for a MC-CDMA system without EST when the system is with full load and Eb/No= 12 dB. The EST based MC-CDMA system also facilitates transmit diversity and MIMO techniques when multiple transmit and receive antennas are available. In particular, we develop symbol shuffle and combined schemes for spatial diversity for a EST based MC-CDMA system with multiple transmit antennas at the base station. Compared with a MC-CDMA system without EST, the required Eb/Nofor 1% BER for the developed scheme is improved by about 4 dB.

An iterative pilot-symbol aided modulation (PSAM) channel estimation approach is proposed for vertical Bell Laboratories layered space-time (V-BLAST) orthogonal frequency division multiplexing systems operating on frequency-selective fading channels. Since the signals at the receive antennas are the superposition of signals from multiple transmit antennas, accurate channel estimates are crucial for good error performance. Furthermore, the time selectivity of the fading channels leads to inter-carrier interference (ICI). While ICI can be ignored for slow fading channels, it should be mitigated for fast fading channels. This paper proposes an ICI mitigation scheme for time-varying channels. We also propose an iterative channel estimator with low-complexity. Simulation results demonstrate the usefulness of the proposed algorithm on frequency-selective fading channels.

In this letter, we consider cooperative spectrum sensing based on energy detection in cognitive radio networks. Soft combination of the observed energies from different cognitive radio users is investigated. Based on the Neyman-Pearson criterion, we obtain an optimal soft combination scheme that maximizes the detection probability for a given false alarm probability. Encouraged by the performance gain of soft combination, we further propose a new softened hard combination scheme with two-bit overhead for each user and achieve a good tradeoff between detection performance and complexity.

Channel parameters, which are usually estimated at the receiver, are required in signal detection in multi-carrier (MC) code division multiplex access (CDMA). The accuracy of channel estimation directly affects the performance of the overall system. In this paper, we present some practical techniques to improve channel estimation in up-link MC-CDMA systems. For most of multipath channels, there are only a few significant taps in time domain that determine the frequency response. Based on this characteristic, we detect and keep these significant taps and discard the trivial ones, which are usually very noisy or contain only noise components. After channel parameters are estimated at the pilot blocks, channels at the data blocks can be obtained by interpolation. To improve the performance of simple linear interpolation, we apply optimal interpolation, which takes channel estimation error and channel correlation into consideration. We then investigate the impact of channel estimation error on minimum mean-square-error (MMSE) successive interference cancelation (SIC) detector and find that more accurate channel is required to further improve the performance of the MMSE SIC detector. Therefore, we develop soft-decision-directed (SDD) channel estimation, which exploits the information at the data blocks to improve channel estimation.

In this paper, we present a simple reservation scheme for multicarrier networks employing channel aware Aloha. We consider the effect of channel measurement errors in the existing channel aware Aloha scheme with collision resolution and show that any uncertainty in channel conditions, however small it may be, leads to zero asymptotic throughput. We then describe a simple reservation scheme specially suited for multicarrier networks and show how multicarrier diversity can be effectively used to offset degradation in the throughput performance of channel aware Aloha operating with channel uncertainties.

In this paper, we consider cooperative spectrum sensing based on energy detection in cognitive radio networks. Soft combination of the observed energy values from different cognitive radio users is investigated. Maximal ratio combination (MRC) is theoretically proved to be nearly optimal in low signal- to-noise ratio (SNR) region, an usual scenario in the context of cognitive radio. Both MRC and equal gain combination (EGC) exhibit significant performance improvement over conventional hard combination. Encouraged by the performance gain of soft combination, we propose a new softened hard combination scheme with two-bit overhead for each user and achieve a good tradeoff between detection performance and complexity. While traditionally energy detection suffers from an SNR wall caused by noise power uncertainty, it is shown in this paper that an SNR wall reduction can be achieved by employing cooperation among independent cognitive radio users.

While most existing channel aware Aloha schemes focus on wireless networks where each user intends to send only one traffic flow, and interferes with all the other users, some wireless networks may have more complicated traffic distribution and the transmissions of different users may interfere with different groups of users. In this paper, we consider schemes for the decentralized cross-layer optimization of multichannel random access, in which users are not necessarily within the transmission ranges of all other users, and each user may choose to send packets to or receive packets from different users simultaneously. With cross-layer design, users are configured according to local neighborhood information to adapt to inhomogeneous network characteristics. It is demonstrated by simulation that the proposed scheme significantly outperforms existing channel aware Aloha schemes due to its exploitation of both multiuser diversity and the inhomogeneous characteristics of traffic distribution in the network.

