Channel knowledge map (CKM) is a key enabler for environmental awareness in future wireless systems. However, reconstructing high-fidelity CKM from sparse and noisy measurements poses a significant challenge. While Graph Neural Networks (GNNs) have emerged as a potent tool for this task, existing methods often lack physical consistency and generalization due to reliance on single graph structures and purely data-driven approaches. To tackle this challenge, in this paper, we propose a physics-informed multi-view graph neural network (PIMV-GNN) framework. This framework innovatively integrates two mechanisms within a unified GNN backbone: a multi-view learning (MVL) module that builds a rich spatial representation of the complex environment by fusing two complementary graph structures, namely a "spatial line-of-sight" graph and a "physical proximity" graph; and a physics-informed neural network (PINN) module that enforces physical consistency by imposing constraints derived from the Helmholtz equation in the graph domain. Extensive simulations demonstrate that our proposed PIMV-GNN framework significantly outperforms baseline models under various levels of data sparsity and measurement noise. Furthermore, the results reveal a profound synergistic effect between the MVL and PINN modules, where high-quality multi-view features significantly improve the regularization efficiency of the physics-based constraints.

The rapid development of low-altitude economy has placed higher demands on the sensing of small-sized unmanned aerial vehicle (UAV) targets. However, the complex and dynamic low-altitude environment, like the urban and mountainous areas, makes clutter a significant factor affecting the sensing performance. Traditional clutter suppression methods based on Doppler difference or signal strength are inadequate for scenarios with dynamic clutter and slow-moving targets like low-altitude UAVs. In this paper, motivated by the concept of channel knowledge map (CKM), we propose a novel clutter suppression technique for orthogonal frequency division multiplexing (OFDM) integrated sensing and communication (ISAC) system, by leveraging a new type of CKM named clutter angle map (CLAM). CLAM is a site-specific database, containing location-specific primary clutter angles for the coverage area of the ISAC base station (BS). With CLAM, the sensing signal components corresponding to the clutter environment can be effectively removed before target detection and parameter estimation, which greatly enhances the sensing performance. Besides, to take into account the scenarios when the targets and clutters are in close directions so that pure CLAM-based spatial domain clutter suppression is no longer effective, we further propose a two-step CLAM-enabled joint spatial-Doppler domain clutter suppression algorithm. Simulation results demonstrate that the proposed technique effectively suppresses clutter and enhances target sensing performance, achieving accurate parameter estimation for sensing slow-moving low-altitude UAV targets.

As wireless communication systems evolve toward intelligent operation, the growing complexity of dynamic and non-stationary propagation environments inherent in large-scale and heterogeneous Internet of Things (IoT) scenarios imposes stringent demands on link adaptation robustness. To address these challenges, we propose a joint bandwidth and modulation and coding scheme (MCS) optimization framework that leverages autonomous scenario identification (ASI) and dueling double deep Q-network (D3QN), named as ASI-D3QN. Specifically, a lightweight deep convolutional neural network (CNN) is tailored for ASI to achieve high identification accuracy while maintaining low computational complexity. This design is particularly suited for resource-constrained devices. Then, a D3QN-based optimization strategy is developed to seamlessly integrate ASI-derived environmental context with intrinsic channel metrics. The proposed framework explicitly expands the decision space by integrating signal bandwidth as an additional optimization dimension, facilitating adaptive optimization over multi-dimensional parameters. Finally, an intelligent communication prototype is established to comprehensively validate the proposed approach under representative standardized channel models. Experimental results demonstrate that, compared to existing methods, the proposed ASI achieves at least a 0.27% accuracy improvement with fewer model parameters. Moreover, relative to conventional link adaptation schemes, the ASI-D3QN strategy achieves superior throughput gains in complex dynamic environments.

Multiple-input multiple-output (MIMO) has been a key technology for wireless systems for decades. For typical MIMO communication systems, antenna array elements are usually separated by half of the carrier wavelength, thus termed as co-located MIMO. In this paper, we investigate the performance of multi-user sparse MIMO communication, with sparse arrays at both the transmitter and receiver side, i.e., the array elements are separated by more than half wavelength. Given the same number of array elements, the performance of sparse MIMO is compared with co-located MIMO. On one hand, sparse MIMO has a larger aperture, which can achieve narrower main lobe beams that make it easier to resolve densely located users. Besides, increased array aperture also enlarges the near-field communication region, which can enhance the spatial multiplexing gain, thanks to the spherical wavefront property in the near-field region. On the other hand, element spacing larger than half wavelength leads to undesired grating lobes, which, if left unattended, may cause severe multi-user interference (MUI). Specifically, we first study the spatial multiplexing gain of the basic single-user sparse MIMO communication system, where a closed-form expression of the near-field effective degree of freedom (EDoF) is derived. The result shows that EDoF increases with the array sparsity for sparse MIMO before reaching its upper bound, which equals to the minimum value between the transmit and receive antenna numbers. Furthermore, the scaling law for the achievable data rate with varying array sparsity is analyzed and an array sparsity-selection strategy is proposed.We then consider the more general multi-user sparse MIMO communication system. It is shown that sparse MIMO is less likely to experience severe MUI than co-located MIMO, especially when users are densely located, thanks to the non-uniform distribution of spatial angle difference among users. Finally, numerical results are provided to validate our theoretical analysis.

The classic orthogonal frequency division multiple access (OFDMA) faces critical challenges in doubly-selective millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, and excessive cyclic prefix (CP) overhead and stringent synchronization requirements substantially degrade spectral efficiency. To address these challenges, recent studies have explored delay alignment modulation (DAM), which exploits joint spatial-delay processing to mitigate inter-symbol interference (ISI) and reduce CP overhead without relying on conventional channel equalization structure in single-user systems. Building upon this concept, we leverage delay-Doppler alignment modulation (DDAM), which is a generalization of DAM suitable for doubly-selective channels, to enhance multi-user uplink OFDMA performance. Specifically, we propose a novel flexible synchronization multi-user uplink transmission framework based on DDAM-OFDMA, which integrates DDAM with OFDMA to jointly reduce CP overhead and relax synchronization constraints. First, we analyze the synchronization and CP overhead limitations of uplink OFDMA and highlight the potential of DDAM at the BS to mitigate these issues. To gain further insights, we first consider a DDAM-OFDMA system under the special case of a single-path channel model and derive its input-output relationship, illustrating how DDAM reduces CP overhead while enhancing synchronization robustness. We then formulate a joint optimization problem for time-domain and frequency-domain beamforming to maximize the sum rate and derive the closed-form expression. The proposed framework is then extended to multi-path scenarios by designing the perfect synchronization and the flexible synchronization. Specifically, the perfect synchronization is the extension of the single-path model. Then, the flexible synchronization additionally achieves the controllable channel delay spread, which is achieved through DDAM’s delay compensation and path-based beamforming. Simulation results validate that the proposed DDAM-OFDMA scheme outperforms various benchmark schemes in terms of spectral efficiency and bit error rate (BER).

