In this paper, we propose an unmanned aerial vehicle (UAV) and bird recognition scheme with signal processing and deep learning for integrated sensing and communications (ISAC) system. We first provide the basic scene of low-altitude targets monitoring, and formulate the motion equations and echo signals for UAVs and birds. Next, we extract the centralized micro-Doppler (cmD) spectrum and the high resolution range profile (HRRP) of the low-altitude target from the echo signals. Then we design a dual feature fusion enabled low-altitude target recognition network with convolutional neural network (CNN), which employs both the images of cmD spectrum and HRRP as inputs to jointly distinguish between UAV and bird. Meanwhile, we generate 237600 cmD and HRRP image samples to train, validate, and evaluate the designed low-altitude target recognition network. The proposed scheme is termed asAirGuard, whose effectiveness has been demonstrated by simulation results.

In this paper, we explore the fine-grained channel state information (CSI) obtained through the sensing function in an reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system to support the efficient covert communications in the system. We first construct a new fine-grained CSI model for the RIS-assisted ISAC system and propose a novel covert communication scheme based on the new CSI model. We then develop theoretical models for the detection error probability, Cramér-Rao Bound and covert rate to depict the covertness, sensing and covert communication performances under the proposed scheme. Based on these theoretical models, we further formulate an optimization problem for covert rate maximization through optimizing the reflection coefficient in RIS and the transmit powers for covert/probing signals. With the help of the homogenization for quadratic constrained quadratic programming, semi-definite relaxation and Dinkelbach transform, an efficient alternating optimization (AO) algorithm is devised to tackle this complex optimization problem. Finally, extensive numerical results are presented to demonstrate the performance enhancement for covert communication in the RIS-assisted ISAC system from exploring the fine-grained CSI and AO-based parameter optimization therein.

Multi-user millimeter-wave communication relies on narrow beams and dense cell deployments to ensure reliable connectivity. However, tracking optimal beams for multiple mobile users across multiple base stations (BSs) results in significant signaling overhead. Recent works have explored the capability of out-of-band (OOB) modalities in obtaining spatial characteristics of wireless channels and reducing pilot overhead in single-BS single-user/multi-user systems. However, applying OOB modalities for multi-BS selection towards dense cell deployments leads to high coordination overhead, i.e, excessive computing overhead and high latency in data exchange. How to leverage OOB modalities to eliminate pilot overhead and achieve efficient multi-BS coordination in multi-BS systems remains largely unexplored. In this paper, we propose a novel OOB modality synergy (OMS) based mobility management scheme to realize multi-user beam prediction and proactive BS selection by synergizing two OOB modalities, i.e., vision and location. Specifically, mobile users are initially identified via spatial alignment of visual sensing and location feedback, and then tracked according to the temporal correlation in image sequence. Subsequently, a binary encoding map based gain and beam prediction network (BEM-GBPN) is designed to predict beamforming gains and optimal beams for mobile users at each BS, such that a central unit can control the BSs to perform user handoff and beam switching. Simulation results indicate that the proposed OMS-based mobility management scheme enhances beam prediction and BS selection accuracy and enables users to achieve 91% transmission rates of the optimal with zero pilot overhead and significantly improve multi-BS coordination efficiency compared to existing methods.

In this paper, we propose a tensor-based framework for multi-user reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC), designed to enable efficient data transmission and accurate localization across far- and near-field communications. The proposed scheme introduces a two-phase nested tensor-based ISAC transmission protocol, comprising the precoding phase and the joint symbol detection and target localization (JSDTL) phase. During the precoding phase, a third-order tensor is constructed to extract angle information from the far-field base station (BS)-RIS channel links, which is then utilized to design a precoding strategy that mitigates inter-user interference. In the JSDTL phase, the received signals are constructed into a fourth-order nested tensor incorporating angular, temporal, and coding dimensions. The algebraic structure of the nested tensor, combined with the second-order Fresnel approximation for the near-field channel model between the RIS and user equipment (UE), is leveraged to perform data recovery and target localization. Simulation results confirm that the proposed scheme achieves superior ISAC performance with reduced computational complexity, outperforming benchmark algorithms.

In this paper, we design the wideband hybrid-analog-digital (HAD) beamforming for integrated sensing and communication (ISAC) systems. Specifically, we incorporate the phase shifters (PSs) and true-time delay lines (TTDs) to combat the wideband beam squint effect, which are able to provide frequency-dependent phase shift in the analog beamforming stage. The fully-digital (FD) beamformers with guaranteed sensing and communication signal-to-interference-plus-noise ratios (SINRs) are first designed. Then, the HAD beamforming is formulated as a least squares (LS) problem to approximate the designed FD beamformers with constant-modulus constraints, whose main challenges are the complicated objective function and the non-convex constraints. To tackle these issues, we majorize the objective function to decouple the optimization variables. Then, the beamformer for PSs can be solved with a closed-form solution, whereas the beamformer for TTDs can be obtained by a simple grid-search. Finally, we adjust the PS and TTD beamformers by the Riemannian conjugate gradient method (RCGM) to improve the performance. Simulation results demonstrate the superior performance of the proposed algorithm over the conventional algorithm.

The reconfigurable intelligent surface (RIS) has been widely studied in far-field communications (FFC), and further extended to near-field communications (NFC). With the distinct electromagnetic properties in FFC and NFC, it remains debatable whether the RIS deployment strategies established for FFC are still applicable to NFC. To bridge this gap, we examine RIS-aided cooperative relaying in NFC through two representative configurations, single-RIS and multi-RIS relaying, in which a decode-and-forward relay forwards data from the source to the user with the assistance of RIS For the single RIS deployment, we show that the achievable rates with RIS deployed near the base station (BS) or near relay are completely different. It is revealed that, with a single-antenna relay, the multi-RIS is significantly preferable over the single RIS in NFC without the requirement of massive RIS reflection elements, which is completely different from that of FFC. Furthermore, we derive closed-form expressions for the achievable rates under both single-antenna and multi-antenna relay configurations to explicitly determine the distance threshold. The presented analytical results demonstrate that locating RIS near the relay yields substantial performance gains when the transmission distance exceeds a certain threshold. This conclusion is validated by numerical simulations, which systematically illustrates the distinct impact of RIS deployment strategies in near-field versus far-field regions.

In this paper, we propose a novel integrated sensing and communications (ISAC) scheme to perform bridge micro-deformation monitoring (BMDM) in complex environments. We first provide an excitation-bridge coupling model to represent the micro-deformation process of the bridge. Next, we design a novel frame structure for BMDM applications, and construct the OFDM echo channel model for basic scene of BMDM, including micro-deformation, dynamic objects, and static environment. Then, we develop a phasor statistical analysis method based on average cancellation algorithm to suppress the interference of dynamic objects, as well as a circle fitting method based on least squares algorithm to remove the interference of static environment near the monitoring area. Furthermore, we extract the micro-deformation feature vector from the OFDM echo signals after inverse discrete fourier transform (IDFT), and derive vertical micro-deformation value with the time-frequency phase resources. Simulation results demonstrate the effectiveness of the proposed BMDM scheme and its robustness against both dynamic interferences and static interferences.

In this paper, we propose an asynchronous unmanned aerial vehicle (UAV) trajectory monitoring scheme with multi-base station (BS) feature fusion in a cellular integrated sensing and communications (ISAC) system. Different from distributed radar systems that rely on wideband radar waveforms and synchronous joint processing, the proposed scheme considers practical cellular ISAC settings such as narrowband orthogonal frequency division multiplexing (OFDM) signaling and transceiver discrepancies-induced offsets. We develop a single-BS signal pre-processing method that estimates target motion parameters and effectively compensates for time offsets (TOs) and carrier frequency offsets (CFOs) caused by transceiver discrepancies. Next, we design a multi-BS feature fusion method that aligns spatial features across BSs and accurately estimates the positions and velocities of targets based on time delay and Doppler frequency features. By operating at the feature level, the fusion process circumvents the need for coherent signal-level processing as well as the extensive data-level fusion commonly required in distributed radar systems. Furthermore, we propose a cooperative trajectory tracking method that associates asynchronous trajectory observations into consistent local and global trajectories, thereby enabling reliable cross-BS trajectory fusion. Simulation results demonstrate that the proposed cooperative scheme significantly enhances the accuracy of UAV trajectory monitoring compared to traditional algorithms.

In this paper, we present a framework to enable unmanned aerial vehicle (UAV) trajectory monitoring for an integrated sensing and communications (ISAC) system. Specifically, the base station (BS) first performs beam-scanning to acquire the echo signals from dynamic targets. Static environmental clutter is subsequently filtered out to enable real-time target detection. Next, we propose a phase-rotated discrete Fourier transform (PRDFT) algorithm to estimate the targets’ motion parameters, including distance, horizontal angle, pitch angle, radial velocity, horizontal angular velocity, and pitch angular velocity. We then convert the estimated parameters into a common Cartesian coordinate system to extract the targets’ positional and velocity features. To associate the targets with their corresponding trajectories, we propose a position wave gate and velocity differences nearest neighbor (WGVDNN) algorithm that matches targets based on similar position and velocity features relative to the trajectories. Afterward, we apply the interactive multiple model unscented Kalman filter (IMMUKF) algorithm to identify the targets’ motion model and predict their positions in the next time slot, thereby directing the beam to track the discovered ones. Simulation results demonstrate that the proposed framework effectively enables the real-time discovery of new targets and the continuous tracking of the discovered targets, thereby monitoring the complete trajectories of all targets.

In the upcoming next-generation wireless communication systems, high-mobility support serves as an essential part. OTFS (Orthogonal Time Frequency Space) modulation is regarded as a candidate waveform for 6G systems for its robustness in high mobility scenarios. However, its performance degrades when Doppler spreads beyond the subcarrier spacing with ultra-high speed, a condition referred to as the out-of-range Doppler (OD) channel. We analyze the input-output relationship under OD channels and reveal that the Delay-Doppler (DD) domain received signal is a superposition of the transmitted signal with an additional phase term. Based on this insight, we design a cross-frame OTFS framework for channel estimation, utilizing coprime subcarrier configurations to observe varying responses across subframes, and the out-of-range parameters are resolved using the Chinese Remainder Theorem (CRT). Furthermore, an effective channel estimation method is developed to handle multipath scenarios by decomposing subframes and matching response taps using fractional and amplitude information, which transforms the OD channel estimation problem into multiple single-domain problems by processing DD domain signals in layers. Simulation results demonstrate the effectiveness of the proposed method in estimating OD channels, outperforming existing approaches that fail under such conditions.

Orthogonal time-frequency space (OTFS) is a potential waveform for integrated sensing and communications (ISAC) systems because it can manage communication and sensing metrics in one unified domain, and has better performance in high mobility scenarios. In practice, a target might come from far distance or with ultra-high speed. However, the max unambiguous range and max tolerable velocity of OTFS-ISAC system is limited by the unambiguous round-trip delay and Doppler shift, which are related to OTFS frame, i.e., time slots and subcarrier spacing, respectively. To enlarge the sensing range, a novel OTFS cross-frame ranging and velocity estimation model as well as its corresponding method based on the Chinese remainder theorem (CRT) are proposed in this paper. By designing co-prime numbers of subcarriers and time slots in different subframes, the difference in the responses of the subframes for a target can be used to estimate the distance and velocity of an out-of-range target. Several frame structures are further designed for specific sensing scenarios, such as target with ultra-high speed or at far distance. Simulation results show that the proposed method can achieve significantly better performance in NMSE compared with the classic sensing methods under the condition of same time and frequency resources.

Bo Lin, Huanming Zhang, Yuhua Jiang, Yucong Wang, Tengyu Zhang, Shaoqiang Yan, Hongyao Li, Yihong Liu, Feifei Gao. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Reconfigurable antennas have excellent dynamic adaptability to alter their operational state in response to environmental changes, and is considered as one potential technology towards future communication systems. However, acquiring accurate channel state information (CSI) for all radiation patterns with low pilot overheads imposes a significant challenge in pattern reconfigurable MIMO (PR-MIMO) communication systems. To address this issue, in this paper we propose a novel two-stage channel estimation approach based on antenna grouping (AG) to obtain the CSIs for all radiation patterns efficiently. In the first stage, all antennas at the transmitter employ the same radiation pattern, while in the second stage, the antennas at the transmitter are divided into groups according to the number of radiation patterns, where antennas in different groups employ different radiation patterns, while antennas within the same group employ the same radiation pattern. When the exact number of channel paths is known, a closed-form channel extrapolation algorithm and a singular value decomposition (SVD)-based channel extrapolation algorithm are proposed, depending on the value of the channel paths and whether the angle information is known. Extensive simulation results illustrate that the proposed algorithms can accurately extrapolate the CSI of all radiation patterns, with dramatically reduced pilot overheads compared to the conventional channel estimation methods.

Exploring the mutual benefit and reciprocity of sensing and communication (S&C) functions is fundamental to realizing deeper integration for integrated sensing and communication (ISAC) systems. This paper investigates a novel communication-assisted sensing (CAS) system within 6G perceptive networks, where the base station actively senses the targets through device-free wireless sensing and simultaneously transmits the estimated information to end-users. In such a CAS system, we first establish an optimal waveform design framework based on the rate-distortion (RD) and source-channel separation (SCT) theorems. After analyzing the relationships between the sensing distortion, coding rate, and communication channel capacity, we propose two distinct waveform design strategies in the scenario of target impulse response estimation. In the separated S&C waveforms scheme, we equivalently transform the original problem into a power allocation problem and develop a low-complexity one-dimensional search algorithm, shedding light on a notable power allocation tradeoff between the S&C waveform. In the dual-functional waveform scheme, we conceive a heuristic mutual information optimization algorithm for the general case, alongside a modified gradient projection algorithm tailored for the scenarios with independent sensing sub-channels. Additionally, we identify the presence of both subspace tradeoff and water-filling tradeoff in this scheme. Finally, we validate the effectiveness of the proposed algorithms through numerical simulations.

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

Reconfigurable intelligent surface (RIS) is a key potential technology for the sixth generation wireless communication. The deployment of RIS in wideband communication systems can effectively mitigate severe path loss and against the blockage of line-of-sight path, which can improve transmission gain and enhance communication quality. However, with the increase of RIS array and bandwidth, beam-squint effect will occur and seriously damage the performance of communication systems. In this paper, we first establish a holographic RIS-aided wideband communication system model from the perspective of the electromagnetic wave propagation theory. Then, we analyze the holographic RIS electromagnetic characteristics under the beam-squint effect. Further, we derive the angle spread range, 3dB beam bandwidth, and beam coverage range to analyze the regularities of beam offset. Besides, we propose a new codebook design scheme to address the impact of the beam-squint effect. Finally, we introduce the true-time-delay (TTD) lines into the holographic RIS structure to mitigate the beam-squint effect. The simulation results reveal the influence of the beam-squint, and also verify the mitigation effect of TTD lines on the beam-squint effect.

Precoding in massive multiple-input-multiple-output (mMIMO) systems relies on accurate estimation of downlink channel state information (CSI). However, when the number of antennas is large, obtaining CSI data incurs significant pilot and feedback overhead. In this paper, we propose a transformer based precoding network (TPN) that infers the future precoding matrices by exploring the time-frequency characteristic of historical wireless channels. Next, we design a suitable precoding matrix switching scheme based on channel correlation to further reduce the pilot overhead. Moreover, we leverage the network pruning technique to reduce the computational complexity of the proposed TPN. Simulations demonstrate that the sum-rate can be improved by 15% compared with the traditional zero-order holding method.

In this paper, we propose a novel scheme to estimate the six-dimensional (6D) motion parameters of the dynamic target for monostatic integrated sensing and communications (ISAC) system. We first provide a generic ISAC framework for dynamic target sensing based on massive multiple input and multiple output (MIMO) array. Next, we derive the relationship between the sensing channel of ISAC base station (BS) and the 6D motion parameters of the dynamic target. Then, we employ the array signal processing methods to estimate the horizontal angle, pitch angle, distance, and virtual velocity of the dynamic target. Since the virtual velocities observed by different antennas are different, we adopt plane fitting to estimate the dynamic target’s radial velocity, horizontal angular velocity, and pitch angular velocity from these virtual velocities. Simulation results demonstrate the effectiveness of the proposed 6D motion parameters estimation scheme, which also confirms a new finding that one single BS with a massive MIMO array is capable of estimating the horizontal angular velocity and pitch angular velocity of the dynamic target.

Deep Learning (DL) achieves significant performance in estimating the direction of arrival (DOA) in array signal processing. However, many existing DL methods require a large amount of data to train a specialized DL network. To reduce data requirements for training, this paper presents a novel DL-based DOA estimation algorithm for limited training data(LTDDOA-net). The proposed algorithm utilizes the properties of second-order derivatives of the loss function and the ‘learn to learn’ approach to construct a framework that can achieve good performance with minimal data training. Initially, we developed a neural network designed for DOA estimation. This network was subsequently trained using proposed method and loss function on a limited dataset. Ultimately, we validated the practicality and benefits of our approach through simulations and hardware experiment. The results of simulations and hardware experiment have verified the superiority of the proposed approach.

In this paper, we present a novel multi-hop link scheduling framework that utilizes the vision perception from cameras of the road-side unit (RSU) as well as cameras of the vehicle to support the large-capacity and reliable transmission of high-speed dynamic vehicle network. Specifically, we propose a vision based link state identification method to determine whether the communications links among RSU and different vehicles are blocked or connected. We firstly utilize the 3D detection technique to obtain the vehicle spatial distribution in surrounding environment. Then, the geometric calculation is adopted to accurately analyze the link states between RSU and different vehicles. Moreover, we design an environmental statistical information based low-complexity link scheduling method, and utilize the joint statistical distribution of the residual transmission distance and the residual multi-hop latency to optimize the total transmission latency. Simulation results show that the proposed vision based link state identification method significantly outperforms the exiting methods, and the proposed link scheduling method can approximately achieve the optimal performance as that from the exhaustive search method but with much less computation overhead.

Leveraging cameras and LiDARs has been proved as an effective way to achieve beam management without training overhead for millimeter wave (mmWave) communications. However, existing methods place sensors at base station (BS) and/or mobile station (MS), which may induce privacy concerns and augment communications system expenses. In this paper, we propose a novel third-party camera aided mmWave beam management framework and self-build an RFSoC prototype for validation. Specifically, we design third-party camera aided beam alignment and blockage prediction algorithms that could work with random third-party perspective. Then, we leverage the proposed beam alignment to design initial access and beam recovery, and utilize the proposed blockage prediction to realize failure prediction. Hand-off from mmWave to sub-6G communications is conducted once the blockage is predicted. To validate the proposed framework, we develop an RFSoC prototype from scratch independently. The real-world real-time experimental results show that the beam alignment achieves over 98% in top-5 accuracy and reduces the time consumption to below$1/50$of that incurred by exhaustive beam sweeping. Meanwhile, the prototype maintains 410 MHz dynamic mmWave communications, and can seamlessly switch to sub-6G communications before a mmWave blockage happens.

This paper aims to answer a fundamental question in the area of Integrated Sensing and Communications (ISAC):What is the optimal communication-centric ISAC waveform for ranging?Towards that end, we first established a generic framework to analyze the sensing performance of communication-centric ISAC waveforms built upon orthonormal signaling bases and random data symbols. Then, we evaluated their ranging performance by adopting both the periodic and aperiodic auto-correlation functions (P-ACF and A-ACF), and defined the expectation of the integrated sidelobe level (EISL) as a sensing performance metric. On top of that, we proved that among all communication waveforms with cyclic prefix (CP), the orthogonal frequency division multiplexing (OFDM) modulation is the only globally optimal waveform that achieves the lowest ranging sidelobe for quadrature amplitude modulation (QAM) and phase shift keying (PSK) constellations, in terms of both the EISL and the sidelobe level at each individual lag of the P-ACF. As a step forward, we proved that among all communication waveforms without CP, OFDM is a locally optimal waveform for QAM/PSK in the sense that it achieves a local minimum of the EISL of the A-ACF. Finally, we demonstrated by numerical results that under QAM/PSK constellations, there is no other orthogonal communication-centric waveform that achieves a lower ranging sidelobe level than that of the OFDM, in terms of both P-ACF and A-ACF cases.

In this paper, we propose an environment sensing-aided beam prediction model for smart factory that can be transferred from given environments to a new environment. In particular, we first design a pre-training model that predicts the optimal beam by sensing the present environmental information. When encountering a new environment, it generally requires collecting a large amount of new training data to retrain the model, whose cost severely impedes the application of the designed pre-training model. Therefore, we next design a transfer learning strategy that fine-tunes the pre-trained model by limited labeled data of the new environment. Simulation results show that when the pre-trained model is fine-tuned by 30% of labeled data from the new environment, the Top-10 beam prediction accuracy reaches 94%. Moreover, compared with the way to completely re-training the prediction model, the amount of training data and the time cost of the proposed transfer learning strategy reduce 70% and 75% respectively.

Integrated sensing and communications (ISAC) has emerged as a transformative paradigm for next-generation wireless systems. In this paper, we present a novel ISAC scheme that leverages the diffusion Schr¨odinger bridge (DSB) to realize the sensing of electromagnetic (EM) property of a target as well as the reconstruction of the wireless channel. The DSB framework connects EM property sensing and channel reconstruction by establishing a bidirectional process: the forward process transforms the distribution of EM property into the channel distribution, while the reverse process reconstructs the EM property from the channel. To handle the difference in dimensionality between the high-dimensional sensing channel and the lower-dimensional EM property, we generate latent representations using an autoencoder network. The autoencoder compresses the sensing channel into a latent space that retains essential features, which incorporates positional embeddings to process spatial context. The simulation results demonstrate the effectiveness of the proposed DSB framework, which achieves superior reconstruction of the targets shape, relative permittivity, and conductivity. Moreover, the proposed method can also realize accurate channel reconstruction given the EM property of the target. The dual capability of accurately sensing the EM property and reconstructing the channel across various positions within the sensing area underscores the versatility and potential of the proposed approach for broad application in future ISAC systems.

Integrated sensing and communications (ISAC) has opened up numerous game-changing opportunities for future wireless systems. In this paper, we develop a novel ISAC scheme that utilizes the diffusion model to sense the electromagnetic (EM) property of the target in a predetermined sensing area. Specifically, we first estimate the sensing channel by using both the communications and the sensing signals echoed back from the target. Then we employ the diffusion model to generate the point cloud that represents the target and thus enables 3D visualization of the target’s EM property distribution. In order to minimize the mean Chamfer distance (MCD) between the ground truth and the estimated point clouds, we further design the communications and sensing beamforming matrices under the constraint of a maximum transmit power and a minimum communications achievable rate for each user equipment (UE). Simulation results demonstrate the efficacy of the proposed method in achieving high-quality reconstruction of the target’s shape, relative permittivity, and conductivity. Besides, the proposed method can sense the EM property of the target effectively in any position of the sensing area.

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for future wireless systems. In this paper, we develop a novel scheme that utilizes orthogonal frequency division multiplexing (OFDM) pilot signals to sense the electromagnetic (EM) property of the target and thus identify the materials of the target. Specifically, we first establish an EM wave propagation model with Maxwell equations, where the EM property of the target is captured by a closed-form expression of the channel. We then build the mathematical model for the relative permittivity and conductivity distribution (RPCD) within a predetermined region of interest shared by multiple base stations (BSs). By leveraging the Lippmann-Schwinger equation, we propose an EM property sensing method that reconstructs the RPCD using compressive sensing techniques. This approach exploits the joint sparsity of the EM property vector, which enables the proposed method to effectively handle the high dimensionality and ill-posed nature of the inverse scattering problem. We then develop a fusion algorithm to combine data from multiple BSs, which can enhance the reconstruction accuracy of EM property by efficiently integrating diverse measurements. Moreover, the fusion is performed at the feature level of RPCD and features low transmission overhead. We further design the pilot signals that can minimize the mutual coherence of the equivalent channels and enhance the diversity of incident EM wave patterns. Simulation results demonstrate the efficacy of the proposed method in achieving high-quality RPCD reconstruction and accurate material classification.

In this paper, we study the deep learning (DL) based channel extrapolation problem and conduct the over-the-air (OTA) antenna extrapolation and frequency channel interpolation test for the 3rd generation partnership project (3GPP) long-term evolution (LTE) time-division duplex (TDD)-like orthogonal frequency division multiplexing (OFDM) massive MIMO prototype. We first present measurement campaigns using universal software radio peripherals (USRP) at 3.5 GHz, where the base station (BS) is composed of a 64-element antenna array. A DL-based antenna extrapolation network is then designed to approximate the inner deterministic function among antennas from the attained channel data within the “training” pilots. We present an antenna selection network (ASN) that can select a limited number of antennas for the best extrapolation, which outperforms the uniform antenna selection in terms of channel reconstruction and signal detection. We also design a deep residual neural network for channel interpolation. The performance of the extrapolated channel is evaluated in terms of normalized mean squared error (NMSE) in comparison to the measured channels on all antenna ports or the full pilot-aided channels in all OFDM subcarriers. Experimental results show that ASN can reduce an average of 87.5% antenna ports and maintain channel estimation NMSE by$10^{-2}$when compared to 3GPP channel estimation protocols.

In low-altitude economy (LAE), the widespread use of various types of unmanned aerial vehicles (UAVs) could provide convenience and enhance efficiency. However, the existence of unauthorized or illegal UAVs would pose significant challenges to urban privacy and security. In this paper, we propose a networked integrated sensing and communications (ISAC) based UAV tracking and handover scheme towards LAE. We define avirtual sensing cell (VSC)where oneprimary base station (PBS)transmits sensing signals, while both the PBS and twosecondary base stations (SBS)receive echoes. Since the echoes contain the clutter of static environment, each base station (BS) would first filter out the clutter and then estimate the UAV’s horizontal angle, elevation angle, distance, and radial velocity with the multiple signal classification (MUSIC) algorithm. Next, we employ the centralized extended Kalman filter (EKF) to fuse the estimations from the three BSs and leverage the one-step prediction results of the EKF to distinguish and track multiple UAVs. When the UAV flies within the coverage of a VSC, we design aPBS handoverstrategy to select the optimal BS from three BSs as the new PBS in real-time. Moreover, we propose aVSC handoverstrategy to track the UAV continuously when it flies from one VSC to another. Simulation results demonstrate the effectiveness of the proposed scheme and provide valuable reference for UAV tracking and handover in LAE.

In time division duplexing (TDD) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) can be obtained from uplink channel estimation thanks to channel reciprocity. However, under high-mobility scenarios, frequent uplink channel estimation is needed due to channel aging. Additionally, large amounts of antennas and subcarriers result in high-dimensional CSI matrices, aggravating pilot training overhead. To address this, we propose a three-domain (3D) channel extrapolation framework across spatial, frequency, and temporal domains. First, considering the effectiveness of traditional knowledge-driven channel estimation methods and the marginal effects of pilots in the spatial and frequency domains, a knowledge-and-data driven spatial-frequency channel extrapolation network (KDD-SFCEN) is proposed for uplink channel estimation via joint spatial-frequency channel extrapolation to reduce spatial-frequency domain pilot overhead. Then, leveraging channel reciprocity and temporal dependencies, we propose a temporal uplink-downlink channel extrapolation network (TUDCEN) powered by generative artificial intelligence for slot-level channel extrapolation, aiming to reduce the tremendous temporal domain pilot overhead caused by high mobility. Numerical results demonstrate the superiority of the proposed framework in significantly reducing the pilot training overhead by 16 times and improving the system’s spectral efficiency under high-mobility scenarios compared with state-of-the-art channel estimation/extrapolation methods.

Ultra-reliable low-latency communication is the key technology for smart factories and autonomous vehicles. However, traditional beam training approaches in millimeter-wave communications generally cause significant latency and communication overhead, especially in the case of multi-user communications. To tackle this problem, we propose a novel Vision-aided Multi-user Beam Tracking (VA-MUBT) framework for mmWave massive MIMO system, which leverages deep learning based visual object detection and multiple objects tracking algorithm to enable fast beam tracking of multi-user. In addition, a prototype is constructed to evaluate the proposed VA-MUBT framework and the experimental results based on this prototype show that the accuracy of 3-time beam search can reach near 90% with only 8% overhead of the exhaustive beam search method. Hence, the proposed VA-MUBT demonstrates the superiority in achieving fast multi-user beam tracking and significantly reducing the communication overhead.

In this paper, we present a novel multi-hop link scheduling framework that utilizes the vision perception from cameras of the road-side unit (RSU) to support the large-capacity and reliable transmission of the high-speed dynamic vehicle network. Specifically, we propose a vision based link state identification method to determine whether the communication links between RSU and different vehicles are blocked or connected. The 3D detection technique is firstly used to obtain the vehicle spatial distribution in surrounding environment. Then, the geometric calculation is adopted to accurately analyze the link states between RSU and different vehicles. Moreover, we design an environmental statistical information based low-complexity link scheduling method. The joint statistical distribution of the residual transmission distance and the residual multi-hop latency is used to optimize the total multi-hop latency. Simulation results show that the proposed vision based link state identification method can significantly outperform the exiting methods, and the proposed link scheduling method can approximately achieve the optimal performance as that from the exhaustive search method but with much less computation overhead.

The utilization of unmanned aerial vehicles (UAVs) for communication data collection across all areas can be modeled as a complete coverage path planning (CCPP) problem. To address the challenge of lengthy coverage time in traditional CCPP algorithms, we propose a weighted balanced graph partitioning based complete coverage path planning scheme (WBGPP), which consists of two sub-algorithm: weighted balanced graph partitioning (Weighted B-GRAP) and single agent path planning (SAPP). The Weighted B-GRAP algorithm can decompose the multi-UAV CCPP problem into multiple single UAV CCPP problems by assigning each UAV a responsibility area according to its capability. Then, we optimize the backtracking strategy through breadth-first search and design a SAPP algorithm to reduce the number of repeated visits and shorten the coverage time. The simulation results show that the proposed WBGPP scheme effectively reduce the coverage time of multiple UAVs in CCPP problems and can be applied to various maps.

Smart factory is a new intelligent platform requiring high throughput and millimeter wave (mmWave) technology has become an enabler for high speed communications in Industry 4.0. However, the sensitivity of mmWave signals to blockage poses serious challenges to the reliability of wireless networks in these frequency ranges. In this paper, we propose a vision-aided reference signal receiving power prediction (RSRP) framework for smart factory to avoid communications interruption caused by unexpected blockage. In particular, we design a feature extraction method to obtain communications-related features in environmental images. Then, we construct a joint image-channel dataset based on Blender and Wireless Insite software. Simulations show that the root mean square error (RMSE) of RSRP prediction 400 ms ahead reaches 2.88 dB. RSRP prediction can assist base station (BS) handover to avoid communications interruption. Hence, the proposed study provides a promising direction for enabling ultra-reliable communications under mmWave and even Terahertz bands in smart factory of Industry 4.0.