Cooperative relay networks have been shown to improve performance in wireless communication systems as a form of spatial diversity. In this paper, we investigate the error rate performance in a single path relay network and a multiple path relay network using orthogonal frequency division multiplexing (OFDM) signals. Using the amplify-and-forward and decode-and-forward relay algorithms, we derive input-output relations for the two networks. For the amplify-and-forward case, we consider two relay power allocation schemes. The first is constant gain allocation, where the amplifying gain is constant for all subcarriers. The second is equal power allocation, where each subcarrier transmits the same power. The former scheme does not require channel state information (CSI), while the latter one does. For the decode-and-forward case, the transmitter and each relay are assumed to have uniform power allocations. We simulate the word error rate (WER) performance for the two networks. For the single path relay network, amplify-and-forward gives very poor performance, because as we increase the distance between the transmitter and receiver (and thus, add more relays), more noise and channel distortion enter the system. Decode- and-forward gives significantly better performance because noise and channel distortion are eliminated at each relay. For the multiple path relay network, decode-and-forward again gives better performance than amplify-and-forward. However, the performance gains are small compared to the single path relay network case. Therefore, amplify-and-forward may be a more attractive choice in this case due to its lower complexity.

Cognitive radio networks need to continuously monitor spectrum to detect the presence of the licensed users. In this paper, we have exploited spatial diversity in multiuser networks to improve the spectrum sensing capabilities of centralized cognitive radio (CR) networks. We develop a fixed and a variable relay sensing scheme. The fixed relay scheme employs a relay that is fixed in location to help the cognitive network base station detect the presence of the primary user. The variable relay sensing scheme employs cognitive users distributed at various locations as relays to sense data and to improve the detection capabilities. We theoretically prove that the proposed variable relay sensing scheme effectively reduces the average detection time which is also illustrated by an insightful example. Finally, we introduce a useful metric to measure the performance of fixed relay and variable relay schemes.

In this paper, the authors investigated the effect of multicarrier diversity on the stability region of channel aware Aloha. The authors show that the stability region of a two-user multicarrier network always includes and is much larger than that of a two-user single carrier network. Therefore, multicarrier diversity significantly improves stability of Aloha networks.

In this paper, we investigate transmit diversity for MC-CDMA down-link mobile communications. We apply recently developed energy spreading transform (EST) in MC-CDMA systems to pick up diversity in frequency-selective wireless channels and to improve signal detection performance. It is shown by computer simulation that the required Eb/No's for a 10-2bit-error-rate (BER) are improved by about 5 dB for both an indoor channel and an urban channel compared with a MC-CDMA system without an EST. For a system with two transmit antennas and Alamouti's code, the required Eb/Nofor an EST based MC-CDMA system is improved by 2.7 dB for an indoor channel and 3.3 dB for an outdoor channel compared with a MC-CDMA system without an EST. For a system with multiple transmit antennas, symbol shuffling scheme, together with an EST, has been proposed for transmit diversity. Even though symbol shuffling scheme alone is not as good as Alamouti's code, it can be combined with Alamouti's code to reach matched filter bound (MFB) for systems with over four transmit antennas. Comparison of our transmit-diversity approach with the widely used MMSE detection for MC-CDMA systems is also provided.

With channel state information (CSI) at the transmitter, beamforming can be used for spatial diversity and multiple spatial access. Due to latency and feedback bandwidth limitation, the CSI at the transmitter is often known with some ambiguity. In this paper, we develop a robust method for downlink beamforming that takes the ambiguity of the CSI into consideration. It is shown by computer simulation that, compared with the existing method, the required signal-to-noise ratio (SNR) for a 1% bit-error rate (BER) is reduced by over 2 dB for a system with 4 transmit antennas and 2 users when the variance of the CSI is -20 dB. The performance gain increases with the number of transmit antennas when the number of users is fixed. The required SNR for a 1% BER is reduced by over 4 dB if the the number of transmitter antennas is 8. We also study the impact of power allocation on the downlink beamforming.