Low-altitude unmanned aerial vehicle (UAV) swarms are expected to play important role for future intelligent aerial systems due to their great potential to cooperatively accomplish complicated missions effectively. However, there are important challenges to be addressed to enable their efficient operation: the large-scale nature of swarms usually leads to excessive spectrum consumption, and ultra-low-cost requirements for individual UAVs renders it necessary to develop cost-effective communication modules. In addition, the densely located swarm UAVs require high resolution for localization and sensing. In order to address the above challenges and simultaneously achieve spectrum- and energy-efficient communication and accurate sensing, we investigate low-altitude UAV swarm with integrated super-resolution sensing and symbiotic communication technology. Specifically, one leading UAV may act as a primary transmitter (PT) to transmit communication signals to the base station (BS), and the remaining nearby UAVs in the swarm act as passive backscatter devices (BDs), which can modulate their information by efficiently backscattering the radio frequency (RF) signals from the PT without consuming extra spectrum or power. In addition, to achieve efficient three-dimensional (3D) super-resolution sensing for the densely located UAV swarm, 3D sparse multiple-input multiple-output (MIMO) technology and super-resolution signal processing algorithms are further exploited, where both L-shaped nested array (LNA) and planar nested arrays (PNA) are considered at the BS. To evaluate the communication and sensing performance for the UAV-symbiotic radio (SR) system, the achievable rates of UAV swarm are derived and the beam patterns of sparse LNA, PNA and the benchmarking compact uniform planar array (UPA) are compared. Furthermore, efficient channel estimation methods assisted by super-resolution sensing are proposed. Simulation results are provided to demonstrate that 3D sparse MIMO with both LNA and PNA can provide significantly better sensing and communication performance than conventional compact MIMO for UAV-SR systems.

Collaborative access points (APs) enabled cell-free networks can provide stable and uniform communication services for all user locations, making them a promising network architecture for the sixth-generation (6G) mobile communication systems. While the performance of pure cell-free networks has been extensively studied, it remains unclear whether deploying large-scale cell-free APs in legacy cellular networks can effectively boost communication performance. Besides, the realization of a cell-free network is considered to be a gradual long-term evolutionary process in which APs will be incrementally introduced and form a hybrid communication network with the existing cellular base stations (BSs). Such a collaboration will bridge the gap between the established cellular network and the innovative cell-free network. Therefore, hybrid cellular and cell-free networks (HCCNs) emerge as a feasible solution for advancing cell-free network development, and it is worthwhile to further explore its performance limits. Different from heterogeneous networks or multipoint coordinated networks, the characterization of HCCNs needs to take both inter- and intra-layer collaboration into account. This paper presents a stochastic geometry-based HCCN model to analyze the distributions of signal and interference and reveal their mutual coupling. Specifically, in order to benefit the user equipments (UEs) from both the cellular BSs and the cell-free APs, a conjugate beamforming design is employed, and the aggregated signal is analyzed using moment matching. Then, the coverage probability of the hybrid network is characterized by deriving the Laplace transforms and their higher-order derivatives of interference components. Furthermore, the average achievable rate of the hybrid network over channel fading is derived based on the interference coupling analysis. Simulation results demonstrate that compared to traditional cellular networks, HCCN effectively narrows communication quality differences between different UEs and improves overall communication performance.

With advancements in wireless communication and localization technologies, cellular networks are evolving towards integrated sensing and communication (ISAC) capabilities. To address the challenges of sensing-assisted communication, we introduce the digital twin of channel (DToC). Specifically, locations of user terminals (UTs) and their statistical channel state information (sCSI) are treated as physical objects and virtual counterparts in the concept of digital twin (DT), respectively. In this work, we establish a probabilistic model that characterizes sCSI as a location-conditioned distribution. To enable precise sCSI generation, we enhance the latent diffusion model (LDM) and propose an improved latent diffusion model (ILDM) with deterministic sampling. We further propose an accelerated LDM method to speed up the generation process by skipping certain sampling steps. Simulation results demonstrate that the proposed ILDM achieves high accuracy in generating sCSI, while the accelerated LDM delivers significant speedups with minor performance degradation. Our results also validate that the DToC framework can effectively generate sCSI without pilot overhead.

Movable antenna (MA), which can flexibly change the position of antenna in three-dimensional (3D) continuous space, is an emerging technology for achieving full spatial performance gains. In this paper, a prototype of MA communication system with ultra-accurate movement control is presented to verify the performance gain of MA in practical environments. The prototype utilizes the feedback control to ensure that each power measurement is performed after the MA moves to a designated position. The system operates at 3.5 GHz or 27.5 GHz, where the MA moves along a one-dimensional horizontal line with a step size of 0.01λ and in a two-dimensional square region with a step size of 0.05λ, respectively, with λ denoting the signal wavelength. The scenario with mixed line-of-sight (LoS) and non-LoS (NLoS) links is considered. Extensive experimental results are obtained with the designed prototype and compared with the simulation results, which validate the great potential of MA technology in improving wireless communication performance. For example, the maximum variation of measured power in the considered scenario reaches over 40 dB and 23 dB at 3.5 GHz and 27.5 GHz, respectively, thanks to the flexible antenna movement. In addition, experimental results indicate that the power gain of MA system relies on the estimated path state information (PSI), including the number of paths, their elevation and azimuth angles of arrival (AoAs), as well as the complex gain of each path.

This paper proposes a novel multi-antenna architecture, termed ray antenna array (RAA), which practically enables flexible beamforming and also enhances wireless communication performance for high-frequency systems in a cost-effective manner. RAA consists of a large number of inexpensive antenna elements and a few radio frequency (RF) chains. These antenna elements are arranged in a novel ray-like structure, where each ray corresponds to onesimple uniform linear array(sULA) with a carefully designed orientation. The antenna elements within each sULA are directly connected, so that each sULA is able to form a beam towards a direction matching the ray orientation without relying on any analog or digital beamforming. By further designing a ray selection network (RSN), appropriate sULAs are selected and connected to the RF chains. Compared to conventional multi-antenna architectures such as the uniform linear array (ULA) with hybrid analog/digital beamforming (HBF), the proposed RAA enjoys four appealing advantages: (i) finer and uniform angular resolution for all signal directions; (ii) enhanced beamforming gain by using antenna elements with higher directivity, as each sULA is only responsible for a small portion of the total angle coverage range; (iii) dramatically reduced hardware cost since no phase shifters are required, which are expensive and difficult to design in high-frequency systems such as millimeter wave (mmWave) and Terahertz (THz) systems; (iv) beam squint free, enabling stable beamforming performance in high-frequency wideband communications. To validate such advantages, we first present the input-output mathematical model for RAA-based wireless communications. Efficient algorithms for joint RAA beamforming and ray selection are then proposed for single-user and multi-user RAA-based wireless communications. Simulation results demonstrate that RAA achieves superior performance compared to the conventional ULA with HBF, while significantly reducing hardware cost.

