Wireless backhauling is envisioned to play a pivotal role in 6G non-terrestrial networks (NTNs) due to its ability to deliver cable-free connectivity between edge nodes and gateways. However, the dynamic network topology and time-varying channels inherent to NTNs pose significant challenges for real-time network status monitoring. To address these challenges, we propose PhyINT, a novel in-band network telemetry approach that collects telemetry data at the physical layer for time-slotted NTNs. PhyINT allows network nodes to encode telemetry data onto resource elements (REs) in a distributed manner. Since REs are consistently available in every time slot, regardless of wireless channel variability, the encoding process can be made highly predictable and faithfully reconstructed at the gateway for decoding. Moreover, we formulate a multi-objective optimization problem that jointly minimizes the resource consumption and the telemetry collection completion latency. Extensive simulations across NTNs demonstrate that PhyINT significantly outperforms existing methods in reliability, latency, and goodput.

Semantic communication has been introduced into collaborative perception systems for autonomous driving, offering a promising approach to enhancing data transmission efficiency and robustness. Despite its potential, existing semantic communication approaches predominantly rely on analog transmission models, rendering these systems fundamentally incompatible with the digital architecture of modern vehicle-to-everything (V2X) networks and posing a significant barrier to real-world deployment. To bridge this critical gap, we propose CoDS, a novel collaborative perception framework based on digital semantic communication, designed to realize semantic-level transmission efficiency within practical digital communication systems. Specifically, we develop a semantic compression codec that extracts and compresses task-oriented semantic features while preserving downstream perception accuracy. Building on this, we propose a novel semantic analog-to-digital converter that converts these continuous semantic features into a discrete bitstream, ensuring integration with existing digital communication pipelines. Furthermore, we develop an uncertainty-aware network (UAN) that assesses the reliability of each received feature and discards those corrupted by decoding failures, thereby mitigating the cliff effect of conventional channel coding schemes under low signal-to-noise ratio (SNR) conditions. Extensive experiments demonstrate that CoDS significantly outperforms existing semantic communication and traditional digital communication schemes, achieving state-of-the-art perception performance while ensuring compatibility with practical digital V2X systems.

As a promising solution for global coverage, low-earth orbit (LEO) satellite communication networks can provide reliable communication and stable control for Internet of Things (IoT) mobile devices. Compared to terrestrial networks, the limited transmission resources, extended propagation delay, and severe signal loss in satellite systems make it challenging to achieve energy-efficient wireless control for satellite IoT applications. In this article, we consider a satellite-based wireless control system (WCS) and propose a control-aware energy-efficient transmission scheme. Different from traditional satellite communication, this scheme ensures the control stability of the satellite IoT systems and minimize the transmission energy consumption through the joint design of satellite beamforming, power allocation, and user scheduling. To reduce energy consumption while satisfying control stability, we first transform the constraint of the control stability into a communication reliability requirement. Then, we use a Lyapunov drift-plus-penalty optimization framework to convert the long-term resource allocation into a deterministic one-shot problem. Finally, we solve the transformed problem through alternating optimization in each time slot. Specifically, user scheduling scheme is designed by utilizing a semidefinite relaxation approach and beamforming and power allocation are carried out by applying a path-following approach. Simulation results illustrate that the proposed schemes can achieve control stability of satellite IoT systems and obtain the lower energy consumption compared to existing schemes.

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

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

Modern networks require frequent configuration updates due to dynamic events, like network expansion and evolving traffic patterns. Existing network reconfiguration tools are effective in certain scenarios, but their practical deployment still has several limitations: (i) They are restricted to specific network topologies, protocols and specifications; (ii) They can cause transient violations; (iii) Their practical deployment is limited by huge computational overheads and the specialized hardware support. To address these limitations, this paper presents a Multi-Objective Optimizing Network Reconfiguration (MONR) framework, which comprises a translator and an optimizer, to automatically generate reconfiguration command sequences. The translator represents various input types into a unified graph format based on Datalog-like facts, which are regardless of the input format. Therefore, MONR supports diverse routing protocols and network specifications. The optimizer employs deep reinforcement learning techniques to simultaneously maximize the specification satisfaction of intermediate configurations and minimize both traffic shifts and the number of command updates to reduce computational overheads. It models the reconfiguration task as a multi-objective Markov Decision Process (MOMDP) and introduces a Dueling Prioritized Experience Replay Double Deep Q-Network (DPER-DDQN) algorithm to balance multiple objectives. We compare MONR with Snowcap, AED and ConfigReco. The evaluation demonstrates that MONR is 2x, 9x, and 56x faster than Snowcap, AED and ConfigReco. Furthermore, MONR maintains 100% specification consistency while reducing the traffic shifts (< 0.1) and the number of update commands (0.8 of Snowcap's).