Transmit and receive antenna arrays can be used to form multiple-input and multiple-output (MIMO) systems for improving the reliability and capacity of data transmission. Layered space-time coding with decision-feedback detection is a promising technique for future wireless communications. In this paper, we investigate power allocation at the transmitter to improve the performance of the decision-feedback detection. The proposed power allocation method may only depend on or is even independent of the signal-to-noise ratio (SNR) of the MIMO systems. When the SNR at the transmitter is not available, the power of each data stream is allocated according to the required bit-error-rate (BER) of the system. Computer simulation shows that the proposed method can improve the performance of a 2-input and 2-output system by 4 dB at 1% BER and that of a 4-input and 4-output system by 3.5 dB.

We propose a modified layered structure for multiple-input multiple-output systems, where the layer detection order is fixed and the data rate for each layer is allocated based on the detection order and channel statistics. Using a Gaussian approximation of the layer capacities, we derive an asymptotic optimum data-rate-allocation approach. For optimum data-rate allocation, the amount of backoff from the mean layer capacity is proportional to the standard deviation of the layer capacity. With statistical data-rate allocation, only limited channel feedback is needed to update channel statistics at the transmitter. Simulation results show significant performance improvement with the proposed algorithm. We also find that the performance gap between the layered structure and the channel capacity diminishes with increasing ergodicity within each codeword. Numerical results show a singal-to-noise ratio improvement of 6.3 and 3.6 dB for TGn channels B and D, respectively, for 1% outage probability and 9 b/s/Hz spectral efficiency.

In this correspondence, we have studied the stability properties of multicarrier channel aware Aloha. For a simple two-user network, the stability region${\cal S}_2$under the classical collision model has been well-studied. In this correspondence, using multiple carriers, we show how to achieve any input rate vector in${\cal S}_2$with$1$-persistent Aloha. We then show how multicarrier diversity can be exploited to improve upon the existing stability region.

Joint maximum-likelihood (JML) detector may be used in memoryless multiple-input multiple-output (MIMO) systems to obtain optimal detection performance. However, JML detector performs an exhaustive search and has prohibitively large decoding complexity. To reduce the complexity of MIMO signal detection, minimum mean-square-error (mmse) linear detector (LD), decision-feedback detector (DFD), group detector, and sphere detector (SD) may be used. In this correspondence, we propose a truncation based detector for low-complexity MIMO signal detection, and give theoretical insight into the design and performance of such a detector. We study bitruncation in detail and present two bitruncation approaches. These approaches have low-complexity, and computer simulation results show that they outperform mmse-LD and mmse-DFD

In cognitive radio networks, cognitive (unlicensed) users need to continuously monitor spectrum for the presence of primary (licensed) users. In this paper, we illustrate the benefits of cooperation in cognitive radio. We show that by allowing the cognitive users operating in the same band to cooperate we can reduce the detection time and thus increase the overall agility. We first consider a two-user cognitive radio network and show how the inherent asymmetry in the network can be exploited to increase the agility. We show that our cooperation scheme increases the agility of the cognitive users by as much as 35%. We then extend our cooperation scheme to multicarrier networks with two users per carrier and analyze asymptotic agility gain. In Part II of our paper [1], we investigate multiuser single carrier networks. We develop a decentralized cooperation protocol which ensures agility gain for arbitrarily large cognitive network population.

In cognitive networks, cognitive (unlicensed) users need to continuously monitor spectrum to detect the presence of primary (licensed) users. In part I, we have illustrated the benefits of cooperation in cognitive radio by considering a simple two-user network and showing improvement in agility. In part II, we investigate multiple cognitive user networks. We first consider multiuser single carrier networks and develop sufficient conditions for agility gain when the cognitive population is arbitrarily large. We then propose a practical algorithm which allows cooperation between cognitive users in random networks. Finally, we provide an example to illustrate the concepts developed in this paper.

A low-complexity iterative maximum a posteriori (MAP) channel estimator is proposed whose complexity increases linearly with the symbol alphabet size 'M. Prediction-based MAP channel estimation is not appropriate with a high-order prediction filter or a large modulation alphabet size, since the computational complexity increases with ML, where L is the predictor order. In contrast, the proposed channel estimator has a constant number of trellis states regardless of the prediction filter order, and is shown to provide comparable error performance to the prediction-based MAP estimator