Ray antenna array (RAA) is a novel multi-antenna architecture comprising massive low-cost antenna elements, no phase shifters and a few radio-frequency (RF) chains. Compared to the classic hybrid analog/digital beamforming based on conventional antenna arrays, RAA has three appealing advantages: (i) dramatically reduced hardware cost since no phase shifters are needed; (ii) enhanced beamforming gain as antenna elements with higher directivity can be used; (iii) uniform angular resolution across all signal directions. Such benefits make RAA especially appealing for integrated sensing and communication (ISAC), particularly for low-altitude unmanned aerial vehicle (UAV) swarm ISAC, where high-mobility aerial targets may easily move away from the boresight of conventional antenna arrays, causing severe communication and sensing performance degradation. Therefore, this paper studies RAA-based ISAC for low-altitude UAV swarm systems. First, we establish an input-output mathematical model for RAA-based UAV ISAC and rigorously show that RAA achieves uniform angular resolution for all directions through beam pattern analysis. Besides, we design the RAA orientation and ray selection network (RSN) to fully reap its advantages. Furthermore, RAA-based ISAC with orthogonal frequency division multiplexing (OFDM) for UAV swarm is studied, and an efficient algorithm is proposed for sensing target parameter estimation. Extensive simulation results are provided to demonstrate the significant performance improvement by RAA system over the conventional antenna arrays, in terms of sensing angular resolution and communication spectral efficiency, highlighting the great potential of the novel RAA system to meet the growing demands of low-altitude UAV ISAC.

Unmanned aerial vehicle (UAV) is regarded as a key enabling platform for low-altitude economy, due to its advantages such as three-dimensional (3D) maneuverability, flexible deployment, and line-of-sight (LoS) air-to-air/ground communication links. In particular, the intrinsic high mobility renders UAV especially suitable for operating as a movable antenna (MA) from the sky. In this paper, by exploiting the flexible mobility of UAV swarm and antenna position adjustment of MA, we propose a novel UAV swarm enabled two-level MA system, where UAVs not only individually deploy a local MA array, but also form a larger-scale MA system with their individual MA arrays via swarm coordination. We formulate a general optimization problem to maximize the minimum achievable rate over all ground user equipments (UEs), by jointly optimizing the 3D UAV swarm placement positions, their individual MAs’ positions (or local positions), and receive beamforming for different UEs. To gain useful insights, we first consider the special case where each UAV has only one antenna, under different scenarios of one single UE, two UEs, and arbitrary number of UEs. In particular, for the two-UE case, we derive the optimal UAV swarm placement positions in closed-form that achieves inter-UE interference (IUI)-free communication when the uniform plane wave (UPW) model holds, where the UAV swarm forms a uniform sparse array (USA) satisfying minimum safe distance constraint. While for the general case with arbitrary number of UEs, we propose an efficient alternating optimization algorithm to solve the formulated non-convex optimization problem. Then, we extend the results to the case where each UAV is equipped with multiple antennas. Numerical results verify that the proposed low-altitude UAV swarm enabled MA system significantly outperforms various benchmark schemes, thanks to the exploitation of two-level mobility to create more favorable channel conditions for multi-UE communications.

As one of the key usage scenarios for the sixth generation (6G) wireless networks, integrated sensing and communication (ISAC) provides an efficient framework to achieve simultaneous wireless sensing and communication. However, traditional wireless sensing techniques mainly rely on line-of-sight (LoS) assumptions, i.e., the sensing targets are directly visible to both the sensing transmitter and receiver. This prevents ISAC systems from being applied in complex environments such as the urban low-altitude airspace, which usually suffers from signal blockage and non-line-of-sight (NLoS) multipath propagation. To address this challenge, in this paper, we propose a novel approach to enable environment-aware NLoS ISAC by leveraging the new technique called channel knowledge map (CKM), which was originally proposed for environment-aware wireless communications. One major novelty of our proposed method is that the same CKM built for wireless communication can be directly used to enable NLoS wireless sensing, thus enjoying the benefits of “killing two birds with one stone”. To this end, the sensing targets are treated as virtual user equipment (UE), and the wireless communication channel priors are transformed into the sensing channel priors, allowing one single CKM to serve dual purposes. We illustrate our proposed framework using a specific CKM called channel angle-delay map (CADM). Specifically, the proposed framework utilizes CADM to derive angle-delay priors of the sensing channel by exploiting the relationship between communication and sensing angle-delay distributions, enabling sensing target localization in the challenging NLoS environment. Extensive simulation results demonstrate significant performance improvements over classic geometry-based sensing methods, which are further validated by Cramér-Rao Lower Bound (CRLB) analysis.

Extremely-large scale MIMO (XL-MIMO) is a key technology for 6G networks, thanks to its superior spatial resolution and beamforming gain. While the recently proposed ray antenna array (RAA) with directly-connected ULAs enables cost-effective beamforming without phase shifters, it may suffer from signal blockage due to coplanar element placement. To address this issue, we propose a cylinder directly-connected antenna array (DCAA) composed of multiple simple uniform circular arrays (sUCAs) in a layered three-dimensional (3D) structure. Each sub-array in sUCA forms a beam aligned with its orientation to achieve full spatial coverage. We characterize the structural design, provide an overall design guideline, and analytically analyze its performance. An input-output signal model is developed for uplink and downlink, along with optimization algorithms to maximize the achievable sum rate. Simulations under 3GPP channels validate the superior performance of the proposed structure, showing that the cylinder DCAA achieves uniform spatial resolution, higher achievable rate, lower hardware cost and power consumption than the conventional ULA with hybrid beamforming (HBF), highlighting its potential for XL-MIMO in millimeter wave (mmWave) and Terahertz (THz) systems.

Integrated sensing and communications (ISAC) is a pivotal technology for low-altitude unmanned aerial vehicle (UAV) swarm. As the sensing targets for UAV swarm systems are usually densely located, the conventional compact multi-input multi-output (MIMO) with half-wavelength antenna spacing usually leads to prohibitive hardware, energy and signal processing costs when large array aperture is needed to achieve fine spatial resolution. By relaxing the traditional half-wavelength spacing constraint, sparse MIMO may achieve a larger array aperture without having to increase the number of antenna elements or radio frequency (RF) chains, which improves spatial resolution for both communication and sensing. Besides, sparse MIMO may also result in a larger near-field region. Therefore, in this paper, we study the near-field sparse MIMO bistatic orthogonal frequency division multiplexing (OFDM)-ISAC for low-altitude UAV swarm systems and propose the framework that utilizes physical array for communication while virtual array for sensing. The proposed method forms virtual arrays at the ISAC transmitter and sensing receiver simultaneously, while eliminating the angle and range coupling effect. As a result, high-resolution angle estimation based on virtual array is achieved. Simulation results demonstrate that sparse MIMO simultaneously improves communication sum rates and sensing resolution compared to the conventional compact MIMO.

Integrated sensing and communication (ISAC) is a promising technique for expanding the functionalities of wireless networks with enhanced spectral efficiency. The 3rd Generation Partnership Project (3GPP) has defined six basic sensing operation modes in wireless networks. To further enhance the sensing capability of wireless networks, this paper proposes a new sensing operation mode, i.e., the base station (BS) and user equipment (UE) cooperative sensing. Specifically, after decoding the communication data, the UE further processes the received signal to extract the target sensing information. We propose an efficient algorithm for fusing the sensing results obtained by the BS and UE, by exploiting the geometric relationship among BS, UE and targets as well as the expected sensing quality in the BS monostatic and BS-UE bistatic sensing. The results show that the proposed data fusion method for cooperative sensing can effectively improve the position and velocity estimation accuracy of multiple targets, and provide a new approach on the expansion of the sensing pattern.