Near field computational imaging has been recognized as a promising technique for non-destructive and highly accurate detection of the target. Meanwhile, reconfigurable intelligent surface (RIS) can flexibly control the scattered electro-magnetic (EM) fields for sensing the target and can thus help computational imaging in integrated sensing and communication (ISAC) systems. In this paper, we propose a near-field imaging scheme based on holograghic RIS. To mitigate the inherent ill conditioning of the inverse problem in the imaging system, we design the EM field patterns as masks that help translate the inverse problem into a forward problem. Next, we utilize RIS to generate different virtual EM masks on the target surface and calculate the cross-correlation between the mask patterns and the electric field strength at the receiver. We then provide a RIS design scheme for virtual EM masks by employing a regularization technique. Simulation results demonstrate that the proposed method can achieve high-quality imaging. Moreover, the imaging quality can be improved by generating more virtual EM masks, by increasing the signal-to-noise ratio (SNR) at the receiver, or by placing the target closer to the RIS.

In this paper, we investigate the correlation between the sub-6GHz channel and the millimeter wave (mmWave) chan-nel, and then predict the mmWave downlink hybrid beamforming (HBF) matrices directly from the sub-6GHz uplink channel, based on a sophisticatedly designed deep learning architecture. Specifically, the neural network structure consists of three functional modules: the feature extraction module extracts channel features from a large amount of channel data, the feature fusion module combines multidimensional features, and the prediction module generates the HBF matrix. Moreover, we develop a power constraint module for digital domain and a constant modulus constraint module for analog domain to ensure that the output of the network satisfies the characteristics of HBF. Simulation results show that the proposed sub-6GHz assisted HBF algorithm without mmWave channel estimation saves 75% of channel state information compared to the method using mmWave pilot resources directly. Furthermore, to facilitate deployment, we design a low-complexity structure that achieves a remarkable reduction of 98.52% in parameters and 22.93% in computations.

Millimeter-wave (mmWave) communications with abundant spectrum resources have become an enabling technology for high throughput, ultra-reliable, and low latency communications (URLLC). Since the mmWave signal is sensitive to blockage, accurate base station (BS) selection is the premise of achieving the URLLC. In this paper, we propose a multi-view images assisted proactive BS selection scheme that can predict the optimal BS for the user in the next frame. The proposed scheme utilizes vision sensing and thus does not require the entire pilot resources, such that the latency caused by seeding and receiving pilots reduces. In addition, we design a multitask learning strategy and a prior knowledge based fine tuning method to ensure the accuracy and reliability of BS selection. Simulation results in an outdoor environment demonstrate the superior performance of the proposed scheme in terms of both the accuracy and the robustness.

In this paper, we propose to utilize the beam squint effect to realize fast non-cooperative dynamic target sensing in massive multiple input and multiple output (MIMO) based integrated sensing and communications (ISAC) systems. Specifically, we design a beamforming strategy that controls the range of beam squint by adjusting the values of phase shifters and true time delay lines. With this design, beams at different subcarriers can be aligned along different directions in a planned way. Then the received echo signals at different subcarriers will carry targets information in different directions, based on which the targets' angles can be estimated through sophisticatedly designed algorithm. Moreover, we propose a supporting method based on extended array signal estimation, which utilizes the phase changes of different frequency subcarriers within different OFDM symbols to estimate the distance and velocity of dynamic targets. Interestingly, the proposed sensing scheme only needs to transmit and receive the signals once, which can be termed as You Only Listen Once (YOLO). Compared with the traditional ISAC method that requires time consuming beam sweeping, the proposed one greatly reduces the sensing overhead. Simulation results confirm the effectiveness of the proposed scheme.

In this paper, we consider the moving target sensing problem for integrated sensing and communication (ISAC) sys-tems in clutter environment. Scatterers produce strong clutter, deteriorating the performance of ISAC systems in practice. Given that scatterers are typically stationary and the targets of interest are usually moving, we here focus on sensing the moving targets. Specifically, we adopt a scanning beam to search for moving target candidates. For the received signal in each scan, we employ high-pass filtering in the Doppler domain to suppress the clutter within the echo, thereby identifying candidate moving targets according to the power of filtered signal. Then, we adopt root-MUSIC-based algorithms to estimate the angle, range, and radial velocity of these candidate moving targets. Subsequently, we propose a target detection algorithm to reject false targets. Simulation results validate the effectiveness of these proposed methods.

In this paper, we design a radio frequency system on chip (RFSoC) based scalable integrated sensing and communication (ISAC) prototyping platform with orthogonal time frequency space (OTFS) waveform. Within the ISAC prototype, we conceive an efficient frame structure tailored to streamline the communication and sensing schemes. The prototype utilizes the low-complexity channel equalization algorithm in the time-delay domain and senses the targets using the synchronization preamble and the peak pilot. The RFSoC based platform employs a hybrid architecture, which can be scaled to accommodate di-verse OTFS waveform configurations, facilitating the verification of a wide range of OTFS algorithms. We test the prototyping platform under both static and dynamic scenarios, and the experimental outcomes affirm the prototype's competence in accurately demodulating transmitted data. Moreover, the sensing function attains a velocity resolution of up to 1.65$m$/$S$and a range resolution of up to 0.5 m.

In this paper, we propose a practical integrated sensing and communications (ISAC) framework to sense dynamic targets from clutter environment while ensuring users communications quality. We design multiple communications beams that can communicate with users while one rotating sensing beam can scan entire space, and then we propose the supporting beam-forming design and power allocation strategies for such design. Unlike most existing ISAC studies that ignore the interference of static environmental clutter on target sensing, we construct a mixed sensing channel that includes both static environment and dynamic targets. When base station receives echo signals, we first provide a practical clutter filtering method to filter out static environmental clutter. Then dynamic target detection and angle estimation are realized through angle-Doppler spectrum estimation (ADSE) and joint detection over multiple subcarriers (MSJD), while distance and velocity estimation are realized through the extended subspace algorithm. Simulation results are provided to demonstrate the effectiveness of the proposed scheme.

In this paper, we investigate the covertness and secrecy guarantees of wireless communications in an active attacker scenario where attackers perform detection/eavesdropping and jamming simultaneously. Both detection and eavesdropping attacks need to be counteracted, such that the covertness and secrecy guarantees in wireless communications can be achieved. To understand the covertness and secrecy performances, we provide theoretical modeling for covertness outage probability and secrecy outage probability, respectively. Based on the the-oretical model, we conduct theoretical analysis to identify the covert secrecy rate (CSR) under power control (PC)-based secure transmission scheme. Extensive numerical results are provided to illustrate the achievable performances and also reveal the impact of the active attackers on the CSR.

The integration of reconfigurable intelligent surfaces (RIS) into Unmanned Aerial Vehicles (UAVs) can be deployed to provide ubiquitous communication services in communication-impaired areas, but the limited on-board battery capacity of the UAVs restricts their endurance. The operating time of the UAV can be extended by dividing the metasurface elements on the RIS, but channel variations due to the constant movement of pedestrians present a significant challenge to efficient resource allocation. In this paper, a novel energy harvesting (EH) scheme, called the UAV-EH, is developed based on the UAV-RIS system. The proposed UAV-EH scheme extends the Synchronized Wireless Information and Power Transfer (SWIPT) system with simultaneous signal transmission and energy harvesting on the RIS metasurface. And we develop a robust proximal policy optimization (PPO) algorithm combined with cropping the action space to assign RIS metasurface elements as a way to improve the UAV-EH scheme to ensure quality of service (QoS) in dynamic wireless environments. The simulation results demonstrate the effectiveness of our proposed UAV-EH scheme based on PPO, with average energy harvesting rates of 65.7% for single-user terminals and 65.6% for multi-user terminals. These rates are very close to the performance of exhaustive search algorithms and outperform all other schemes.

Leveraging various sensors, i.e., cameras and Li-DARs, is deemed as a promising way to realize fast millimeter wave (mmWave) beam alignment with no frequency overhead. However, previous methods that place extra sensors at base station (BS) and mobile station (MS) may augment communication system expenses and also induce privacy concerns. In this paper, we propose a novel beam alignment framework that utilizes images taken by camera placed at third-party perspective. We design a deep neural network that extracts and fuses the position and orientation of mobile user in the third-party images to infer the optimal beam pair, and we establish real-world datasets to train and evaluate the proposed network. Specifically, we design a mmWave light-weight beam sweeping system to reduce time expenses and system complexity during dataset collection. To calculate the time consumption of the third-party camera aided beam alignment framework, we independently implement a mmWave communication prototype with 410MHz bandwidth OFDM baseband according to IEEE 802.11 protocol on Xilinx RFSoC. The experimental results show the third-party camera aided beam alignment approach achieves over 98% in top-5 accuracy, and reduces the mmWave beam alignment time consumption to below 1/50 of that incurred by exhaustive beam sweeping.

Utilizing communication signals for indoor human activity recognition (HAS) is an important component of integrated sensing and communication (ISAC). The current majority HAS solutions adopt a single sensing strategy and only work in a simple environment. In this paper, we propose a new HAS method named WiSMLF that can flexibly select multiple sensing strategies and then use multi-level feature fusion for sensing. We first use the high frequency energy (HFE) method to categorize human activities into two types: static activities (SAs) and moving activities (MAs). Subsequently, for SAs, we adopt a joint localization and activity recognition sensing strategy, and use a multi-level feature fusion network based on visual geometry group (VGG). For MAs, we adopt a joint activity recognition and moving distance estimation sensing strategy, and use a multi-level feature fusion network based on long short-term memory (LSTM). The experimental results show that WiSMLF outperforms the existing methods especially in complex environments, and can obtain 92% higher accuracy in location, activity recognition, and distance estimation.

Realizing the wireless environmental sensing is another desired function of reconfigurable intelligent surface (RIS), in addition to enhancing the performance of wireless communication systems. In this paper, we design a holographic RIS-aided computational imaging system, which consists of a transmitter, a holographic RIS, a rectangular target and a receiver. Here, the target is composed of a series of discrete segments, each of which possesses a constant scattering density. The sensing task of the proposed system is to estimate the scattering densities of the target, which corresponds to the termcomputational imaging. The termholographicmeans that the RIS is modeled as a physically continuous surface with a physically continuous phase shift pattern, which can be approximately considered as as having massive (possibly infinite) number of elements within a finite space. Both the RIS and the target are subject to the electromagnetic boundary conditions, whose scattered fields are computed by the equivalent current method and the physical equivalent. Based on the computed scattered fields of the target, we derive the pathloss of the proposed system. In order to perform the imaging, we alter the phase shift pattern of the RIS such that the main energy of its scattered fields is focused towards different segments of the target successively, which then produces multiple measurements of the scattering densities and simultaneously ensures a low pathloss. After all measurements are completed, the scattering densities of the target can be estimated with the observed measurement vector and the reconstructed sensing channel, i.e., the computational imaging is accomplished. Simulation results show that the proposed imaging strategy performs well if the system parameters are designed properly.

Smart factory is a new digital and intelligent platform requiring high throughput and ultra-reliable low-latency communications (URLLC). Industrial communications at sub-6 GHz faces spectrum congestion and bandwidth limitations, which seriously jeopardize the high data rate requirement of smart factory. Recently, millimeter wave (mmWave) and Terahertz technologies have become enablers for high speed communications and intelligent manufacturing in Industry 4.0 and beyond. However, the sensitivity of mmWave signals to blockage and the overhead of large-scale antenna beam sweeping pose serious challenges to the reliability and the latency of wireless networks in these frequency ranges. In this paper, we propose a vision-aided URLLC framework for smart factory that does not incur any overhead from channel training and beam sweeping. In particular, we design a feature extraction method to obtain communications-related features in environmental images for blockage prediction, reference signal receiving power (RSRP) prediction, and beam selection. Then, we construct a joint image-channel dataset covering images, annotations, blockage, and wireless channels based on Blender and Wireless Insite software. Simulations show that the accuracy of blockage prediction 400 ms ahead reaches 99.9%, the root mean square error (RMSE) of RSRP prediction 400 ms ahead reaches 2.78 dB, and the Top-5 accuracy of beam selection reaches 91.8%. Blockage and RSRP prediction can assist base station (BS) handover to avoid communications interruption, while beam selection can eliminate the overhead of channel training and beam sweeping. Hence, the proposed study provides a promising direction for enabling URLLC under mmWave and even Terahertz bands in smart factory of Industry 4.0.

Integrated sensing and communication (ISAC) has become a promising technology for future communication system. In this paper, we consider a millimeter wave system over high mobility scenario, and propose a novel simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) aided ISAC scheme. To improve the communication service of the in-vehicle user equipment (UE) and simultaneously track and sense the vehicle with the help of nearby roadside units (RSUs), a STAR-RIS is equipped on the outside surface of the vehicle. Firstly, an efficient transmission structure for the ISAC scheme is developed, where a number of training sequences with orthogonal precoders and combiners are respectively utilized at BS and RSUs for channel parameter extraction. Then, the near-field static channel model between the STAR-RIS and in-vehicle UE as well as the far-field time-frequency selective BS-RIS-RSUs channel model are characterized. By utilizing the multidimensional orthogonal matching pursuit (MOMP) algorithm, the cascaded channel parameters (i.e., the delays, the Doppler frequency shifts, the angles of arrivals, and the angles of departure of the scattering paths) of the BS-RIS-RSUs links can be obtained at the RSUs. Thus, the vehicle localization and its velocity measurement can be acquired by jointly utilizing these extracted cascaded channel parameters of all RSUs. Note that the MOMP algorithm can be further utilized to extract the channel parameters of the BS-RIS-UE link for communication service. With the help of sensing results, the reflection and refraction phase shifts of the STAR-RIS are delicately designed, which can significantly improve the received signal strength for both the RSUs and the in-vehicle UE, and can finally enhance the sensing and communication performance. Moreover, the trade-off design for sensing and communication is proposed by optimizing the energy splitting factors of the STAR-RIS. Finally, simulation results are provided to validate the feasibility and effectiveness of our proposed STAR-RIS aided ISAC scheme.

Millimeter-wave (mmWave) communications with abundant spectrum resources have become an enabling technology for high throughput, ultra-reliable, and low latency communications (URLLC). Since the mmWave signal is sensitive to blockage, accurate base station (BS) selection and beam searching are the premises of achieving the URLLC. In this paper, we consider the mmWave communications systems where mobile users are served by the roadside unit (RSU). We propose a multi-camera view based proactive RSU selection and beam searching scheme that can predict the optimal RSU for the user in the next frame and search the corresponding beam pair. The proposed scheme utilizes vision sensing and reduces training resources. In addition, the visual information of multiple views makes the selection of the optimal RSU more accurate and reliable compared to the existing single view technologies. Simulation results in an outdoor environment show the superior performance of the proposed scheme in terms of predicting accuracy and achievable rate.

In this paper, a deterministic sampling decoding strategy for multiple-input multiple output (MIMO) systems is studied, which performs probabilistic searching according to a probability threshold in the lattice Gaussian distribution. Motivated by model probabilistic twin (MPT), the randomness in obtaining the target decoding solution is overcome by the proposed probabilistic searching decoding (PSD) algorithm, which brings considerable decoding gains in both performance and complexity. Specifically, the decoding radius of PSD is derived while the decoding complexity in terms of the number of visited nodes during the searching is also upper bounded, leading to an explicit decoding trade-off. Meanwhile, we generalize PSD by the mechanism of candidate protection so that it enjoys a flexible performance between the suboptimal successive interference cancelation (SIC) decoding and the optimal maximum likelihood (ML) decoding by adjusting the initial search size$K$. Methods for further optimization and complexity reduction of the proposed PSD algorithm are also given. Finally, simulation results based on MIMO detection are presented to confirm the tractable and flexible decoding trade-off of the proposed PSD algorithm.

In this paper, we propose a novel vehicle positioning method for commodity multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) WIFI systems in indoor parking lots. To address the limitation of the small number of WIFI antennas, the proposed method first utilizes signal model rearrangement techniques, in which the abundant carrier frequency resources of WIFI are expanded into space resources. Then a rearranged off-grid sparse Bayesian learning (ROG-SBL) algorithm is developed for parameters estimation to achieve vehicle positioning. Specifically, by resorting to the Bayesian inference and Newton method, the position-related parameters are estimated iteratively by fitting the channel state information (CSI) measurement model, and thus the vehicle positioning is realized according to the geometric relationship. Moreover, we derive the Cramér-Rao bound (CRB) as a performance reference for the proposed algorithm. Compared with the existing algorithms, the proposed one improves the positioning performance of the vehicle with fewer carrier numbers and has more stable performance. Simulation results show that the performance curves of the proposed algorithm for parameters estimation are close to the corresponding CRBs, and the proposed algorithm can cope with more challenging cases when the line-of-sight (LOS) path does not exist.

Millimeter wave communications are sensitive to complex scattering environments (e.g., to the presence of obstacles), which can be mitigated by reconfigurable intelligent surfaces (RISs). Traditional nearly passive RISs lack the ability to perform signal processing operations, which makes channel estimation in RIS-aided communications more challenging. Hence, in this paper, we focus on a hybrid RIS architecture, which is equipped with a small number of active elements. These active elements can be connected with baseband processing units through radio frequency (RF) chains. We estimate the whole channel, including the channel between the base station (BS) and the RIS, that between the users and the RIS, and that between the BS and the users. The whole channel is acquired at the hybrid RIS through the transmission of several segments of training pilots. In order to decrease the hardware cost, the BS and the hybrid RIS are equipped with RF chains with low-resolution analog-to-digital converters (ADCs). Since the numbers of BS antennas and RIS elements are very large, the estimation of the full-space channels is not straightforward. To tackle this problem, we propose a channel extrapolation scheme based on a joint selection model. Specifically, we select a BS antenna subset and an RIS element subset to be connected to the RF chains and to estimate the partial-space channels related to these subsets. Then, a reference-based variational auto-encoder model is used to implement the extrapolation from the partial-space channels to the full-space channels. Besides, the optimal joint selection pattern is acquired through a selection network to improve the channel extrapolation performance. Moreover, to overcome the quantization error caused by the use of low-resolution ADCs, we propose a two-stage repair scheme for channel estimation. Simulation results are provided to demonstrate the effectiveness of the designed channel extrapolation scheme.

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for future wireless systems. In this paper, we develop a novel scheme that utilizes orthogonal frequency division multiplexing (OFDM) pilot signals in ISAC systems to sense the electromagnetic (EM) property of the target and thus also identify the material of the target. Specifically, we first establish an end-to-end EM propagation model by means of Maxwell equations, where the EM property of the target is captured by a closed-form expression of the ISAC channel, incorporating the Lippmann-Schwinger equation and the method of moments (MOM) for discretization. We then model the relative permittivity and conductivity distribution (RPCD) within a specified detection region. Based on the sensing model, we introduce a multi-frequency-based EM property sensing method by which the RPCD can be reconstructed from compressive sensing techniques that exploits the joint sparsity structure of the EM property vector. To improve the sensing accuracy, we design a beamforming strategy from the communications transmitter based on the Born approximation that can minimize the mutual coherence of the sensing matrix. The optimization problem is cast in terms of the Gram matrix and is solved iteratively to obtain the optimal beamforming matrix. Simulation results demonstrate the efficacy of the proposed method in achieving high-quality RPCD reconstruction and accurate material classification. Furthermore, improvements in RPCD reconstruction quality and material classification accuracy are observed with increased signal-to-noise ratio (SNR) or reduced target-transmitter distance.

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.

Using communications signals for dynamic target sensing is an important component of integrated sensing and communications (ISAC). In this paper, we propose to utilize the beam squint effect to realize fast non-cooperative dynamic target sensing in massive multiple input and multiple output (MIMO) Terahertz band communications systems. Specifically, we construct a wideband channel model of the echo signals, and design a beamforming strategy that controls the range of beam squint by adjusting the values of phase shifters and true time delay lines. With this design, beams at different subcarriers can be aligned along different directions in a planned way. Then the received echo signals at different subcarriers will carry target information in different directions, based on which the targets’ angles can be estimated through sophisticatedly designed algorithm. Moreover, we propose a supporting method based on extended array signal estimation, which utilizes the phase changes of different frequency subcarriers within different orthogonal frequency division multiplexing (OFDM) symbols to estimate the distances and velocities of dynamic targets. Interestingly, the proposed sensing scheme only needs to transmit and receive the signals once, which can be termed asYou Only Listen Once(YOLO). Compared with the traditional ISAC methods that require time consuming beam sweeping, the proposed one greatly reduces the sensing overhead. Simulation results are provided to demonstrate the effectiveness of the proposed schemes.

Integrated sensing and communication (ISAC) has been regarded as a key technology for 6G wireless communications, in which large-scale multiple input and multiple output (MIMO) array with higher and wider frequency bands will be adopted. However, recent studies show that the beam squint phenomenon can not be ignored in wideband MIMO system, which generally deteriorates the communications performance. In this paper, we find that with the aid of true-time-delay lines (TTDs), the range and trajectory of the beam squint in near-field communications systems can be freely controlled, and hence it is possible to reversely utilize the beam squint for user localization. We derive the trajectory equation fornear-field beam squint pointsand design a way to control such trajectory. With the proposed design, beamforming from different subcarriers would purposely point to different angles and different distances, such that users from different positions would receive the maximum power at different subcarriers. Hence, one can simply localize multiple users from the beam squint effect in frequency domain, and thus reduce the beam sweeping overhead as compared to the conventional time domain beam search based approach. Furthermore, we utilize the phase difference of the maximum power subcarriers received by the user at different frequencies in several times beam sweeping to obtain a more accurate distance estimation result, ultimately realizing high accuracy and low beam sweeping overhead user localization. Simulation results demonstrate the effectiveness of the proposed schemes.

In this paper, we propose a practical integrated sensing and communications (ISAC) framework to sense dynamic targets from clutter environment while ensuring users communications quality. To implement communications function and sensing function simultaneously, we design multiple communications beams that can communicate with the users as well as one sensing beam that can rotate and scan the entire space. To minimize the interference of sensing beam on existing communications systems, we divide the service area intosensing beam for sensing (S4S) sectorandcommunications beam for sensing (C4S) sector, and provide beamforming design and power allocation optimization strategies for each type sector. Unlike most existing ISAC studies that ignore the interference of static environmental clutter on target sensing, we construct a mixed sensing channel model that includes both static environment and dynamic targets. When base station receives the echo signals, it first filters out the interference from static environmental clutter and extracts the effective dynamic target echoes. Then a complete and practical dynamic target sensing scheme is designed to detect the presence of dynamic targets and to estimate their angles, distances, and velocities. In particular, dynamic target detection and angle estimation are realized through angle-Doppler spectrum estimation (ADSE) and joint detection over multiple subcarriers (MSJD), while distance and velocity estimation are realized through the extended subspace algorithm. Simulation results demonstrate the effectiveness of the proposed scheme and its superiority over the existing methods that ignore environmental clutter.

In this paper, we investigate the joint device activity and data detection in massive machine-type communications (mMTC) with a one-phase non-coherent scheme, where data bits are embedded in the pilot sequences and the base station simultaneously detects active devices and their embedded data bits without explicit channel estimation. Due to the correlated sparsity pattern introduced by the non-coherent transmission scheme, the traditional approximate message passing (AMP) algorithm cannot achieve satisfactory performance. Therefore, we propose a deep learning (DL) modified AMP network (DL-mAMPnet) that enhances the detection performance by effectively exploiting the pilot activity correlation. The DL-mAMPnet is constructed by unfolding the AMP algorithm into a feedforward neural network, which combines the principled mathematical model of the AMP algorithm with the powerful learning capability, thereby benefiting from the advantages of both techniques. Trainable parameters are introduced in the DL-mAMPnet to approximate the correlated sparsity pattern and the large-scale fading coefficient. Moreover, a refinement module is designed to further advance the performance by utilizing the spatial feature caused by the correlated sparsity pattern. Simulation results demonstrate that the proposed DL-mAMPnet can significantly outperform traditional algorithms in terms of the symbol error rate performance.

To reduce the downlink beam sweep overhead of mmWave systems, we propose a deep reinforcement learning based dynamic beam selection (DRL-DBS) method. A new learning motivation is presented by analyzing the dynamic change laws of high- and low-frequency channels in the spatial domain: to learn the index offset between the optimal beam of mmWave and sub-6 GHz spatial spectrum. In the DRL-DBS method, we propose a novel action space where actions can dynamically adjust the size of the beam sweep subset according to the high-and low-frequency channel propagation laws. Hence, the DRL-DBS method can predict a mmWave downlink beam sweep subset with dynamic size, and the optimal beamforming index is from beam sweep results on the subset. A dual-input dueling Q-network with noisy networks and prioritized experience replay is designed to select the optimal action. The DRL-DBS method can achieve a dynamic trade-off between mmWave beam selection quality and beam sweep overhead based on the reward function. Simulation results demonstrate the superior performance of the DRL-DBS method compared with the existing strategies. Especially, the DRL-DBS method outperforms the exhaustive search algorithm in achievable rate because the overhead of mmWave beam sweep is considered.

To reap the promising benefits of massive multiple-input multiple-output (MIMO) systems, accurate channel state information (CSI) is required through channel estimation. However, due to the complicated wireless propagation environment and large-scale antenna arrays, precise channel estimation for massive MIMO systems is significantly challenging and costs an enormous training overhead. Considerable time-frequency resources are consumed to acquire sufficient accuracy of CSI, which thus severely degrades systems’ spectral and energy efficiencies. In this paper, we propose a dual-attention-based channel estimation network (DACEN) to realize accurate channel estimation via low-density pilots, by jointly learning the spatial-temporal domain features of massive MIMO channels with the temporal attention module and the spatial attention module. To further improve the estimation accuracy, we propose a parameter-instance transfer learning approach to transfer the channel knowledge learned from the high-density pilots pre-acquired during the training dataset collection period. Experimental results reveal that the proposed DACEN-based method achieves better channel estimation performance than the existing methods under various pilot-density settings and signal-to-noise ratios. Additionally, with the proposed parameter-instance transfer learning approach, the DACEN-based method achieves additional performance gain, thereby further demonstrating the effectiveness and superiority of the proposed method.

Accurate channel state information (CSI) is essential for downlink precoding in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems with orthogonal frequency-division multiplexing (OFDM). However, obtaining CSI through feedback from the user equipment (UE) becomes challenging with the increasing scale of antennas and subcarriers and leads to extremely high CSI feedback overhead. Deep learning-based methods have emerged for compressing CSI but these methods generally require substantial collected samples and thus pose practical challenges. Moreover, existing deep learning methods also suffer from dramatically growing feedback overhead owing to their focus on full-dimensional CSI feedback. To address these issues, we propose a low-overhead Incorporation-Extrapolation based Few-Shot CSI feedback Framework (IEFSF) for massive MIMO systems. An incorporation-extrapolation scheme for eigenvector-based CSI feedback is proposed to reduce the feedback overhead. Then, to alleviate the necessity of extensive collected samples and enable few-shot CSI feedback, we further propose a knowledge-driven data augmentation (KDDA) method and an artificial intelligence-generated content (AIGC) -based data augmentation method by exploiting the domain knowledge of wireless channels and by exploiting a novel generative model, respectively. Experimental results based on the DeepMIMO dataset demonstrate that the proposed IEFSF significantly reduces CSI feedback overhead by 64 times compared with existing methods while maintaining higher feedback accuracy using only several hundred collected samples.

Integrated sensing and communication (ISAC) has become a promising technology for future communication system. In this paper, we consider a millimeter wave system over high mobility scenario, and propose a novel simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) aided ISAC scheme. To improve the communication service of the in-vehicle user and simultaneously track and sense the vehicle with the help of nearby roadside units (RSUs), a STAR-RIS is equipped on the outside surface of the vehicle to transmit and reflect the signal from the base station (BS). Firstly, an efficient transmission structure for the ISAC scheme is designed. Then, the time-frequency selective BS-RIS-RSUs channel model are characterized. Based on the estimated cascaded channel parameters (i.e., the delays, the Doppler frequency shifts, the angles of arrivals, and the angles of departure of the scattering paths) of the BS-RIS-RSUs links, the vehicle localization and its velocity can be acquired. With the help of sensing results, the reflection and refraction phase shifts of the STAR-RIS are designed for performance enhancememt. Moreover, the trade-off design for sensing and communication is proposed by optimizing the energy splitting factors of the STAR-RIS. Finally, simulation results are provided to validate the feasibility and effectiveness of our proposed STAR-RIS aided ISAC scheme.

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

The orthogonal time frequency space (OTFS) technique is an innovative modulation scheme that provides significant advantages in terms of channel delay and Doppler shifts. In this work, we study the sparse delay and Doppler channel estimation problem for OTFS and consider the impact of inheriting initial and iterative parameters on adjacent estimated channel corresponding to previous OTFS transmitted blocks. We propose a parameter-inherited sparse Bayesian learning (SBL) channel estimation algorithm based on unitary approximate message passing (UAMP). Simulation results show that compared to the state-of-art SBL-based algorithms, the proposed algorithm has faster convergence speed and higher accuracy. Furthermore, by exploiting the block circulant matrix with circulant blocks (BCCB) matrix property, we replace the matrix multiplication with two-dimensional (2D) fast Fourier transform (FFT), which leads to a low complexity.

Integrated sensing and communication (ISAC) has been regarded as a key technology of 6G wireless communications, in which large-scale multiple input and multiple output (MIMO) with higher and wider frequency bands will be adopted. However, recent studies show that the beam squint phenomenon can not be ignored in the wideband MIMO system, which generally deteriorates the communications performance. In this paper, we find that with the aid of the true-time-delay lines (TTDs), the range and trajectory of the beam squint in the near-field communications systems can be freely controlled, and hence it is possible to reversely utilize the beam squint for user localization. We derive the trajectory equation for near-field beam squint points and design a way to control such trajectory. With the proposed design, beamforming from different subcarriers would purposely point to different angles and different distances, such that users from different positions would receive the maximum power at different subcarriers. Hence, one can simply localize multiple users from the beam squint effect in frequency domain, and thus reduce the timing overhead as compared to the conventional beam sweeping approach. Simulation results demonstrate the effectiveness of the proposed scheme.

The recently proposed orthogonal time frequency space (OTFS) modulation multiplexes data symbols in the delay-Doppler (DD) domain. Since the range and velocity, which can be derived from the delay and Doppler shifts, are the parameters of interest for radar sensing, it is natural to consider implementing DD signal processing for radar sensing. In this paper, we investigate the potential connections between the OTFS and DD domain radar signal processing. Our analysis shows that the range-Doppler matrix computing process in radar sensing is exactly the demodulation of OTFS with a rectangular pulse shaping filter. Furthermore, we propose a two-dimensional (2D) correlation-based algorithm to estimate the fractional delay and Doppler parameters for radar sensing. Simulation results show that the proposed algorithm can efficiently obtain the delay and Doppler shifts associated with multiple targets.

Semantic communications has received growing interest since it can remarkably reduce the amount of data to be transmitted without missing critical information. Most existing works explore the semantic encoding and transmission for text and apply techniques in Natural Language Processing (NLP) to interpret the meaning of the text. In this paper, we conceive the semantic communications for image data that is much more richer in semantics and bandwidth sensitive. We propose an reinforcement learning based adaptive semantic coding (RL-ASC) approach that encodes images beyond pixel level. Firstly, we define the semantic concept of image data that includes the category, spatial arrangement, and visual feature as the representation unit, and propose a convolutional semantic encoder to extract semantic concepts. Secondly, we propose the image reconstruction criterion that evolves from the traditional pixel similarity to semantic similarity and perceptual performance. Thirdly, we design a novel RL-based semantic bit allocation model, whose reward is the increase in rate-semantic-perceptual performance after encoding a certain semantic concept with adaptive quantization level. Thus, the task-related information is preserved and reconstructed properly while less important data is discarded. Finally, we propose the Generative Adversarial Nets (GANs) based semantic decoder that fuses both locally and globally features via an attention module. Experimental results demonstrate that the proposed RL-ASC is noise robust and could reconstruct visually pleasant and semantic consistent image in low bit rate condition.