Satellite communication stands as an emerging tech-nology pivotal in enabling the seamless global deployment of the Internet of Things (IoT). In this study, we investigate a control-oriented optimization problem in satellite IoT system, such as satellite-enabled automated control for drones and unmanned vehicles, etc. This approach stands as a distinct contrast to conventional studies that lean heavily on communication-oriented issues. Nevertheless, due to the limited power of satellite and the significant transmission distance between satellites and Earth, maintaining control stability poses substantial challenges for satellite control systems. Therefore, our focus is to address the optimization from the angle of minimizing the aggregate linear quadratic regulator (LQR) control cost by designing satellite beamforming. In detail, a developed path-following algorithm is capable of converging to a minimally optimal local solution. When compared with the conventional capacity-oriented optimization framework, the algorithm proposed in this paper has proven to yield a comparatively diminished LQR control cost.

Modern networks are susceptible to configuration errors, such as misconfigurations and policy conflicts due to the complex interactions of diverse devices through various protocols. Control plane verification offers an effective solution to prevent these errors. However, existing tools face several challenges: (i) prolonged verification times, (ii) the verification of only specific policies, and (iii) poor robustness against node and link failures. To address these issues, we propose a control plane verification framework based on a multimodal multitask learning model. This framework enables simultaneous verification of multiple policies directly from various network configuration files. The learning model utilizes modality fusion techniques to capture both topology-related and traffic-related network features. It is trained on datasets augmented with the failure model to enhance robustness against failures. We compare our framework with three state-of-the-art verification tools: Minesweeper, Hoyan, and Tiramisu. Our evaluation shows that our framework is 2600 times faster than Minesweeper, twice as fast as Hoyan, and 19 times faster than Tiramisu, while maintaining 100% verification accuracy. Furthermore, our framework excels in verifying traffic-related network policies and remains effective even under node and link failures.

In-band Network Telemetry (INT) has emerged as a promising network measurement technology. However, existing network telemetry systems lack the flexibility to meet diverse telemetry requirements and are also difficult to adapt to dynamic network environments. In this paper, we propose AdapINT, a versatile and adaptive in-band network telemetry framework assisted by dual-timescale probes, including long-period auxiliary probes (APs) and short-period dynamic probes (DPs). Technically, the APs collect basic network status information, which is used for the path planning of DPs. To achieve full network coverage, we propose an auxiliary probes path deployment (APPD) algorithm based on the Depth-First-Search (DFS). The DPs collect specific network information for telemetry tasks. To ensure that the DPs can meet diverse telemetry requirements and adapt to dynamic network environments, we apply the deep reinforcement learning (DRL) technique and transfer learning method to design the dynamic probes path deployment (DPPD) algorithm. The evaluation results show that AdapINT can flexibly customize the telemetry system to accommodate diverse requirements and network environments. In latency-aware networks, AdapINT effectively reduces telemetry latency, while in overhead-aware networks, it significantly lowers the control overheads.

Unmanned surface vehicles (USVs) have recently found increasing applications in marine scenarios. In this paper, we investigate the cooperative communication of the hybrid terrestrial-maritime wireless system coexisting with a satellite network, where a multi-antenna USV is used as the relay to assist the communication between the terrestrial base station (TBS) and marine users (MUs). Considering the shortage of communication resources, the USV shares the same frequency spectrum with the satellite network. Using the composite maritime two-ray channel, we aim to maximize the throughput over all MUs by optimizing the cooperative beamforming scheme and association jointly with the USV trąjectory, subject to the constraints of USV kinematics, power consumption, quality-of-service requirements, and information-causality. Since the formulated optimization problem is non-convex, we propose an efficient iterative algorithm by applying the block coordinate descent and successive convex optimization methods. Simulation results confirm the significant performance gains of the proposed design as compared to other benchmark methods.