This paper proposes a novel multi-antenna architecture, termed ray antenna array (RAA), which aims to enhance wireless communication performance in a cost-effective manner. RAA is composed of massive cheap antenna elements and a few radio frequency (RF) chains. The massive antenna elements are arranged in a novel ray-like structure, with each ray corresponding to a simple uniform linear array (sULA) with a carefully designed orientation. The antenna elements of each sULA are directly connected to an RF combiner, so that the sULA in each ray is able to form a beam towards a direction matching the ray orientation without relying on any analog or digital beamforming. By further designing a ray selection network (RSN), appropriate sULAs are selected to connect to the RF chains for further baseband processing. Compared to conventional multi-antenna architectures like hybrid analog/digital beamforming (HBF), the proposed RAA has two major advantages. First, it can significantly reduce hardware cost since no phase shifters, which are usually expensive especially in high-frequency systems, are required. Besides, RAA can greatly improve system performance by configuring antenna elements with higher directionality, as each sULA only needs to be responsible for a portion of the total coverage angle. To demonstrate such advantages, in this paper, we first present the input-output model for RAA-based wireless communications, based on which the ray orientations of the RAA are designed. Furthermore, efficient algorithms for joint ray selection and beamforming are proposed for single-user and multi-user RAA-based wireless communications. Simulation results demonstrate the superior performance of RAA compared to HBF while significantly reducing hardware cost.

Channel knowledge map (CKM) is a promising technique that enables environment-aware wireless networks by utilizing location-specific channel prior information to improve communication and sensing performance. A fundamental problem for CKM construction is how to utilize partially observed channel knowledge data to reconstruct a complete CKM for all possible locations of interest. This problem resembles the long-standing ill-posed inverse problem, which tries to infer from a set of limited observations the cause factors that produced them. By utilizing the recent advances of solving inverse problems with generative artificial intelligence (AI), in this paper, we propose generative CKM construction method using partially observed data by solving inverse problems with diffusion models. Simulation results show that the proposed method significantly improves the performance of CKM construction compared with benchmarking schemes.

Integrated sensing and communications (ISAC) in distributed networks presents an efficient solution for multi-target localization. This paper investigates the joint target matching and parameter estimation with multiple cooperative base stations (BSs), where each BS can only estimate the range and radial velocity of a subset of targets. The interdependence of these tasks introduces a mixed measurement-to-target association (MTA) problem, characterized by prohibitively significant computational complexity due to the binary constraints and the growing number of variables with an increasing number of targets and BSs. To address this challenge, we propose an iterative algorithm that alternates between target matching and parameter estimation, supported by a heuristic initialization method to enhance efficiency. Simulation results demonstrate the robustness of the proposed method, achieving high matching accuracy under different system configurations, including scenarios with low sensing resolution and an increasing number of targets.

For orthogonal frequency division multiplexing (OFDM) integrated sensing and communication (ISAC), in order to perform efficient delay and Doppler estimation, the equivalent data symbols on the time-frequency resource element need to be removed, so that the resulting signal obeys the manifold structure with phases linearly increasing across subcarriers/OFDM symbols. However, this task becomes more challenging for multiple-input and multiple-output (MIMO)-OFDM ISAC with spatial multiplexing, since the received equivalent data symbols in time-frequency resource element not only depend on the transmitted information-bearing symbols, but also depend on the transmit steering vectors and the precoding matrix. To address such challenges, this paper proposes an effective sensing signal processing method for MIMO-OFDM ISAC with spatial multiplexing. Specifically, we first estimate the angle of arrival, followed by channel matrix decomposition to obtain transmit steering vector, then reconstruct the equivalent symbol. After removing the equivalent symbol from the received signal, the classical periodogram algorithm can be used to estimate the range and Doppler of targets. Simulation results show that the proposed method achieves effective parameter estimation for MIMO-OFDM ISAC with spatial multiplexing.

In this paper, we propose an integrated sensing and communication (ISAC) target recognition method based on radar cross section (RCS) profile estimation. Compared with traditional recognition methods such as inverse synthetic aperture radar (ISAR), the estimation of target’s RCS features does not require a large signal bandwidth to provide high resolution nor complex signal processing methods. We first study the variation of RCS with the observation angle during the target’s movement. Then, we use Periodogram algorithm for position and RCS estimation based on OFDM echo signal. After that, we create the POFACETS-based RCS database. The result of target recognition is obtained by matching the sampling RCS sequence with the database. Finally, we choose correlation coefficient as the criterion to reduce the complexity of data matching. Simulation results show that an accuracy of approximately 98.7% is achieved in target recognition.

Channel knowledge map (CKM) is a novel technique for achieving environment-aware wireless communication and sensing. CKM exchange among different base stations (BSs) is needed to achieve efficient BS cooperations. This paper proposes an efficient CKM exchange method based on semantic communication to facilitate the utilization of CKM at dynamic users. The proposed semantic communication framework achieves the tradeoff between the bandwidth consumption and reconstruction quality of CKM. Compared to traditional image compression and transmission methods, the semantic communication approach demonstrates higher stability and reliability in CKM exchange, especially under low SNR region. Besides, semantic communication based on deep learning can also effectively mitigate the measurement errors associated with CKM reconstruction. Simulation results show that under 4 times compression ratio, the proposed method outperforms traditional methods in terms of stability and can effectively transmit severely corrupted data, recovering CKMs close to the original values.

Channel knowledge map (CKM) is a novel technique for achieving environment awareness, and thereby improving the communication and sensing performance for wireless systems. A fundamental problem associated with CKM is how to construct a complete CKM that provides channel knowledge for a large number of locations based solely on sparse data measurements. This problem bears similarities to the super-resolution (SR) problem in image processing. In this paper, we propose an effective deep learning-based CKM construction method that leverages the image SR network known as SRResNet. Unlike most existing studies, our approach does not require any additional input beyond the sparsely measured data. In addition to the conventional path loss map construction, our approach can also be applied to construct channel angle maps (CAMs), thanks to the use of a new dataset called CKMImageNet. The numerical results demonstrate that our method outperforms interpolation-based methods such as nearest neighbour and bicubic interpolation, as well as the SRGAN method in CKM construction. Furthermore, only 1/16 of the locations need to be measured in order to achieve a root mean square error (RMSE) of 1.4 dB in path loss.

This paper proposes an advanced framework utilizing channel knowledge map (CKM) to strengthen secure communication and facilitate eavesdropper localization. CKM is established to learn the spatially unique channel characteristics, enabling it to distinguish between legitimate user equipment (UE) and eavesdropper channels without requiring prior knowledge of the latter. This new capability addresses fundamental challenges in conventional physical-layer security methods that rely heavily on prior information of channel state information (CSI). By leveraging CKM, the framework enables multiple critical functionalities, including eavesdropper detection and localization, as well as beamforming design for secure communication. Simulation results validate the advantages of the proposed CKM-enabled approach over several benchmarks, such as statistical model-based method, particularly in terms of signal-to-leakage-and-noise ratio (SLNR) and localization performance.