In this paper, we propose an environment semantics aided wireless communication framework to reduce the transmission latency and improve the transmission reliability, where semantic information is extracted from environment image data, selectively encoded based on its task-relevance, and then fused to make decisions for channel related tasks. As a case study, we develop an environment semantics aidednetwork architecturefor mmWave communication systems, which is composed of a semantic feature extraction network, a feature selection algorithm, a task-oriented encoder, and a decision network. With images taken from street cameras and user’s identification information as the inputs, the environment semantics aided network architecture is trained to predict the optimal beam index and the blockage state for the base station. It is seen that without pilot training or costly beam scans, the environment semantics aided network architecture can realize extremely efficient beam prediction and timely blockage prediction, thus meeting requirements for ultra-reliable and low-latency communications (URLLCs). Simulation results demonstrate that compared with existing works, the proposed environment semantics aided network architecture can reduce system overheads such as storage space and computational cost while achieving satisfactory prediction accuracy and protecting user privacy.

Integrated sensing and communications (ISAC) has attracted tremendous attention for the future 6G wireless communications systems. To improve the transmission rates and sensing accuracy, massive multi-input multi-output (MIMO) technique is leveraged with large transmission bandwidth in millimeter wave (mmWave)/terahertz (THz) band. However, the growing size of antenna array and transmission bandwidth results in the beam-squint effect, which hampers the performance of communications. Moreover, the time overhead of the traditional sensing algorithm is prohibitively high for practical systems. In this paper, instead of alleviating the beam-squint effect, we take advantage of joint beam-squint and beam-split effect and propose a novel integrated sensing and communications scheme for massive MIMO system. Specifically, with the beam-squint effect, the base station (BS) utilizes the true-time-delay (TTD) lines to steer the beams of different OFDM subcarriers towards distributive directions simultaneously. Different users then feedback their respective subcarrier frequency with the maximum array gain to BS, based on which BS could calculate the directions of the users. Moreover, by selecting sub-array with the inter-antenna spacing larger than half-wavelength, the beam-split effect can be introduced and exploited to expand the sensing range. The proposed sensing method operates over frequency-domain, and the intended sensing range is covered by all the subcarriers simultaneously, which significantly reduces the time overhead compared to the conventional sensing scheme. Simulation results have demonstrated the effectiveness as well as the superior performance of the proposed ISAC scheme.

Visual perception is an effective way to obtain the spatial characteristics of wireless channels and to reduce the overhead for communications system. A critical problem for the visual assistance is that the communications system needs to match the radio signal with the visual information of the corresponding user, i.e., to identify the visual user that corresponds to the target radio signal from all the environmental objects. In this paper, we propose a user matching method for environment with a variable number of objects. Specifically, we apply 3D detection to extract all the environmental objects from the images taken by multiple cameras. Then, we design a deep neural network (DNN) to estimate the location distribution of users by the images and beam pairs at multiple moments, and thereby identify the users from all the extracted environmental objects. Moreover, we present a resource allocation method based on the taken images to reduce the time and spectrum overhead compared to traditional resource allocation methods. Simulation results show that the proposed user matching method outperforms the existing methods, and the proposed resource allocation method can achieve 92% transmission rate of the traditional resource allocation method but with the time and spectrum overhead significantly reduced.

mmWave communications systems usually rely on analog or hybrid analog/digital architectures and thus need a predefined codebook to perform beamforming. Traditional codebooks are designed for universal environments, although in practice a particular BS will only serve a particular environment. In this paper, we propose novel site-specific codebook design methods by utilizing the visual information captured through cameras. Different from other site-specific codebook design methods that require a large amount of measured channel state information (CSI), the proposed ones need only a simple snapshot of the environment followed by efficient computer vision (CV) techniques. Thus the proposed CV-aided codebook design reduces the overhead of communications system, such as the cost of time, human resources, as well as the hardware installation and calibration. Specifically, we propose a CV-based approach that detects the LOS area around the BS and reconstructs the LOS channel vectors set (CVS). With this knowledge, we build a vision-based beam codebook using Lloyd algorithm. Further, we design a FusionNet to generate the codebook that can serve the non-line-of-sight (NLOS) users. The simulation results demonstrate the effectiveness of the proposed CV-aided codebook design methods and their superiority compared to the conventional methods.

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.

Reconfigurable intelligent surface (RIS) can improve the communications between a source and a destination. Recently, continuous aperture RIS is proved to have better communication performance than discrete aperture RIS and has received much attention. However, the conventional continuous aperture RIS is designed to convert the incoming planar waves into the outgoing planar waves, which is not the optimal reflecting scheme when the receiver is not a planar array and is located in the near field of the RIS. In this paper, we consider two types of receivers in the radiating near field of the RIS: (1) when the receiver is equipped with a uniform linear array (ULA), we design RIS coefficient to convert planar waves into cylindrical waves; (2) when the receiver is equipped with a single antenna, we design RIS coefficient to convert planar waves into spherical waves. We then propose the maximum likelihood (ML) method and the focal scanning (FS) method to sense the location of the receiver based on the analytic expression of the reflection coefficient, and derive the corresponding position error bound (PEB). Simulation results demonstrate that the proposed scheme can reduce energy leakage and thus enlarge the channel capacity compared to the conventional scheme. Moreover, the location of the receiver could be accurately sensed by the ML method with large computation complexity or be roughly sensed by the FS method with small computation complexity.

In this paper, we propose a novel computer vision-based approach to aid reconfigurable intelligent surface (RIS) for dynamic beam tracking and implement the corresponding prototype verification system. A camera is attached at the RIS to obtain the visual information about the surroundings, with which RIS identifies the incident beam direction and the desired reflected beam direction, and then adjusts the reflection coefficients according to the pre-designed codebooks. We build a 20-by-20 RIS running at 5.4 GHz and develop a high-speed control board to ensure the real-time refresh of the reflection coefficients. Meanwhile we implement an independent peer-to-peer communications system to simulate the physical link between the base station and the user equipment. The vision-aided RIS prototype system is tested in two mobile scenarios: RIS works in near-field conditions as a passive array antenna of the base station; RIS works in far-field conditions to assist the communication between the base station and the user equipment. The experimental results show that RIS can quickly adjust the reflection coefficients for dynamic beam tracking with the help of visual information.

Unmanned aerial vehicle (UAV)-enabled air-ground integrated communication systems are vulnerable to jamming attack, mainly due to the high probability with line-of-sight wireless channel conditions. Although a few UAV anti-jamming transmission schemes have been recently proposed, the fundamental performance limits by simultaneously considering the UAV and jammer’s mobility have not yet been reported. These observations motivate us to formulate an interactive reward region (IRR) characterization problem for the scenario that the UAV and the jammer compete with each other to maximize their respective rewards via power and mobility control. Then, we propose a transmit and jamming power optimization algorithm to characterize the IRR with power control, by leveraging successive convex approximation technique, where two transmit power design schemes are given in closed-form expressions from a game-theoretic perspective. Furthermore, the IRR with joint power and mobility control is characterized based on alternating optimization, in which both the UAV and jammer can simultaneously adjust their power and trajectories. Finally, extensive simulation results reveal that joint transmit power and trajectory optimization can enlarge the IRR, and increasing the UAV mobility and flight time length improves the UAV reward.

Outdoor-to-indoor communications in millimeter-wave (mmWave) cellular networks have been one challenging research problem due to the severe attenuation and the high penetration loss caused by propagation characteristics of mmWave signals. We propose a viable solution to implement the outdoor-to-indoor mmWave communication with the aid of an active intelligent transmitting surface (active-ITS), where the active-ITS allows the incoming signal from an outdoor base station (BS) to pass through the surface and be received by indoor users (UEs) after shifting its phase and magnifying its amplitude. Then, the problem of joint precoding of the BS and active-ITS is investigated to maximize the weighted sum-rate (WSR) of the system. An efficient block coordinate descent (BCD) based algorithm is developed to solve it with the suboptimal solutions in nearly closed-forms. In addition, to reduce the size and hardware cost of active-ITSs, we provide a block-amplifying architecture to partially remove the circuit components for power-amplifying, where multiple transmissive-type elements (TEs) in each block share the same power amplifier. Simulations indicate that active-ITS has the potential of achieving a given performance with much fewer TEs compared to the passive-ITS under the same total system power consumption, which makes it suitable for application to the space-limited and aesthetic-needed scenario, and the performance degradation caused by the block-amplifying architecture is negligible.

Visual information, captured for example by cameras, can effectively reflect the sizes and locations of the environmental scattering objects, and thereby can be used to infer communications parameters like propagation directions, receiver powers, as well as the blockage status. In this paper, we propose a novel beam alignment framework that leverages images taken by cameras installed at the mobile user. Specifically, we utilize 3D object detection techniques to extract the size and location information of the dynamic vehicles around the mobile user, and design a deep neural network (DNN) to infer the optimal beam pair for transceivers without any pilot signal overhead. Moreover, to avoid performing beam alignment too frequently or too slowly, a beam coherence time (BCT) prediction method is developed based on the vision information. This can effectively improve the transmission rate compared with the beam alignment approach with the fixed BCT. Simulation results show that the proposed vision based beam alignment methods outperform the existing LIDAR and vision based solutions, and demand for much lower hardware cost and communication overhead.

Millimeter wave (mmWave) multi-user massive multi-input multi-output (MIMO) is a promising technique for the next generation communication systems. However, the hardware cost and power consumption grow significantly as the number of radio frequency (RF) components increases, which hampers the deployment of practical massive MIMO systems. To address this issue and further facilitate the commercialization of massive MIMO, mixed analog-to-digital converters (ADCs) architecture has been considered, where parts of conventionally assumed full-resolution ADCs are replaced by one-bit ADCs. In this paper, we first propose a deep learning-based (DL) joint pilot design and channel estimation method for mixed-ADCs mmWave massive MIMO. Specifically, we devise a pilot design neural network whose weights directly represent the optimized pilots, and develop a Runge-Kutta model-driven densely connected network as the channel estimator. Instead of randomly assigning the mixed-ADCs, we then design a novel antenna selection network for mixed-ADCs allocation to further improve the channel estimation accuracy. Moreover, we adopt an autoencoder-inspired end-to-end architecture to jointly optimize the pilot design, channel estimation and mixed-ADCs allocation networks. Simulation results show that the proposed DL-based methods have advantages over the traditional channel estimators as well as the state-of-the-art networks.

In this paper, we develop a dynamic detection network (DDNet) based detector for multiple-input multiple-output (MIMO) systems. By constructing an improved DetNet (IDetNet) detector and the OAMPNet detector as two independent network branches, the DDNet detector performs sample-wise dynamic routing to adaptively select a better one between the IDetNet and the OAMPNet detectors for every samples under different system conditions. To avoid the prohibitive transmission overhead of dataset collection in centralized learning (CL), we propose the federated averaging (FedAve)-DDNet detector, where all raw data are kept at local clients and only locally trained model parameters are transmitted to the central server for aggregation. To further reduce the transmission overhead, we develop the federated gradient sparsification (FedGS)-DDNet detector by randomly sampling gradients with elaborately calculated probability when uploading gradients to the central server. Based on simulation results, the proposed DDNet detector consistently outperforms other detectors under all system conditions thanks to the sample-wise dynamic routing. Moreover, the federated DDNet detectors, especially the FedGS-DDNet detector, can reduce the transmission overhead by at least 25.7% while maintaining satisfactory detection accuracy.

In this paper, we consider a multi-user interference network where a central controller allocates power resources to multiple base stations for maximizing the entire network utility under a long-term convex power budget constraint. The optimal power allocation strategy depends on the accurate and instant channel state information (CSI). However, due to users’ mobility and the existence of channel fading and interference, timely channel estimation is unavailable. To overcome the difficulty of unknown channel states, we resort to the Lyapunov drift analysis framework and design an online power allocation algorithm based on historical CSI. The algorithm can be proven to achieve sub-linear performance for both cumulative regret and power budget violation. The sub-linear regret indicates the proposed algorithm can asymptotically achieve the optimal static power allocation performance in hindsight. Simulation results are provided to validate the asymptotic optimal performance of the proposed algorithm, as well as its robustness in the presence of adversarial interference.

Unmanned-aerial-vehicle (UAV)-assisted communications has attracted increasing attention recently. This article investigates air–ground coordinated communications system, in which trajectories of air UAV base stations (UAV-BSs) and access control of ground users (GUs) are jointly optimized. We formulated this optimization problem as a mixed cooperative–competitive game, where each GU competes for the limited resources of UAV-BSs to maximize its own throughput by accessing a suitable UAV-BS, and UAV-BSs cooperate with each other and design their trajectories to maximize the definedfair throughputto improve the total throughput and keep the GU fairness. Moreover, the action space of GUs is discrete, while that of UAV-BS is continuous. To tackle this hybrid action space issue, we transform the discrete actions into continuous action probabilities and propose a multiagent deep reinforcement learning (MADRL) approach, named air–ground probabilistic multiagent deep deterministic policy gradient (AG-PMADDPG). With well-designed rewards, AG-PMADDPG can coordinate two types of agents, UAV-BSs and GUs, to achieve their own objectives based on local observations. Simulation results demonstrate that AG-PMADDPG can outperform the benchmark algorithms in terms of throughput and fairness.

Energy self-sustainability is critically important for future Internet of Things (IoT) networks to support an ever-growing massive number of wireless devices with low maintenance cost and high spectrum/energy efficiency. Power-splitting (PS)-based simultaneous wireless information and power transfer (PS-SWIPT) is a promising solution to realize it. However, the performance of PS-SWIPT is severely influenced by the channel attenuation caused by the detrimental radio propagation environment. Intelligent reflecting surface (IRS) is an emerging technology that can reconfigure the incident signal with considerable array gain so as to improve the PS-SWIPT performance. Thus, in this article, we investigate the weighted sumrate (WSR) maximization problem of the IRS-assisted multi-input–multioutput (MIMO) PS-SWIPT IoT network with multiple low-power IoT PS-based devices (PSDs). The formulated problem is nonconvex and arduous to tackle due to the presence of the intricately coupled variables and the mutually exclusive constraints. To the best of our knowledge, the problem is not addressed yet and cannot be solved by employing the existing methods directly. To cope with the problem, we develop a joint optimization framework that decomposes the original problem into several subproblems that can be solved alternately. Simulation results confirm the effectiveness of IRS to improve the WSR of the PS-SWIPT energy self-sustainable IoT networks and demonstrate that the proposed algorithm outperforms benchmark methods considerably.

In an aerial hybrid massive multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) system, how to design a spectral-efficient broadband multi-user hybrid beamforming with a limited pilot and feedback overhead is challenging. To this end, by modeling the key transmission modules as an end-to-end (E2E) neural network, this paper proposes a data-driven deep learning (DL)-based unified hybrid beamforming framework for both the time division duplex (TDD) and frequency division duplex (FDD) systems with implicit channel state information (CSI). For TDD systems, the proposed DL-based approach jointly models the uplink pilot combining and downlink hybrid beamforming modules as an E2E neural network. While for FDD systems, we jointly model the downlink pilot transmission, uplink CSI feedback, and downlink hybrid beamforming modules as an E2E neural network. Different from conventional approaches separately processing different modules, the proposed solution simultaneously optimizes all modules with the sum rate as the optimization object. Therefore, by perceiving the inherent property of air-to-ground massive MIMO-OFDM channel samples, the DL-based E2E neural network can establish the mapping function from the channel to the beamformer, so that the explicit channel reconstruction can be avoided with reduced pilot and feedback overhead. Besides, practical low-resolution phase shifters (PSs) introduce the quantization constraint, leading to the intractable gradient backpropagation when training the neural network. To mitigate the performance loss caused by the phase quantization error, we adopt the transfer learning strategy to further fine-tune the E2E neural network based on a pre-trained network that assumes the ideal infinite-resolution PSs. Numerical results show that our DL-based schemes have considerable advantages over state-of-the-art schemes.

In this paper, we study the self-healing problem of unmanned aerial vehicle (UAV) swarm network (USNET) that is required to quickly rebuild the communication connectivity under unpredictable external destructions (UEDs). Firstly, to cope with theone-off UEDs, we propose a graph convolutional neural network (GCN) that can find the recovery topology of the USNET in an on-line manner. Secondly, to cope withgeneral UEDs, we develop a GCN based trajectory planning algorithm that can make UAVs rebuild the communication connectivity during the self-healing process. We also design a meta learning scheme to facilitate the on-line executions of the GCN. Numerical results show that the proposed algorithms can rebuild the communication connectivity of the USNET more quickly than the existing algorithms under both one-off UEDs and general UEDs. The simulation results also show that the meta learning scheme can not only enhance the performance of the GCN but also reduce the time complexity of the on-line executions.

Achieving adequate precision in deep learning based communications often requires large network architectures, which results into unacceptable time delay and power consumption. This paper introduces the dynamic neural network (DyNN) into the design of wireless communications systems. DyNN allocates different samples with computation resources on demand by preforming dynamic inferences, thereby reducing the redundant computational cost and enhancing the network efficiency. We design a dynamic depth architecture that allows samples to adaptively skip layers with various dynamic strategies, from which we further develop aconfidence criterion baseddynamicimproved DetNet (CD-IDetNet) and apolicy network baseddynamicimproved DetNet (PD-IDetNet) for multiple-input multiple-output (MIMO) detection. Specifically, in CD-IDetNet, a confidence criterion is adopted to control samples exiting early, while in PD-IDetNet, policy networks are trained by reinforcement learning to selectively skip layers for varying samples. Simulation results demonstrate that CD-IDetNet and PD-IDetNet detectors can respectively reduce 17.4% and 31.1% computational costs while preserving the full accuracy of IDetNet. Desirable tradeoffs between accuracy and computational complexity can be further achieved by fine-tuning the hyper-parameters of CD-IDetNet and PD-IDetNet. Moreover, over-the-air (OTA) tests are conducted to validate the effectiveness of the proposed detectors in practical systems.

For high mobility communication scenario, the recently emerged orthogonal time frequency space (OTFS) modulation introduces a new delay-Doppler domain signal space, and can provide better communication performance than traditional orthogonal frequency division multiplexing system. This article focuses on the joint channel estimation and data detection (JCEDD) for hybrid reconfigurable intelligent surface (HRIS) aided millimeter wave (mmWave) OTFS systems. Firstly, a new transmission structure is designed. Within the pilot durations of the designed structure, partial HRIS elements are alternatively activated. The time domain channel model is then exhibited. Secondly, the received signal model for both the HRIS over time domain and the base station over delay-Doppler domain are studied. Thirdly, by utilizing channel parameters acquired at the HRIS, an HRIS beamforming design strategy is proposed. For the OTFS transmission, we propose a JCEDD scheme over delay-Doppler domain. In this scheme, message passing (MP) algorithm is designed to simultaneously obtain the equivalent channel gain and the data symbols. On the other hand, the channel parameters, i.e., the Doppler shift, the channel sparsity, and the channel variance, are updated through expectation-maximization (EM) algorithm. By iteratively executing the MP and EM algorithm, both the channel and the unknown data symbols can be accurately acquired. Finally, simulation results are provided to validate the effectiveness of our proposed JCEDD scheme.

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.

In this paper, we propose a novel deep unsupervised learning-based approach that jointly optimizes antenna selection and hybrid beamforming to improve the hardware and spectral efficiencies of massive multiple-input-multiple-output (MIMO) downlink systems. By employing ResNet to extract features from the channel matrices, two neural networks, i.e., the antenna selection network (ASNet) and the hybrid beamforming network (BFNet), are respectively proposed for dynamic antenna selection and hybrid beamformer design. Furthermore, a deep probabilistic subsampling trick and a specially designed quantization function are respectively developed for ASNet and BFNet to preserve the differentiability while embedding discrete constraints into the network structures. With the aid of a flexibly designed loss function, ASNet and BFNet are jointly trained in a phased unsupervised way, which avoids the prohibitive computational cost of acquiring training labels in supervised learning. Simulation results demonstrate the advantage of the proposed approach over conventional optimization-based algorithms in terms of both the achieved rate and the computational complexity.

In this paper, we model, analyze and optimize the multi-user and multi-order-reflection (MUMOR) intelligent reflecting surface (IRS) networks. We first derive a complete MUMOR IRS network model applicable for the arbitrary times of reflections, size and number of IRSs/reflectors. The optimal condition for achieving sum rate upper bound with one IRS in a closed-form function and the analytical condition to achieve interference-free transmission are derived, respectively. Leveraging this optimal condition, we obtain the MUMOR sum rate upper bound of the IRS network with different network topologies, where the linear graph (LG), complete graph (CG) and null graph (NG) topologies are considered. Simulation results verify our theories and derivations and demonstrate that the sum rate upper bounds of different network topologies are under a$K$-fold improvement given$K$-piece IRS.

Reconfigurable intelligent surface (RIS) is a revolutionary technology and can be used to assist communication systems by adaptively manipulating the wireless environment. In this paper, we propose a novel cascaded channel estimation algorithm for the RIS-assisted multiple-input multiple-output orthogonal frequency division multiplexing systems. Inspired by the channel compression idea, we can obtain a sub-sampled channel by turning off a fraction of RIS elements and then extrapolate it to the complete one, by which the pilot overhead is greatly reduced. The problem of channel extrapolation is transformed into recovering the physical parameters of the cascaded channel from the partial channel observations and is then formulated by the sparse Bayesian learning (SBL) framework. In order to circumvent the curse of high dimensional matrices inversion in the vector-matrix system, we further introduce the tensor structure into the SBL framework. Specially, by leveraging of the channel sparsity over the angle domain and delay domain, we derive the virtual channel expression in tensor form and model a Kronecker-structured prior distribution for the virtual channels. The multi-domain sparse properties of the virtual channel tensor can be effectively captured by a group of low-dimensional hyper-parameters, and thus reduce the computational complexity. In addition, the Cramer-Rao lower bound is derived for the proposed channel extrapolation. Simulation results show the superior performance of the proposed scheme.

Deep neural networks (DNNs) are shown to be excellent solutions to staggering and sophisticated problems in machine learning. A key reason for their success is due to the strong expressive power of function representation. For piecewise linear neural networks (PLNNs), the number of linear regions is a natural measure of their expressive power since it characterizes the number of linear pieces available to model complex patterns. In this article, we theoretically analyze the expressive power of PLNNs by counting and bounding the number of linear regions. We first refine the existing upper and lower bounds on the number of linear regions of PLNNs with rectified linear units (ReLU PLNNs). Next, we extend the analysis to PLNNs with general piecewise linear (PWL) activation functions and derive the exact maximum number of linear regions of single-layer PLNNs. Moreover, the upper and lower bounds on the number of linear regions of multilayer PLNNs are obtained, both of which scale polynomially with the number of neurons at each layer and pieces of PWL activation function but exponentially with the number of layers. This key property enables deep PLNNs with complex activation functions to outperform their shallow counterparts when computing highly complex and structured functions, which, to some extent, explains the performance improvement of deep PLNNs in classification and function fitting.

As a promising solution for massive machine-type communication, grant-free non-orthogonal multiple access (GF-NOMA) has received considerable attention in recent years. However, the multidimensional constellation design (MCD) and multiuser detection (MUD) in GF-NOMA are usually optimized in adivide and conquerway, leading to local optima and performance degradation. To address this issue, we investigate the joint optimization of MCD and MUD for GF-NOMA. The formulated joint optimization is based on variational inference, which is intractable due to the signal superimposition that makes the optimization variables intricately coupled. Then, we resort to end-to-end deep learning (DL) to obtain the optimal solution. Specifically, we propose a DL-based multi-task variational autoencoder (Mul-VAE) that adopts a variational autoencoder network to optimize the distribution of the constellation points. We further derive the loss function of the proposed network and analyze it from an information-theoretic perspective. On this basis, multi-task learning is employed to deal with mutually conflicting yet related detection processes. Besides, taking heterogeneous transmission rates of users into account, a multi-task prioritizing strategy is designed to balance training performance. Simulation results reveal that the proposed method enables significant gains compared to state-of-the-art techniques.

We thoroughly investigate the outage performance of reconfigurable intelligent surface (RIS) aided multi-input multi-output (MIMO) communications by exploiting statistical channel state information (CSI). Kornecker channel model is adopted to characterize the impact of spatial correlations among MIMO antennas and reconfigurable reflectors. Mellin transform and random matrix theory are then utilized to derive the outage probability, with which we further conduct the asymptotic outage analysis to obtain insightful findings. In particular, the asymptotic analysis reveals that the number of reflecting elements at the RIS should not be smaller than the total number of MIMO transmit and receive antennas to get rid of the rank deficiency of the cascaded MIMO channels. Moreover, the asymptotic outage probability is a monotonically increasing and convex function with respect to the transmission rate. The numerical outcomes not only corroborate our analytical results, but also demonstrate the negative impact of the spatial correlation and the benefit of increasing the number of reconfigurable reflectors. Finally, we apply the asymptotic results to optimally devise the phase shifts with a low computational complexity.

This paper presents the Generative Adversarial Networks (GANs)-based image semantic coding, the goal of which is semantic exchange rather than symbol transmission. State-of-the-art visually pleasing reconstruction and semantic preserving performance are obtained in extreme low bitrate via a rate-perception-distortion optimization framework. In particular, we investigate convolutional encoder, quantizer, conditional SPADE generator, residual coding as well as perceptual losses. In contrast to previous work, we designed a coarse-to-fine image semantic coding model for multimedia semantic communication system. The base layer of the image is fully generated and preserves semantic information while the enhancement layer restores the fine details. We explore the perception and distortion performance trade-off by tuning the rate of base layer and enhancement layer. Different from the existing methods that adopt pixel accuracy as distortion metric, we train and evaluate the proposed image semantic coding model with multiple perception metrics, in line with the purpose of semantic communications. Experimental results demonstrate that our model could achieve visually pleasant and semantic consistent reconstruction, as well as saving times of bitrate, compared to BPG, WebP, JPEG2000, JPEG, and other deep learning-based image codecs.

In this paper, we investigate an air-ground coordination communication system where ground users (GUs) access suitable UAV base stations (UAV-BSs) to maximize their own throughput and UAV-BSs design their trajectories to maximize the total throughput and keep GU fairness. Note that the action space of GUs is discrete, and UAV-BSs’ action space is continuous. To deal with the hybrid action space, we propose a multi-agent deep reinforcement learning (MADRL) approach, named AG-PMADDPG (air-ground probabilistic multi-agent deep deterministic policy gradient), where GUs transform the discrete actions to continuous action probabilities, and then sample actions according to the probabilities. The proposed method enable the users make decisions based on their local information, which is beneficial for user privacy. Simulation results demonstrate that AG-PMADDPG can outperform the benchmark algorithms in terms of fairness and throughput.

This paper aims to investigate the joint optimization of multidimensional constellation design (MCD) and multiuser detection (MUD) for grant-free non-orthogonal multiple access (GF-NOMA). We first formulate the joint optimization problem and derive its explicit expression using variational inference. Due to the intractability of the joint optimization problem, we then resort to deep learning (DL) and approximate the optimal solution in an end-to-end manner. Specifically, we develop a novel variational autoencoder based network, such that the distribution of the multidimensional constellations can be accessed and optimized. We also design a multi-task learning architecture on the decoder side to deal with the complex coupling among signal streams, by taking the MUD process as multiple distinctive yet related tasks. The derivation of the loss function for network training is presented, and simulation results are provided to validate the superior performance of the proposed method over conventional approaches.

In this paper, we study the fast coverage problem of 3D irregular terrain surfaces with a hierarchical UAV swarm. We first build a 3D model of a random irregular terrain and project the 3D terrain surface into many weighted 2D patches. Then we develop a two-level hierarchical UAV swarm architecture, including the low-level follower UAVs (FUAVs) and the high-level leader UAVs (LUAVs). For FUAVs, we adopt the traditional coverage trajectory algorithm to carry out specific coverage tasks within patches based on the star communication topology. For LUAVs, we propose a swarm deep Q-learning (SDQN) reinforcement learning algorithm to select patches. The numerical results show that the total coverage time of the SDQN is less than that of existing methods, which demonstrates the effectiveness of the proposed algorithm.

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.

Existing deep learning (DL) based downlink channel prediction algorithms for frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems mainly utilize single-source sensing information, e.g., the uplink channels, to predict the downlink channels. With the aid of multi-source sensing information (MSI) in communication systems, this paper explores deep multimodal learning (DML) technologies to improve the accuracy of downlink channel prediction. By leveraging various modality combinations and fusion levels, we design several DML based architectures for downlink channel prediction, which can also be easily extended to other communication problems like beam prediction. Simulation results demonstrate that the proposed DML based architectures can effectively exploit the constructive and complementary information of multimodal sensory data, thus achieving better performance than existing works.

Multi-input multi-output orthogonal frequency division multiplexing (MIMO OFDM) is a key technology for mobile communication systems. However, due to the issue of high peak-to-average power ratio (PAPR), the OFDM symbols may suffer from nonlinear distortions of the power amplifier (PA) at the transmitters, which degrades the channel estimation and detection performances of the receivers. To mitigate the clipping distortions at the receivers end, we leverage deep learning (DL) and devise a DL based receiver which is aided by the traditional least square (LS) channel estimation and the zero-forcing (ZF) equalization models. Moreover, a data driven DL based receiver without explicit channel estimation is proposed and combined with the model aided DL based receiver to further improve the performance. Simulation results showcase that the proposed model aided DL based receiver has superior performance of bit error rate and has robustness over different levels of clipping distortions.

The emerging backscatter communication technology is recognized as a promising solution to the battery problem of Internet of Things (IoT) devices. For example, the wireless sensor network with backscatter communication technology can monitor the environment in remote areas without battery maintenance or replacement. Unfortunately, the transmission range of backscatter communication is limited. To tackle this challenge, we propose a multi-UAV-aided data collection scenario where the unmanned aerial vehicle (UAV) can fly close to the backscatter sensor node (BSN) to activate it and then collects the data. We aim to minimize the total flight time of the rechargeable UAVs when the collection mission is finished. During the data collection process, the UAVs can return to the charging station to recharge itself when the energy of UAV is not sufficient to complete the mission. To reduce the complexity of the task, we first use the Gaussian mixture model clustering method to divide the BSNs into multiple clusters. Then we consider the deterministic boundary and ambiguous boundary for the UAV flying regions, respectively. For the deterministic boundary scenario, we propose a single-agent deep option learning (SADOL) algorithm, where each UAV cannot fly beyond the deterministic boundary. For the ambiguous boundary scenario, we propose a multiagent deep option learning (MADOL) algorithm to enable the UAVs to cooperatively learn the ambiguous BSNs assignment. In the simulation, we compare the proposed algorithms with multiagent deep deterministic policy gradient (MADDPG), deep deterministic policy gradient (DDPG), and deep Q-network (DQN) algorithms, which proves the proposed algorithms can achieve better performance.

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.