In this paper, we investigate the effects of the spatial fading correlation on the performance of a multiple-input multiple-output (MIMO) adaptive antenna system with optimum combining (OC) in the presence of multiple cochannel interferers over a correlated Rayleigh fading channel. Based on the Khatri's distribution functions of quadratic forms in complex Gaussian random matrices, we develop a unified determinant representation of those joint eigenvalue distributions. Taking into account the spatial correlation among the antenna elements at the transmitter or receiver, we derive the closed-form formulas for the probability density function and outage probability of the maximum output signal-to-interference ratio (SIR) in an interference-limited MIMO-OC system. Furthermore, the average output SIR and error probability are also investigated. From numerical examples, we show that a new theoretical approach gives a simple and accurate way to assess the performance of the MIMO-OC system over arbitrarily correlated fading channels

To obtain the closed-loop capacity of a multiple-input and multiple-output (MlMO) orthogonal frequency division multiplexing (OFDM) system, channel state information (CSI) is required at the transmitter. To reduce the data rate of CSI feedback, preceding matrix approach has been proposed for MIMO systems in flat fading channels. In this paper, we develop a novel approach for MIMO-OFDM systems in frequency-selective fading channels. The proposed approach exploits the correlation of frequency responses at different subchannels in MIMO-OFDM systems to reduce CSI feedback. It is not only flexible to multiple data stream transmission but also has better performance than the existing approaches

Channel aware medium access protocols perform decisions primarily based on the instantaneous channel state information (CSI) available. In practice, channel measurements and quantization introduce distortion in the available CSI at the transmitter. In this paper, we investigate the effect of imperfect CSI on the asymptotic throughput of a particularly important medium access protocol - the channel aware Aloha. We show that any degree of uncertainty in the channel estimate leads to zero throughput asymptotically in the existing single carrier Aloha scheme; however, multicarrier diversity is effective in compensating for lack of perfect CSI.

In this paper, an improved scheme for energy spreading transform (EST) based equalization is proposed. In the improved scheme, optimal frequency-and time-domain filters that maximize signal-to-interference-noise ratio (SINR) are employed to enhance the performance while the filters used in the original scheme [1] can be regarded as an approximation of the optimal filters.

This paper aims at the system design on the MC-CDMA uplink. In this paper, we compare the performance and the complexity of MC-CDMIA systems with and without iterative detectors and multiple receive antenna arrays. Through extensive computer simulation, we demonstrate that the following four combinations are good solutions: a single-antenna receiver with an iterative PIC detector initialized by the MF and with 3 iterations, a two-antenna receiver with MMSE-MUD or an iterative PIC detector initialized by the MF and with 2 iterations, and a four-antenna receiver with a simple MF detector. Therefore, non-iterative detectors can be used with multiple receive antenna arrays to substitute complicated iterative detectors and it is a promising solution for the 4G up-link MC-CDMA systems where multiple receive antennas are available. In this paper, we have also considered pilot-aided channel estimation with weighted delay profile technique and investigated the impact of channel estimation error on different systems.

Nonideal sampling clocks in orthogonal frequency-division multiplexing (OFDM)-based ultra-wideband (UWB) systems introduce random timing jitter, which results in interchannel interference (ICI) and degrades the system performance. In this paper, we investigate the impact of timing jitter on OFDM-based UWB systems. We first derive an exact expression for the ICI power due to timing jitter. From the exact ICI power expression, we then develop various bounds on the ICI power under different situations. When timing jitters at different samples are independent, we obtain tight upper and lower bounds. When timing jitters at different samples are dependent, the ICI powers are different at different subcarriers and a universal upper bound for the ICI power is derived in this case. Compared with the exact expression for the ICI power, the developed bounds are easy to evaluate and provide insights. Their accuracy is confirmed by numerical examples. We also discuss the potential and the limitations of using oversampling to reduce the ICI power at the end.

In this paper, we provide an asymptotic performance analysis of channel-aware packet scheduling based on extreme value theory. We first address the average throughput of systems with a homogeneous average signal-to-noise ratio (SNR) and obtain its asymptotic expression. Compared with the exact throughput expression, the asymptotic one, which is applicable to a broader range of fading channels, is more concise and easier to get insights. Furthermore, we confirm the accuracy of the asymptotic results by theoretical analysis and numerical simulation. For a system with heterogeneous SNRs, normalized-SNR-based scheduling needs to be used for fairness. We also investigate the asymptotic average throughput of the normalized-SNR-based scheduling, and prove that the average throughput in this case is less than that in the homogeneous case with a power constraint.