The fifth-generation mobile communication technology (5G) requires the ability to support a variety of different types of services in parallel. This paper considers the problem of enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC) services being jointly precoded under a multiple-input single-output (MISO) system, where the optimization objective is to minimize the precoding power at the base station (BS). However, the problem is difficult to be solved directly due to the complexity caused by interference between URRLC and eMBB users. Thus, we first transform it into a quadratic constraint quadratic programming (QCQP) problem, and then relax it into a convex problem through semidefinite relaxation (SDR). After that, an SDR-Precoding Power Minimization (SDR-PPM) algorithm is proposed to obtain the optimal solution iteratively. Meanwhile, a low-complexity (LC) comparison algorithm is also proposed. Simulation results verify the effectiveness of the proposed algorithms and show the performance of the algorithms as well as the impact of the parameters on precoding power.

In the power grid networks, mobile edge computing (MEC) is a critical technology to improve processing capacity and real-time business processing while Intelligent Reconfigurable Surface (IRS) is a promising approach which can effectively improve the propagation environment. This paper considers an IRS-assisted MEC system, which minimizes the transmission energy consumption of Base Station (BS) and mobile devices (MDs) by jointly optimizing the transmission power of MDs, the receiving beamforming vector of BS, computing resource allocation, and the phase shift of IRS. The computation resource allocation and phase shift are optimized by using quadratic transformation and Lagrange dual transformation while the transmitted power of MDs is optimized by using Difference of Convex function Algorithm (DCA). Simulation results verify the effectiveness of the optimization method and the IRS-assisted MEC system.

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

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

Traditional Canny edge detection algorithm has been widely used for the extraction of hyperbolic edges in ground penetrating radar (GPR). However, the Canny edge detection algorithm cannot adaptively determine the segmentation thresholds. Moreover, multiple hyperbolic edges are usually detected for each target, which increases the complexity of subsequent processing. In this letter, we propose a novel method that obtains only one hyperbolic edge for each target and automatically determines the segmentation threshold. Both simulation and real data results show that the proposed method is able to extract the target hyperbolic edges precisely and significantly improves the efficiency of target detection combined with the Hough transform in impulse GPR systems.

To reduce the computational complexity and channel estimation overhead for multi-intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication, we consider a joint design of the hybrid precoders at the base station and the passive precoders at the IRSs to maximize the ergodic spectral efficiency by exploiting the twin-timescale channel state information (CSI). Specifically, the digital precoder is designed according to the instantaneous CSI of a reduced-dimensional assist channel matrix, while the IRS passive reflection coefficient matrices and the analog precoder are optimized using the statistical CSI of all links. However, such a design problem is challenging to solve due to the non-convexity and the twin timescale. This work proposes efficient algorithms to jointly design the precoders, where the update of the IRS reflection coefficient matrices is independent of the hybrid precoders and the design of the analog precoder is independent of the digital precoder. Simulation results demonstrate the effectiveness of the proposed algorithms and provide the application scenes of the fully-connected and subarray-connected architectures. The results also show that the ergodic spectral efficiency for the fully-connected architecture using the twin-timescale CSI can approach that using the existing CSI schemes with less channel estimation overhead and computational complexity.

A key challenge for millimeter wave (mmWave) multiple-input multiple-output (MIMO) communication is that the signals at mmWave band are highly susceptible to blockage. To address this challenge, we introduce intelligent reflecting surface (IRS) to increase coverage area and improve communication performance. This paper considers a joint design of hybrid precoders at the base station and the passive precoder at the IRS to maximize the average spectral efficiency in an IRS-assisted mmWave MIMO system by exploiting the mixed timescale channel state information (CSI). Specifically, the hybrid precoders are designed according to the instantaneous CSI of the overall channel, while the IRS reflection coefficient matrix is optimized using the statistical CSI of all links. However, such a design problem is challenging to solve due to the non-convexity and the mixed timescale. This work proposes efficient algorithms to design jointly the hybrid precoders and the IRS reflection coefficient matrix where the update of the IRS reflection coefficient matrix is independent of the hybrid precoders. Simulation results demonstrate the effectiveness of the proposed algorithms. More interestingly, the results also show that adding low-cost reflector elements at the IRS can reduce the number of required high-cost radio frequency chains.