This article focuses on a new path division multiple access (PDMA) for both uplink (UL) and downlink (DL) massive multiple-input multiple-output network over a high mobility scenario, where the orthogonal time frequency space (OTFS) is adopted. First, the 3D UL channel model and the received signal model in the angle-delay-Doppler domain are studied. Secondly, the 3D-Newtonized orthogonal matching pursuit algorithm is utilized for the extraction of the UL channel parameters, including channel gains, directions of arrival, delays, and Doppler frequencies, over the antenna-time-frequency domain. Thirdly, we carefully analyze energy dispersion and power leakage of the 3D angle-delay-Doppler channels. Then, along UL, we design a path scheduling algorithm to properly assign angle-domain resources at user sides and to assure that the observation regions for different users do not overlap over the 3D cubic area, i.e., angle-delay-Doppler domain. After scheduling, different users can map their respective data to the scheduled delay-Doppler domain grids, and simultaneously send the data to base station (BS) without inter-user interference in the same OTFS block. Correspondingly, the signals at desired grids within the 3D resource space of BS are separately collected to implement the 3D channel estimation and maximal ratio combining-based data recovery over the angle-delay-Doppler domain. Then, we construct a low complexity beamforming scheme over the angle-delay-Doppler domain to achieve inter-user interference free DL communication. Simulation results are provided to demonstrate the validity of our proposed unified UL/DL PDMA scheme.

This paper proposes a model-driven deep learning (MDDL)-based channel estimation and feedback scheme for wideband millimeter-wave (mmWave) massive hybrid multiple-input multiple-output (MIMO) systems, where the angle-delay domain channels' sparsity is exploited for reducing the overhead. First, we consider the uplink channel estimation for time-division duplexing systems. To reduce the uplink pilot overhead for estimating high-dimensional channels from a limited number of radio frequency (RF) chains at the base station (BS), we propose to jointly train the phase shift network and the channel estimator as an auto-encoder. Particularly, by exploiting the channels' structured sparsity from an a priori model and learning the integrated trainable parameters from the data samples, the proposed multiple-measurement-vectors learned approximate message passing (MMV-LAMP) network with the devised redundant dictionary can jointly recover multiple subcarriers' channels with significantly enhanced performance. Moreover, we consider the downlink channel estimation and feedback for frequency-division duplexing systems. Similarly, the pilots at the BS and channel estimator at the users can be jointly trained as an encoder and a decoder, respectively. Besides, to further reduce the channel feedback overhead, only the received pilots on part of the subcarriers are fed back to the BS, which can exploit the MMV-LAMP network to reconstruct the spatial-frequency channel matrix. Numerical results show that the proposed MDDL-based channel estimation and feedback scheme outperforms state-of-the-art approaches.

Unmanned aerial vehicle (UAV) technology is recognized as a promising solution to area coverage problems (ACPs) and has been extensively studied recently. In this paper, we study the 3D irregular terrain surface coverage problem with a hierarchical UAV swarm. We first build the 3D model of a random irregular terrain and propose a geometric way to project the 3D terrain surface into many weighted 2D patches. Then we develop a two-level hierarchical UAV swarm architecture, including the low-level follower UAVs (FUAVs) and the high-level leader UAVs (LUAVs). For FUAVs, we design a coverage trajectory algorithm to carry out specific coverage tasks within patches based on the star communication topology. For LUAVs, we propose a swarm deep Q-learning (SDQN) reinforcement learning algorithm to select patches. Moreover, an observation history model based on convolutional neural networks (CNNs) and the mean embedding method is integrated into SDQN to address the communication limitation problems of LUAVs. The numerical results show that FUAVs can cover the entire area of each patch with little redundancies, and the total coverage time of the SDQN is less than that of existing methods, which demonstrates the effectiveness of the proposed algorithms.

Existing work in intelligent communications has recently made preliminary attempts to utilize multi-source sensing information (MSI) to improve the system performance. However, the research on MSI aided intelligent communications has not yet explored how to integrate and fuse the multimodal sensory data, which motivates us to develop a systematic framework for wireless communications based on deep multimodal learning (DML). In this paper, we first present complete descriptions and heuristic understandings on the framework of DML based wireless communications, where core design choices are analyzed in the view of communications. Then, we develop several DML based architectures for channel prediction in massive multiple-input multiple-output (MIMO) systems that leverage various modality combinations and fusion levels. The case study of massive MIMO channel prediction offers an important example that can be followed in developing other DML based communication technologies. Simulation results demonstrate that the proposed DML framework can effectively exploit the constructive and complementary information of multimodal sensory data to assist the current wireless communications.

In order to reduce the downlink training overhead of mmWave communications, we propose a novel downlink beamforming strategy using the uplink sub-6GHz channel and downlink mmWave pilots that are sent from a few active antennas. Specifically, we design a novel dual-input neural network architecture, called FusionNet, to merge the sub-6GHz channel and the channel of a few active mmWave antennas. The proposed fusion model could intelligently adjust the attention paid (by the neural network) for sub-6GHz channel and mmWave channel by an attention mechanism. The output of the FusionNet represents the probability for each beam being the optimal one. We also propose an antenna selection model that can choose better active antennas to send the downlink pilots, in which the gradient of antenna selection vector is approximated by that of an antenna probability vector. Simulation results demonstrate the superior performance of the proposed strategy compared to the existing one that purely relies on the sub-6GHz information or compared to the shallow model that directly adds uniform pilots.

Implementing wireless body area networks (WBANs) is very challenging, due to limited power supply, inadequate computation capability, and imperfect channel state information (CSI). In this paper, we propose a hybrid offloading scheme with backscatter communication (BackCom) under imperfect CSI, where each sensor firstly receives radio frequency (RF) energy and then offloads body data task via low-power BackCom to the access point (AP) for edge computing. Aiming to minimize the end-to-end system latency, we jointly optimize the computation speed of AP for processing computation tasks, the power of the signal transmitted by the AP, and the power reflection coefficient under energy and data rate chance constraints. To solve the proposed distributionally robust chance-constrained optimization problem, we approximate chance constraints by the Bernstein-type-inequality (BTI) method and Conditional value-at-risk (CVaR) method in the Gaussian distribution and arbitrary distribution of channel estimation errors, respectively. To tackle the NP-hard problem efficiently, the original problem can be decomposed into two subproblems, which are solved by successive linear programming and iterative algorithm, respectively. Simulation results show that the CVaR method outperforms the other methods for the non-Gaussian CSI mismatch, and the Bernstein method is more suitable for the Gaussian distribution of CSI errors.

We propose a holographic version of a reconfigurable intelligent surface (RIS) and investigate its application to terahertz (THz) massive multiple-input multiple-output systems. Capitalizing on the miniaturization of THz electronic components, RISs can be implemented by densely packing sub-wavelength unit cells, so as to realize continuous or quasi-continuous apertures and to enable holographic communications. In this paper, in particular, we derive the beam pattern of a holographic RIS. Our analysis reveals that the beam pattern of an ideal holographic RIS can be well approximated by that of an ultra-dense RIS, which has a more practical hardware architecture. In addition, we propose a closed-loop channel estimation (CE) scheme to effectively estimate the broadband channels that characterize THz massive MIMO systems aided by holographic RISs. The proposed CE scheme includes a downlink coarse CE stage and an uplink finer-grained CE stage. The uplink pilot signals are judiciously designed for obtaining good CE performance. Moreover, to reduce the pilot overhead, we introduce a compressive sensing-based CE algorithm, which exploits the dual sparsity of THz MIMO channels in both the angular domain and delay domain. Simulation results demonstrate the superiority of holographic RISs over the non-holographic ones, and the effectiveness of the proposed CE scheme.

Channel covariance matrix (CCM) is one critical parameter for designing the communications systems. In this paper, a novel framework of the deep learning (DL) based CCM estimation is proposed that exploits the perception of the transmission environment without any channel sample or the pilot signals. Specifically, as CCM is affected by the user’s movement, we design a deep neural network (DNN) to predict CCM from user location and user speed, and the corresponding estimation method is named as ULCCME. A location denoising method is further developed to reduce the positioning error and improve the robustness of ULCCME. For cases when user location information is not available, we propose an interesting way that uses the environmental 3D images to predict the CCM, and the corresponding estimation method is named as SICCME. Simulation results show that both the proposed methods are effective and will benefit the subsequent channel estimation.

Reconfigurable intelligent surface (RIS) is a revolutionary technology for achieving high rate and large coverage in future wireless networks by smartly reflecting the signals with adjustable phase shifts. To design the reflection beamforming, accurate individual channel state information is required at the RIS, which is a challenge task due to the lack of signal processing ability in passive mode. In this paper, we add signal processing units for a few antennas at the RIS to partially acquire the channels and extrapolate them to the full channels, in which the active antenna selection is a key point but has not been addressed yet. We construct an active antenna selection network that utilizes the probabilistic sampling theory to select the optimal locations of these active antennas. With this active antenna selection network, we further design two deep learning-based schemes, i.e., the channel extrapolation scheme and the beam searching scheme. The former utilizes the selection network and a convolutional neural network to extrapolate the full channels from the partial channels, while the latter adopts a fully-connected neural network to achieve the direct mapping from the partial channels to the optimal beamforming vector with maximal transmission rate. Simulation results show that the proposed optimal antenna selection outperforms the trivial uniform antenna selection, and the performance of beam searching is more stable than that of channel extrapolation with fewer active antennas.

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.

A critical bottleneck of massive multiple-input multiple-output (MIMO) system is the huge training overhead caused by downlink transmission, like channel estimation, downlink beamforming and covariance observation. In this paper, we propose to use the channel state information (CSI) of a small number of antennas to extrapolate the CSI of the other antennas and reduce the training overhead. Specifically, we design a deep neural network that we call an antenna domain extrapolation network (ADEN) that can exploit the correlation function among antennas. We then propose a deep learning (DL) based antenna selection network (ASN) that can select a limited antennas for optimizing the extrapolation, which is conventionally a type of combinatorial optimization and is difficult to solve. We trickly designed a constrained degradation algorithm to generate a differentiable approximation of the discrete antenna selection vector such that the back-propagation of the neural network can be guaranteed. Numerical results show that the proposed ADEN outperforms the traditional fully connected one, and the antenna selection scheme learned by ASN is much better than the trivially used uniform selection.

Multi-user millimeter wave (mmWave) massive multi-input multi-output (MIMO) is a promising technology for the next generation mobile communication systems. However, there are still unsolved problems before such commercial MIMO networks are rolled out. One main issue is the hardware cost and power consumption which grow significantly as the number of radio frequency (RF) components increases. To tackle this issue, we consider to deploy one-bit analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) at the base station (BS), and study uplink (UL)/downlink (DL) channel estimation and DL precoding techniques for the associated MIMO systems with one-bit ADCs/DACs. Specifically, we first formulate the UL channel estimation as an one-bit compressed sensing problem, and then devise an efficient gridless generalized approximate message passing-based (GL-GAMP) algorithm to handle it. Additionally, we develop an exhaustive search based proximal gradient descent method (PGM) for DL channel estimation. Note that with slight modifications, we show that PGM can also be applied to solve the DL precoding problem. Simulation results showcase that our methods have advantages over the state-of-the-art techniques and are able to offer good trade-offs between accuracy and computational complexity, which ultimately indicates their superiority in the application of mmWave MIMO systems with one-bit ADCs/DACs.

Unmanned Aerial Vehicle (UAV)-assisted communication is a promising technique for future communication. In this paper, the UAV serves as base station (BS) to provide energy-efficient and fair communication service for ground users (GUs). We first derive the energy consumption model of a quad-rotor UAV as a function of UAV's 3D movement. Then we formulate the problem where UAV aims to maximize the defined fair throughput within limited on-board energy through 3D trajectory and frequency band allocation. The formulated problem is hard to deal with for the GUs' movement and complicated nonconvex objective function. Then we propose a deep reinforcement learning (DRL) based method to transform the original problem into maximizing accumulative reward. Simulation results demonstrate that the proposed method outperforms two baselines in terms of fairness and total throughput.

This paper proposes an angle domain framework for the design of an intelligent reflecting surface (IRS) system. The maximum likelihood (ML) principle is applied to derive the estimators for the effective angles among the base station (BS), IRS and user. It is demonstrated that the accuracy of the estimated angles improves with the number of BS antennas. Also, deploying the IRS closer to the BS increases the accuracy of the estimated angle from the IRS to the user. Then, exploiting the estimated angles, we propose a joint design of BS beamforming and IRS beamforming. Simulation results show that our proposed algorithm, which only needs few angle information, achieves nearly the same performance as the algorithm requiring full channel state information (CSI). Moreover, the optimized BS beam becomes more focused towards the IRS direction as the number of reflecting elements increases.

This paper proposes a novel location information aided design framework for multiple intelligent reflecting surface (IRS) systems. Assuming practical and imperfect user location information, the effective angles from the IRS to the users are estimated, which is then used to design the transmit beam and IRS beam. Furthermore, closed-form expressions for the achievable rate are derived. The analytical findings indicate that the achievable rate can be improved by increasing the number of base station (BS) antennas or reflecting elements. Specifically, a power gain of order NM2is achieved, where N is the number of BS antennas and M is the number of reflecting elements. Moreover, with a large number of reflecting elements, the individual signal to interference plus noise ratio (SINR) is proportional to M. Also, it has been shown that high location uncertainty would significantly degrade the achievable rate. Besides, IRSs should be deployed at distinct directions (relative to the BS) and be far away from each other to reduce the interference from multiple IRSs.

In this paper, we propose a distributionally robust chance-constrained design for the backscatter communication-aided computation offloading scheme in wireless body area networks (WBANs), where each sensor firstly receives radio frequency (RF) energy and then offloads body physiological computation tasks via low-power BackCom to the access point (AP) for edge computing. Specifically, only rough first and second-order moment statistics are obtained for the estimation errors of CSI. Based on all the possible distributions of CSI errors, we aim to minimize the end-to-end system latency by jointly optimizing the power of the signal transmitted by the AP and the power reflection coefficient with energy chance restrictions and throughput requirement constraints. In order to solve the proposed non-convex chance-constrained optimization problem, we approximate chance constraints by the conditional value-at-risk (CVaR), and apply an efficient block coordinate descent (BCD) algorithm to solve it. Simulation results are provided to corroborate that the proposed method outperforms other methods for the non-Gaussian mismatch.

With the explosive growth of the Internet of Things (IoT), a massive number of IoT devices are deployed so as to realize a variety of advanced applications, i.e., environmental monitoring and smart traffic. There are two main characteristics in these typical applications, namely the massive connectivity and the sporadic transmission. The massive connectivity usually leads the IoT system to be spatially undersampled, since the number of devices is much larger than the number of receiver antennas, which brings difficulties and challenges to symbol detection. Fortunately, the sporadic transmission in IoT communication introduces sparsity into transmitted symbols, thanks to which we are able to perform symbol detection even in a spatially undersampled scenario. In this paper, we attempt to incorporate deep learning (DL) into the symbol detection of spatially undersampled IoT with sporadically transmitting devices. Specifically, we propose a novel DL-based detector named DeepIoT that employs a variant autoencoder network to recover both the indices of active devices and their transmitting symbols by using only the received signal. Simulation results show that the DeepIoT can outperform various existing methods and has only a 1.5- 2dB signal-to-noise ratio (SNR) loss compared to the optimal maximum likelihood (ML) detector.

In this paper, we examine the acquisition of uplink and downlink channel parameters for the massive multiple-input multiple-output (MIMO) networks in the high mobility scene. We firstly formulate the time domain massive MIMO signal model along the uplink and adopt the expectation maximization based variational Bayesian (EM-VB) framework to recover the uplink channel parameters including angle, delay, Doppler frequency, and channel gain for each physical scattering path. Then, we fully exploit the angle, delay and Doppler reciprocity between uplink and downlink and reconstruct angles, delays, and Doppler frequencies for the downlink massive channels at the base station. Various numerical examples are presented to confirm the validity and robustness of the proposed scheme.

In this paper, we investigate a multi-unmanned aerial vehicle (UAV) cooperation mechanism for secure communications, where the UAV transmitter moves around to serve the multiple ground users (GUs) while the UAV jammers send the 3D jamming signals to the ground eavesdroppers (GEs) to protect the UAV transmitter from being wiretapped. The 3D jamming guarantees the GEs not being interfered by the jamming signals. It is challenging to make a joint trajectory design and power control for a UAV team without central control. To this end, we propose a multi-agent deep reinforcement learning approach to achieve the maximum sum secure rate by designing the dynamic trajectory of each UAV. The proposed multi-agent deep deterministic policy gradient (MADDPG) technique is centralized training at high altitude platforms (HAPs) and distributed execution at each UAV, which enables the fully distributed cooperation among UAVs. Finally, the simulation results show the proposed method can efficiently solve the multi-UAV cooperation trajectory design problem in secure communication scenarios.

Massive multiple‐input multiple‐output (MIMO) is currently entering the practical implementation phase, and key implementation issues for this technology have yet to be fully addressed. Crucial among these is the practical problem of frequency synchronisation, which refers to the adjustment of the clock frequency of local nodes to the clock frequency of a reference node by estimating and compensating carrier frequency offset. Existing theoretical studies on massive MIMO generally assume perfect frequency synchronisation; however, the potentially very high complexity of this process poses a major challenge for massive MIMO systems. Therefore, new frequency synchronisation techniques are urgently needed to make the practical implementation of massive MIMO feasible. In this study, the authors provide a comprehensive classification of the existing research efforts along this line, considering different antenna architectures and modulation schemes. They also highlight the key challenges in frequency synchronisation for massive MIMO, and they outline future research directions on this topic.

In high-mobility massive multiple-input multiple-output (MIMO) systems, Doppler shifts compensation can be combined with beamforming network to effectively suppress the channel time variation. The key of the beamforming network lies in the optimization of the common configurable amplitudes and phases (CCAP) parameter. In this paper, we reveal more insights of this approach by conducting the in-depth analysis. First, we demonstrate that the CCAP parameter optimizes the beamforming network to reduce channel time variation and approximates in a semi-sinusoidal form. Then, a scaling law between the asymptotic Doppler spread and the number of antennas M is derived, revealing that the asymptotic Doppler spread decreases at a rate of 1/M. We further prove that the optimal CCAP parameter obtained from Jakes' channel model can be directly applied to more general cases, while the inverse proportionality between the resulting asymptotic Doppler spread and the number of antennas remains valid. Numerical results confirm the correctness of the theoretical analysis.

Although it is often used in the orthogonal frequency division multiplexing (OFDM) systems, application of massive multiple-input multiple-output (MIMO) over the orthogonal time frequency space (OTFS) modulation could suffer from enormous training overhead in high mobility scenarios. In this paper, we propose one uplink-aided high mobility downlink channel estimation scheme for the massive MIMO-OTFS networks. Specifically, we firstly formulate the time domain massive MIMO-OTFS signal model along the uplink and adopt the expectation maximization based variational Bayesian (EM-VB) framework to recover the uplink channel parameters including the angle, the delay, the Doppler frequency, and the channel gain for each physical scattering path. Correspondingly, with the help of the fast Bayesian inference, one low complex approach is constructed to overcome the bottleneck of the EM-VB. Then, we fully exploit the angle, delay and Doppler reciprocity between the uplink and the downlink and reconstruct the angles, the delays, and the Doppler frequencies for the downlink massive channels at the base station. Furthermore, we examine the downlink massive MIMO channel estimation over the delay-Doppler-angle domain. The channel dispersion of the OTFS over the delay-Doppler domain is carefully analyzed and is utilized to associate one given path with one specific delay-Doppler grid if different paths of any user have distinguished delay-Doppler signatures. Moreover, when all the paths of any user could be perfectly separated over the angle domain, we design the effective path scheduling algorithm to map different users' data into the orthogonal delay-Doppler-angle domain resource and achieve the parallel and low complex downlink 3D channel estimation. For the general case, we adopt the least square estimator with reduced dimension to capture the downlink delay-Doppler-angle channels. Various numerical examples are presented to confirm the validity and robustness of the proposed scheme.

Source number detection is a critical problem in array signal processing. Conventional model-driven methods e.g., Akaikes information criterion and minimum description length, suffer from severe performance degradation when the number of samples is small or the signal-to-noise ratio is low. In this letter, we exploit the model-aided based deep neural network to estimate the source number. Specifically, we propose two eigenvalue based networks, i.e., a regression network (ERNet) and a classification network (ECNet), for source number detection, where the eigenvalues of the received signal covariance matrix and the source number are used as the input and the label of the networks, respectively. Furthermore, ERNet and ECNet can be easily generalized to handle coherent sources by adopting, e.g., the forward-backward spatial smoothing technique. Numerical results are included to showcase the remarkable improvements of ERNet and ECNet over the existing methods.

Artificial intelligence (AI) based downlink channel state information (CSI) prediction for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems has attracted growing attention recently. However, existing works focus on the downlink CSI prediction for the users under a given environment and is hard to adapt to users in new environment especially when labeled data is limited. To address this issue, we formulate the downlink channel prediction as a deep transfer learning (DTL) problem, and propose the direct-transfer algorithm based on the fully-connected neural network architecture, where the network is trained in the manner of classical deep learning and is then fine-tuned for new environments. To further improve the transfer efficiency, we propose the meta-learning algorithm that trains the network by alternating inner-task and across-task updates and then adapts to a new environment with a small number of labeled data. Simulation results show that the direct-transfer algorithm achieves better performance than the deep learning algorithm, which implies that the transfer learning benefits the downlink channel prediction in new environments. Moreover, the meta-learning algorithm significantly outperforms the direct-transfer algorithm, which validates its effectiveness and superiority.

Unmanned Aerial Vehicle (UAV)-assisted communication has drawn increasing attention recently. In this paper, we investigate 3D UAV trajectory design and band allocation problem considering both the UAV's energy consumption and the fairness among the ground users (GUs). Specifically, we first formulate the energy consumption model of a quad-rotor UAV as a function of the UAV's 3D movement. Then, based on the fairness and the total throughput, the fair throughput is defined and maximized within limited energy. We propose a deep reinforcement learning (DRL)-based algorithm, named as EEFC-TDBA (energy-efficient fair communication through trajectory design and band allocation) that chooses the state-of-the-art DRL algorithm, deep deterministic policy gradient (DDPG), as its basis. EEFC-TDBA allows the UAV to: 1) adjust the flight speed and direction so as to enhance the energy efficiency and reach the destination before the energy is exhausted; and 2) allocate frequency band to achieve fair communication service. Simulation results are provided to demonstrate that EEFC-TDBA outperforms the baseline methods in terms of the fairness, the total throughput, as well as the minimum throughput.

This paper proposes a Bayesian downlink channel estimation framework for time-varying massive MIMO networks. In particular, the quantization effects at the receiver are considered. In order to fully exploit the sparsity and time correlations of channels, we formulate the time-varying massive MIMO channel as the simultaneously sparse signal model. Then, we propose a sparse Bayesian learning (SBL) framework to estimate the model parameters of the sparse virtual channel. The expectation maximization (EM) algorithm is employed to reduce complexity. Specifically, the factor graph and the general approximate message passing (GAMP) algorithms are used to compute the desired posterior statistics in the expectation step, so that high-dimensional integrals over the marginal distributions can be avoided. The non-zero supporting vector of the virtual channel is then obtained from channel statistics by a k-means clustering algorithm. After that, the reduced dimensional GAMP-based scheme is designed to make the full use of the channel temporal correlation so as to enhance the virtual channel tracking accuracy. Finally, the efficacy of the proposed framework is demonstrated through simulations.

Millimeter wave (mmWave) communication is a promising technology for future outdoor cellular systems. However, the abrupt channel change is more prone to occur over mmWave band compared to conventional frequencies, which will degrade the system performance. Hence, it is important to detect the channel change accurately for preparation of re-estimating the channel state information (CSI). In this paper, we attempt to solve the channel change problem from the perspective of change-point detection. By exploiting the low-rank characteristic of mmWave massive multiple-input multiple-output (MIMO) channel, the received signals can be viewed as data points in a subspace superimposed with noises. Then a cumulative sum (CUSUM)-based subspace change-point detection algorithm is designed to detect the mmWave channel change. The method can work without the aid of pilot signals and is of low computational complexity and memory requirement. Results are provided to validate the satisfactory of the proposed method.

This paper focuses on learning the representing dictionaries for sparse channel estimation in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. To overcome the energy leakage problem in traditional sparse channel estimation, we propose a geographical dictionary- based spatial channel model to efficiently represent the cell-specific geographical characteristics. Based on that, the properties, especially the block structure, of the expected dictionaries are analyzed, and we design a data-driven joint block-structured dictionary learning algorithm (JBSDL) to obtain the expected representing dictionaries. The simulation environment is generated according to 3GPP standard, and we systematically study the properties of the learned dictionaries, which reveals the physical meaning of the dictionary learning results in massive MIMO systems. The proposed method demonstrates superior downlink channel estimation performance through the simulations.

In this paper, we propose a Bayesian cooperative detection algorithm for ambient backscatter assisted generalized spatial modulation (AB-GSM) system, which recovers information from both the ambient backscatter sensor (ABS) and the generalized spatial modulation (GSM) source. To exploit the inherent sparsity of GSM, we adopt a two-layer hierarchical prior model for source symbol. Moreover, we derive linear detectors for comparison. Simulation results show that the proposed algorithm can achieve a superior detection accuracy than linear detectors in under-determined AB-GSM systems.

We develop a direction of arrival (DoA) and channel estimation algorithm for the one-bit quantized millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system. By formulating the estimation problem as a noisy one-bit compressed sensing problem, we propose a computationally efficient gridless solution based on the expectation-maximization generalized approximate message passing (EM-GAMP) approach. The proposed algorithm does not need the prior knowledge about the number of DoAs and outperforms the existing methods in distinguishing extremely close DoAs for the case of one-bit quantization. Both the DoAs and the channel coefficients are estimated for the case of one-bit quantization. The simulation results show that the proposed algorithm has effective estimation performances when the DoAs are very close to each other.

This paper presents a novel channel estimation scheme for massive multiple input multiple output (MIMO) millimeter wave (mmWave) communication system with massive uniform linear array (ULA) at base station (BS) and hybrid architecture. Through practical channel modeling, each channel path is composed of angle information and channel gain information that can be estimated separately. We first propose a general iterative index detection-based channel estimation algorithm (IDCEA) that can obtain both direction of arrival (DOA) and channel gain of each channel path. We then design an enhanced hybrid precoding scheme from the angle domain viewpoint to reduce the inter-beam interferences. Simulation results show that the proposed channel estimation can be better than traditional methods. Finally, numerical examples are provided to corroborate the proposed studies.

Multiple-input multiple-output (MIMO) millimeter wave (mmWave) communication is a key technology for next generation wireless networks. As the number of antennas becomes larger and the transmission bandwidth becomes wider, the array steering vectors would vary at different subcarriers, causing the beam squint effect. In this case, the conventional channel model is no longer applicable, especially for the mmWave massive MIMO system. In this paper, we first explain the influence of the beam squint effect from the array signal processing perspective and then investigate the angle-delay sparsity of mmWave transmission. We next design a compressive sensing (CS) algorithm based on shift-invariant block-sparsity that can jointly compute the off-grid angles, the off-grid delays, and the complex gains of the multi-path channel. Compared to either the conventional channel model, or the existing on-grid algorithms, the proposed one more accurately reflects the mmWave channel and is shown to yield better performance of uplink channel estimation.

Ambient backscatter (AB), making use of both energy harvesting and backscattering, has recently become a promising solution to communications among low-power devices and demonstrates its potential application in the Internet of Things. Existing AB systems adopt two-state amplitude shift keying or phase shift keying (PSK), where data are transmitted at the rate of one bit per symbol period. To increase the data rate, we investigate the high-order modulation where M -PSK is employed for backscattering. We derive the optimal multilevel energy detector and compute the closed-form symbol error rate. To show the realizability of the proposed design, we build a 4PSK-AB hardware prototype, in which the selection of load impedance is discussed with the aid of phasor diagram illustration. The hardware prototype can achieve the date rate of 20 kb/s. Besides, higher date rate is achievable for 98.7% of the time compared with binary AB communications, and the mean number of distinguishable symbols is 3.66.

Backscatter communications have been considered as one of the key technologies in the Internet of Things (IoT) applications. In this paper, we consider a multitag ambient backscatter system, where the multiple tags can harvest radio frequency (RF) energy from the power station and backscatter the RF signals to the reader for data transmission. In order to guarantee the throughput requirements, we aim to maximize the minimum user rate among all the tags by jointly optimizing the backscatter time allocation and power reflection coefficient. Channel state information (CSI) mismatch is taken into account in our proposed optimization problem, which leads to a robust chance-constrained optimization problem. To deal with the nonconvex chance constraints, we propose two safe approximation methods: 1) Bernstein-type-in-equality and 2) conditional value-at-risk (CVaR), applying to the Gaussian distribution and arbitrary distribution of channel estimation errors, respectively. In addition, we develop an alternating optimization algorithm to obtain the optimal value of minimum throughput. Finally, simulation results reveal that the CVaR-based method outperforms the Bernstein-type-inequality-based method for the non-Gaussian channel estimation error.

In recent years, unmanned aerial vehicle (UAV) communication technology has played an important role in both military and civilian applications. However, with the rapid development of military equipment, the execution efficiency of single UAVs is often limited, for which complex combat missions cannot be completed well. Therefore, UAV swarm has become an important research trend in the field of UAVs. In this paper, we consider the problem of channel estimation and self-positioning for the UAV swarm, where multiple small UAVs are displaced by arbitrarily unknown displacements due to the dynamic moving. To explore the physical characteristics of UAV swarm, the parameters of the channel are decomposed into the direction of arrival (DOA) information, the relative position information, and the channel gain information. Utilizing the rank reduction (RARE) estimator, DOAs of the different target users can be estimated efficiently, regardless of the position of the UAVs. After obtaining the DOA information, we estimate the channel gain information using small amount of training resources, which significantly reduces the training overhead and the feedback cost. Moreover, the unknown displacements among UAVs can be self-recovered from the mixed integer nonlinear programming (MINLP). To reduce the computational complexity, we develop both the sphere decoding (SD) and the least square (LS) based methods. The deterministic Cramér-Rao bound (CRB) of the self-positioning estimation is derived in closed-form. Finally, numerical examples are provided to corroborate the proposed studies.

Communications in high-mobility environments have received a lot of attention recently. In this paper, fast time-varying channels for massive multiple-input multiple-output (MIMO) systems are addressed. We derive the exact channel power spectrum density (PSD) for the uplink from a high-speed railway (HSR) to a base station (BS) and propose to further reduce the channel time variation via beamforming network optimization. A large-scale uniform linear array (ULA) is equipped at the HSR to separate multiple Doppler shifts in the angle domain through high-resolution transmit beamforming. Each branch comprises a dominant Doppler shift, which can be compensated to suppress the channel time variation, and we derive the channel PSD and the Doppler spread to assess the residual channel time variation. Interestingly, the channel PSD can be exactly expressed as the product of a pattern function and a beam-distortion function. The former reflects the impact of array aperture and is the converted radiation pattern of ULA, while the latter depends on the configuration of the beamforming directions. Inspired by the PSD analysis, we introduce a common configurable amplitudes and phases (CCAP) parameter to optimize the beamforming network, by partly removing the constant modulus quantized phase constraints of matched filter (MF) beamformers. In this way, the residual Doppler shifts can be ulteriorly suppressed, further reducing the residual channel time variation. The optimal CCAP parameter minimizing the Doppler spread is derived in a closed form. Numerical results are provided to corroborate both the channel PSD analysis and the superiority of the beamforming network optimization technique.