Integrated super-resolution sensing and communication (IS2AC) in symbiotic radio (SR) systems enables highly efficient simultaneous localization and information transmission for both primary and secondary user devices. However, accurate estimation of the two-dimensional (2-D) directions of the backscatter devices (BDs) poses a great challenge when they are densely located. To address this issue, in this paper, we propose a sparse multiple-input multiple-output (MIMO)-based IS2AC SR system with L-shaped nested array (NA) deployed at the base station (BS) to achieve both super-resolution 2-D sensing for multiple secondary BDs and efficient communication for all users. To achieve efficient channel estimation, the elevation and azimuth angles of the primary and secondary users in SR systems are firstly estimated and paired. After that, by matching the paired angles to obtain beamforming gain, the channel gains of both direct and backscatter links are estimated. To evaluate the performance of channel estimation with L-shaped NA, the sum mean square error (MSE) of all channels are analyzed. Simulation results are provided to demonstrate that sparse MIMO can provide significant performance gain than conventional compact MIMO.

Delay alignment modulation (DAM) is recently proposed as an effective technique to address the inter-symbol interference (ISI) issue, which circumvents the conventional channel equalization and multi-carrier transmission. Moreover, wireless communications are vulnerable to malicious eavesdropping and attacks due to their inherent open and broadcast nature. In particular, DAM not only eliminates the ISI at the desired receiver but may also introduce ISI to other locations, and thus is quite promising for secure communications. This paper considers the near-field secure wireless communication with DAM. To gain useful insights, it is first shown that when the antenna number of Alice is much larger than the number of multipaths for Bob and Eve, the delay compensation and low-complexity path-based maximal-ratio transmission (MRT) beamforming achieve a communication free of ISI and information leakage, owing to the asymptotically orthogonal property brought by the near-field nonuniform spherical wave (NUSW). The secrecy rate performance of path-based zero-forcing (ZF) beamforming toward ISI-free communication is then evaluated. Furthermore, the path-based optimized DAM beamforming scheme is proposed to maximize the secrecy rate, by considering the general case in the presence of some tolerable ISI. As a comparison, the benchmarking scheme of the artificial noise (AN) based orthogonal frequency-division multiplexing (OFDM) is considered. Simulation results show that DAM achieves a higher secrecy rate and lower peak-to-average-power ratio (PAPR) than the AN-based OFDM.

This study addresses the challenges of near-field interference suppression and resource allocation in extremely large-scale multiple-input multiple-output (XL-MIMO) communication systems, particularly under dense-user scenarios. We propose a quality-of-service (QoS)-aware joint user scheduling and power control scheme. Leveraging the spherical wave (SW) characteristics of near field channels, a dual-domain interference suppression strategy is developed by analyzing the spatial correlation of beam focusing vectors in terms of both angular separation and distance constraints. Based on this, a spatial correlation-based scheduling (SCS) algorithm is designed. By integrating this user selection strategy with a dynamic power allocation mechanism, the proposed approach optimizes the sum spectral efficiency while ensuring the user QoS. This framework is further extended to modular XL-MIMO systems. We show how modular deployment can enhance spatial resolution and develop an adapted QoS-aware user scheduling algorithm, called modular SCS (SCS-mod), for this architecture. Simulation results validate that the proposed algorithms significantly outperform existing schemes in terms of sum spectral efficiency and the number of scheduled users, especially under high user density and high transmission power conditions.

In this paper, we investigate the far-field and near-field codebook-based beam training for multi-user modular extremely large-scale multiple-input multiple-output (XL-MIMO) communications, utilizing a modular extremely large-scale uniform linear array (XL-ULA) at the base station (BS). Unlike conventional collocated XL-ULA with all adjacent elements separated by signal wavelength scale, the modular XL-ULA has different inter-module and intra-module spacings, rendering the existing near-field polar-domain codebook design ineffective. To cater to the modular array architecture, one straightforward approach to beam codebook design is to remove those elements corresponding to the modular space in the conventional polar-domain codebook. However, such a naive polar-domain codebook for modular XL-ULA results in undesired grating lobes. To overcome this challenge, we propose a novel near-field optimization-based codebook design, by exploiting the priori knowledge about the users’ potential angular/distance range to minimize the levels of side lobes while maintaining the main lobe beamforming gain. Furthermore, based on the designed near-field codebooks, an efficient multi-beam training scheme enabled by grating lobes is devised for multi-user modular XL-MIMO communications. Numerical results verify the effectiveness of the proposed near-field optimization-based codebook for modular XL-MIMO in mitigating inter-user interference (IUI) for ultra-dense users, as well as the efficiency of the multi-beam training scheme.

With the increasing demand for real-time channel state information (CSI) in sixth-generation (6G) mobile communication networks, channel knowledge map (CKM) emerges as a promising technique, offering a site-specific database that enables environment-awareness and significantly enhances communication and sensing performance by leveraging a priori wireless channel knowledge. However, efficient construction and utilization of CKMs require high-quality, massive, and location-specific channel knowledge data that accurately reflects the real-world environments. Inspired by the great success of ImageNet dataset in advancing computer vision and image understanding in artificial intelligence (AI) community, we introduce CKMImageNet, a dataset developed to bridge AI and environment-aware wireless communications and sensing by integrating location-specific channel knowledge data, high-fidelity environmental maps, and their visual representations. CKMImageNet supports a wide range of AI-driven approaches for CKM construction with spatially consistent and location-specific channel knowledge data, including both supervised and unsupervised, as well as discriminative and generative AI methods. The dataset is built using advanced ray-tracing techniques, ensuring high fidelity and environmental accuracy. By addressing key challenges in CKM construction and enabling AI models to learn environment-aware propagation patterns, CKMImageNet may serve as a foundational tool for advancing environment-aware 6G systems, ranging from network planning such as communication base station (BS) site selection and sensing anchor node placement, to pro-active resource allocation such as beam alignment, power allocation, interference avoidance, clutter rejection, and robot trajectory planning. Compared with existing datasets like RadioMapSeer, CKMImageNet not only provides numerical and visual representation to channel gain values, but also more diversified channel knowledge like multipath angles of arrival (AoAs) and path delays. Moreover, the dataset offers images with multiple sizes to cater to different application scenarios.

Channel knowledge map (CKM) is a promising technology to enable environment-aware wireless communications and sensing. Link state map (LSM) is one particular type of CKM that aims to learn the location-specific line-of-sight (LoS) link probability between the transmitter and the receiver at all possible locations, which provides the prior information to enhance the communication quality of dynamic networks. This paper investigates the LSM construction for cellular-connected unmanned aerial vehicles (UAVs) by utilizing both the expert empirical mathematical model and the measurement data. Specifically, we first model the LSM as a binary spatial random field and its initial distribution is obtained by the empirical model. Then we propose an effective binary Bayesian filter to sequentially update the LSM by using the channel measurement. To efficiently update the LSM, we establish the spatial correlation models of LoS probability on the location pairs in both the distance and angular domains, which are adopted in the Bayesian filter for updating the probabilities at locations without measurements. Simulation results demonstrate the effectiveness of the proposed algorithm for LSM construction, which significantly outperforms the benchmark scheme, especially when the measurements are sparse.