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

Intelligent reflecting surface (IRS) is a revolutionizing technology for improving the spectrum and energy efficiency in wireless communications. In this paper, a new communication framework enabled by multiple unmanned aerial vehicle (UAV)-carried intelligent reflecting surfaces (IRSs) is proposed to enhance multiuser downlink transmissions. To take full advantage of multi-UAV-IRS assisted system, we formulate the problem as maximizing the downlink sum rate by jointly optimizing the placement of UAVs, active beamforming and power allocation at the BS, and passive beamforming at the IRSs. An efficient algorithm is proposed by invoking successive convex approximation technique to decompose the joint optimization problem into several subproblems, which can be solved in an iterative manner. Simulation results show that the proposed scheme achieves considerable sum rate gain, which outperforms other benchmark schemes.

In this paper, we investigate a low earth orbit satellite (LEO SAT) and high altitude platform (HAP) integrated edge computing network to provide computing services for ground mobile devices (GMDs). We propose to minimize the weighted sum energy consumption via jointly optimizing the GMD association, precoding design, computation task assignment and computation resource allocation. To solve the nonconvex problem, we propose an algorithm that decomposes the optimization problem into four subproblems and solves each sub-problem iteratively. Specially, the GMD association subproblem is solved by quadratic transform based fractional programming (QTFP) and difference of convex function; the precoding design subproblem is obtained via QTFP and weighted minimum mean square (WMMSE) method; the computation task assignment is solved by the interior point method and the computation resource allocation is derived in closed form. The numerical results show that the proposed algorithms can solve the problems quite well and the energy consumption is maintained at a relative low level.

In maritime wireless communications, unmanned surface vehicles (USVs) can improve coverage and transmission performance due to their agile maneuverability and flexible deployment. This paper considers a USV-enabled maritime wireless network, where a USV is employed to assist the communication between the terrestrial base station and ships. Considering the maritime environment characteristics and earth curvature, we establish the systematic USV kinetics and information transmission models. To guarantee fairness, we aim to maximize the minimum expected throughput overall ships by jointly optimizing the trajectory and communication resource allocation, subject to the constraints of the USV kinetics, safe sailing, breakpoint distances, line-of-sight links, resource allocation, and information-causality. Due to the complexity of the maritime two-ray signal propagation model, we propose a channel approximation method to find an upper bound of the throughput for the original problem. By the problem decomposition, two sub-problems are derived and solved iteratively using successive convex approximation and interior-point methods. Simulation results confirm the effectiveness of the proposed method and show that USV can significantly improve transmission performance in maritime wireless networks.

Mobile edge computing is considered as a promising method to release the computation burden of mobile devices (MDs) by transferring the computation tasks to the nearby edge server. In this paper, we address the computation offloading problem by jointly optimizing offloading-decision making, multi-user multiple input and multiple output (MU-MIMO) precoding and computation resource allocation. The optimization problem is formulated as the minimization of the weighted sum of energy consumption and time delay of MDs, which is a mixed-integer non-linear programming problem. Due to the complexity of offloading time delay, we consider two special cases namely, the lower bound and upper bound of offloading time delay for the original problem, and exploit semidefinite relaxation and rounding methods to obtain the offloading decisions. Specially, we adopt the quadratic transform based fractional programming and the weighted minimum mean square error methods to solve the MU-MIMO precoding design problem for the two cases of offloading time delay, respectively. Simulation results confirm the effectiveness of the proposed method, and show that the application of multi-antenna MU-MIMO communication into MEC can sufficently reduce the energy consumption and time delay during computation offloading.

In this paper, a hovering unmanned aerial vehicle (UAV)-based free-space optical (FSO) serial multi-hop decode-and-forward relaying system is investigated. Considering the joint effect of atmospheric loss, atmospheric turbulence, pointing error and angle-of-arrival (AOA) fluctuation, the novel closed-form expressions of the link outage probabilities for ground-to-UAV, UAV-to-UAV, and UAV-to-ground links are derived, and the expression of the end-to-end outage probability for the UAV-based relaying system is also obtained. The asymptotic outage performance bounds for each link and the considered system are presented to reveal insights into the impact of AOA fluctuations. Based on the derived theoretical results, an optimization problem of receiver's field-of-view (FOV) is formulated to alleviate the impairment of AOA fluctuation. Numerical results show that the derived theoretical expressions are accurate to evaluate the outage performance of UAV-based FSO system. Moreover, the derived FOV can improve performance significantly.