The low-rank property of the channel covariances can be adopted to reduce the overhead of the channel training in massive MIMO systems. In this paper, with the help of the virtual channel representation, we apply such property to both time-division duplex and frequency-division duplex systems, where the time-varying channel scenarios are considered. First, we formulate the dynamic massive MIMO channel as one sparse signal model. Then, an expectation maximization-based sparse Bayesian learning framework is developed to learn the model parameters of the sparse virtual channel. Specifically, the Kalman filter (KF) and the Rauch-Tung-Striebel smoother are utilized to track the model parameters of the uplink (UL) spatial sparse channel in the expectation step. During the maximization step, a fixed-point theorem-based algorithm and a low-complex searching method are constructed to recover the temporal varying characteristics and the spatial signatures, respectively. With the angle reciprocity, we recover the downlink (DL) model parameters from the UL ones. After that, the KF with the reduced dimension is adopt to fully exploit the channel temporal correlations to enhance the DL/UL virtual channel tracking accuracy. A monitoring scheme is also designed to detect the change of model parameters and trigger the relearning process. Finally, we demonstrate the efficacy of the proposed schemes through the numerical simulations.

In this paper, we analyze the achievable rate for ambient backscatter communications under three different channels: the binary input and binary output (BIBO) channel, the binary input and signal output (BISO) channel, and the binary input and energy output (BIEO) channel. Instead of assuming Gaussian input distribution, the proposed study matches the practical ambient backscatter scenarios, where the input of the tag can only be binary. We derive the closed-form capacity expression as well as the capacity-achieving input distribution for the BIBO channel. To show the influence of the signal-to-noise ratio (SNR) on the capacity, a closed-form tight ceiling is also derived when SNR turns relatively large. For BISO and BIEO channel, we obtain the closed-form mutual information, while the semi-closed-form capacity value can be obtained via one dimensional searching. Simulations are provided to corroborate the theoretical studies. Interestingly, the simulations show that: (i) the detection threshold maximizing the capacity of BIBO channel is the same as the one from the maximum likelihood signal detection; (ii) the maximal of the mutual information of all channels is achieved almost by a uniform input distribution; and (iii) the mutual information of the BIEO channel is larger than that of the BIBO channel, but is smaller than that of the BISO channel.

In this paper, we consider a radio-frequency identification (RFID) backscatter system with two scenarios: single input multiple output (SIMO) and multiple input single output (MISO). We investigate the impact of channel correlation between the forward and backscatter links on the symbol error rate (SER), and we compare the performance of SIMO and MISO RFID systems with maximum ratio combining reception over arbitrarily correlated Nakagami-m fading channels. In order to gain an insight for the system design, closed-form expressions for the asymptotic SER and an upper bound are derived for M-ary phase-shift keying and quadrature amplitude modulation. The diversity order in the fully correlated cases, revealed from these expressions, is half of that in the partially correlated and uncorrelated cases. Finally, simulations are performed to validate the theoretical analysis.

In this paper, we consider a downlink orthogonal frequency division multiplexing system from a base station to a high-speed train equipped with fully/partly calibrated massive uniform linear antenna-array (ULA) in wireless environments with abundant scatterers. Multiple Doppler frequency off- sets (DFOs) stemming from intensive propagation paths together with transceiver oscillator frequency offset (OFO) result in a fast time-varying frequency-selective channel. We develop an angle domain carrier frequency offset (CFO, a general designation for DFO and OFO) estimation approach. A high-resolution beamforming network is designed to separate different DFOs into a set of parallel branches in angle domain such that each branch is mainly affected by a single dominant DFO. Then, a joint estimation algorithm for both maximum DFO and OFO is developed for fully calibrated ULA. Next, its estimation mean square error performance is analyzed under inter-subarray mismatches. To mitigate the detrimental effects of inter-subarray mismatches, we introduce a calibration-oriented beamforming parameter and develop the corresponding modified joint estimation algorithm for partly calibrated ULA. Moreover, the Cramér-Rao lower bound of CFO estimation is derived. Both theoretical and numerical results are provided to corroborate the proposed method.

In this paper, we apply angle-domain Doppler compensation for high-mobility wideband massive multi-input multi-output (MIMO) uplink communications. The time-varying multipath channel is considered between high-speed terminal and static base station (BS), where multiple Doppler frequency offsets (DFOs) are associated with distinct angle of departures (AoDs). With the aid of large-scale uniform linear array (ULA) at the transmitter, we design a beamforming network to generate multiple parallel beamforming branches, each transmitting signal pointing to one particular angle. Then, the transmitted signal in each branch will experience only one dominant DFO when passing over the time-varying channel, which can be easily compensated before transmission starts. We theoretically analyze the Doppler spread of the equivalent uplink channel after angle-domain Doppler compensation, which takes into account both the mainlobe and sidelobes of the transmit beam in each branch. It is seen that the channel time-variation can be effectively suppressed if the number of transmit antennas is sufficiently large. Interestingly, the asymptotic scaling law of channel variation is obtained, which shows that the Doppler spread is proportional to the maximum DFO √ and decreases approximately as 1/√(M) (M is the number of transmit antennas) when M is sufficiently large. The numerical results are provided to corroborate the proposed scheme.

In this paper, we design an uplink/downlink channel estimation method for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems and investigate the impact of beam squint effect that accompanies large array configuration. Specifically, we adopt the off-grid sparse Bayesian learning (SBL) that directly works on the continuous angle-delay parameter domain and avoids the grid mismatch problem. Hence, the proposed method achieves good channel estimation accuracy and handles the wideband direction of arrival (DOA) estimation problem for mmWave massive MIMO communications, where beam squint effect was previously ignored by many existing literatures. The Cramér-Rao bound for unknown parameters is derived to make the proposed study complete. More importantly, a much simplified downlink channel estimation scheme is designed with the aid of angle-delay reciprocity, which significantly reduces training and feedback overhead. The simulation results are provided to demonstrate the superior performance of the proposed method over existing ones.

Unmanned aerial vehicles (UAVs) have attracted increasing attention in wireless communications due to the high mobility. This paper investigates a fixed-wing UAV-to-UAV (U2U) communications system, with the aim of minimizing the information transmission time via proactively designing the UAV paths. First, we propose a general optimization framework for U2U communications, which covers the communication throughput requirement, interference from terrestrial transmitters, UAV maximum/minimum speeds and accelerations, and minimum U2U distance. To tackle the formulated optimization, the communication throughput constraint that contains uncertain locations of terrestrial transmitters is transformed into a deterministic expression with the aid of S-procedure, and the nonlinear equality constraints on the UAV paths are replaced by linear equality constraints with additional positive semidefinite matrix constraints. Then, we develop a path planning algorithm based on the exact penalty method and successive convex approximation. Furthermore, we design a heuristic path planning algorithm that solves the completion time minimization problem by iteratively addressing a series of throughput maximization problems. The proposed heuristic algorithm strikes a good tradeoff between the computational complexity and the achievable performance. Finally, the simulation results are presented to verify the proposed path planning algorithms under various parameter configurations.

We investigate the worst-case robust beamforming for simultaneous wireless information and power transfer in a multiuser beamspace massive multiple-input multiple-output (MIMO) system. The objective is to minimize the transmit power of the base station subject to the individual signal-to-interference-plus-noise ratio and the energy-harvesting constraints under imperfect channel state information. Instead of directly resorting to semi-definite relaxation, we convert the initial non-convex optimization to a power allocation problem, which greatly reduces the computational complexity. The beamforming vectors are proven to be scaled versions of the estimated channels. The optimal scaling factors are then derived in closed-form. The simulations demonstrate that the proposed robust beamforming method achieves the globally optimal point for the initial design when the channel estimation errors are small while leads to satisfactory performance when the channel estimation errors are large.

Recently, ambient backscatter has been introduced as an attractive technology that allows small devices, such as battery-less sensors and passive tags, to communicate by using radio-frequency (RF) signals over the air. It is worth noting that multiple-antenna tags could perform energy harvesting and backscattering simultaneously and thus are advantageous for ambient backscatter communication systems. Therefore, in this paper, we consider the ambient backscatter communication systems with multiple-antenna tags and focus on signal detection problem. Multiple antennas imply multiple channel parameters, which are difficult to estimate because the tags have limited power and can transmit few training symbols. To address this problem, a semi-blind detector is designed for readers to recover tag signals without any knowledge of the multiple channel parameters between the reader and the tag. We also derive the bounds on the detection probabilities. Moreover, an antenna selection scheme is suggested to optimize the detection performance. Finally, simulation results are provided to corroborate our theoretical studies.

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

Radio frequency identification (RFID) backscatter communication systems play an important part in the future Internet of Things (IoT) applications. In this paper, we consider a multiple input single output (MISO) backscatter system, where the reader is equipped with multiple antennas and the tag is equipped with single antenna. The impact of the channel correlation between the forward and backscatter links on the symbol error rate (SER) is investigated. Then, we derive expressions for asymptotic SER and an upper bound, assuming M-ary phase-shift keying (M-PSK) and quadrature amplitude modulation (M-QAM) with maximum ratio combining (MRC) reception, over arbitrarily correlated Nakagami-m fading channels. From these expressions, we can directly obtain the diversity order. Finally, numerical results and simulations are provided to demonstrate the accuracy of the theoretical expressions.

Recently, ambient backscatter that utilizes surrounding radio frequency (RF) signals for both power and communications, has attracted vast interest since it can free sensors and tags from batteries and has extensive applications in Internet of Things (IoT), Existing studies about ambient backscatter often assume single antenna for each tag. Actually, as we show in this paper, equipping tags with multiple antennas can enlarge communication distance, enhance detection performance, and thus be practically useful. One key challenge of using multiple-antenna tags is the signal detection at the reader because the tag may have limited power and can transmit few training symbols. Therefore, in this paper, we design a blind detector based on F-test for the reader to recover tag signals without any knowledge of RF signals power, noise variance and all channel state information (CSI). Furthermore, we derive the lower and upper bounds of detection probabilities, and its exact expression in a special case. The optimal antenna selection scheme is also proposed to maximize the detection probability. Finally, simulation results are provided to corroborate our theoretical studies.

In this paper, we consider the problem of self- positioning for the unmanned aerial vehicle (UAV) swarm, where multiple small UAVs are arranged by unknown displacement due to the dynamic moving. These multiple small UAVs also formulate a virtual massive antenna array that can estimate the direction of arrivals (DOAs) of target users efficiently, regardless of the relative position of the UAVs. After obtaining the DOA information, the unknown displacements among UAVs can also be self-recovered, automagically realizing the important functionality of self-positioning for UAV swarm. The self-positioning problem falls into the category of the mixed integer nonlinear programming (MINLP). To reduce the computational complexity, we develop a novel self-positioning algorithm based on least square (LS) method. Moreover, the deterministic Cramer-Rao bound (CRB) of the self-positioning estimation is derived in closed- form. Finally, numerical examples are provided to corroborate the proposed studies.

In this paper, we design a novel angle-domain adaptive filtering (ADAF)-based frequency synchronization method for massive multiple-input multiple-output (MIMO) multiuser uplink, which is suitable for users with either separate or overlapped angle-of-arrival (AoA) regions. We first introduce the angle-constraining matrix (ACM), which consists of a set of selected match-filter (MF) beamformers pointing to the AoAs of the interested user. Then, the adaptive beamformer can be acquired by appropriately designing the ADAF vector. Such beamformer can achieve a two-stage adaptive multiuser interference (MUI) suppression, i.e., inherently suppressing the MUI from distant users by ACM in the first stage and substantially mitigating the MUI from adjacent overlapping users by ADAF vector in the second stage. For both separate and mutually overlapping users, the carrier frequency offset (CFO) estimation and subsequent data detection can be performed individually for each user, which reduces the computational complexity. Moreover, ADAF is rather insensitive to imperfect AoA knowledge, making itself promising for multiuser uplink transmission. Numerical results are provided to show the effectiveness of the proposed method, as well as its superiority over existing competitors.

In this paper, we design a compressed sensing (CS) based channel estimation method for millimeter wave (mmWave) massive MIMO systems and investigate the impact of dual-wideband effect (frequency-wideband and spatial-wideband) that appears in large array communications. Specifically, we adopt the off-grid sparse Bayesian learning (SBL) that directly works on the continuous angle-delay parameter domain and avoids the basis mismatch problem. Hence, the proposed method achieves better channel estimation accuracy compared to most state-of-the-art algorithms that rely on on-grid CS approach. Moreover, the proposed method could successfully handle the spatial-wideband effect (sometimes known as beam squint effect) for wideband massive MIMO communications that was previously ignored by many existing literatures. Simulation results are provided to demonstrate the superior performance of the proposed method.

In this paper, we derive the closed-form capacity expression as well as the capacity-achieving input distribution for an ambient backscatter system with memoryless binary input and binary output (BIBO) channel. The discrete inputs are restricted to two mass points and the outputs are binary results obtained from energy detection with certain threshold. To show the influence of the signal to noise ratio (SNR) on the capacity, a closed-form tight capacity ceiling is also derived when SNR turns relatively large. Simulations are provided to corroborate the theoretical studies. Interestingly, simulations show: (i) the detection threshold maximizing the capacity is the same to the one from the maximum likelihood detector; (ii) the capacity is achieved by a uniform distribution for the inputs.

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 this paper, a multi-tag selection combining (SC) scheme is proposed in a radio frequency identification (RFID) backscatter communication system which contains a reader and L tags. The proposed scheme could efficiently combat the double-fading channel in RFID system since the diversity of multiple tags is utilized. Different from the conventional one-way communication system, the forward link channel and backscatter link channel could be correlated in backscatter communication system. Hence, we investigate the system performance under fully correlated and partially correlated Nakagami-m fading channels. The closed-form analytical outage probabilities for the two correlation circumstances are derived. Furthermore, the asymptotic outage probability is derived in a high signal-to- noise ratio (SNR) range, from which the insight on how the channel and system parameters affect the outage performance is gained. Finally, the simulation results are presented to verify the theoretical analysis.

Unmanned aerial vehicle (UAV)-satellite communication has drawn dramatic attention for its potential to build the integrated space-air-ground network and the seamless wide-area coverage. A key challenge to UAV-satellite communication is its unstable beam pointing due to the UAV navigation, which is a typical SatCom on-the-move scenario. In this paper, we propose a blind beam tracking approach for Ka-band UAV-satellite communication system, where UAV is equipped with a hybrid large-scale antenna array. The effects of UAV navigation are firstly released through the mechanical adjustment, which could approximately point the beam towards the target satellite through beam stabilization and dynamic isolation. Specially, the attitude information for mechanical adjustment can be realtimely derived from data fusion of low-cost sensors. Then, the precision of beam pointing is blindly refined through electrically adjusting the weight of the massive antennas, where an array structure based simultaneous perturbation algorithm is designed. Simulation results are provided to demonstrate the superiority of the proposed method over the existing ones.

In this paper, we design robust beamforming to guarantee the physical layer security for a multiuser beam division multiple access (BDMA) massive multiple-input multiple-output (MIMO) system, when the channel estimation errors are taken into consideration. With the aid of artificial noise, the proposed design are formulated as minimizing the transmit power of the base station, while providing legal users and the eavesdropper with different signal-to-interference-plus-noise ratio. It is strictly proved that, under BDMA massive MIMO scheme, the initial non-convex optimization can be equivalently converted to a convex semi-definite programming problem and the optimal rank-one beamforming solutions can be guaranteed. In stead of directly resorting to the convex tool, we make one step further by deriving the optimal beamforming direction and the optimal beamforming power allocation in closed-form, which greatly reduces the computational complexity and makes the proposed design practical for real world applications. Simulation results are then provided to verify the efficiency of the proposed algorithm.

Wireless power transfer (WPT) is an emerging and promising technique for power supplies to mobile and portable devices. Among all approaches, magnetic resonant coupling (MRC) is an excellent one for midrange WPT, which provides high mobility, flexibility, and convenience due to its simplicity in hardware implementation and longer transmission distances. In this letter, we consider an MRC-WPT system with multiple power transmitters, one intended power receiver and multiple unintended power receivers. The optimal robust beamforming design of the complex transmit currents is investigated to achieve the minimal total source power with the worst-case mutual inductances measurement, whereas the unintended receiving powers are constrained by certain bounds. Numerical results demonstrate that the proposed algorithm can significantly improve the performance and the robustness of the MRC-WPT systems.

This paper considers the beamspace channel estimation problem in three-dimensional (3D) lens antenna array under a millimeter-wave communication system. We analyze the focusing capability of the 3D lens antenna array and the sparsity of the beamspace channel response matrix. Considering the analysis, we observe that the channel matrix can be treated as a two-dimensional (2D) natural image; that is, the channel is sparse and the changes between most of adjacent elements are subtle. Thus, for the channel estimation, we incorporate an image reconstruction technique called sparse noninformative parameter estimator-based cosparse analysis approximate message passing for imaging (SCAMPI) algorithm. The SCAMPI algorithm is faster and more accurate than earlier algorithms such as orthogonal matching pursuit and support detection algorithms. To further improve the SCAMPI algorithm, we model the channel distribution as a generic Gaussian mixture (GM) probability and embed the expectation-maximization learning algorithm into the SCAMPI algorithm to learn the parameters in the GM probability. We show that the GM probability outperforms the common uniform distribution used in image reconstruction. We also introduce a phase-shifter-reduced selection network structure to decrease the power consumption of the system and prove that the SCAMPI algorithm is robust even if the number of phase shifters is reduced by 10%.

In this paper, we design robust downlink beamforming against the imperfect channel state information (CSI) for beam division multiple access (BDMA) massive multiple-input multiple output (MIMO) systems. Following a worst-case deterministic model, the proposed design is formulated as minimizing the power consumption of base station (BS) under different signal-to-interference-plus-noise ratio (SINR) constraints. The S-Procedure and semi-definite relaxation (SDR) are used to convert the initial non-convex optimization to a convex semi-definite programming problem. Then the optimality of SDR is strictly proved by showing the rank-one property of the optimal beamforming thanks to the orthogonal channels under BDMA scheme. More importantly, we make one step further by deriving the optimal beamforming directions and optimal beamforming power allocation of the SDR in closed-form, which greatly reduces the optimization complexity and makes the proposed design practical for a real word massive MIMO system. Simulation results are then provided to verify the efficiency of the proposed robust beamforming algorithm.

Soft iterative detection/decoding algorithms are fundamentally necessary for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) adopted in the Third Generation Long Term Evolution (LTE)-Advanced in order to increase the capacity and achieve high data rates. However, their high performance critically requires log likelihood ratio computations with prohibitive complexity. This challenge will be addressed in this paper. We first use the assumption of Gaussian transmit symbols to show the equivalence among several existing algorithms. We next develop a non-Gaussian approximation for high-order constellations, which paves the way for interference cancellation-based detectors. Based on both Gaussian and non-Gaussian approximations, we thus develop several capacity-achieving iterative MIMO-OFDM demodulation and decoding algorithms. To this end, we adopt K-best algorithms to take advantage of both the types of approximations and the list decoder. Unlike existing algorithms, our proposed K-best algorithms make use of the a priori probabilities to generate the list. Simulations of standard-compliant LTE systems demonstrate that the proposed algorithms outperform the existing ones.

This paper proposes a novel direction-of-arrival (DOA)-aided channel estimation for a hybrid millimeter-wave (mm-wave) massive multiple-input multiple-output system with a uniform planar array at the base station. To explore the physical characteristics of the antenna array in mm-wave systems, the parameters of each channel path are decomposed into the DOA information and the channel gain information. We first estimate the initial DOAs of each uplink path through the 2-D discrete Fourier transform and enhance the estimation accuracy via the angle rotation technique. We then estimate the channel gain information using a small amount of training resources, which significantly reduces the training overhead and the feedback cost. More importantly, to examine the estimation performance, we derive the theoretical bounds of the mean squared errors (MSEs) and the Cramér-Rao lower bounds (CRLBs) of the joint DOA and channel gain estimation. The simulation results show that the performances of the proposed methods are close to the theoretical MSEs' analysis. Furthermore, the theoretical MSEs are also close to the corresponding CRLBs.

Hybrid interweave-underlay spectrum access in cognitive radio networks can explore spectrum opportunities when primary users (PUs) are either active or inactive, which significantly improves spectrum utilization. The practical wireless systems, such as long-term evolution-advanced, usually operate at multiple transmission power levels, leading to a multiple primary transmission power scenario. In such a case, the two fundamental issues in hybrid interweave-underlay spectrum access are to detect the “ON/OFF” status of PUs and to recognize the operating power level of PUs, which are challenging due to non-Gaussian transmitted signals. In this paper, we exploit high-order cumulants (HOCs) to efficiently perform spectrum sensing and power recognition. Specifically, for a given order and time lag, we first propose a single HOC-based spectrum sensing and power recognition scheme with low computational complexity, by leveraging minimum Bayes risk criterion. Moreover, we propose a hybrid multiple HOCs-based spectrum sensing and power recognition scheme with multiple orders and time lags, to further improve the detection performance. Both the proposed schemes can eliminate the adverse impact of the noise power uncertainty. Finally, simulation results are provided to evaluate the proposed schemes.

In this paper, we propose a new channel estimation scheme for TDD/FDD massive MIMO systems by reconstructing (sometimes also referred to as covariance computing or covariance fitting) uplink/downlink channel covariance matrices (CCMs) with the aid of array signal processing techniques. Specifically, the angle parameters and power angular spectrum (PAS) of channel are extracted from the instantaneous uplink channel state information (CSI). Then, the uplink CCM is reconstructed and can be used to improve the uplink channel estimation without any additional training cost. By virtue of angle reciprocity as well as PAS reciprocity between uplink and downlink channels, the downlink CCM could also be inferred with a similar approach even for the FDD massive MIMO systems. Then, the downlink instantaneous CSI can be obtained by training toward the dominant eigen-directions of each user. The proposed strategy is applicable to various PAS distributions. Numerical results are provided to demonstrate the superiority of the proposed methods over the existing ones.

In this paper, we propose a frequency synchronization scheme for multiuser orthogonal frequency division multiplexing uplink with a large-scale uniform linear array at base station (BS) by exploiting the angle information of users. Considering that the incident signal at BS from each user can be restricted within a certain angular spread, the proposed scheme could perform carrier frequency offset (CFO) estimation for each user individually through a joint spatial-frequency alignment procedure and can be completed efficiently with the aid of fast Fourier transform. A multi-branch receive beamforming is further designed to yield an equivalent single user transmission model for which the conventional single-user channel estimation and data detection can be carried out. To make the study complete, theoretical performance analysis of the CFO estimation is also conducted. We further develop a user grouping scheme to deal with the unexpected scenarios that some users may not be separated well from the spatial domain. Finally, various numerical results are provided to verify the proposed studies.

In this paper, we design a channel tracking method for massive multiple-input multiple-output systems under both time-varying and spatial-varying circumstances. By exploiting the characteristics of massive antenna array, a spatial-temporal basis expansion model is proposed to reduce the effective dimension of uplink/downlink channel, which decomposes channel state information into time-varying spatial information and gain information. We first model the user's movement as the one-order unknown Markov process, whose parameters are blindly obtained by expectation and maximization learning. Then, the uplink time-varying spatial information can also be blindly tracked by unscented Kalman filter and Taylor series expansion of the steering vector, while the rest of uplink channel gain information can be trained by only a few pilot symbols. Due to physical angle reciprocity, the spatial information of the downlink channel can be immediately computed from the uplink counterpart, which greatly reduces the complexity of downlink channel tracking. Various numerical results are provided to demonstrate the effectiveness of the proposed method.

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.

This paper proposes a novel direction of arrival (DOA) estimation for hybrid millimeter wave (mmWave) massive MIMO systems with the uniform planar array (UPA) at base station (BS). To explore the physical characteristics of antenna array in mmWave systems, the parameters of each channel path are decomposed into the DOA information and the channel gain information. We first estimate the initial DOAs of each uplink path through the two dimension discrete Fourier transform (2D-DFT) efficiently, and then the estimation accuracy can be further enhanced via the angle rotation technique. To examine the estimation performance, we derive the theoretical bounds of the mean squared error (MSE) performance of the DOA estimation in high signal-to-noise ratio (SNR) region. Simulation results are provided to corroborate the proposed studies, and show that the proposed DOA estimation method is close to the theoretical MSE performance.

In this paper, we propose a Doppler pre-compensation scheme for high-mobility orthogonal frequency division multiplexing (OFDM) uplink, where a high-speed terminal transmits signals to the base station (BS). Considering that the time-varying multipath channel consists of multiple Doppler frequency offsets (DFOs) with different angle of departures (AoDs), we propose to perform DFO pre-compensation at the transmitter with a large-scale uniform linear array (ULA). The transmitted signal passes through a beamforming network with high- spatial resolution to produce multiple parallel branches. Each branch transmits signal towards one direction thus the transmitted signal is affected by one dominant DFO when passing over the time-varying channel. Therefore, we can compensate the DFO for each branch at the transmitter previously. Theoretical analysis for the Doppler spread of the equivalent uplink channel is also conducted. It is found that when the number of transmit antennas is sufficiently large, the time-variation of channel can be efficiently suppressed. Therefore, the performance will not degrade significantly if we apply the conventional time- invariant channel estimation and equalization methods at the receiver. Simulation results are provided to verify the proposed scheme.

The low-rank property of the channel covariances can be adopted to reduce the overhead of the channel training in massive MIMO system. In this paper, we exploit such low-rank property through virtual channel representation (VCR) under the time-varying channel scenario. Firstly, we reformulate the dynamic massive MIMO channel as one sparse signal model through VCR. Then, an expectation maximization (EM) based sparse Bayesian learning (SBL) framework is developed to estimate the statistical parameters of the sparse virtual channel. Specifically, the Kalman filter (KF) and the Rauch-Tung-Striebel smoother (RTSS) are applied to track the posterior statistics of the angle domain sparse channel in the expectation step, while a fixed-point theorem based algorithm and a low-complexity searching algorithm are separately developed to recover the temporal varying characteristics and the spatial signatures in the maximization step. Finally, we demonstrate the efficacy of the proposed schemes through simulations.

mmWave massive multiple-input multiple-output (MIMO) system has gained much attention for its considerable improvement in system throughput. However, the cost of the complex hardware, e.g., the radio frequency (RF) chains, hinders it from the practical deployment. In this paper, we propose an angle space channel tracking method for mmWave massive MIMO systems with limited RF chains (hybrid scheme). Specifically, the users can be scheduled according to their DOA information, i.e. angle division multiple access (ADMA). Besides, the channel information can be divided into direction of arrival (DOA) information and gain information respectively, where DOA can be tracked through unscented Kalman filter (UKF), while the gain information can be obtained from beam training and spatial rotation. Numerical results are provided to corroborate our studies.

In this paper, we design robust downlink beamforming against the imperfect channel state information (CSI) for beam division multiple access (BDMA) massive multiple-input multiple output (MIMO) systems. Different from conventional approach, the optimality of semi-definite relaxation (SDR) is strictly proved by showing the rank-one property of the optimal beamforming with the orthogonal BDMA massive channels, where globally optimal robust beamforming solutions are derived. More importantly, we make one step further by deriving the optimal beamforming directions and optimal beamforming power allocation of the SDR in closed-form, which greatly reduces the optimization complexity and makes the proposed design practical for a real word massive MIMO system. Simulation results are provided to demonstrate the efficiency of the proposed algorithm.

This paper proposes a practical channel estimation scheme for downlink 60GHz indoor systems with the massive uniform rectangular array (URA) at base station (BS). Through array signal processing theory, the parameter of each channel path can be decomposed into the angular information and the channel gain information that can be estimated separately. We first prove that the two dimensional Discrete Fourier transform (2D-DFT) with phase rotation operation can be applied to efficiently estimate the angular information. Then, the channel gain information could be easily obtained with small amount of training resources in a linear manner. Interestingly, since uplink and the downlink angular information is reciprocal, the proposed method applicable for both TDD and FDD systems. Simulation results are provided to corroborate the proposed studies.

In this paper, we propose a new channel tracking method for massive multiple-input multiple-output (MIMO) systems under both the time-varying and spatial-varying circumstance. With spatial-temporal basis expansion model (ST-BEM), the channel information is decomposed into the spatial information and gain information, where the former is determined by the central angle as well as the angular spread of the incoming signal. We first blindly track the central angle by the extended Kalman filter (EKF) and obtain the angular spread through Taylor series expansion of the steering vector. Then, the channel gain information can be estimated with only a few pilot symbols. Various numerical results are provided to demonstrate the effectiveness of the proposed method.

In this paper, we address the orthogonal frequency division multiplexing (OFDM) downlink transmission in scenarios abundant of scatters when the high- speed rail (HSR), equipped with partly calibrated massive uniform linear array (ULA), is in high- speed motion relative to the base station. A high- resolution beamforming network is designed to separate multiple Doppler frequency offsets (DFOs) in spatial domain into a series of parallel beamforming branches, such that each branch is mainly affected by single dominant DFO, wherein the conventional carrier frequency offset (CFO) compensation and subsequent data detection could be carried out individually. In view of this, a joint- estimation algorithm is proposed to jointly estimate the CFO and equivalent channel of each beamforming branch. Moreover, the calibration- oriented beamforming parameter (COBP) is introduced to mitigate the detrimental effects in presence of inter-subarray uncertainties. Both numerical and theoretical results are provided to corroborate the effectiveness of the proposed method.

This paper proposes a practical channel estimation for 60-GHz indoor systems with the massive uniform rectangular array at base station. Through antenna array theory, the parameters of each channel path can be decomposed into the angular information and the channel gain information. We first prove that the true direction of arrivals of each uplink path can be extracted via an efficient array signal processing method. Then, the channel gain information could be obtained linearly with small amount of training resources, which significantly reduces the training overhead and the feedback cost. More importantly, the proposed scheme unifies the uplink/downlink channel estimations for both the time duplex division and frequency duplex division systems, making itself particularly suitable for protocol design. Compared with the existing channel estimation algorithms, the newly proposed one does not require any knowledge of channel statistics and can be efficiently deployed by the 2-D fast Fourier transform. Meanwhile, the number of user terminals simultaneously served can be increased from a sophisticatedly designed angle division multiple access scheme. Simulation results are provided to corroborate the proposed studies.

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.

Magnetic resonance coupling (MRC) has been utilized in wireless power transfer (WPT) to achieve mid-range contactless power supply. However, unintended users might also draw power from the transmission devices. In this letter, an MRC-WPT system with multiple power transmitters, one intended power receiver, and one unintended power receiver is investigated. We formulate a power security problem by limiting the unintended receiving power and, at the same time, maximizing the power of the intended user. Such an optimization problem is in general nonconvex. Nevertheless, a global optimal solution can be efficiently achieved with the aid of semidefinite relaxation approach. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

In this paper, we consider the sequential detection problem in a new cognitive radio scenario when the primary user (PU) works with more than one transmit power level. Different from most existing literature where PU is assumed to operate with a constant transmit power only, this new consideration well matches the practical standards, e.g., IEEE 802.11 Series, LTE, LTE-A, and so on, as well as the adaptive powering concept that a user would vary its transmit power under different situations. The targets of the secondary user here are not only to detect the presence of PU but also to recognize PU's transmit power levels. We first formulate a valid sequential detection approach via the modified Neyman-Pearson criterion and then derive the closed-form decision region for each PU's transmit power level. Many interesting discussions are raised. Moreover, the average sample number, a key metric for any sequential detection method, is derived in closed form to facilitate the performance evaluation. The performance comparison of the sequential detection and the fixed sample detection for this multiple primary transmit power levels scenario is analyzed. Finally, the simulation results are presented to verify the correctness of the proposed studies.