In this paper, we provide an asymptotic performance analysis of channel-aware packet scheduling based on the extreme value theory. We first address the average throughput of systems with a homogeneous average signal-to-noise ratio (SNR), and obtain its asymptotic expression. Compared with the exact throughput expression, the asymptotic one, which is applicable to a broader range of fading channels, is more concise and easier from which to get insights. Furthermore, we confirm the accuracy of the asymptotic results by theoretical analysis and numerical simulation. For a system with heterogeneous SNRs, normalized-SNR-based scheduling needs to be used for fairness. We also investigate the asymptotic average throughput of the normalized-SNR-based scheduling, and prove that the average throughput in this case is less than that in the homogeneous case with a power constraint

Multiple transmit and receive antenna arrays can be used to form multiple input and multiple output (MIMO) systems for diversity and multiplexing in wireless communications. In this paper, we develop iterative signal-detection schemes based on energy spreading transform (EST) (T. Hwang and Y. Li) for MIMO channels. The EST in a MIMO system improves signal-detection performance by spreading the symbol energy over the space and time domain. It also enables iterative signal detection without employing channel coding. Analytical and simulation results demonstrate that the performance of the proposed schemes is very close to that of the genie-aided receiver when there are a sufficiently large number of receive antennas and signal-to-noise ratio (SNR) is above a threshold

Ultra-wideband (UWB) communication is a potential technique for future high-speed networks. In this paper, we investigate channel estimation and interference suppression for OFDM based UWB systems. In particular, we modify an existing channel estimation approach for UWB systems and develop an exponential window based approach to estimate correlation of the receive signals for interference suppression. Computer simulation results show that these approaches can be effectively used in OFDM based UWB systems.

Multiple transmit and receive antennas (MIMO) have been used with orthogonal frequency division multiplexing (OFDM) for capacity improvement in frequency-selective channels. In certain propagation environments, there exists spatial correlation among channels corresponding to different pairs of transmit and receive antennas. In this paper, we investigate training sequence design for channel estimation in MIMO-OFDM systems. We develop necessary conditions for a training sequence to minimize the mean-square error (MSE) of channel estimation when spatial correlation of MIMO channel is known to the transmitter and discuss training sequence design for some special cases. The performance improvement of the designed training sequences is confirmed by simulation examples.

In this paper, we illustrate the benefits of cooperation in cognitive radio. Cognitive (unlicensed) users need to continuously monitor spectrum for the presence of primary (licensed) users. We show that by allowing the cognitive radios operating in the same band to cooperate we can reduce the detection time and thus increase the overall agility. We first consider the case of two cognitive users and show how the inherent asymmetry in the network can be exploited to increase the agility. We then extend our protocol to study multi-user multi-carrier cognitive network. We compare our cooperation scheme with the non-cooperation scheme and derive expressions for agility gain. We show that our cooperation scheme reduces the detection time for the cognitive users by as much as 35%

A robust iterative pilot symbol aided modulation (PSAM) channel estimation (CE) approach is proposed for vertical Bell Laboratories layered space-time (V-BLAST) multiple-input multiple-output orthogonal frequency division multiplexing systems operating on time-varying wireless channels. Since the signals at receive antennas are the superposition of the transmitted signals from multiple transmit antennas in V-BLAST systems, accurate CE is crucial for good error rate performance. Furthermore, since the channel estimator is independently implemented for a MIMO system, the proposed iterative algorithm has an inherent iterative interference cancellation characteristic. Simulation results demonstrate excellent performance of the proposed algorithm on frequency selective fading channels

In this paper, we provide an asymptotic performance analysis of channel-aware packet scheduling based on extreme value theory. We first address the average throughput of systems with a homogeneous average signal-to-noise ratio (SNR) and obtain its asymptotic expression. Compared to the exact throughput expression, the asymptotic one, which is applicable to a broader range of fading channels, is more concise and easier to get insights. The accuracy of the asymptotic results is confirmed by numerical simulation. Furthermore, we investigate the asymptotic average throughput of the normalized-SNR-based scheduling, which can provide fairness for a system with heterogeneous SNRs. The results show that the average throughput in this case is less than that in the homogeneous case with a power constraint.

We propose a modified layered structure for multiple-input multiple-output (MIMO) systems, where the layer detection order is fixed and the data rate for each layer is allocated based on the detection order and channel statistics. With Gaussian approximation of layer capacities, we derive the optimum data rate allocation and the amount of backoff from mean layer capacity is proportional to the standard deviation of the layer capacity. The minimum overall outage probability of a layered system is uniquely determined by the normalized capacity margin. We then investigate how to select the total information rate to maximize effective throughput. Simulation results show significant performance improvement with the proposed algorithm, and the performance gap between layered structure and the channel capacity diminishes with increasing ergodicity within each codeword.