Intelligent reflecting surface (IRS) is a new technique to improve the ergodic capacity in wireless networks. IRS consists of a large number of passive elements which digitally manipulate electromagnetic waves, and thus can act as a passive precoder in the communication. This paper introduces the IRS to millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems. Specifically, we consider a joint design of hybrid precoders at the base station (BS) and the passive precoder at the IRS to maximize the ergodic capacity of the system. In particular, since the instantaneous channel state information (CSI) of the BS-IRS link and the IRS-user link is challenging to obtain in practice, the statistical CSI is exploited for the joint hybrid and passive precoder design. However, such a design problem is challenging to solve due to the non-convexity. Thus, the block-coordinate-descent based algorithms are proposed to solve the problem efficiently. Simulation results demonstrate that, compared with the traditional systems without IRSs, the joint design of hybrid and passive precoding improves the ergodic capacity significantly. The results also show that adding some low-cost reflector elements at the IRS can help reduce the number of high-cost RF chains in the BS of the IRS-assisted mmWave MIMO systems.

In a multiuser massive multiple-input multipleoutput (MIMO) system, hybrid analog-and-digital structure is widely applied due to the high cost of deploying a large number of radio-frequency (RF) chains to drive the large antenna array. Phase shifter network is a common way of accomplishing the analog component. However, phase shifters impaired by hardware constrains can seriously degrade the performance of the system. This paper investigates the influence of imperfect phase shifters on the uplink achievable rate of the system with fullyconnected and sub-connected architectures. We derive a tractable expression for the uplink achievable sum rate. The proposed studies show that massive antennas are able to compensate for the performance degradation caused by imperfect phase shifters in the hybrid massive MIMO system. Our analytical results are verified by extensive simulations.

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

Multi-resolution codebook based hierarchical beam training is an attractive solution to the heavy overhead of millimeter-wave (mmWave) beam alignment. However, most existing codebooks suffer from either undesired main-lobefluctuation or high hardware-complexity. To address these issues, this paper proposes a closed-form phase-shifted discrete Fourier transformation (PS-DFT/CF) codebook design for mmWave links with hybrid structures. The proposed codebook is of hybrid analog/digital architecture. Analog components are fixed-size DFT vectors, which can be readily implemented with radiofrequency (RF) phase shifters. Digital components at baseband select unitary subbeams shaped by the analog components and tackle the impact of the phase difference between adjacent subbeams, such that multi-resolution flat beam patterns can be synthesized. Moreover, the closed-form PS-DFT codebook design enables joint transceiver design for mmWave beam alignment. Numerical results verify that the proposed PS-DFT/CF codebook approaches the performance of the ideal PS-DFT counterpart.

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

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

The tradeoff between narrow beams and low training overhead is deemed to a bottleneck for millimeter-wave (mmWave) systems. Particularly, in multi-user (MU) scenarios, the training overhead grows linearly with the number of users. To address this issue, this paper proposes a codebook based simultaneous beam training scheme for mmWave MU systems with split/partially-connected structures. In this scheme, the multi-resolution codebook based hierarchical beam searching is adopted to reduce the training overhead and acquire the angle of departure (AoD) for each user-group. Meanwhile, the concurrent beams, offered by the corresponding subarray-groups of split structures, enable simultaneous beam training for all served user-groups. Consequently, the overall training overhead is significantly reduced. Moreover, a two-phase training protocol is elegantly built, where the whole array is used to reap large array gain in the initial phase. Furthermore, in the subsequent parallel training phase, multiple subarray-groups conduct simultaneous beam training for the corresponding user-groups. Hence, advantages on both low training overhead and large array gain are achieved. Numerical results show the proposed scheme outperforms traditional counterparts, especially in moderate-high signal-to-noise ratio (SNR) regimes with limited transmission blocks.

Hierarchical beam training methods are imposed with some limitations on antenna spacing, the number of antennas and/or radio-frequency (RF) chains, which can not be directly extended to general deployments. To address these issues, this paper investigates a coordinated subarray based beam training scheme for indoor sub-Terahertz (sub-THz) multi-user (MU) communications with split hybrid structures. The capability of concurrent beams offered by multiple subarrays in the hybrid structure is exploited to partition the whole angle domain into multiple orthogonal subregions on the basis of subarrays, and each subregion is exclusively covered with a corresponding subarray without explicit interference among them, such that these multiple narrow beams can be simultaneously shaped in their reduced subregions. Consequently, an increased success rate of angle of departure (AoD) estimation can be obtained with affordable overhead in typical deployments, without limitation on the number of antennas in each subarray. Analysis and simulation results verify the feasibility and the superiority of the proposed scheme.