We study a novel communication technique, ambient backscatter, that utilizes radio frequency signals transmitted from an ambient source as both energy supply and information carrier to enable communications between low-power devices. Different from existing noncoherent schemes, we here design the semi-coherent detection, where channel-related parameters can be obtained from unknown data symbols and a few pilot symbols. In order to obtain a benchmark for overall detection, we first derive a maximum likelihood detector assuming a complex Gaussian ambient source, and the closed-form bit error rate (BER) is computed. To release the dependence on prior knowledge of the ambient source, we next derive a type of robust design, called an energy detector, with the ambient signal being either complex Gaussian or phase shift keying (PSK). The closed-form detection thresholds, analytical BERs, and outage probability are provided correspondingly. Interestingly, the complex Gaussian source would cause an error floor, while the PSK source does not, which brings nontrivial indication of constellation design as opposed to popular Gaussian-embedded literatures. We also propose an effective approach to estimate detection-required parameters rather than channels themselves. Numerical simulations are finally presented to verify theoretical results.

Ultra-dense networks are envisioned as essential to embrace the skyrocketed traffic for the next-generation wireless networks. In this paper, we consider the uplink transmissions in ultra-dense networks, where the increased interference along with the increased density significantly challenges the efficient utilization of network resources as well as the provisioning of users' quality of services (QoS). Targeting these issues, we consider the QoS in terms of target signal-to-interference-plus-noise ratio (SINR) and power consumption simultaneously for each user within a multi-objective optimization model. We then investigate the interactions among users by leveraging the non-cooperative game-theoretical framework. By characterizing the properties of Nash equilibrium, we develop the target-SINR oriented resource allocation (TORA) algorithm, which features distributed implementation. Moreover, we obtain the condition to guarantee the convergence of our proposed TORA algorithm and demonstrate that it adapts to different interfering scenarios. Furthermore, considering the heterogeneous service requirements in real practice, we also design the target-SINR constrained resource allocation (TCRA) algorithm, such that TORA and TCRA are able to cope with voice and data services, respectively. Also provided are the simulation results, which demonstrate that, compared with the counterparts, our proposals more effectively guarantee the target-SINR for users with efficient power utilization.

Small-cell networks and massive multiple-input multiple-output (MIMO) systems are regarded as important candidate techniques for 5G communication systems. This paper considers a heterogeneous network composed of a macrocell tier overlaid with an extremely dense tier of small-cells. In the network, the macrocell base station (BS), which applies massive MIMO, does not only serve macro user equipment units but also provides wireless backhaul for small-cell access points (APs). The wireless backhaul shares the same spectrum resource with radio access networks without creating extra spectrum resources. However, due to the densification of small-cells, the inter- and intra-tier interferences become severe. To mitigate the interferences, we use the regularized zero-forcing precoding combined with a projection technique is used at the BS in downlink (DL) to avoid interference to the APs in uplink (UL). Meanwhile, the joint linear minimum mean square error detection is applied in UL to mitigate the inter-tier interference. We derive deterministic expressions for ergodic UL and DL sum rates (SRs) by leveraging the large-dimensional random matrix theory. The expressions only depend on statistical channel information and can be used to optimize the bandwidth division between radio access links and wireless backhaul, as well as the time allocation between DL and UL operation intervals. Numerical results show that the deterministic SR equivalents are accurate and that the proposed resource allocation method can significantly improve system performance.

Due to the difficulty in manipulating the distribution of Wishart random matrices, the performance analysis of multiple-input-multiple-output (MIMO) channels has mainly focused on deriving capacity bounds via Jensen's inequality. However, to the best of our knowledge, the tightness of Jensen's bounds has not yet been rigorously quantified in the general MIMO context. This paper proposes a new methodology for measuring the tightness of Jensen's bounds via the sandwich theorem. In particular, we first compare the tightness of two different pairs of upper/lower bounds for a general class of MIMO channels based on the unordered eigenvalue of the instantaneous correlation matrix and for arbitrary numbers of antennas. The tightness of Jensen's bounds in different channel scenarios is investigated including multiuser MIMO with maximal ratio combining. Our analysis is facilitated by deriving some new results for finite-dimensional Wishart matrices, i.e., for the arbitrary moments of the unordered eigenvalue of central and non-central Wishart matrices. Our results provide very interesting insights into the implications of the system parameters, such as the number of antennas, and signal-to-noise ratio, on the tightness of Jensen's bounds, and showcase the suitability and limitations of Jensen's bounds.

Large-scale multi-input multi-output (MIMO) technique has drawn considerable research interest recently. However, multiuser transmissions in large-scale MIMO systems would face challenging estimation and compensation for multiple carrier frequency offsets (CFOs) that co-exist at the receiver. In this paper, we design a new frequency synchronization scheme in multiuser orthogonal frequency division multiplexing uplink with the aid of scattered pilot symbols. We specifically consider a base station with a large number of antennas and propose to assign a number of scattered pilot subcarriers to each user. With sufficient spatial dimensions offered by the large number of antennas, the designed scheme could estimate CFO for each user individually and eliminate the necessity of the multidimensional search. Moreover, after the CFO estimation, the receive beamforming matrix is further designed for inter-user interference cancelation, which yields an equivalent single user transmission model. The conventional single user channel estimation and data detection can then be performed. Finally, the numerical results are provided to verify the proposed studies.

We consider the problem of downlink training and channel estimation in frequency division duplex (FDD) massive MIMO systems, where the base station (BS) equipped with a large number of antennas serves a number of single-antenna users simultaneously. To obtain the channel state information (CSI) at the BS in FDD systems, the downlink channel has to be estimated by users via downlink training and then fed back to the BS. For FDD large-scale MIMO systems, the overhead for downlink training and CSI uplink feedback could be prohibitively high, which presents a significant challenge. In this paper, we study the behavior of the minimum mean-squared error (MMSE) estimator when the channel covariance matrix has a low rank or an approximate low-rank structure. Our theoretical analysis reveals that the amount of training overhead can be substantially reduced by exploiting the low-rank property of the channel covariance matrix. In particular, we show that the MMSE estimator is able to achieve exact channel recovery in the asymptotic low-noise regime, provided that the number of pilot symbols in time is no less than the rank of the channel covariance matrix. We also present an optimal pilot design for the single-user case, and an asymptotic optimal pilot design for the multi-user scenario. Last, we develop a simple model-based scheme to estimate the channel covariance matrix, based on which the MMSE estimator can be employed to estimate the channel. The proposed scheme does not need any additional training overhead. Simulation results are provided to verify our theoretical results and illustrate the effectiveness of the proposed estimated covariance-assisted MMSE estimator.

In this paper, we propose three automatic modulation classification classifiers based on order-statistics and reduced order-statistics, where the order-statistics are the random variables sorted by ascending order and the reduced order-statistics represent a subset of the original order-statistics. Specifically, the linear support vector machine classifier applies the linear combination of the order-statistics of the received signals, while the approximate maximum likelihood and the backpropagation neural networks (BPNNs) classifier resort to the reduced order-statistics to decrease the computational complexity. Moreover, BPNN is applicable for modulation classification both in known and unknown channel scenarios. It is shown that in the known channel scenario, the proposed classifiers provide a good tradeoff between performance and computational complexity, while in the unknown channel scenario, the proposed BPNN classifier outperforms the expectation maximization classifier in terms of both classification performance and computational complexity. Simulations results are provided to evaluate the proposed classifiers.

Ambient backscatter, an emerging communication mechanism where battery-free devices communicate with each other via backscattering ambient radio frequency (RF) signals, has achieved much attention recently because of its desirable application prospects in the Internet of Things. In this paper, we formulate a practical transmission model for an ambient backscatter system, where a tag wishes to send some low-rate messages to a reader with the help of an ambient RF signal source, and then provide fundamental studies of noncoherent symbol detection when all channel state information of the system is unknown. For the first time, a maximum likelihood detector is derived based on the joint probability density function of received signal vectors. In order to ease availability of prior knowledge of the ambient RF signal and reduce computational complexity of the algorithm, we design a joint-energy detector and derive its corresponding detection threshold. The analytical bit error rate (BER) and BER-based outage probability are also obtained in a closed form, which helps with designing system parameters. An estimation method to obtain detection-required parameters and comparison of computational complexity of the detectors are presented as complementary discussions. Simulation results are provided to corroborate theoretical studies.

The millimeter-wave (mm-wave) massive multiple-input multiple-output (MIMO) system has gained much attention for its considerable improvement in system throughput. However, the cost of complex hardware, e.g., radio frequency (RF) chains, hinders it from practical deployment. In this paper, we propose an angle domain hybrid precoding and channel tracking method by exploring the spatial features of the mm-wave massive MIMO channel. The number of the effective spatial beams, or equivalently the RF chains, is enormously decreased via the operation of spatial rotation. The users are then scheduled by the angle division multiple access scheme, which groups users according to their direction of arrivals (DOAs). Meanwhile, a channel tracking method is designed for the subsequent data transmission through a small number of pilot symbols. Specifically, the channel information is divided into the DOA information and the gain information, where the DOA information is tracked by a modified unscented Kalman filter and the gain information is estimated from beam training. Numerical results are provided to corroborate our studies.

In this paper, we consider a heterogeneous network in which a macro-cell tier is overlaid with a very dense tier of small cells. The macro-cell base station (BS) that applies a massive MIMO scheme not only serves the macro user equipment but also provides a wireless backhual for small-cell access points (APs). These APs serve their associated small-cell user equipment. A reverse time division duplex transmission protocol is utilized. To avoid interference toward the APs in the uplink (UL), regularized zero-forcing precoding combined with a projection technique is utilized at the BS in the downlink (DL). We derive deterministic expressions for ergodic UL and DL sum rates (SRs) under the assumption that perfect channel state information is available and use these results to optimize the spectrum division between radio access links and the wireless backhaul. Simulation results suggest that the deterministic SR approximations are accurate and that system performance can be significantly improved through the optimization of spectrum division.

This paper proposes a new channel estimation scheme for the multiuser massive multiple-input multiple-output (MIMO) systems in time-varying environment. We introduce a discrete Fourier transform (DFT) aided spatial-temporal basis expansion model (ST-BEM) to reduce the effective dimensions of uplink/downlink channels, such that training overhead and feedback cost could be greatly decreased. The newly proposed ST-BEM is suitable for both time division duplex (TDD) systems and frequency division duplex (FDD) systems thanks to the angle reciprocity, and can be efficiently deployed by fast Fourier transform (FFT). Various numerical results have corroborated the proposed studies.

In this paper, we propose a new frequency synchronization scheme for multiuser orthogonal frequency division multiplexing (OFDM) uplink with a large-scale uniform linear array (ULA) at base station (BS). Considering that the incident signal at BS from each user can be restricted within a narrow angular spread, the proposed scheme performs carrier frequency offset (CFO) estimation for each user individually through a joint spatial-frequency alignment procedure. The basic idea behind is that with sufficient spatial dimension, the multiuser interference (MUI) effect can be effectively mitigated via beamforming with the steering vectors corresponding to any direction of arrival (DOA) of each user. A multi-branch receive beamforming is further designed for each user which results in an equivalent single user transmission model and the conventional single-user channel estimation and data detection can be carried out. We show that the proposed CFO estimation can be applied efficiently with the aided of fast Fourier transform (FFT). The theoretical CFO estimation performance analysis is also conducted. Various numerical results are provided to verify the proposed studies.

We study the problem of signal detection for the ambient backscatter system (ABS) when data are transmitted with differential modulation. An implementation of the maximum likelihood (ML) detection algorithm is proposed. To reduce the computational complexity, we further design a suboptimal detector and derive its bit error rate (BER) closed-form expression. Moreover, both the upper and the lower bounds of the BER, which can tell more insight of how system parameters affect the detection performance, are obtained. Simulations are then provided to corroborate the studies.

In this paper, we propose a new blind carrier frequency offset (CFO) estimation method for multiuser orthogonal frequency division multiplexing (OFDM) uplink transmissions. The spatial multiplexing is supported in the considered model that allows the subcarriers to be simultaneously occupied by multiple users. We propose to assign different null subcarriers to different users and design algorithm that can perform blind CFO estimation for each individual user with the aid of large number of receive antennas, which then removes the necessity of multidimensional searching. Numerical results are provided to corroborate the proposed studies.

In this paper, we consider the sequential spectrum sensing in a practice-matching cognitive raio (CR) scenario, where the primary user (PU) can operate under more than one transmit power levels as regulated in IEEE 802.11 Series, LTE, LTE-A, etc. In this case, the sensing targets not only include detecting the presence of PU but also include recognizing the transmit power level of PU. We propose two different sequential sensing schemes, i.e., detection before recognition and recognition before detection, and also derive the closed form decision regions. Performance of both schemes are compared. Numerical examples are then provided to corroborate the proposed studies.

The paper presents a new channel estimation scheme for downlink 60GHz indoor environment with Massive Multiple Input Multiple Output (Massive MIMO) systems by exploiting the physical characteristics of the uniform planar array (UPA) and 2-dimensional discrete Fourier Transform (2D-DFT) operation. The proposed channel estimation algorithm can be carried out with very small number of training resources, which significantly reduces the training overhead and the feedback cost. Meanwhile, the number of user terminals (UTs) served simultaneously can be increased by exploiting the spatial pattern of different UTs. Compared to the existing channel estimation algorithms, the newly proposed one does not require any knowledge of channel statistics and can be efficiently deployed by the fast Fourier transform (FFT). Finally, simulation results are provided to corroborate the proposed studies.

In this paper, we propose two low-complexity automatic modulation classification (AMC) classifiers based on order-statistics: the linear support vector machine (LSVM) and the approximate maximum likelihood (AML). Specifically, LSVM applies the linear combination of the entire order-statistics of the received signals for the classification, while AML resorts to the asymptotic distribution of the reduced order- statistics to decrease the computational complexity. The Simulations show that the performance of our proposed classifiers is close to that of the maximum likelihood (ML) classifier and outperforms the Kolmogorov-Smirnov (KS) and cumulant-based classifiers. While the complexity of our proposed classifiers is much lower than that of the ML classifier.

This paper proposes a novel channel estimation scheme for the multiuser massive multiple-input multiple-output (MIMO) systems. A discrete Fourier transform (DFT) aided spatial basis expansion model (SBEM) is first introduced to represent the uplink (UL)/downlink (DL) channels with much few parameter dimensions by exploiting the physical characteristics of the uniform linear array (ULA). With SBEM, pilot contamination in the UL training can be relieved by user scheduling exploiting their spatial information. Moreover, the UL spatial information can help to simplify the DL training based on the angle reciprocity. Compared to existing low-rank models, the newly proposed SBEM does not need any information of channel statistics and is suitable for both time division duplex (TDD) and frequency division duplex (FDD) systems. Moreover, the proposed method can be efficiently deployed by the fast Fourier transform (FFT) followed by linear estimator. Various numerical results are provided to corroborate the proposed studies.

This paper presents energy efficient resource allocation schemes for multi-user cognitive radio networks. The aim is to achieve the fairness in power consumption of different secondary users. A binary integer programming problem is formulated to jointly allocate sub-carriers to users and power loading over different sub-carriers. Energy fairness is achieved subject to individual power constraints at secondary nodes, interference constraint of primary network, and minimum required rate of the secondary system. Dual decomposition based approach is used to find a joint optimization solution. Further, a sub-optimal scheme is also designed where the power is optimized for fixed sub-carrier allocation. Simulation results are presented to evaluate the performance of proposed schemes.

In this paper, we consider the Physical Layer Security (PLS) problem in orthogonal frequency division multiple access (OFDMA) based dual-hop system which consists of multiple users, multiple amplify and forward relays, and an eavesdropper. The aim is to enhance PLS of the entire system by maximizing sum secrecy rate of secret users through optimal resource allocation under various practical constraints. Specifically, the sub-carrier allocation to different users, the relay assignments, and the power loading over different sub-carriers at transmitting nodes are optimized. The joint optimization problem is modeled as a mixed binary integer programming problem subject to exclusive sub-carrier allocation and separate power budget constraints at each node. A joint optimization solution is obtained through Lagrangian dual decomposition where KKT conditions are exploited to find the optimal power allocation at base station. Further, to reduce the complexity, a sub-optimal scheme is presented where the optimal power allocation is derived under fixed sub-carrier-relay assignment. Simulation results are also provided to validate the performance of proposed schemes.

This paper considers an orthogonal frequency division multiplexing (OFDM) based two-way cognitive radio (CR) multiuser multiple relay network. The target is to jointly solve the problem of power allocation, subcarrier assignment, selection of best relay/s, and the sub-carrier pairing at the relay nodes. The aim of the optimization is to maximize the end-to-end sum rate of the network under exclusive sub-carrier allocation constraint, maximum power constraint at each node, and per carrier based primary user interference constraint. We solve the mixed integer programming problem by duality approach and obtain joint optimization solution. Further, we also propose a suboptimal algorithm to reduce the overall complexity with little sacrificing on performance. At the end, simulation results are provided to show the performance comparison of our proposed schemes.

In this paper, we introduce a new spatial spectrum-sharing strategy for massive multiple-input multiple-output (MIMO) cognitive radio (CR) systems. Different from the conventional MIMO CR system, CR terminals can be discriminated by their angular information with the help of high spatial resolution of massive antennas at CR base station (CBS). Moreover, the discrete Fourier transform can be applied to efficiently obtain such angular information thanks to the massive antennas, again. We then formulate a 2-D spatial basis expansion model to represent the uplink/downlink channels of CRs with reduced parameter dimensions, which immediately alleviates the general headaches of massive MIMO systems, such as uplink pilot contamination and downlink training overhead. Moreover, we present a full-space coverage concept by employing two CBSs at the adjacent sides of each cell, which diminishes the sheltering effect from the primary radio. We also design two greedy CR scheduling algorithms for the dual CBSs to improve the spectral efficiency and enhance the scheduling probability of CRs. Since the proposed strategy exploits angular information and since the angle reciprocity holds for two frequency carriers with moderate distance, the proposed strategy is applied for both time division duplex and frequency division duplex systems.

Ambient backscatter technology that utilizes the ambient radio frequency signals to enable the communications of battery-free devices has attracted much attention recently. In this paper, we study the problem of signal detection for an ambient backscatter communication system that adopts the differential encoding to eliminate the necessity of channel estimation. Specifically, we formulate a new transmission model, design the data detection algorithm, and derive two closed-form detection thresholds. One threshold is used to approximately achieve the minimum sum bit error rate (BER), while the other yields balanced error probabilities for “0” bit and “1” bit. The corresponding BER expressions are derived to fully characterize the detection performance. In addition, the lower and the upper bounds of BER at high signal-to-noise ratio regions are also examined to simplify a performance analysis. Simulation results are then provided to corroborate the theoretical studies.

In this paper, the problem of joint transmit power and bandwidth allocation over multiple channels is investigated for a secondary user in underlay mode, when partial information of interference channel is known. The target is to maximize the capacity of a secondary user under a probabilistic constraint of the interference to the primary user. A robust optimization problem is formulated, which is nondeterministic and cannot be solved directly. We then transform the original optimization problem into an equivalent convex optimization problem. For general case, an optimal solving algorithm, which is a combination of analytical and bisection-search methods, is given. For some special cases, simple and optimal solving algorithms are also devised. Numerical results are presented to show that our proposed optimal algorithm has low computation complexity when the number of channels is not large and can achieve global optimal utility in general case and our proposed simple algorithms have much lower computation complexity and can achieve global optimal utility in special cases.

In this paper, we propose a new computationally efficient blind carrier frequency offset (CFO) estimator for multi-input multi-output orthogonal frequency division multiplexing systems. A cost function is carefully designed and can be exactly expressed as the superposition of very few harmonically related cosine waves even with the effect of the noise. Using this property, the minimization of the designed cost function can be solved in a quite computationally efficient manner without any exhaustive grid search procedure. It is seen that the proposed estimator can achieve comparable estimation performance as existing competitors with substantially reduced computational burden. Moreover, in order to improve the estimation performance, we further develop an enhanced CFO estimator by introducing an additional one-step adjustment. We find that the enhanced estimator can attain almost the same estimation performance as the existing maximum likelihood estimator but with a lower order of computational burden. We provide both numerical results and theoretical performance analysis to corroborate the proposed studies.

In this paper, we design transmit beamforming for a full duplex base station (FD-BS) considering both self-interference mitigation and physical-layer security. The proposed design is formulated as minimizing the power consumption of FD-BS under different signal-to-interference-and-noise-ratio (SINR) constraints. Semidefinite relaxation (SDR) is used to convert the initial nonconvex optimization to be a convex semidefinite programming (SDP) problem. Then the optimality of SDR is strictly proved by showing the existence of the rank-one optimal solutions. To reduce the computational complexity, we develop zero forcing beamforming-based suboptimal algorithms, where the solutions can be obtained using golden search and closed-form solutions can be derived in each step. Simulation results are then provided to verify the efficiency of the proposed algorithms.

The massive multiple input multiple output (MIMO) technique is a promising candidate to enormously increase the capacity of wireless networks. In a massive MIMO system, coordinated signal processing among different antennas is crucial to cope with the inevitable co-channel interference. However, it is normally difficult to perform perfect coordination in practical applications, due to the challenging requirement of global channel state information at the transmitter (CSIT). To solve this problem, this paper considers a massively distributed MIMO model, and presents dynamic-cell (DC)-based macro coordination, which requires only the instantaneous intra-DC CSIT and the slowly-varying large-scale inter-DC CSIT. Particularly, we first divide the system into a number of coupled user-centric DCs. Perfect coordination in the form of maximum ratio transmission (MRT) is adopted locally within each DCs based on instantaneous intra-DC CSIT. Then, we propose an inter-DC coordination approach termed as enhanced MRT to mitigate the inter-DC interference. The coordination is designed on the basis of large-scale inter-DC CSIT, which thus is referred to as macro coordination. Simulation results demonstrate that the proposed DC-based macro coordination can achieve a satisfactory performance gain in terms of system sum rate, while requiring much less CSIT than traditional schemes.

In this paper, we target at cognitively detecting the presence of the primary user (PU) as well as recognizing PU's signal modulation. Since the existing modulation classification methods rely on fixed sensing period which may waste time when the modulations are easier to distinguish, we propose an automatic modulation classification (AMC) approach using likelihood-based (LB) and feature- based (FB) sequential detection methods, where SU calculates the likelihood ratio (LLR) sequentially to determine whether or not to stop listening. Referring to asymptotic analysis of the sequential methods, we formulate an optimization problem and derive the minimum sensing time under a constrained misclassification rate. Simulation results demonstrate that both LB and FB methods could significantly reduce the sensing time compared to fixed sensing period method.

Ambient backscatter is a new communication technology that utilizes ambient radio frequency signals to enable battery-free devices to communicate with each other. In this paper, we study the problem of signal detection and bit error rate (BER) performance for this new communication system where the differential encoding is adopted to eliminate the necessity of channel estimation. We formulate a new transmission model, design the data detection approach, and derive the optimal/approximate closed-form detection thresholds. In addition, the performance at high signal-to-noise region (SNR) is also analyzed, where the lower and the upper bounds of BERs are found. Simulation results are then provided to corroborate our theoretical studies.

In this paper, we design transmit beamforming for a full-duplex base station (FD-BS) considering joint self-interference mitigation and physical-layer security enhancement. The proposed designs are formulated to minimize the power consumption of FD-BS, under different signal- to-interference-and-noise-ratio (SINR) constraints. We strictly prove the optimality of SDR by showing the existence of rank-one solutions. Simulation results are provided to demonstrate the efficiency of the proposed algorithms.

In this paper two-path relay channels with direct link between the source and the destination are considered. We present a decode-and-forward cooperative transmission scheme based on nested lattice codes. The proposed scheme performs complete inter-relay interference (IRI) cancellation at the relays as well as successive decoding at the destination, which efficiently combines the signals from the source and the relays. We derive the achievable rates of the proposed scheme, and show that our scheme considerably outperforms the existing schemes in the literature.

Cloud radio access networks (C-RANs) have drawn considerable interests due to the significant improvements of spectral and energy efficiencies. Since most signal processing functions are moved to the centralized baseband unit (BBU), remote radio heads (RRHs) in C-RANs can be regarded as soft relays to transfer the received signals. The centralization characteristics in C-RANs make traditional channel estimation and training design approaches inefficient, and the requirements of perfect channel state information (CSI) would not be satisfied in turn. To solve this problem, a segment training based individual channel estimation scheme and the corresponding training design are proposed for C-RANs in this paper. Particularly, the channel estimator in terms of the sequential minimum mean-square-error (MMSE) is developed through a prior knowledge of long-term channel correlation statistics and previous channel estimates. The optimal training design for the developed estimator is derived by minimizing the estimation mean-square-error (MSE). Further, the optimal training design for the channel estimation of radio access links is computed by applying the eigenvalue decomposition (EVD). Numerical results show that performance gains of the proposed channel estimation and training design schemes are significant.

This paper studies transmit optimization for a multiple-input multiple-output (MIMO) femtocell that coexists with a macrocell by concurrently accessing the same frequency bands in an uncoordinated manner. Due to the lack of active cooperation between macrocell and femtocell, a stochastic strategy that exploits channel distribution is considered, in an attempt to optimize the femtocell performance as well as guarantee the macrocell service quality in the form of outage-probability. By investigating the upperbound/lowerbound of the macrocell outage-probability, a suboptimal femtocell transmission strategy is proposed, whose solution leads to the single eigenmode transmission and thus aggressively simplifies the original problem. Moreover, it is shown that when single antenna is equipped for the macro-link, the upper and lower bounds become tight to each other. Numerical results are provided to validate the efficiency of the proposed strategy.

In this paper, we consider the multiple antenna based spectrum sensing problem for a cognitive radio (CR) network. Different from conventional CR, we here consider a more practice-matching scenario when the primary user (PU) could work under more than one transmit power levels, depending on the communication environments. Consequently the spectrum sensing at the secondary user (SU), besides checking the on/off status of PU, could also identify PU's transmit power level if it is “detected”, making the overall sensing problem much more challenging. We design the optimal spectrum sensing algorithm when the channel information is known and partially known. New performance metrics under this new multiple primary transmit power (MPTP) scenario are defined to better evaluate the proposed algorithm, and quite a number of closed-form results are derived. Finally simulation results are presented to verify the proposed studies.

In this paper, we study the performance of ultradensely distributed antenna system in multi-floor buildings with high user density. To reduce the pilot overhead, we consider multi-floor pilot reuse. We derive the closed-form approximations of the sum-rate for the system using linear receivers, including the linear minimum-mean-squared-error receiver and the maximal ratio combining (MRC) receiver. We demonstrate the spectral efficiency per unit volume of the system and show that the ultradensely distributed antenna system is a promising way to achieve the spectral efficiency target of 5G.

The cognitive radio technology allows secondary user (SU) to share the licensed spectrum by adapting its transmission power in a sensing-based spectrum sharing manner. Reliable spectrum prediction and channel selection could alleviate the processing delays and enhance the spectrum utilization. In this paper, we propose a new strategy for spectrum prediction and channel selection using online machine learning techniques, which consists of three stages: 1) SU utilizes online learning techniques for the regression of received transmit power on different licenced frequency bands; 2) SU predicts the probability of each primary user's status (busy/idle) based on the power regression results; 3) SU optimizes channel selection in terms of expected ergodic capacities from the prediction outcomes. The proposed strategy can not only save time and energy, but also enhance the throughput of SU. The performance of the proposed strategy is evaluated through extensive simulations.

2-D principal component analysis based on l1 -norm (2DPCA-L1) is a recently developed approach for robust dimensionality reduction and feature extraction in image domain. Normally, a greedy strategy is applied due to the difficulty of directly solving the l1 -norm maximization problem, which is, however, easy to get stuck in local solution. In this paper, we propose a robust 2DPCA with non-greedy l1 -norm maximization in which all projection directions are optimized simultaneously. Experimental results on face and other datasets confirm the effectiveness of the proposed approach.

In this paper, we investigate the robust transceiver design for a downlink multiuser multiple-input multiple-output (MIMO) amplify-and-forward (AF) relay system with imperfect channel state information (CSI). We consider that the actual channel is within the neighborhood of a nominal channel and formulate two optimization problems following the philosophy of worst-case robustness, i.e., minimizing the sum mean-square-error (SMSE) and minimizing the total transmit power for any channel realization in the uncertainty region. In order to efficiently find the solutions, we transform the original problems into suitable forms using the sign-definiteness lemma and then propose an alternating iterative algorithm with guaranteed convergence. To further reduce the computational complexity, the cutting-set method is developed, which alternates between transceiver design and channel determination steps. Moreover, some interesting extensions of the proposed methods are discussed, including various types of uncertainty models and transmit power constraints, nonlinear transceiver structure, and uncertain noise covariance. Simulation results are presented to demonstrate the effectiveness of the proposed robust designs.

In this paper, the channel estimation in uplink multiple access relay networks (MARNs) with analog network coding protocol has been researched. We apply the superimposed training (ST) scheme where each relay puts a separate training sequence on the top of the received one before forwarding to destination, and design a maximum likelihood based channel estimation algorithm for the composite source-relay-destination and individual relay-destination links. The optimal training sequences as well as the superimposed training power are also derived in closed forms. To make our study more complete, the channel estimation in the time-selective fading environment is further considered, and a correlation-based channel estimation (CBCE) algorithm is developed by taking advantage of time-domain channel autocorrelation nature. Simulation results show that the presented ST scheme can effectively improve the performance of multi-user detection in MARNs, and the proposed CBCE algorithm significantly outperforms the existing channel estimation methods.

In this paper, we examine the channel estimation in an amplify-and-forward (AF) one-way relay network (OWRN) under time selective flat fading scenario, where the distributed space-time coding (DSTC) is adopted at relay nodes. Different from most existing works, our target is to estimate and track the individual channels of each relay hop instead of the composite channels. To reduce the number of the channel parameters to be estimated, we apply the polynomial basis-expansion-model (P-BEM) and convert the problem to estimating the channel coefficient-vectors (called in-BEM-CVs) of each relay hop. With the aid of the autoregressive (AR) model, we formulate the dynamic state space for the in-BEM-CV estimation. Specifically, we adopt the unscented Kalman filter (UKF) to track the in-BEM-CV dynamic variations in an forward manner, and utilize the unscented Rauch-Tung-Striebel smoother (URTSS) to smooth the UKF's estimations in an backward manner. To make the study complete, we also derive Bayesian Cramér lower bounds (BCRBs) for the in-BEM-CV estimation. Finally, numerical results are provided to corroborate the proposed studies.