The unmanned aerial vehicle (UAV)-enabled data collection system with a rotary-wing UAV and multiple ground nodes (GNs) is investigated in this paper. The average transmission data rate is maximized through the coordinated optimization of the GNs’ transmit power and the UAV’s trajectory. In particular, the UAV dynamic constraints and physical constraints are imposed. The UAV dynamics, which are governed by a group of differential equations, are usually ignored in existing works. As a consequence, the planned trajectory cannot be fully tracked by the controller in real world applications, which could lead to severe performance degradation. Thus, a control-based method is devised to address this issue. Specifically, by adopting the state-space model from control theory, the data collection problem is established as a dynamic optimization problem subject to state constraints, in which both of the decision variables and constraints are infinite-dimensional in nature. The key idea of the solution method is to convert the infinite-dimensional dynamic program into a finite-dimensional static nonlinear problem. This is achieved by deriving the required gradients of the dynamic optimization problem based on the control parametrization scheme and an exact penalty function method. The effectiveness and superiority of the proposed design are validated via numerical experiments.

Integrated sensing and communication (ISAC) systems may face a heavy computation burden since the sensory data needs to be further processed. This paper studies a novel system that integrates sensing, communication, and computation, aiming to provide services for different objectives efficiently. This system consists of a multi-antenna multi-functional base station (BS), an edge server, a target, and multiple single-antenna communication users. The BS needs to allocate the available resources to efficiently provide sensing, communication, and computation services. Due to the heavy service burden and limited power budget, the BS can partially offload the tasks to the nearby edge server instead of computing them locally. We consider the estimation of the target response matrix, a general problem in radar sensing, and utilize Cramér-Rao bound (CRB) as the corresponding performance metric. To tackle the non-convex optimization problem, we propose both semidefinite relaxation (SDR)-based alternating optimization and SDR-based successive convex approximation (SCA) algorithms to minimize the CRB of radar sensing while meeting the requirement of communication users and the need for task computing. Furthermore, we demonstrate that the optimal rank-one solutions of both the alternating and SCA algorithms can be directly obtained via the solver or further constructed even when dealing with multiple functionalities. Simulation results show that the proposed algorithms can provide higher target estimation performance than state-of-the-art benchmarks while satisfying the communication and computation constraints.

To enhance global data transmission, uncrewed aerial vehicle (UAV)-aided space-air-ground integrated networks (SAGIN) represent a pivotal direction for future advancements. In this paper, we focus on the trajectory optimization problem with the goal of maximizing the energy efficiency (EE), thereby balancing the system capacity with energy expenditure. To this end, we first introduce a cell-free SAGIN network where UAVs function as flying access points to serve ground user equipment (GUE). Given that the transmission power of satellite direct-to-cell devices typically exceeds that of GUEs, we investigate the interference effect and derive exact closed-form expressions for the uplink spectral efficiency. In order to improve the service access efficiency, a GUE grouping scheme based on density distribution is proposed. Then, an effective EE analysis model is established considering the power consumption of fixed-wing UAVs. To solve the UAV trajectory optimization problem, two algorithms over two timescales are proposed: a successive convex approximation strategy and a multi-agent reinforcement learning (MARL)-based algorithm. In particular, to reduce the algorithmic complexity, we employ a shared Critic network in the proposed MARL algorithm to reduce the training parameters. Importantly, our approach comprehensively optimizes the UAV trajectory, acceleration, and velocity parameters. The results show that the proposed GUE grouping algorithm and the MARL-based optimization algorithm demonstrate adaptability in dynamic time-varying environments.

Passive metal reflectors for communication enhancement have appealing advantages such as ultra low cost, zero energy expenditure, maintenance-free operation, long life span, and full compatibility with legacy wireless systems. To unleash the full potential of passive reflectors for wireless communications, this paper proposes a new passive reflector architecture, termedflexible reflector(FR), for enabling the flexible adjustment of beamforming direction via the FR placement and rotation optimization. We consider the multi-FR aided area coverage enhancement and aim to maximize the minimum expected receive power over all locations within the target coverage area, by jointly optimizing the placement positions and rotation angles of multiple FRs. To gain useful insights, the special case of movable reflector (MR) with fixed rotation is first studied to maximize the expected receive power at a target location, where the optimal single-MR placement positions for electrically large and small reflectors are derived in closed-form, respectively. It is shown that the reflector should be placed at the specular reflection point for electrically large reflector. While for area coverage enhancement, the optimal placement is obtained for the single-MR case and a sequential placement algorithm is proposed for the multi-MR case. Moreover, for the general case of FR, joint placement and rotation design is considered for the single-/multi-FR aided coverage enhancement, respectively. Numerical results are presented which demonstrate significant performance gains of FRs over various benchmark schemes under different practical setups in terms of receive power enhancement.

For integrated sensing and communication (ISAC) systems, channel information that is essential for communication and sensing tasks fluctuates at different timescales. Specifically, the composite channel state information (CSI) for wireless communication is static during channel coherence time. However, this concept is less appropriate for describing the wireless channel for sensing. To this end, in this paper, we first introduce a new timescale to study the real-time variations of the path state information (PSI) (e.g., delay, angle, and Doppler) of individual multi-path, termed path-invariant time, during which the PSI remains constant. As the goal of environment sensing for PSI essentially aligns with the channel estimation for the recently proposed delay-Doppler alignment modulation (DDAM) technique, we introduce a novel framework for a bi-static ISAC system, which refers to as DDAM-based ISAC. To acquire the PSI, in this paper, by capitalizing on the dual timescales of wireless channels, we propose a novel algorithm, termed as adaptive simultaneously orthogonal matching pursuit algorithm with support refinement (ASOMP-SR). The performance of DDAM with the imperfectly sensed PSI is analyzed, where the signal-to-interference-plus-noise ratio (SINR) and the achievable spectral efficiency are derived. Numerical results unveil that the proposed ASOMP-SR algorithm achieves better sensing performance than the conventional orthogonal matching pursuit (OMP) algorithm, in terms of the normalized mean squared error (NMSE) and the number of multi-paths resolved. In addition, DDAM-based ISAC can achieve superior spectral efficiency and a reduced peak-to-average power ratio (PAPR) compared to standard orthogonal frequency division multiplexing (OFDM).

For millimeter wave (mmWave) or Terahertz (THz) communications, by leveraging the high spatial resolution offered by large antenna arrays and the multi-path sparsity of mmWave/THz channels, a novel inter-symbol interference (ISI) mitigation technique called delay alignment modulation (DAM) has been recently proposed. The key ideas of DAM aredelay pre-compensationandpath-based beamforming. However, existing research on DAM is mainly based on fully digital beamforming, which requires the number of radio frequency (RF) chains to be equal to the number of antennas. This paper proposes the hybrid analog/digital beamforming based DAM, including both fully and partially connected structures. The analog and digital beamforming matrices are designed to achieve performance close to DAM based on fully digital beamforming. While DAM was considered for the path-based channel model with integer delays in the previous work, this paper extends DAM to a more general tap-based model that accounts for fractional path delays. To further reduce the cost of channel estimation and improve the performance for wireless channels with fractional delays, DAM with codebook-based beam alignment and DAM-orthogonal frequency division multiplexing (DAM-OFDM) with hybrid beamforming are proposed. The effectiveness of the proposed techniques is verified by extensive simulation results.