Indoor positioning for visible light communication (VLC) has gained significant attentions recently with the popularity of light-emitting diodes (LEDs). In this paper, we consider a typical application of VLC by proposing a three-dimensional positioning scheme for a target terminal equipped with multiple photodiodes (PDs). Given the relative coordinates between the target terminal and receiving PDs along with positions of fixed transmitting LEDs, precise location estimation of the terminal device can be achieved via measuring received signal strength (RSS) through line-of-sight (LoS) channels. Moreover, multipath reflections from interior walls are considered as a major interference in non-LoS environment. It is discovered that the positioning error increases linearly with respect to the reflection coefficient of walls, which also verified by simulation results. The positioning error is achieved in millimeter scale under an ideal condition and in decimeter scale with multipath reflections.

One-bit digital-to-analog converter (DAC) has been a promising potential for both cost- and power-efficient massive multiple-input multiple-output (MIMO) implementation. We investigate the performance of a downlink massive MIMO with the 1-bit DAC using regularized zero-forcing (RZF) precoding serving quantized receivers. By taking the quantization errors at both transmitter and receivers, regularization parameter for the RZF is optimized with closed-form solution by applying asymptotic random matrix theory. The optimal parameter is discovered as linearly increasing w.r.t. the user loading ratio. Furthermore, asymptotic sum rate performance is derived and a closed-form expression of the optimal user loading ratio is achieved specifically for low SNR. The optimal user loading is found decreasing with increasing receiver quantization resolutions. Numerical simulations verify our observations.

In a visible light communication (VLC) system, the nonlinearity of LED is one of the challenges that prevents reliable communication. In order to mitigate the nonlinearity of LED, in this paper, the concept of spatial summing pulse amplitude modulation (PAM) is developed where PAM signals are separated into several on#x002F;off keying (OOK) signals transmitted by different LED groups. The signal streams from different LED groups are designed to sum in space into a single signal stream which is detected by a conventional optimal maximum likelihood detector. A scheme is proposed to cope with the distortion caused by the mismatch among the LED array in the spatial summing PAM system and the error performance of the spatial summing PAM is analyzed. Simulation results verify that the proposed scheme can remarkably improve the error performance.

White light-emitting diode (LED) consisting of red, green, blue, and amber chips (RGBA-LED) has recently been adopted as transmitter in visible light communication (VLC) systems. This paper proposed a reshaped orthogonal frequency division multiplexing (R- OFDM) scheme for RGBA-LED-based VLC systems. In the R- OFDM, the signal is adjusted by separation and biasing after clipping (BAC) operations for transmitting in RGBA-LED. Then, the biasing factor of BAC is derived according to the color mix ratio (CMR) of RGBA-LED. At the receiver, We develop a direct detection algorithm to recover the original data, and analyze its the theoretical bit error rate (BER). Furthermore, a lower bound of the BER is analyzed under high clipping ratio (CR) by using SNR upper bound. Finally, simulation results confirm our theoretical analysis and verify that R-OFDM outperforms the ACO-OFDM in term of BER.

This paper considers the optimal energy efficient association for HetNets by applying almost blank subframes (ABSs) techniques. Aiming at maximizing energy efficiency (EE) without the quality of service (QoS) constraints, we obtain a closed-form optimal solution, which indicates that the optimal choice of blank resource block (RB) fraction for EE maximization is binary without individual user QoS requirements. We further incorporate QoS constraints and equivalently transform the corresponding complicated fractional EE optimization problem to a single linear program (LP) by introducing some new auxiliary variables. Finally, we confirm the validity of the assumption that a user is served by at most one BS in a given RB both in theory and by numerical results.