In this paper, we study a more practical cognitive radio (CR) scenario where the primary user (PU) operates under more than one transmit power levels. Different from the existing research where PU is assumed to have only one constant transmit power, the new consideration well matches the existing and ongoing standards, i.e., IEEE 802.11 Series, GSM, LTE, LTE-A, etc. The primary target in this new consideration is still to detect the presence of PU, while a secondary target to identify the PU's transmit power level could also be achieved. By doing this, the secondary user (SU) could realize more "cognition" compared to the conventional strategy where only the `on-off" status of PU is detected. To make the study complete, we derive closedform results for multiple thresholds as well as the performance analysis. Numerical examples are provided to corroborate the proposed studies.

In this paper, we examine the pilot-based channel estimation in an amplify-and-forward (AF) one-way relay network (OWRN) under time selective flat fading scenario. Different from most existing works, our target is to estimate and track the individual channel of each relay hop instead of the composite channel. To reduce the number of the channel parameters to be estimated, we apply the polynomial basis-expansion-model (P-BEM) and convert the problem to estimating the channel coefficient-vector (called in-BEM-CV) of each relay hop. With the aid of the autoregressive (AR) model, we formulate the dynamic state space for the pilot-based in-BEM-CV estimation. Specifically, we adopt the unscented Kaiman filter (UKF) to track the in-BEM-CV dynamic variations in an online manner, and utilize the unscented Rauch-Tung-Striebel smoother (URTSS) to smooth the UKF's estimations in an offline manner. Finally, numerical results are provided to corroborate the proposed studies.

Model based sparse signal recovery requires fewer measurements and has attracted lots of attention recently. One prototypical sparsity model is block sparsity whose stability is guaranteed from block restricted isometry property (RIP). However, the existing block RIP methods in the l2norm space only consider Gaussian measurement case. In this paper, we extend the block RIP to the Fourier measurement case and demonstrate that the minimum number of measurements satisfying block RIP is as low as O (sd log q log(sd log q)log2s), where d is the block size, s represents the block sparsity, and N is the length of unknowns satisfying N = qd for some integer q.

With the emerging demands for extensive mobile applications, Device-to-Device (D2D) communication is viewed as an important technology for the next-generation cellular communication and mobile networks to increase spectral efficiency and enhance network capacity. Existing works usually study D2D communications under a network consisting of four nodes. In this paper, we jointly investigate the problems of optimal system resource allocation and mode selection for large scale networks. Specifically, by formulating a max-flow optimization problem that maximizes the system throughput through any possible of transmission modes with the decisions of resource allocations, we obtain the optimal system performance. Under realistic scenario driven simulations, we demonstrate the efficiency of our proposed scheme for combined mode selection and resource allocation.

This paper considers resource allocation and transmission optimization for femtocell networks, where the orthogonal frequency division multiple access (OFDMA) strategy is used to cope with the multi-user communication. In an attempt to mitigate cross-tier interference and optimize multi-user network performance, a two-layered beamforming scheme is proposed, facilitated by the multiple-input-multiple-output (MIMO) transmission. Two different operation models are discussed, namely open and closed access. It is shown that the beamforming design problem for closed access turns out to be convex and can be efficiently solved, whereas a non-convex optimization has to be treated when adopting the open access. For the latter non-convex problem, we propose an iterative algorithm, which has ensured convergence and closed-form solutions for subproblems of each alternating step. Numerical results validate the efficiency of the proposed approach.

Compressive sensing (CS) has recently attracted lots of attention and has been extended to more structured architectures, for example the linear time-invariant system identification. However, prevalent CS methods used for channel estimation, such as Basis Pursuit Denoising (BPDN) and Dantzig selector (DS), require computational complexity as high as O(N3), where N is the length of the channel. When N is very large, the complexity will aggravate the hardware burden. In this paper, we propose a new channel estimation scheme that uses the expander graph based compressive sensing. The computation complexity is demonstrated to be as low as O((P - N)N), where P is the length of the training vector.

In this paper, we investigate the individual channel estimation for three-node one-way relay network (OWRN), where both source and destination are equipped with multiple antennas. Without resorting to the composite channel estimation, as did in the traditional work, we directly estimate the individual channels from an iterative linear minimum mean-square-error (LMMSE) estimator. The closed-form least square (LS) channel estimator is also derived through matrix unitary diagonalization to provide a good initialization for the iterative LMMSE estimator. To make the work more complete, we present two performance lower bounds: Bayesian Cramér lower bound (BCRB) and linear estimation lower bound (LELB), for the proposed algorithm. Numerical results are provided to corroborate our proposed studies.

In this paper, we investigate the pricing and resource allocation strategies in the two-tier sensing-based cognitive fem-tocell networks, where the macrocell and femtocells are operating over the same frequency band. The macrocell base station protects itself by setting the maximum aggregate interference constraint and makes profit by pricing the interference from femtocell users. Different from the conventional underlay-based networks where there is one price only, in the proposed sensing-based networks two prices are adopted corresponding to the idle and busy states of the macrocell. We consider both cases of perfect and imperfect sensing at the femtocells and solve the pricing and power allocation for both macrocell and femtocells using the energy efficiency as the utility function. Simulation results show that the proposed scheme can improve the energy efficiency significantly in spectrum sharing femtocell networks.

In this paper, we consider the spectrum sensing for cognitive radio (CR) when the primary user (PU) has a set of discrete transmit powers. Different from the traditional sensing approach where the binary hypothesis testing was used, we here apply the the multiple hypotheses testing in order to not only check the ``on/off'' status of PU but also to identify its power level. To better illustrate our idea, we set the price of erroneous sensing in Bayes risk to be unit, such that maximum a posterior (MAP) detection is optimal in terms of minimizing the total error rate. The closed expression of the decision regions as well as the performance metrics are derived. Simulation results are provided to corroborate the proposed studies.

In this paper, we consider a multiuser cognitive radio (CR) transmission where the users transmit and receive information with the help of a two-way half duplex cognitive relay node. The problem of power allocation, user assignment, and the transmission mode selection is investigated in underlay transmission scheme such that the secondary and the primary networks share the spectrum simultaneously. The joint solution is proposed to enhance the secondary system's throughput where the relay power allocation, user to base station assignment, and the transmission mode selection are optimized under a unified framework subject to peak power and inference power constraints. Finally, simulations results are provided to evaluate the proposed schemes.

In this study, the authors consider the robust transceiver design in a dual‐hop multi‐user multiple‐input multiple‐output amplify‐and‐forward relay system. By incorporating the antenna correlation at both ends of the channel and taking the channel estimation errors into account, the authors propose two robust design approaches under the minimum sum mean‐square‐error criterion. Specifically, they decouple the original problem into three convex sub‐problems in the first approach and develop an iterative algorithm with guaranteed convergence. The second approach converts the original problem into simple power allocation problems utilising a certain relaying structure, and the approximate closed‐form solutions are derived. Some practical implementation issues are also discussed. Simulation results are provided to demonstrate the effectiveness of the proposed approaches.

In order to achieve better statistical Quality-of-Service (QoS) provisioning for cognitive radio networks (CRN), in this paper, we develop a hybrid underlay/overlay transmission mode for CRNs. Specifically, by applying the theory of effective capacity and taking PN's activity statistics into consideration, we first analyze the maximum achievable throughput of the CRN under two dominant transmission modes, namely underlay and overlay, respectively, and provide efficient algorithms to derive optimal transmission strategies for the two modes. Following the analyses, we then propose a hybrid underlay/overlay transmission mode, through which the cognitive users' QoS requirements can be better guaranteed and network throughput can be further improved. Moreover, we analyze the optimal transmission strategies for both underlay and overlay modes under two limiting cases. Analyses indicate that 1) for the loose QoS requirement, optimal transmission strategies for both underlay and overlay modes become the water-filling algorithm; and 2) for the stringent QoS requirement, the cognitive user will transmit with constant rate. Furthermore, the impact of imperfect channel estimations on our proposed transmission mode is discussed. Simulation results are provided to demonstrate the impacts of delay QoS requirements and PN's activity statistics on maximizing the delay-constrained throughput for both underlay and overlay modes and verify the effectiveness of our proposed transmission mode. Moreover, for the overlay mode, we observe that 1) a unique optimal sensing time exists under the given QoS constraint; and 2) the optimal sensing time surprisingly increases as the QoS constraint gets more stringent.

In this paper, the outage performance of an opportunistic amplify-and-forward cooperative downlink cellular system is analyzed. Different from prior works, the randomness of the network topology is taken into account by modeling the user nodes as a homogeneous Poisson point process. Based on this model, outage probability is derived and the impacts of several system parameters are investigated. Under certain conditions, the closed form expression of outage probability is derived. It is found from our results that the diversity order of this opportunistic cooperative system at high signal-to-noise-ratio (SNR) is one. Moreover, optimal power allocation can be found from our results to minimize the outage probability.

This paper considers a two-phase two-way relay network (TWRN) with two sources and multiple amplify-and-forward (AF) relays, where the direct link between the sources exists and one best relay is chosen for data communication to maximize the minimum signal-to-noise ratio (SNR) of bidirectional communication. To efficiently exploit the direct link within two phases, a switch-and-stay combining (SSC) protocol is employed. In SSC, one branch out of the relay and direct branches is activated for data communication, and the branch switching occurs when the end-to-end SNRs fall below the given thresholds. We analyze the system performances over the independent but not necessarily identically distributed (i.n.i.d.) Rayleigh fading channels, by deriving lower bounds and asymptotic expressions with high SNR for the outage probability and bit error rate (BER). It is shown that SSC can preserve the same spectral efficiency as analog network coding (ANC), while can concurrently achieve the full diversity order as the optimal selection (OS) with less implementation complexity. Numerical and simulation results verify the proposed studies.

In this paper, we adopt the time-multiplexed-superimposed training and investigate channel estimation for amplify-and-forward (AF) two-way relay network (TWRN) under doubly-selective channel scenario. With the aid of the complex-exponential basis-expansion-model (CE-BEM), we first develop the estimation model for BEM coefficient-vectors (BEM-CVs) of the individual channels between both sources and the relay. A two-step coarse estimator is proposed to obtain the BEM-CVs of the individual channels. Finally, numerical results are provided to corroborate the above studies.

In this paper, we propose an Alamouti coded orthogonal frequency-division multiplexing (OFDM) scheme for amplify-and-forward (AF) relay networks that contain multiple distributed relay nodes. The frequency asynchronous nature of the distributed system is considered in our design. By implementing simple operations, e.g., time reversal, conjugation and amplification, the proposed scheme eliminates the needs of knowledge of both channel states and the frequency offsets at the relay nodes. We prove that when the frequency offsets among the relay nodes are smaller than a certain threshold, full spatial diversity can be achieved at the destination node even with the linear receivers, e.g., zero-forcing (ZF) receiver. Numerical results are provided to corroborate the proposed studies.

In this paper, we design downlink (DL) beamforming vectors for a multiuser multicell cognitive radio (CR) network with imperfect channel state information (CSI) at base stations (BS). Specifically, we consider deterministic error in both channel gain and channel covariance. Our objective is to minimize the total DL transmit power subject to quality of service (QoS) constraints of each secondary user (SU) and primary user (PU). The optimization problems for both uncertainty models can be transformed into convex semidefinite programming (SDP) from the standard rank relaxation approach. Interestingly, numerical results show that the obtained solutions fulfill the rank constraint and are therefore exact.

As a promising solution to the spectrum scarcity problem, cognitive radio (CR) has received much attention recently, The key component of CR is the spectrum sensing technique that can detect the idling spectrum of the authorized user. Currently, the accuracy of spectrum sensing remains a significant constraint that limits the practical application of CR. In this paper, we propose an enhanced spectrum sensing scheme based on decoding feedback strategy, where the secondary receiver decodes the desired signal and utilizes the remaining part for spectrum sensing. Specifically, we embed this concept into the underlay scheme with no silent slot in order to improve the throughput of the secondary user. The corresponding sensing performance is analyzed, based on which we formulate the optimization problems and derive the optimal transmission parameters. Simulation results show that, the proposed scheme can significantly improve the achievable throughput of the secondary system comparing to the traditional underlay scheme.

In this paper, we design a null-space based robust interference avoiding strategy for the Device-to-Device (D2D) communication underlaying network. Thanks to the coordination between D2D user and the regular user, the interfering channel state information (CSI) among the base station (BS), cellular user equipment (CUE) and the D2D user equipments (DUEs) can be estimated from the training approach. Then, the null-space based transmit and receive beamformings are designed at appropriate terminals to mitigate the interference caused in the future data transmission. To make the design practical, we also characterize the null-space uncertainty that is resulted from the imperfect channel estimation. Moreover, we derive the optimal transmission strategy that can achieve the best training-throughput tradeoff. Simulation results are provided to corroborate the proposed studies.

This paper studies robust beamforming design in a relay-assisted multiuser multi-antenna cognitive radio (CR) network that coexists with a primary radio (PR) system via opportunistic spectrum sharing (OSS). The robustness is with respect to imperfect channel knowledge at transmitter. Since the CR-to-PR interference needs to be carefully mitigated in OSS, the recently developed cognitive beamforming (CB) technique is employed by CR base station (BS), CR relay station (RS), and secondary receivers (SRs). Meanwhile, joint CR-BS and CR-RS transmit precoding is adopted to solve the weighted sum rate maximization (WSRM) problem. To cope with the non-convex problem, we apply the classic altering optimization process, namely the Blahut-Arimoto algorithm. Interestingly, it is shown that within each iterative step, we solely need to treat some semidefinite programming (SDP) subproblems with linear matrix inequalities (LMIs), which can be efficiently solved by standard convex optimization methods. Numerical results demonstrate significant performance gain of our approach over non-robust beamforming strategy.

In this letter, we propose a power allocation scheme for statistical quality-of-service (QoS) provisioning in multi-relay decode-and-forward (DF) cognitive networks (CN). By considering the direct link between the source and destination, the CN first chooses the transmission mode (direct transmission or relay transmission) based on the channel state information. Then, according to the determined transmission mode, efficient power allocation will be performed under the given QoS requirement, the average transmit and interference power constraints as well as the peak interference constraint. Our proposed power allocation scheme indicates that, in order to achieve the maximum throughput, at most two relays can be involved for the transmission. Simulation results show that our proposed scheme outperforms the max-min criterion and equal power allocation policy.

In this paper, we design a segment training based individual channel estimation (STICE) scheme in the classical three-node it amplify-and-forward (AF) one-way relay network (OWRN). The linear minimum mean-square-error (LMMSE) channel estimator is used to obtain a good initialization, and an iterative maximum a posteriori (MAP) channel estimator is developed to improve the estimation accuracy. We then investigate the underlying power allocation at the relay node both to minimize the mean-square-error (MSE) of the individual channel estimation and to maximize the average effective signal-to-noise ratio (AESNR) of the data detection. The closed-form Bayesian Cramér-Rao Bound (CRB) is also derived to evaluate the proposed algorithm. Finally, numerical results are provided to corroborate the proposed studies.

In this paper, we design a new distributed angle estimation method for localization in wireless sensor networks (WSNs) under multipath propagation environment. We employ a two-antenna anchor that can emit two linear chirp waves simultaneously, and propose to estimate the angle of departure (AOD) of the emitted waves at each receiving node via frequency measurement of the local received signal strength indication (RSSI) signal. An improved estimation method is further proposed where multiple parallel arrays are adopted to provide the space diversity. The proposed methods rely only on radio transceivers and do not require frequency synchronization or precise time synchronization between the transceivers. More importantly, the angle is estimated at each sensor in a completely distributed manner. The performance analysis is derived and simulations are presented to corroborate the proposed studies.

In this paper, we address a resource allocation problem, where a pair of device-to-device terminals are integrated into a time division duplex (TDD) cellular network. By introducing an incremental relay transmission scheme for the D2D communication, the D2D transmitter, traditionally believed to be the source of interference, are coordinated with other cellular user equipments (CUEs) in the uplink session. In consequence, both the D2D receiver and the central base station (CBS) are able to decode the message sent from the D2D transmitter. The CBS, in the following downlink session, may forward this message to the D2D receiver if the direct D2D link is in outage. We formulate and solve the cell throughput maximization problem for three transmission modes: cellular, underlay transmission, and incremental relay mode. Simulation results show that the proposed incremental relay is not only with higher spectral efficiency, but also provides more reliable D2D transmission than the cellular relay and the underlay scheme.

In this paper, we design a segment training based individual channel estimation scheme in the classical three-node amplify-and-forward one-way relay network (OWRN). We investigate the underlying power allocation at the relay to minimize the mean-square-error (MSE) of the individual channel estimation and to maximize the average effective signal-to-noise ratio (AESNR) of the data detection. The optimal/sub-optimal power allocation schemes are also derived for the two objectives. Extensive numerical results are provided to corroborate the proposed studies.

In this paper, we design downlink (DL) beam-forming vectors for a multiuser multicell cognitive radio (CR) network with imperfect channel state information (CSI) at base stations (BS). Specifically, we model channel estimation error as a random vector with known statistical distribution. Our objective is to minimize the total DL transmit power subject to probabilistic quality of service (QoS) constraints of every secondary user (SU) and primary user (PU). Utilizing Bernstein-type inequalities [12], we replace the probabilistic constraints with conservative deterministic constraints. By applying rank relaxation, the original problem is reformulated as semidefinite programming (SDP). Interestingly, numerical results show that the obtained solutions fulfill the rank constraint.

In this paper, we study the joint resource allocation problem in multi-carrier multi-relay aided dual hop single-user communication. We adopt orthogonal frequency division multiplexing (OFDM) as the transmission modulation and consider amplify-and-forward (AF) relaying scheme. The optimization is performed over power allocation at source node, beamforming at relay nodes and subcarrier pairing at two hops, such that the overall system throughput is maximized under limited power budget at the source and relay nodes. The optimization is a mixed integer programming problem which is solved through dual decomposition approach. To further reduce the complexity, we propose a suboptimal algorithm which sacrifices very little on the performance. Finally, simulation results are provided to corroborate the proposed studies.

In this paper, we develop a new carrier frequency offset (CFO) estimation scheme for interleaved orthogonal frequency division multiple access (OFDMA) transmission. We employ multi-antenna at the receiver and exploit the rank reduction approach to blindly estimate the CFOs of multiple users. The proposed scheme supports full load transmission that allows all sub-carriers being allocated to users, which is a significant advantage over the existing schemes. Both performance analysis and numerical results are provided to corroborate the proposed studies.

In this paper, spectrum-sensing based resource allocation for a cognitive radio network that contains multiple secondary users (SUs) and a primary user (PU) is investigated. Each SU firstly performs the individual spectrum sensing and forwards the result to a fusion center to make the global decision. If PU is determined absent, then SUs access the primary band with their regular transmit power. Otherwise SUs still access the licensed band but with a limited transmit power to avoid harmful interference to PU. We target at designing the optimal sensing time, bandwidth allocation, power allocation, and the detection threshold to maximize the total achievable rate of SUs subject to the constraints of their peak transmit powers as well as the interference constraint to PU. The original optimization is divided into several subproblems that can be solved separately with low computational complexity. Numerical results demonstrate the superiority of the proposed one over the existing candidates.

This paper investigates the resource allocation problem in a relay-aided cognitive radio (CR) system under the orthogonal frequency division multiplexing (OFDM) transmission. Different from the conventional CR resource allocation problem, the relay node here is capable of performing subcarrier permutation over two hops such that the signal received over a particular subcarrier is forwarded on a different subcarrier. The objective is to maximize the throughput of the CR network subject to a limited power budget at the secondary source and relay node, as well as the interference constraints at the primary receiver. The optimization is performed under a unified framework where the power allocation at the source node, power allocation at the relay node, and subcarrier pairing at the two hops are optimized jointly. Finally, numerical examples are provided to corroborate the proposed studies.

In this paper, the estimation of doubly selective channel is considered for amplify-and-forward (AF) relay networks. The complex exponential basis expansion model (CE-BEM) is chosen to describe the time-varying channel, from which the infinite channel parameters are mapped onto finite ones. Since direct estimation of these coefficients encounters high computational complexity and large spectral cost, we develop an efficient estimator targeting at some specially defined channel parameters. The training sequence design that can minimize the channel estimation mean-square error is also proposed.

In this paper, we consider the robust transceiver design with Tomlinson-Harashima precoding (THP) for multi-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relaying systems. THP is adopted at the source to mitigate the spatial inter-symbol interference and then a joint Bayesian robust design of THP at source, linear forwarding matrices at relays and linear equalizer at destination is proposed. Based on the elegant characteristics of multiplicative convexity and matrix-monotone functions, the optimal structure of the nonlinear transceiver is first derived. Based on the derived structure, the optimization problem is greatly simplified and can be efficiently solved. Finally, the performance advantage of the proposed robust design is assessed by simulation results.

In this paper, robust transceiver design with Tomlinson-Harashima precoding (THP) for multi-hop amplify-and-forward (AF) multiple-input multiple-output (MIMO) relaying systems is investigated. At source node, THP is adopted to mitigate the spatial intersymbol interference. However, due to its nonlinear nature, THP is very sensitive to channel estimation errors. In order to reduce the effects of channel estimation errors, a joint Bayesian robust design of THP at source, linear forwarding matrices at relays and linear equalizer at destination is proposed. With novel applications of elegant characteristics of multiplicative convexity and matrix-monotone functions, the optimal structure of the nonlinear transceiver is first derived. Based on the derived structure, the transceiver design problem reduces to a much simpler one with only scalar variables which can be efficiently solved. Finally, the performance advantage of the proposed robust design over non-robust design is demonstrated by simulation results.

In this paper, we study resource scheduling in a relay-assisted cognitive radio network with the orthogonal frequency division multiple access (OFDMA) scheme adopted to cope with the multi-user configuration. We discuss the optimization over the relay assignment, subcarrier allocation, per-node power control, and heterogenous quality-of-service (QoS) provisioning. The discrete characters of relay assignment and subcarrier allocation lead to a mixed integer nonlinear program (MINLP) whose computational complexity grows exponentially with the number of subcarriers. In an attempt to treat this complication, an asymptotically optimal solution based on the dual-analysis framework is proposed, for which we investigate the optimality of the dual method in the two scenarios: with and without subcarrier pairing. For both cases, it is shown that zero-duality-gap is achievable and the joint scheduling problem can be solved through a series of subproblems whose closed-form solutions are found. Moreover, for the scenario without subcarrier pairing we prove that the relaxation of allocation variables does not affect global optimality, i.e., even if the allocation variables are relaxed into continuous ones, binary variables are always retrievable as optimal solutions. Numerical examples are provided to corroborate the efficiency of the proposed approach.

There is a strong need to explore green and harvestable energy in computer communications. However, adapting wireless network performance to harvested energy has largely been ignored in literature. In this paper, we propose a new resource allocation scheme to improve data delivery quality in energy harvesting enabled wireless networks. In the proposed approach, packet Automatic Repeat reQuest (ARQ) limit of each wireless node is adaptively adjusted according to harvested energy. To achieve such optimal retry adaptation, energy neutrality constraint is considered in the overall optimization process. Simulation results show that the proposed retry adaptation approach significantly improves packet delivery ratio by exploring the harvested energy.

In this paper, we design downlink (DL) beamforming vectors for a multiuser multicell network when only imperfect knowledge of the channel covariance is available at base stations. Specifically, we consider two different models for covariance errors: (a)deterministic error bounded in a spherical region and (b) stochastic error with known probability distribution. Our objective is to minimize the total DL transmit power subject to quality of service (QoS) constraint of every user. It is shown that for both uncertainty models, the optimization can be formulated as a convex semidefinite programming (SDP) problem using the standard rank relaxation approach. Interestingly, numerical results show that the obtained solutions fulfill the rank constraint and are therefore exact.

In this paper, we study the problem of resource allocation in orthogonal frequency division multiple access (OFDMA) based multi-user dual-hop uplink transmission where a single relay station is deployed between the mobile users and the base station which operates under amplify-and-forward (AF) relaying mode. The optimization targets to maximize the overall system throughput through joint optimization of sub-carrier allocation, sub-carrier pairing, and the power allocation, subject to individual power constraint at each node. The optimization results in mixed integer programming problem and a near optimal solution is obtained through dual decomposition approach. Further, we develop a suboptimal scheme to trade the performance for lower complexity. Finally, numerical examples are provided to demonstrate the performance gain of the proposed schemes.

In this paper, we develop a distributed space-time coding scheme to cope with both time and frequency asynchronism in two-way relay network (TWRN). The convolutional coding is employed to handle multiple timing errors in the networks. We prove that both the cooperative and the multipath diversities can be achieved by linear receivers, e.g., linear zero-forcing (ZF) or minimum mean square error (MMSE) receivers. Moreover, the diversity can be guaranteed almost surely under frequency asynchronism. Numerical results are then provided to corroborate the proposed studies.

In this paper, we consider channel estimation for amplify-and-forward (AF) relay network with orthogonal frequency division multiplexing (OFDM) modulation. We propose a superimposed training strategy at relay that allows the destination node to obtain the separate channel information of the source-->;relay link and the relay-->;destination link. The proposed training strategy only requires two transmission phases and is thus compatible with the two-phase data transmission scheme, i.e., the training can be embedded into data transmission. Since the optimal minimum mean square error (MMSE) estimator and the maximum a posteriori (MAP) estimator cannot be expressed in close-forms, we propose to obtain the initial channel estimates from the low complexity suboptimal linear estimators, e.g., linear minimum mean-square error (LMMSE) or least square (LS), and then resort to iterative approaches to improve the estimation accuracy.

In this paper, we consider the problem of channel estimation for two-way relay networks (TWRN) under time-selective environment. We first parameterize the time-varying channels by the basis expansion model (BEM) and then propose a novel pilot symbol aided modulation (PSAM) for TWRN. A linear approach to estimate the cascaded channels is designed and the optimal training sequences are derived based on minimizing the mean-square error (MSE) criterion. Moreover, we develop an algorithm to recover the individual channel knowledge with which both the channel estimation accuracy and the system performance can be improved. Various simulations are provided to corroborate the proposed studies.

In this paper, we apply the data-dependent superimposed training (DDST) in amplify-and-forward (AF) relay networks with cyclic-prefix single carrier (CPSC) modulation. We consider various issues such as channel estimation, training design and data detection. A sub-optimal training sequence that can minimize the upper bound of the mean square error of the estimator is derived. Since the DDST estimator can only find the overall channel information, we further propose a doubly cooperative estimator (DCE) to track the individual channel knowledge at the cost of some performance loss. Simulations are then provided to corroborate the proposed studies.

Knowing accurate noise variance and signal power is crucial to most spectrum-sensing algorithms such as energy detection, matched filter detection, and cyclostationary detection. In this paper, we consider a practical scenario when these two parameters are unknown and are needed to be estimated before the spectrum sensing. This task is non-trivial without knowing the status of the primary user, and we categorize the related spectrum sensing as a blind one. We develop the estimation algorithms for unknown parameters by exploiting the signal constellation of the primary user. Three different parameter estimators that do not require any training are then proposed based on the moments of the received signals. Since the secondary user may not know the primary user's signal constellation, we develop a robust approach that approximates a finite quadrature amplitude modulation (QAM) constellation by a continuous uniform distribution. We also derive the modified Cramer-Rao bound (CRB) for noise variance estimation. Then the optimal moment pair is found from minimizing the mean squared error (MSE) of the signal-to-noise ratio (SNR). The method of choosing the spectrum sensing threshold by taking into consideration the estimation error is also discussed.

In this paper, we consider the channel estimation for the classical three-node relay networks that employ the amplify-and-forward (AF) transmission scheme and the orthogonal frequency division multiplexing (OFDM) modulation. We propose a superimposed training strategy that allows the destination node to separately obtain the channel information of the source→relay link and the relay→destination link. Specifically, the relay superimposes its own training signal over the received one before forwarding it to the destination. The proposed training strategy can be implemented within two transmission phases and is thus compatible with the two-phase data transmission scheme, i.e., the training can be embedded into data transmission. We also derive the Cramér-Rao bound for the random channel parameters, from which we compute the optimal training sequence as well as the optimal power allocation. Since the optimal minimum mean square error (MMSE) estimator and the maximum a posteriori (MAP) estimator cannot be expressed in closed-form, we propose to first obtain the initial channel estimates from the low complexity linear estimators, e.g., linear minimum mean-square error (LMMSE) and least square (LS) estimators, and then resort to the iterative method to improve the estimation accuracy. Simulation results are provided to corroborate the proposed studies.

In this letter, we study the problem of resource allocation in amplify-and-forward (AF) based multiuser two-way relay network that is operated under orthogonal frequency division multiple access (OFDMA) modulation. We formulate an end-to-end throughput maximization problem subject to limited power constraint at individual user and relay. The optimization targets to find the best sub-carrier allocation to each user, sub-carrier pairing at the relay, as well as the power allocation at all nodes, which turns out to be a mixed integer programming problem. We then derive an asymptotically optimal solution through Lagrange dual decomposition approach and further design a suboptimal algorithm to trade the performance for computational complexity. Finally, simulation results are provided to demonstrate the performance gain of the proposed algorithms.

This paper proposes a fast recursive algorithm for Group-wise Space-Time Block Code (G-STBC), which takes full advantage of the Alamouti structure in the equivalent channel matrix to reduce the computational complexity. With respect to the existing efficient algorithms for G-STBC, the proposed algorithm achieves better performance and usually requires less computational complexity.

In this paper, channel estimation and training sequence design are considered for amplify-and-forward (AF)-based two-way relay networks (TWRNs) in a time-selective fading environment. A new complex-exponential basis expansion model (CE-BEM) is proposed to represent the mobile-to-mobile time-varying channels. To estimate such channels, a novel pilot symbol-aided transmission scheme is developed such that a low complex linear approach can estimate the BEM coefficients of the convoluted channels. More essentially, two algorithms are designed to extract the BEM coefficients of the individual channels. The optimal training parameters, including the number of the pilot symbols, the placement of the pilot symbols, and the power allocation to the pilot symbols, are derived by minimizing the channel mean-square error (MSE). The selections of the system parameters are thoroughly discussed in order to guide practical system design. Finally, extensive numerical results are provided to corroborate the proposed studies.

Joint estimation of the carrier frequency offset (CFO) and the channel is developed for a two-way relay network (TWRN) that comprises two source terminals and an amplify-and-forward (AF) relay. The terminals use orthogonal frequency division multiplexing (OFDM). New zero-padding (ZP) and cyclic-prefix (CP) transmission protocols, which maintain the carrier orthogonality and ensure low estimation and detection complexity, are proposed. Both protocols lead to the same estimation problem which can be solved by the nulling-based least square (LS) algorithm and perform identically when the block length is large. We present detailed performance analysis by proving the unbiasedness of the LS estimators at high signal-to-noise ratio (SNR) and by deriving the closed-form expression of the mean-square-error (MSE). Simulation results are provided to corroborate our findings.

A fast algorithm for inverse Cholesky factorization is proposed, to compute a triangular square-root of the estimation error covariance matrix for Vertical Bell Laboratories Layered Space-Time architecture (V-BLAST). It is then applied to propose an improved square-root algorithm for V-BLAST, which speedups several steps in the previous one, and can offer further computational savings in MIMO Orthogonal Frequency Division Multiplexing (OFDM) systems. Compared to the conventional inverse Cholesky factorization, the proposed one avoids the back substitution (of the Cholesky factor), and then requires only half divisions. The proposed V-BLAST algorithm is faster than the existing efficient V-BLAST algorithms. The expected speedups of the proposed square-root V-BLAST algorithm over the previous one and the fastest known recursive V-BLAST algorithm are 3.9 ~ 5.2 and 1.05 ~ 1.4, respectively.