Channel gain map (CGM) is a promising technique that enables the environment-aware communications by providing a priori channel gain information for users at arbitrary locations. The construction of CGM can be viewed as spatial channel prediction from the communication perspective, which motivates the channel model-based construction algorithm. On the other hand, it can also be viewed as spatial data interpolation from the geostatistic perspective, where a bunch of model-free methods can be adopted. This paper compares model-based and model-free approaches for CGM construction. We extend the existing model-based channel prediction by introducing a multi-mode channel model to address the spatial inconsistency issue in conventional models. The model-based methods are then compared with various model-free spatial interpolation methods, including a deep learning method. The results show that the channel model may introduce substantial bias for CGM construction in complex environments.

Cell-free wireless communication is envisioned as one of the most promising network architectures, which can achieve stable and uniform communication performance while improving the system energy and spectrum efficiency. The deployment of cell-free networks is envisioned to be a long-term evolutionary process, in which cell-free access points (APs) will be gradually introduced into the communication network and collaborate with the existing cellular base stations (BSs). To further explore the performance limits of hybrid cellular and cell-free networks, this paper develops a hybrid network model based on stochastic geometric toolkits, which reveals the coupling of the signal and interference from both the cellular and cell-free networks. Specifically, the conjugate beamforming is applied in hybrid cellular and cell-free networks, which enables user equipment (UE) to benefit from both cellular BSs and cell-free APs. The aggregate signal received from the hybrid network is approximated via moment matching, and coverage probability is characterized by deriving the Laplace transform of the interference. The analysis of signal strength and coverage probability is verified by extensive simulations.

Delay alignment modulation (DAM) is a promising communication technology to mitigate inter-symbol interference (ISI) without relying on sophisticated channel equalization or multi-carrier transmissions. The key ideas of DAM are delay pre-compensation and path-based beamforming, so that the multi-path signal components will arrive at the receiver simultaneously and constructively, rather than causing the detrimental ISI. However, the practical implementation of DAM requires channel state information (CSI) at the transmitter side. Therefore, in this paper, we study an efficient channel estimation method for DAM based on block orthogonal matching pursuit (BOMP) algorithm, by exploiting the block sparsity of the channel impulse response (CIR) vector. Based on the imperfectly estimated CSI, the delay pre-compensations and path-based beamforming are designed for DAM, and the resulting performance is studied. Simulation results demonstrate that with the BOMP-based channel estimation method, the CSI can be effectively acquired with low training overhead, and the performance of DAM based on estimated CSI is comparable to the ideal case with perfect CSI.

Integrated super-resolution sensing and communication (ISSAC) is a promising technology to achieve extremely high sensing performance for critical parameters, such as the angles of the wireless channels. In this paper, we propose an ISSAC-based channel estimation method, which requires little or even no pilot, yet still achieves accurate channel state information (CSI) estimation. The key idea is to exploit the fact that subspace-based super-resolution algorithms such as multiple signal classification (MUSIC) do not require a priori known pilots for accurate parameter estimation. Therefore, in the proposed method, the angles of the multi-path channel components are first estimated in a pilot-free manner while communication data symbols are sent. After that, the multi-path channel coefficients are estimated, where very little pilots are needed. The reasons are two folds. First, compared to the conventional channel estimation methods purely relying on channel training, much fewer parameters need to be estimated once the multi-path angles are accurately estimated. Besides, with angles obtained, the beamforming gain is also enjoyed when pilots are sent to estimate the channel path gains. To rigorously study the performance of the proposed method, we first consider the basic line-of-sight (LoS) channel. By analyzing the minimum mean square error (MMSE) of channel estimation and the resulting beamforming gains, we show that our proposed method significantly outperforms the conventional methods purely based on channel training. We then extend the study to the more general multipath channels. Simulation results are provided to demonstrate our theoretical results.

Delay alignment modulation (DAM) is a novel transmission technique for wireless systems with high spatial resolution by leveraging delay compensation and path-based beamforming, to mitigate the inter-symbol interference (ISI) without resorting to complex channel equalization or multi-carrier transmission. However, most existing studies on DAM consider a simplified sce-nario by assuming that the channel multi-path delays are integer multiples of the signal sampling interval. This paper investigates DAM for the more general and practical scenarios with fractional multi-path delays. We first analyze the impact of fractional multi-path delays on the existing DAM design, termed integer DAM (iDAM), which can only achieve delay compensations that are integer multiples of the sampling interval. It is revealed that the existence of fractional multi-path delays renders iDAM no longer possible to achieve perfect delay alignment. To address this issue, we propose a more generic DAM design called fractional DAM (fDAM), which achieves fractional delay pre-compensation via upsampling and fractional delay filtering. By leveraging the Farrow filter structure, the proposed approach can eliminate ISI without real-time computation of filter coefficients, as typically required in traditional channel equalization techniques. Simulation results demonstrate that the proposed fDAM outperforms the existing iDAM and orthogonal frequency division multiplexing (OFDM) in terms of symbol error rate (SER) and spectral efficiency, while maintaining a comparable peak-to-average power ratio (PAPR) as iDAM, which is considerably lower than OFDM.

Reliability and latency are two key performance indicators of communications. This paper investigates the tradeoff between them over a random packet erasure channel in the absence of feedback. In contrast to the instant feedback case where guaranteed reliability with low latency can be achieved by simple automatic repeat query (ARQ), we show that in the absence of feedback, the reliability increases with the coding window size, at the cost of the degraded latency performance. Specifically, we propose a sliding window network coding (SWNC) scheme that works in the absence of feedback and achieves various reliability and latency tradeoff by adjusting the coding window size. The tradeoff between the reliability and latency are investigated by deriving the achievable performance of the proposed SWNC scheme as a function of the coding window size. We further show that the proposed scheme degenerates to the existing benchmark schemes that are superior in either latency or reliability, and it has much higher flexibility.

Thanks to the great potential to alleviate the intercell interference issue, cell-free wireless network is regarded as one of the most promising networking architectures in the future. In the meantime, the dramatic increase in the number of wireless devices and their diversified communication rate requirements pose new challenges for cell-free networks. In this article, we integrate the new symbiotic radio (SR) transmission technique into cell-free networks. In particular, spectral- and energy-efficient passive backscatter communication in SR is achieved by passive reflective beamforming over multiple reconfigurable intelligent surfaces (RISs). On the one hand, distributed access points (APs) in cell-free network collaboratively perform active communication through direct links, and on the other hand, RISs reuse the spectrum and energy of active communication to passively backscatter its own information-bearing signal. Considering the coexistence of active and passive communication demands, we define the rate region of the RIS-based cell-free SR system as the union of all rate pairs achieved by the active and passive communication devices. To characterize the achievable rate region, we formulate an optimization problem to maximize the passive communication rate given a minimum active rate constraint, by jointly optimizing the active transmit beamforming and passive reflective beamforming. An efficient alternating algorithm is proposed to solve the formulated problem. Finally, simulation results are presented to show the rate region of RIS-based cell-free SR systems and demonstrate the effectiveness of the proposed joint beamforming algorithm.