In this paper, we investigate a multiuser MIMO downlink with imperfect channel state information (CSI) from the physical-layer security provision. In a classical transmitter (Alice)-legitimate receiver (Bob)-eavesdropper (Eve) model using artificial noise to disturb Eve's reception, we consider a general scenario where all nodes are equipped with multiple antennas, and Bob's CSI is acquired by pilot-assisted channel estimation. For designing the secure transmit beamforming, we utilize random matrix quantization (RMQ) to quantize Bob's channel estimate, and then Bob feeds it back to Alice. Due to the effects of imperfect CSI at Alice, the secrecy performance is upper bounded in the high signal-to-noise ratio (SNR) regions. In order to avoid the interference-limited phenomenon, we present a scaled strategy for the secrecy system utilizing derived upper bound on the secrecy rate loss. Moreover, it is shown that our derived results can be easily extended to a special case where both legitimate and eavesdropping receivers equip a single antenna each, where random vector quantization (RVQ) is utilized instead. By employing our proposed feedback strategy, the secrecy rate increases with transmit power, and the certain secrecy requirement can be guaranteed.

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

With the rapidly increasing demand for high speed data transmission and a growing number of terminals in one cell, massive multiple-input multiple-output (MIMO) has been shown promising owing to its high spectrum efficiency. Although massive MIMO can efficiently improve the system performance, usage of orthogonal pilots and growing terminals cause large resource consumption especially when the coherence interval is short. This paper presents a semi-orthogonal pilot design with simultaneous data and pilot transmission. In the proposed technique, we exploit the asymptotic channel orthogonality in massive MIMO systems, with which the mutual interference between data and pilot can be mitigated by successive interference cancellation (SIC). Theoretical analysis and simulation results show that the proposed pilot design can achieve a significant performance improvement with reduced pilot resource consumption compared with the popular orthogonal pilots.

DFT-Spread OFDM has been well studied in radio frequency (RF) systems thanks to its effectiveness in reducing the peak-to-average power ratio (PAPR). However, due to the inherent positive real signal constraint in visible light communications (VLC), the implementation of DFT-Spread OFDM in VLC faces some different challenges with respect to its high PAPR. This paper presents an LED array assisted visible light communication system using DFT-Spread OFDM. With this technique, theoretical analysis are firstly derived to help characterize the system PAPR reduction in VLC systems. Specifically, two different subcarrier allocation modes namely localized DFT-Spread and interleaved DFT-Spread OFDM are explicitly studied with their time domain signal expressions. Moreover, based on the results, detailed comparisons of DFT-Spread OFDM in terms of PAPR reduction are made for VLC and RF systems. Simulation results show that the proposed system not only leads to a reduced PAPR, but also achieves a performance gain in BER without any loss of system transmission rate.

In this paper, we investigate the performance enhanced transceiver design in a Device-to-Device (D2D) communication system underlaying a cellular network. The problem of joint transmitter and receiver optimization via maximizing the entire D2D and cellular transmission rate is considered. Due to the non-convexity of the primal problem, we resort to decomposing the problem into a sequence of standard convex quadratic programs. An iterative sequential optimization algorithm is accordingly presented for joint transceiver design at both the base station and the device terminals. Computer simulations convinced the performance enhancement of our proposed scheme compared with conventional transmission schemes.

This paper considers device-to-device (D2D) communications underlaying cellular networks with a multi-antenna base station (BS). The BS serves its own cellular users while letting another remote terminal directly transmit signals to its nearby receiver via a D2D link. Two transmit strategies including beamforming (BF) and interference cancellation (IC) are considered at the BS for performance evaluation in terms of achievable channel capacity. The capacity performance of two different cases with perfect and quantized channel knowledge at the transmitter is derived with closed-form expressions. Based on these results, an adaptive transmission scheme to switch between BF and IC is proposed. Numerical results verify the accuracy of the derived expressions and draw the operating regions of BF/IC strategies.

This paper studies the capacity performance of coordinated multi-point (CoMP) downlink transmissions based on limited feedback. We consider both path loss effects and channel imperfections including feedback delay and quantization error. Closed-form expressions are derived to characterize ergodic achievable rates for joint processing (JP) and coordinated beamforming (CBF) techniques, respectively. According to the derived expressions, an adaptive transmission strategy to switch between JP and CBF is proposed to maximize cell throughput. Simulation shows the CBF scheme is preferred at medium SNR with varying switch points jointly determined by the feedback size, delay, and locations of CoMP users.