In this paper, we highlight the advantage of multichannel random access schemes by jointly exploiting the inherent multiuser and multicarrier diversity. For a deeper understanding of network behavior, we define a parameter called the population density of the network and consider multichannel systems under different types of network density. We show that by exploiting the inherent diversity of the multicarrier networks, the asymptotic stable throughput (AST) of any single carrier medium access protocol can be increased. We have developed closed form expressions for the amount of gain that can be obtained using multicarrier diversity. We then take an example of the traditional Aloha systems to demonstrate the advantages of multicarrier systems over single carrier systems and show using simulation results that the maximum throughput gain achievable is as high as 50%.

Multiple transmit and receive antennas (MIMO) have been used in OFDM systems for capacity improvement. In practice, channel state information has to be estimated for diversity combining or space-time decoding. Previous work on channel estimation assumes that MIMO channels are independent and identically distributed (i.i.d.). In certain propagation environments, there exists spatial correlation among channels corresponding to different pairs of transmit and receive antennas. The spatial correlations can he exploited to improve channel estimation. In this paper, we develop a minimum mean-square-error (MMSE) channel estimator for MIMO-OFDM systems that can make full use of the spatial correlation. We also design optimum training sequences that minimize the channel estimation error. When MIMO channels are i.i.d., the training sequences for different transmit antennas are orthogonal and with equal power. However, when MIMO channels are spatially correlated, the power allocation for training sequences can be further optimized. Our simulation results show that the proposed MMSE estimator can exploit spatial and frequency correlations of MIMO channels in OFDM systems and therefore has good performance.

Channel-aware packet scheduling is investigated in this paper. Based on extreme value theory, we propose asymptotic analyses for average throughput and delay in both single-carrier and multicarrier networks. Those analyses provide accurate results and have concise expressions, which explicitly show the multiuser diversity gain inherent in channel-aware scheduling. Although multicarrier networks with channel-aware scheduling have the same throughput as single-carrier networks, multicarrier networks can provide better delay performance than single-carrier networks

To obtain the closed-loop capacity of a multiple-input and multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, channel state information (CSI) is required at the transmitter. Sometimes, CSI can be only obtained through frequent feedback from the receiver and it occupies a large bandwidth in the reverse link to completely feedback CSI. To reduce the data rate of CSI feedback, precoding matrix has been proposed for MIMO systems in flat fading channels and it is also extended to MIMO-OFDM systems in frequency-selective fading channels by interpolation. In this paper, we develop a novel precoding matrix tracking approach for MIMO-OFDM systems. The proposed approach is based on subspace tracking in the Grassman manifold and requires a limited data rate feedback. It not only is flexible to multiple data transmission but also has better performance than the existing approaches

In this letter, we investigate the issue of selecting the number of antennas at the base station and at the mobile to optimize ergodic capacity of a multiple-input/multiple-output system, when the costs of implementing antennas at the base station and at the mobile are unequal. Total system capacity, defined as a linear combination of the uplink and downlink ergodic capacity, is used as the objective function to be maximized. The asymptotic expression for the ergodic capacity is used as an approximation. The limiting case gives some insight on how the ratio of the number of antennas at the base station to the number of antennas at the mobile may change with signal-to-noise ratio and cost ratio when the total system capacity is maximized.

Due to the characteristics of wireless channels, utility-based resource management in wireless networks requires a set of mechanisms that are different from those for wireline networks. This paper explores in detail why and how the requirements are different. In particular, we analyze the wireless network performance to find out the scheduling algorithm that maximizes total utility of the system. Unlike previous studies, this paper focuses on scenarios in which wireless networks are not fully loaded and all of the users are best-effort data users, i.e., there is no streaming user. Our first key conclusion is that Kleinrock's Conservation Law provides a valuable means to accurately capture the perceived rates of best-effort users in such systems. The queueing analysis further indicates that, within periods during which channel conditions are stable for each user, albeit differ from user to user, the max-utility scheduling algorithm can be derived using queueing theorem and can be readily implemented in actual systems for utility functions that are of exponential or log format. When further taking into account the time-variant nature of wireless channel conditions, our simulation results demonstrate that dynamic weighted fair queueing, with weights adjusted according to the channel conditions, can achieve highly desirable performance with great flexibility.