In this paper, we design the optimal precoding matrices for amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay networks. Specifically, we consider a dual-hop relay network and minimize the total power consumed by source and relay under predetermined quality of service (QoS) constraints, i.e., mean square error (MSE) constraints. By using majorization theory, we simplify the matrix-valued problem into a scalar-valued one. Since the problem is non-convex, we then propose two convex suboptimal problems that provide the upper and lower bound of the original objectives. Numerical results demonstrate that the lower bound and the upper bound are tight in high signal-to-noise ratio (SNR).

In this paper, we consider the problem of precoding design for amplify-and-forward (AF) relay network with imperfect channel state information (CSI). We find a general rank precoding matrix at the relay such that the relay transmit power is minimized subject to quality of service (QoS) constraint as the worst case signal-to-noise ratio (SNR) at the destination. Since the direct optimization is nonconvex, we apply conservative methods to reformulate it as a semi-definite programming (SDP) problem which provides the upperbound of the original objective. Specifically, we suggest two SDP formulations that can be solved efficiently via convex optimization tools. We numerically compare the proposed suboptimal methods with the existing method, i.e., collaborative robust relay beamforming (CRBF), and show that the proposed schemes achieve a significant performance gain for a majority of feasible uncertainty sizes.

In this paper, channel estimation for two-way relay networks (TWRNs) over time-varying channels is investigated. We consider the amplify-and-forward (AF) relaying scheme and adopt the complex-exponential basis expansion model (CE-BEM) that represents the time-varying channel by a finite number of parameters. We develop the estimation methods for both the cascaded channels and the individual channels and also apply the total least square (TLS) algorithm to improve the estimation accuracy. Moreover, the training design is discussed and a heuristic criterion is proposed to minimize the condition number of the estimation matrix. The simulation results verify the goodness of the criterion.

This paper proposes a superimposed training strategy to estimate the individual frequency and channel parameters in an amplify-and-forward (AF) two-way relay network (TWRN). Two efficient suboptimal estimation algorithms and an iterative process to further improve the performance are proposed. The estimation Cramér-Rao Bound (CRB) on the proposed estimation strategy is also derived. The simulations confirm that the iterative estimation process converges rapidly and that the resultant estimation mean square error (MSE) approaches the CRB, especially for the case when the carrier frequency offset between the two source terminals is small.

In this paper, we propose a distributed adaptive interference coordination algorithm for a practical orthogonal frequency division multiple access (OFDMA)-based mobile cellular systems. The designed algorithm can achieve an efficient frequency reuse for any user distribution and traffic load. Since no a priori frequency planning is required, the minimal coordination between base stations is also achieved. Moreover, the proposed algorithm can adapt to different network interference conditions and is power-saving in some degree. We also develop a way to decompose a multi-cell optimization problem into distributed single-cell optimization problems, which greatly reduces the computational complexity.

In this paper, we study the problem of joint carrier frequency offset (CFO) and channel estimation for amplify-andforward (AF) two-way relay network (TWRN) that comprises two source terminals and one relay node. Both the system design and the estimation problem become more challenging when CFO is non-zero in a frequency-selective environment, as compared to the conventional point-to-point communication systems. By introducing some redundancy, we propose a cyclic prefix (CP) based OFDM modulation for TWRN that is capable of maintaining the advantage of using multi-carrier transmission and at the same time facilitates the system initialization, e.g., synchronization and channel estimation. We then apply a least square (LS) approach to solve the estimation problem. The approximated Cramér-Rao Bound (CRB) has been derived as the performance benchmark of the proposed estimator. Finally, simulations are provided to corroborate the theoretical studies. ©2010 IEEE.

Existing works on joint carrier frequency offset (CFO) and channel estimation in two-way relay networks (TWRN) only deal with the composite channel parameters and the mixed CFO values. In this paper, we design a superimposed pilot based training strategy such that the individual frequency and channel parameters can be obtained at the source terminals. We consider the amplify-and-forward (AF) relaying scheme and discuss the zero-padding (ZP) based orthogonal frequency division multiplexing (OFDM) modulation in order to cope with the frequency selective fading channels. We build up the system model and propose the joint estimation method. An iterative process is also proposed to further improve the estimation accuracy. To make the study complete, we also derive the Cram\'er-Rao Bound (CRB) and compare with the mean square error of our algorithms. Finally, simulation results are provided to corroborate our studies.

In this paper, we consider an interesting and practical scenario for spectrum sensing in cognitive radio network, where both the signal power of the primary user and the noise variance are treated as unknowns before the detection. Knowing accurate noise variance and signal power is crucial in most sensing algorithms, e.g., energy detection. By exploiting the received signal structure, we propose blind spectrum sensing methods in the sense that both the signal power of the primary user and the noise variance are estimated, which is a non-trivial task before knowing the status of the primary user. Three different algorithms, direct estimator, approximate maximum likelihood (ML) estimator and pseudo linear minimum mean square error (MMSE) estimator, are proposed based on the moments of received signals. Simulation results confirm that the proposed algorithms can estimate the noise variance and the primary user's signal power with high accuracy.

In this paper, we develop a simple blind channel estimation algorithm for two-way relay network (TWRN) that consists of two terminal nodes and one relay node. We consider the frequency selective channels and adopt the orthogonal frequency-division multiplexing (OFDM) modulation to compensate the inter-symbol interference (ISI). By applying a non-redundant linear precoding at both terminals, we propose an algorithm that is effective of estimating two cascaded channels and is consistent with the two-phase two-way transmission protocols. The method to remove the inherent ambiguity of the blind channel estimation is discussed. Finally, the numerical results are provided to corroborate the proposed studies.

In this paper, we study the resource allocation problem in relay aided two-hop uplink multi-user transmission with a single relay node and a single destination node. The orthogonal frequency division multiple access (OFDMA) is adopted as the main transmission modulation. We investigate the problems of subcarrier allocation, subcarrier coupling, and power allocation. The optimization is formulated as maximizing the capacity under constraints of limited number of available subcarriers and a limited amount of power for each source node. A low complexity, suboptimal solution is presented to solve the problem. Simulation results are presented to evaluate the performance of the proposed algorithm.

In this paper, we study the problem of joint carrier frequency offset (CFO) and channel estimation for two-way relay network (TWRN). We consider the frequency selective fading channels and adopt the zero padding (ZP) based orthogonal frequency division multiplexing (OFDM) as the modulation of the transmission. Due to the mixture of the first and the second transmission phases, the joint estimation problem becomes much challenging than that in the traditional point-to-point communication systems. By introducing some redundancy, we modify the structure of ZP-OFDM to cope with non-zero frequency synchronization errors. We then propose a nulling-based least square (NLS) method for joint CFO and channel estimation. A detailed performance analysis of NLS has been conducted, where we prove that the unbiasedness of NLS and derive the closed-form estimation mean-square-error (MSE) at high signal-to-noise ratio (SNR). Finally, simulations are provided to corroborate the proposed studies.

This paper studies the transmit strategy for a secondary link or the so-called cognitive radio (CR) link under opportunistic spectrum sharing with an existing primary radio (PR) link. It is assumed that the CR transmitter is equipped with multi-antennas, whereby transmit precoding and power control can be jointly deployed to balance between avoiding interference at the PR terminals and optimizing performance of the CR link. This operation is named as cognitive beamforming (CB). Unlike prior study on CB that assumes perfect knowledge of the channels over which the CR transmitter interferes with the PR terminals, this paper proposes a practical CB scheme utilizing a new idea of effective interference channel (EIC), which can be efficiently estimated at the CR transmitter from its observed PR signals. Somehow surprisingly, this paper shows that the learning-based CB scheme with the EIC improves the CR channel capacity against the conventional scheme even with the exact CRto- PR channel knowledge, when the PR link is equipped with multi-antennas but only communicates over a subspace of the total available spatial dimensions. Moreover, this paper presents algorithms for the CR to estimate the EIC over a finite learning time. Due to channel estimation errors, the proposed CB scheme causes leakage interference at the PR terminals, which leads to an interesting learning-throughput tradeoff phenomenon for the CR, pertinent to its time allocation between channel learning and data transmission. This paper derives the optimal channel learning time to maximize the effective throughput of the CR link, subject to the CR transmit power constraint and the interference power constraints for the PR terminals.

In this letter, we investigate the problem of CFO estimation in OFDM systems when the timing offset and channel length are not exactly known. Instead of explicitly estimating the timing offset and channel length, we employ a multi-model approach, where the timing offset and channel length can take multiple values with certain probabilities. The effect of multimodel is directly incorporated into the CFO estimator. Results show that the proposed estimator outperforms the estimator selecting only the most probable model and the method taking the maximal model.

In this paper, we propose a new channel estimation prototype for the amplify-and-forward (AF) two-way relay network (TWRN). By allowing the relay to first estimate the channel parameters and then allocate the powers for these parameters, the final data detection at the source terminals could be optimized. Specifically, we consider the classical three-node TWRN where two source terminals exchange their information via a single relay node in between and adopt the maximum likelihood (ML) channel estimation at the relay node. Two different power allocation schemes to the training signals are then proposed to maximize the average effective signal-to-noise ratio (AESNR) of the data detection and minimize the mean-square-error (MSE) of the channel estimation, respectively. The optimal/sub-optimal training designs for both schemes are found as well. Simulation results corroborate the advantages of the proposed technique over the existing ones.

In this paper, we propose a semi-blind channel estimation algorithm for amplify-and-forward (AF) relay networks. The algorithm fits well for the recently developed space-time coding (STC) technique in AF relay network that serves for small size terminal and achieve the transmission diversity. Compared to the optimal training based estimators, e.g., maximum likelihood (ML) or linear minimum mean square (MMSE), the proposed semi-blind approach requires less training for successful channel estimation, or it yields better estimates if the same amount of training is used. The channel ambiguity issue as well as its relationship with the traditional semi-blind method is discussed in detail. We then provide various numerical examples to corroborate the proposed studies.

In this paper, we study the problem of joint carrier frequency offset (CFO) and channel estimation for two-way relay network (TWRN) that comprises two source terminals and one relay node. We build up the signal model, from which we identify the CFO and channels at the two source terminals. As the very first attempt to discuss the joint CFO and the channel estimation for TWRN, we consider relay node that purely amplifies and forwards, which is also known as the repeater. The new model is different from the traditional ones in that the unknown CFO is combined with only part of the channel parameters. We then propose two joint estimation methods, i.e., the approximate maximum-likelihood (ML) method and the nulling-based method. The Cramer-Rao Bounds (CRB) of both methods are derived in closed-form. Simulations are then provided to corroborate the proposed studies.

This paper presents a multi-antenna cognitive radio (CR) system that is capable of operating concurrently with the primary radio (PR) link. The operation of the CR system consists of three stages: environmental learning, CR channel training and CR data transmission. In environmental learning stage, partial channel information between PR and CR are obtained blindly, based on which the transmit beamforming and the receive beamforming strategies are designed at CR to remove/reduce the interference to and from PR, respectively. We characterize all the interference values analytically and study the problem of learning/training tradeoff associated with the proposed scheme. The optimal balancing between learning and training is examined via the minimum mean square error (MSE) of the channel estimation. It is shown that for a given total learning/training time, there indeed exists a optimal learning time that minimizes the MSE of the channel estimation, yet the interference power to the PR is regulated.

We consider the channel estimation issues for two-way relay network (TWRN) that employs orthogonal frequency division multiplexing (OFDM) modulation. We propose a two-phase training protocol for channel estimation, which is compatible with two-phase data transmission scheme associated with TWRN. It will be seen that channel estimation in TWRN is quite different from that in the traditional point-to-point system or even that in the one-way relay network (OWRN). The identifiability issue of the channel estimation, which particularly exists for TWRN, is studied. Simulation results corroborate the effectiveness of the proposed method.

In this work, we consider channel estimation for an amplify-and-forward (AF) two way relay network (TWRN), where two terminal nodes exchange information via a single relay node in between. A new concept of channel estimation by performing the power allocation at the relay node is introduced. As an example, we consider the maximum likelihood (ML) channel estimation at relay node and derive the power allocation factor such that the average effective signal-to-noise ratio (AESNR) at the terminal nodes is maximized. Nonetheless, the idea of using power allocation at relay node can be straightforwardly extended to more general scenarios. The simulation results show the advantages of the proposed method over the existing techniques.

In this paper, we study a multi-antenna-based cognitive radio (CR) system that is able to operate concurrently with the primary radio (PR) system. We propose a novel CR transmission frame structure consisting of three stages, including a new environment learning stage in addition to the conventional channel training and data transmission stages. During the environment learning stage, the CR terminals blindly learn the spatial knowledge of the PR-CR channels, based on which cognitive beamforming is designed at CR transceivers to restrict the interference to and from the PR, respectively, in the subsequent channel training and data transmission stages. Considering the learning and training errors from the first two stages, we derive a lower bound on the ergodic capacity achievable for the CR link subject to a predefined interference-power constraint at the PR and the CR's own transmit power constraint. We then characterize a general learning/training/throughput (LTT) tradeoff associated with the proposed scheme, pertinent to transmit power allocation between training and transmission stages, as well as time allocation among learning, training, and transmission stages.

This work considers two-way relay channels (TWRC), where two terminals transmit simultaneously to each other with the help of a relay node. For single antenna systems, we propose several new transmission schemes for both amplify-and-forward (AF) protocol and decode-and-forward (DF) protocol where the channel state information is not required. These new schemes are the counterpart of the traditional noncoherent detection or differential detection in point-to-point communications. Differential modulation design for TWRC is challenging because the received signal is a mixture of the signals from both source terminals. We derive maximum likelihood (ML) detectors for both AF and DF protocols, where the latter can be considered as performing differential network coding at the physical layer. As the exact ML detector is prohibitively complex, we propose several suboptimal alternatives including decision feedback detectors and prediction-based detectors. All these strategies work well as evidenced by the simulation results. The proposed protocols are especially useful when the required average data rate is high. In addition, we extend the protocols to the multiple-antenna case and provide the design criterion of the differential unitary space time modulation (DUSTM) for TWRC.

In this work, we consider the two-way relay network (TWRN) where two terminals exchange their information through a relay node in a bi-directional manner and study the training-based channel estimation under the amplify-and-forward (AF) relay scheme. We propose a two-phase training protocol for channel estimation: in the first phase, the two terminals send their training signals concurrently to the relay; and in the second phase, the relay amplifies the received signal and broadcasts it to both terminals. Each terminal then estimates the channel parameters required for data detection. First, we assume the channel parameters to be deterministic and derive the maximum-likelihood (ML) -based estimator. It is seen that the newly derived ML estimator is nonlinear and differs from the conventional least-square (LS) estimator. Due to the difficulty in obtaining a closed-form expression of the mean square error (MSE) for the ML estimator, we resort to the Crameacuter-Rao lower bound (CRLB) on the estimation MSE for design of optimal training sequence. Secondly, we consider stochastic channels and focus on the class of linear estimators. In contrast to the conventional linear minimum-mean-square-error (LMMSE) -based estimator, we introduce a new type of estimator that aims at maximizing the effective receive signal-to-noise ratio (SNR) after taking into consideration the channel estimation errors, thus referred to as the linear maximum SNR (LMSNR) estimator. Furthermore, we prove that orthogonal training design is optimal for both the CRLB- and the LMSNR-based design criteria. Finally, simulations are conducted to corroborate the proposed studies.

In this work, we consider differential modulation in two-way relay channels (TWRC). In single antenna systems, we propose non-coherent schemes for both amplify-and forward (AF) and decode-and-forward (DF) where the channel state information is not required. These new schemes are counterparts of the traditional non-coherent detection in point to point communications. The difficulty with differential modulation design in TWRC is that the received signal is a mixture of the signals from both source terminals. We derive maximum likelihood (ML) detectors for both AF and DF. The DF protocol can be considered as performing differential network coding at the physical layer. In addition, we propose several suboptimal alternatives including decision feedback and prediction based detectors. All these strategies work well as evidenced by simulation results. We also extend the schemes to the multiple-antenna case and provide design criterion of differential unitary space time modulation.

Recently, a maximum likelihood (ML) detection rule for differential unitary space time modulation (DUSTM) under the existence of unknown carrier frequency offset (CFO) has been derived. However, the ML detection is based on the exhaustive search over all the unitary group codes. In this paper, we design an efficient detection algorithm for newly derived ML rule, by modifying the bound intersection detector (BID). Our proposed algorithm is seen as a generalization of the existing BID that is known to be an optimal detector for the conventional DUSTM. The simulation results show that the proposed algorithm can save a large portion of the computational complexity compared to the naive searching method.

In this paper, we study the channel estimation problem for the two-way wireless relay network (TWRN) where two terminals exchange their information through a relay node in a bi-directional manner. We derive the maximum likelihood (ML) channel estimator as well as a new estimator called the linear maximum signal-to-noise ratio (LMSNR) estimator. It is shown that our proposed methods give superior performance compared to the common channel estimators like the least-square (LS) and the linear minimum-mean-squared-error (LMMSE) in the TWRN scenario. The provided study is based on any given training sequence, while the optimal training sequence design will be presented in a separate work due to the lack of the space. Simulations are conducted to corroborate the proposed studies.

We consider distributed space-time coding for two-way wireless relay networks, where communication between two terminals is assisted by relay nodes. We compare existing and new protocols that operate over 2, 3 or 4 times slots. Particularly, a new class of relaying protocols, termed as partial decode-and- forward (PDF), is proposed for the 2 time slots transmission. We show that the proposed amplify and forward (AF) protocols achieve the diversity order of min{N,T} ( 1- (loglogP)/(logP))> where N is the number of relays, P is the total power of the network, and T is the number of symbols transmitted during each time slot. When linear dispersion (LD) codes with random unitary matrices are used, the proposed PDF protocols resemble random linear network coding, where the former operates on unitary group and the latter works on finite field.

We provide studies on the training based channel estimation for decode-and-forward relay networks. Since multiple relay nodes are geographically distributed over the service region, channel estimation is different from the traditional way in that each relay has its own individual power constraint. Since the general optimization for the minimum mean square error based channel estimation is hard to solve, we consider three special yet reasonable scenarios. The problem in the first scenario lies in the so called semidefinite programming and could be efficiently solved by state of the art optimization tools. Closed-form waterfilling type solutions are found in the remaining two scenarios, of which the first one has a special physical meaning and its corresponding structure is named as cave-filling.

In this paper, we provide a complete study on the training based channel estimation for relay networks that employ the decode-and-forward (DF) scheme. Since multiple relay nodes are geographically distributed over the service region, channel estimation is different from the traditional way in that each relay has its own individual power constraint. We consider the maximum likelihood (ML) channel estimation and derive closed form solutions for the optimal training as well as for the optimal power allocation. It is seen that the optimal power allocation follows a multi-level waterfilling structure.

In this paper, we consider the problem of spectrum sensing in cognitive radio, where unlicensed (secondary) users are allowed to share the vacant frequency bands from the licensed (primary) users. We propose two cooperation protocols to improve the detection probability compared to an existing protocol. In the first protocol, secondary users with higher detection probability constantly act as relays to help those with lower detection probability, while in the second protocol, the help-oriented users choose to relay signals according to the decision made during the first time slot. Analytical studies are provided to demonstrate the enhanced performance of our proposed protocols. Finally, simulation examples are presented to corroborate our analytical results.

A novel subspace (SS) based blind channel estimation method for multi-input, multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems is proposed in this work. With an appropriate re-modulation on the received signal blocks, the SS method can be effectively applied to the cyclic prefix (CP) based MIMO-OFDM system when the number of the receive antennas is no less than the number of transmit antennas. These features show great compatibility with the coming fourth generation (4G) wireless communication standards as well as most existing single-input single-output (SISO) OFDM standards, thus allow the proposed algorithm to be conveniently integrated into practical applications. Compared with the traditional SS method, the proposed algorithm exhibits many advantages such as robustness to channel order over-estimation, capability of guaranteeing the channel identifiability etc. Analytical expressions for the mean-square error (MSE) and the approximated Cramer-Rao bound (ACRB) of the proposed algorithm are derived in closed forms. Various numerical examples are conducted to corroborate the proposed studies.

In this paper, we propose a novel carrier frequency offset (CFO) tracking algorithm for orthogonal frequency division multiplexing (OFDM) systems by exploiting scattered pilot carriers and virtual carriers embedded in the existing OFDM standards. Assuming that the channel remains constant during two consecutive OFDM blocks and perfect timing, a CFO tracking algorithm is proposed using the limited number of pilot carriers in each OFDM block. Identifiability of this pilot based algorithm is fully discussed under the noise free environment, and a constellation rotation strategy is proposed to eliminate the c-ambiguity for arbitrary constellations. A weighted algorithm is then proposed by considering both scattered pilots and virtual carriers. We find that, the pilots increase the performance accuracy of the algorithm, while the virtual carriers reduce the chance of CFO outlier. Therefore, the proposed tracking algorithm is able to achieve full range CFO estimation, can be used before channel estimation, and could provide improved performance compared to existing algorithms. The asymptotic mean square error (MSE) of the proposed algorithm is derived and simulation results agree with the theoretical analysis.

Recently, the advantages of the discrete cosine transform (DCT) based orthogonal frequency-division multiplexing (OFDM) have come to the light. We thus consider DCT- OFDM with non-circular transmission (our results cover circular transmission as well) and present two blind joint maximum- likelihood frequency offset and phase offset estimators. Both our theoretical analysis and numerical comparisons reveal new advantages of DCT-OFDM over the traditional discrete Fourier transform (DFT) based OFDM. These advantages, as well as those already uncovered in the early works on DCT-OFDM, support the belief that DCT-OFDM is a promising multi-carrier modulation scheme.

Can conventional differential unitary space time modulation (DUSTM) be applied when there is an unknown carrier frequency offset (CFO)? This paper answers this question affirmatively and derives the necessary maximum likelihood (ML) detection rule. The asymptotic performance of the proposed ML rule is analyzed, leading to a code design criterion for DUSTM by using the modified diversity product. The resulting proposed decision rule is a new differential modulation scheme in both the temporal and spatial domains. Two sub-optimal multiple-symbol decision rules with improved performance are also proposed. For the efficient implementation of these, we derive a modified bound intersection detector (BID), a generalization of the previously derived optimal BID for the conventional DUSTM. The simulation results show that the proposed differential modulation scheme is more robust against CFO drifting than the existing double temporal differential modulation.

In this paper, we provide a complete study on the training based channel estimation issues for relay networks that employ theamplify-and-forward(AF) transmission scheme. We first point out that separately estimating the channel from source to relay and relay to destination suffers from many drawbacks. Then we provide a new estimation scheme that directly estimates the overall channels from the source to the destination. The proposed channel estimation well serves the AF based space time coding (STC) that was recently developed. There exists many differences between the proposed channel estimation and that in the traditional single input single out (SISO) and multiple input single output (MISO) systems. For example, a relay must linearly precode its received training sequence by a sophisticatedly designed matrix in order to minimize the channel estimation error. Besides, each relay node is individually constrained by a different power requirement because of the non-cooperation among all relay nodes. We study both the linear least-square (LS) estimator and the minimum mean-square-error (MMSE) estimator. The corresponding optimal training sequences, as well as the optimal preceding matrices are derived from an efficient convex optimization process.

In this paper, we study training based channel estimation for relay networks using the amplify-and-forward (AF) transmission scheme. We first point out that separately estimating the channel from source to relay and the channel from relay to destination incurs several problems. We then propose a new estimation scheme that directly estimates the overall channel from source to destination. The proposed channel estimation well matches the AF based space time coding that was developed recently. Both linear least-square estimator and minimum mean- square-error estimator are studied. The corresponding optimal training sequences and the optimal precoding matrices are also derived.

In this paper, we answer the question that "Can conventional differential unitary space time modulation (DUSTM) be applied when there is an unknown carrier frequency offset (CFO)?" and present a maximum likelihood (ML) detection rule for this scenario. We analyze the asymptotical performance of our ML detection and provide the code design criterion by using the modified diversity product. The analysis also brings the insight that our proposed decision rule is a new differential modulation scheme in both temporal and spatial domains. Various simulations are conducted, and the proposed algorithm is shown to be more robust to the CFO drifting than the existing double temporal differential modulation.

In this paper, we propose a new carrier frequency offset (CFO) tracking algorithm for orthogonal frequency division multiplexing (OFDM) systems. Assuming that the channel remains constant during two consecutive OFDM blocks, a CFO estimation algorithm is proposed based on the limited number of pilots in each OFDM block. Identiflability of this pilot based algorithm is fully discussed under the noise free environment. A weighted algorithm is then developed by considering both pilot carriers and virtual carriers. The asymptotic mean square error (MSE) of the proposed algorithm is provided, and simulation results clearly show the performance improvement of the proposed algorithm over the existing methods.

Frequency synchronization is one of the most important components in orthogonal frequency-division multiplexing (OFDM) systems. Recently, the discrete cosine transform (DCT) based OFDM system has received wide attentions due to several advantages. Hence, the study of frequency synchronization issue for this newly raised system is well on its time. To provide a thorough study, we consider the non-circular transmissions, and the results can be easily generated to circular transmissions if the elliptic variance is set to zero. We present three joint maximum likelihood (ML) carrier frequency offset (CFO) and phase offset (PO) estimators. From both the theoretical analysis and the numerical comparisons, we found new advantages of the DCT-OFDM over the traditional discrete Fourier transform (DFT) based OFDM. These advantages, as well as those already studied in the early works on DCT-OFDM, support the belief that the DCT-OFDM is a new promising multi-carrier modulation (MCM) scheme.

In this paper, we consider the problem of blind channel estimation for multi-input, multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems via second order statistics (SOS) only. By an appropriate re-modulation on the received signal blocks, we find an effective way to apply the subspace based channel estimation for the cyclic prefix (CP) based MIMO-OFDM system when the number of the receive antennas is no less than the number of transmit antennas. Suitable relationships are built between the proposed algorithm and one existing technique (called ZPSOS in the future) for zero-padding (ZP) based MIMO-OFDM systems, thanks to the re-modulation considered. Consequently, many advantages could be directly inherited from ZPSOS, for example, robustness to channel order over-estimation, guaranteeing the estimation identiflability. By comparing these two algorithms, we found that they could provide similar channel estimation accuracy but the proposed method could beat the other in terms of bit error rate (BER). Moreover, adopting CP-based OFDM is compatible with many existing standards and applications, which shows great potential of our proposed algorithm.

The present authors reply to a comment by Attallah and Thiagarajan (IEEE Signal Process. Lett., vol.14, no.4, April 2006) on the original paper by Gao and Nallanathan (IEEE Signal Process. Lett., vol.13, no.2, p.73-6, Feb. 2006).

In this paper, we consider channel estimation and identification problem for multi-input multi-output (MIMO) finite impulse response (FIR) system based on second order statistics (SOS) only. By assigning different block-precoders to different transmitters, we develop two simple techniques that allows blind MIMO channel identification up to a scalar ambiguity for each transmitter. A special design of precoders is developed by which the performance accuracy of both algorithms can be dramatically improved. The results of computer simulations clearly show the effectiveness of our proposed algorithms.

Frequency synchronization is one of the most important issues for reliable transmission in most practical communication systems. Carrier frequency offset (CFO) must be compensated before channel estimation and coherent detection. Normally, training sequences are sent for CFO estimation and channel estimation before the data transmission. However, an improper selection of training sequences may cause failure in CFO estimation, resulting in the identifiability problem. In this paper, we present a detailed study on identifiability issue relate with data-aided CFO estimation. We firstly propose a theorem that is applicable for all training sequences. Then, the theorem is modified to deal with a popular set of training sequences that is deemed as optimal for channel estimation. Simulation results are provided to validate the proposed study.

We develop a simple subspace-based blind channel estimation technique for single-input single-output (SISO) and multi-input multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems based on a non-redundant linear block precoding. A novel contribution is that the proposed method can be applied for channel estimation in multi-input single-output (MISO) systems, where the traditional subspace based methods cannot be applied. Further consideration that can eliminate the multi-dimensional ambiguity for multiple transmitter scenarios is also provided. The numerical results clearly show the effectiveness of our proposed algorithm.

Based on the assumption that the transmitted symbols are independent and identically distributed (i.i.d), we develop a simple blind channel estimation technique for OFDM systems. Instead of using partial information from the signal covariance matrix, as done in previous works where a fixed precoder is used and channel is only estimated from one column of the signal covariance, our work jointly consider all entries in the signal covariance matrix, and is applicable for much general precoders. A design criteria of the precoders by which the performance can be greatly improved is provided, and the stochastic Cramér-Rao Bound (CRB) is derived in a close form. The numerical results clearly show the effectiveness and the improvement of our proposed algorithm in reducing the estimation errors.

The non-zero carrier frequency offset (CFO) must be compensated for orthogonal frequency-division multiplexing (OFDM) communications since it may destroy the orthogonality among subcarriers and cause severe degradation in system performance. Several blind CFO estimation methods were developed by exploiting the virtual carriers in practical OFDM transmissions, including a highly efficient approach by rooting a polynomial. However, this rooting method is suboptimal when the noise is present. In this work, we propose an improved polynomial rooting method that is shown to be the maximum likelihood (ML) estimator for both the noisy and the noise-free case. The simulation results clearly show the effectiveness and the better performance of the newly proposed method

In this paper, we consider the problem of blind channel estimation for single-input single-out (SISO) orthogonal frequency-division multiplexing (OFDM) systems via second order statistics (SOS) only. Based on the assumption that the transmitted symbols are independent and identically distributed (i.i.d), we develop a simple subspace based blind channel estimation technique by utilizing a non-redundant linear block precoding. The proposed method offers improved performance over the existing method using similar precoding technique. Additionally, the design criteria for the precoders by which the performance can be greatly improved is discussed, and the stochastic Cramer-Rao bound (CRB) is derived. The numerical results clearly show the effectiveness of our proposed algorithm

Blind channel estimation for cyclic prefixed single-carrier (CP-SC) systems via second order statistics (SOS) is considered in this paper. By fixing one or more symbols in the transmitted block to be real, we develop a simple technique for blind channel estimation that does not need redundant precoding or virtual carriers. A side-benefit, yet a novel contribution of the proposed algorithm, is that the phase ambiguity, an inherent problem in traditional blind channel estimation, is also resolved. The elimination of phase ambiguity facilitates signal detection for digital communications where symbols are usually taken from the finite alphabet. The results of the computer simulations clearly show the effectiveness of our proposed algorithm

The blind carrier frequency offset estimation problem has been well studied by exploiting the virtual carriers existing in practical orthogonal frequency division multiplexing transmissions. A highly efficient approach by rooting a polynomial has been proposed in the literature. However, this rooting method is suboptimal when noise is present. In this letter, we propose an improved polynomial rooting method that is shown to be the maximum likelihood estimator for both the noisy and the noise-free case.

A new spectral search-based direction-of-arrival (DOA) estimation method is proposed that extends the idea of the conventional ESPRIT DOA estimator to a much more general class of array geometries than assumed by the conventional ESPRIT technique. A computationally efficient polynomial rooting-based search-free implementation of the proposed algorithm is also developed.