Multicast service is a key function of the next generation broadband mobile system. The performance of wireless multicasting is severely degraded due to the wireless channel fading. Multiple antennas and cooperative communication are promising techniques that can be used to improve the multicast performance. Most cooperative schemes are based on the decode-and-forward (DF) strategy that requires the cooperating users being able to correctly decode the multicast data first. In this paper, we study the cooperative multiple antenna multicast scheme based on amplify-and-forward (AF) strategy. A cooperative protocol for multicast systems with multiple transmit antennas is proposed. We analyze the performance of the protocol with two multiple antenna transmission schemes: the diversity transmission and multiplexing transmission. The close form expression and two bounds on the multicast outage probability are derived. Thorough numerical evaluation shows that our proposed protocol can take full advantage of the degree of freedom provided by the multiple transmit antennas. And a larger outage capacity is obtained at moderate and high signal-to-noise ratio.

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

In this paper we investigate the channel estimation and data transmission scheme for bi-directional relaying applied in cellular systems. We propose a novel pilot-aided transmission strategy to realize the efficient data exchange between Base Station (BS) and Mobile Station (MS) via a fixed Relay Node (RN). Based on the characteristics of the BS-RN link, the transmitted signals at the BS are pre-equalized. At the RN, part of the CSI is estimated and used to equalize the relayed signals. We then propose a semi-orthogonal pilot structure for channel estimation. With the proposed transmission scheme and pilot structure, the pilot number is reduced by half and the channel estimation at the MS has much low complexity. Simulation results show that the BER performance of the proposed scheme at the MS is a little better than the conventional scheme.

In this paper, we address a sub-optimum distributed power allocation (SODPA) scheme for two-hop parallel relay networks, which includes a source-destination pair and multiple relay nodes. The system employs Decode-and-Forward (DF) as relaying protocol. In the proposed scheme, each relay is assumed to be able to decode the received signal if it satisfies the given SNR threshold and may make decision individually on whether to forward the source code to destination. Compared with the work in, the proposed scheme requires only the statistical CSI at source node instead the exact instantaneous CSI. Therefore, the SODPA reduces the amount of feedback information significantly. Simulation results show that the proposed scheme can reduce the total transmit power effectively.

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

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

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

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

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

We investigate clustered orthogonal frequency-division multiplexing (OFDM) with adaptive antenna arrays for interference suppression. To calculate weights for interference suppression, instantaneous correlations of the received signals and channel responses corresponding to the desired signals have to be estimated. However, due to smaller size of each cluster for clustered OFDM than for classical OFDM, the discrete Fourier transform (DFT)-based estimator has large leakage and results in severe performance degradation. Therefore, a polynomial-based parameter estimator is proposed to combat the severe leakage of the DFT based estimator. We study the impact of the polynomial order and window size on the estimation error. An approximately optimal window size for the polynomial-based estimator is obtained and an adaptive algorithm for the optimal window size is developed. With the adaptive algorithm, the polynomial-based estimator has no leakage and does not require channel statistics. Simulation results show that the developed algorithm improves the performance significantly.

Time-variance of wireless channels destroys the orthogonality among subchannels in OFDM system and causes the inter-channel interference (ICI), which results in an error floor. In this paper, we study frequency-domain partial response coding (PRC) for reducing the effect of the ICI. Based on the general expression of the ICI power for OFDM with PRC, the optimum weights for PRC that minimize the ICI power are derived. From numerical and simulation results, optimum PRC for OFDM can reduce the ICI effectively by 4.0 dB for 2-tap PRC and 6.2 dB for 3-tap PRC, respectively.

In this paper, we investigate clustered OFDM with adaptive antenna arrays for interference suppression. To calculate weights for interference suppression, instantaneous correlations of the received signals and channel responses corresponding to the desired signal have to be estimated. Therefore, a polynomial based parameter estimator is proposed to combat the severe leakage of the traditional DFT based estimator. The approximately optimal window size for the polynomial estimator is obtained and an adaptive algorithm of optimal window size is developed. Simulation results show that the developed algorithm improves performance significantly.

Time variance of wireless channels destroys the orthogonality among subchannels in orthogonal frequency-division multiplexing (OFDM) systems and causes interchannel interference (ICI), which results in an error floor. In this paper, we study frequency-domain partial-response coding (PRC) for reducing the effect of the ICI. Based on the general expression of the ICI power for OFDM with PRC, the optimum weights for PRC that minimize the ICI power are derived. From the numerical and simulation results, optimum PRC for OFDM can reduce the ICI effectively by 4.0 dB for two-tap PRC and 6.2 dB for three-tap PRC, respectively.