In this paper, we provide a theoretical framework for cross-layer optimization for orthogonal frequency division multiplexing (OFDM) wireless networks. The utility is used in our study to build a bridge between the physical layer and the media access control (MAC) layer and to balance the efficiency and fairness of wireless resource allocation. We formulate the cross-layer optimization problem as one that maximizes the average utility of all active users subject to certain conditions, which are determined by adaptive resource allocation schemes. We present necessary and sufficient conditions for utility-based optimal subcarrier assignment and power allocation and discuss the convergence properties of optimization. Numerical results demonstrate a significant performance gain for the cross-layer optimization and the gain increases with the number of active users in the networks.

We have established a theoretical framework for cross-layer optimization in orthogonal frequency division multiplexing (OFDM) wireless networks. In this paper, we focus on effective and practical algorithms for efficient and fair resource allocation in OFDM wireless networks. We have taken various conditions into account and developed a variety of efficient algorithms, including sorting-search dynamic subcarrier assignment, greedy bit loading, and power allocation, as well as objective aggregation algorithms. We have also modified those algorithms for a certain type of nonconcave utility functions. To further improve performance by exploiting time diversity, a low-pass time filter can be easily incorporated into all of the algorithms. Simulation results have confirmed that the utility-based cross-layer optimization can significantly enhance the system performance and guarantee fairness. The gains come from multiuser diversity, frequency diversity, as well as time diversity. The fairness is automatically achieved by the behavior of marginal utility functions.

We present a novel, adaptive subchannel allocation algorithm for the downlink channel of a multiuser, OFDM-based wireless home network. Since channel resources are not adaptively allocated in a traditional OFDM system, some of the users with fixed frequency channel allocation may experience deep fading in a frequency-selective multipath environment. This problem can be mitigated using an adaptive algorithm, which can be modeled as an optimization problem that allocates the proper number of subchannels to achieve maximum channel capacity. We employ the classical bipartite matching algorithm to achieve optimal OFDM subchannel allocation. Simulation results show that the total capacity increases remarkably, and that higher channel capacity is achieved when the number of users is increased. In a wireless environment, the reallocation overhead can be large. As such, we propose a hybrid method for subchannel allocation to support more users while reducing the reallocation overhead. We simulated the performance of the hybrid algorithm and compared it with the matching-only algorithm.

We use a space-time energy spreading transform (ST-EST) for iterative signal detection in multiple input and multiple output (MIMO) wireless systems. Theoretical and simulation results demonstrate that when the signal-to-noise ratio (SNR) is above a threshold, the performance of the system is very close to that of the genie-aided (interference-free) receiver.

The joint maximum-likelihood (JML) detector may be used in memoryless multiple input multiple output (MIMO) systems to obtain optimal detection performance. However the JML detector needs an exhaustive search and causes prohibitively large decoding complexity. To reduce the complexity of MIMO signal detection, the minimum mean-square-error (MMSE) linear detector (LD), decision-feedback detector (DFD) and sphere detector (SD) may be used. In this paper we develop bi-truncation based approaches for MIMO signal detection. The new approaches have low-complexity, and computer simulation results show that they outperform MMSE-LD and MMSE-DFD.

Multiple-input-multiple-output (MlMO) techniques promise huge capacity increase in fading channels. Group-wise space-time coding is a tradeoff between complexity and performance. In this paper, structure of particular component space-time code trellises is exploited using partial information from Viterbi decoder of the simultaneously decoded interfering component code. We have shown that calculating the a posteriori probabilities of different states of the encoder at each time, and predicting the conditional mean and covariance of interference for the next time index, the system performance is improved by soft interference cancellation. The word-error-rate (WER) performance improvement for a system with 4 transmit 4 receive antennas is 1.7 dB and 1.5 dB for 2-space-time codes with 8 and 16 states, respectively. In addition, the parallel structure does not have decoding delay as in successive interference cancellation and is highly modular for VLSI implementation.

In this paper, a novel iterative equalization is proposed. Different from the existing turbo-like equalization, the proposed one uses an energy spreading transform (EST) instead of a (soft) channel decoder, which separates equalization and decoding. The complexity of the proposed equalization approach is comparable to that of decision-feedback equalization (DFE). However, analytical and simulation results demonstrate that its performance is very close to the matched filter bound (MFB) when the signal-to-noise ratio (SNR) is above a threshold.