Abstract Near-field technology is increasingly recognized due to its transformative potential in communication systems, establishing it as a critical enabler for sixth-generation (6G) telecommunication development. This paper presents a comprehensive survey of recent advancements in near-field technology research. First, we explore the near-field propagation fundamentals by detailing definitions, transmission characteristics, and performance analysis. Next, we investigate various near-field channel models—deterministic, stochastic, and electromagnetic information theory based models, and review the latest progress in near-field channel testing, highlighting practical performance and limitations. With evolving channel models, traditional mechanisms such as channel estimation, beamtraining, and codebook design require redesign and optimization to align with near-field propagation characteristics. We then introduce innovative beam designs enabled by near-field technologies, focusing on non-diffractive beams (such as Bessel and Airy) and orbital angular momentum (OAM) beams, addressing both hardware architectures and signal processing frameworks, showcasing their revolutionary potential in near-field communication systems. Additionally, we highlight progress in both engineering and standardization, covering the primary 6G spectrum allocation, enabling technologies for near-field propagation, and network deployment strategies. Finally, we conclude by identifying promising future research directions for near-field technology development that could significantly impact system design. This comprehensive review provides a detailed understanding of the current state and potential of near-field technologies.

This paper investigates the near-field spatial correlation (SC) for extremely large-scale multiple-input multiple-output (XL-MIMO) communications, where the positions of scatterers and/or users may be in the radiative near-field region due to the large size of the antenna array. Therefore, the conventional far-field uniform plane wave (UPW) assumption is no longer valid. By discarding the UPW assumption, we consider the generic non-uniform spherical wave (NUSW) model for accurate characterization of signal amplitude and phase variations on different antenna elements. A novel expression is derived for the near-field SC in terms of the scatterer distribution, which generalizes the conventional far-field SC. It is revealed that the developed near-field SC is determined by the power location spectrum (PLS), which is characterized not only by the scatterers’ directions but also by their distances from the array, while the conventional far-field model is only dependent on the power angular spectrum (PAS). In addition, the near-field SC no longer exhibits spatial wide-sense stationarity (SWSS), since the SC coefficient between each pair of transmit-receive antenna components is determined by their specific positions, not just by their relative locations. Furthermore, the developed near-field SC is utilized to obtain the optimal transmission strategy to maximize the ergodic spectral efficiency based on statistical channel state information (CSI). Besides, we consider the specific multi-ring scattering model for scatterer distribution, where semi-closed expressions for the near-field SC can be obtained. Simulation results are given to validate the developed near-field SC for XL-MIMO communications.

In this paper, we consider an intelligent reflecting surface (IRS) assisted downlink multiuser communication system with improper Gaussian signaling (IGS) that serves as generalized Gaussian signaling and can effectively combat multiuser interference. We focus on the max-min achievable rate optimization problem by jointly optimizing the transmit beamforming vectors and reflecting phase shifts, subject to the transmit power budget constraint at the access point (AP). We propose a low-complexity iterative algorithm based on a two-layer iterative procedure, which differs from these existing algorithms that rely on inefficient alternating optimization framework and high computational complexity convex optimization tools. Specifically, in the outer layer procedure, we employ a tractable lower bound of user communication rate to reformulate the original problem and repeatedly update the lower bound in each iteration. In the inner layer procedure, based on the alternating direction method of multipliers (ADMM), we decompose the reformulated problem into several convex sub-problems, which can be alternately solved by closed-form solutions. Furthermore, we study the initialization, convergence, and computational complexity of the proposed algorithm. Additionally, we simplify the algorithm to make it applicable for the cases of conventional proper Gaussian signaling (PGS) and without IRS. Finally, numerical results validate the advantages of the proposed algorithm over benchmarking algorithm in terms of rate performance and average execution time.

Intelligent reflecting surface (IRS) is an emerging technology for wireless communications, thanks to its powerful capability to engineer the radio environment. However, in practice, this benefit is attainable only when the passive IRS is of sufficiently large size, for which the conventional uniform plane wave (UPW)-based far-field model may become invalid. In this paper, we pursue a near-field modelling and performance analysis for wireless communications with extremely large-scale IRS (XL-IRS). By taking into account the directional gain pattern of IRS’s reflecting elements and the variations in signal amplitude across them, we derive both the lower- and upper-bounds of the resulting signal-to-noise ratio (SNR) for the generic uniform planar array (UPA)-based XL-IRS. Our results reveal that, instead of scaling quadratically and unboundedly with the number of reflecting elementsMas in the conventional UPW-based model, the SNR under the new non-uniform spherical wave (NUSW)-based model increases withMwith a diminishing return and eventually converges to a certain limit. To gain more insights, we further study the special case of uniform linear array (ULA)-based XL-IRS, for which a closed-form SNR expression in terms of the IRS size and locations of the base station (BS) and the user is derived. Our result shows that the SNR is mainly determined by the two geometric angles formed by the BS/user locations with the IRS, as well as the dimension of the IRS. Numerical results validate our analysis and demonstrate the necessity of proper near-field modelling for wireless communications aided by XL-IRS.

Integrated sensing and communication (ISAC) is studied in the airborne domain, where Unmanned Aerial Vehicles (UAVs) act as communication base stations and radars simultaneously. The UAV transmits signals to users while leveraging these signals to localize targets. This research focuses on jointly improving communication and sensing (C&S) performances by designing the UAV trajectory and allocating user’s bandwidth. Since UAV’s sustainability is determined by its onboard battery, energy supply is considered as a constraint in the trajectory design. Communication performance is evaluated by total transmitted data, while sensing performance is assessed through Cramér-Rao bound (CRB). A tradeoff objective is formulated with normalization. To achieve a flexible tradeoff between C&S, the trajectory design is formulated as a weighted sum optimization problem. To improve the formulation accuracy of trajectory design, a multi-stage trajectory design (MSTD) is proposed. While the resultant design problem is difficult to solve directly, an iterative algorithm is developed to obtain a local optimal solution of UAV trajectory. Finally, numerical results are presented to show UAV trajectories determined by the tradeoff between C&S and the energy supply. Benefits of ISAC-based UAV scenario are highlighted by comparing the single-functional UAV scenarios.

In this paper, we investigate multi-user modular extremely large-scale multiple-input multiple-output (XL-MIMO) communication systems, where modular extremely large-scale uniform linear array (XL-ULA) is deployed at the base station (BS) to serve multiple single-antenna users. By exploiting the unique modular array architecture and considering the potential near-field propagation, we develop sub-array based uniform spherical wave (USW) models for distinct versus common angles of arrival/departure (AoAs/AoDs) with respect to different sub-arrays/modules, respectively. Under such USW models, we analyze the beam focusing patterns at the near-field observation location by using near-field beamforming. The analysis reveals that compared to the conventional XL-MIMO with collocated antenna elements, modular XL-MIMO can provide better spatial resolution by benefiting from its larger array aperture. However, it also incurs undesired grating lobes due to the large inter-module separation. Moreover, it is found that for multi-user modular XL-MIMO communications, the achievable signal-to-interference-plus-noise ratio (SINR) for users may be degraded by the grating lobes of the beam focusing pattern. To address this issue, an efficient user grouping method is proposed for multi-user transmission scheduling, so that users located within the grating lobes of each other are not allocated to the same time-frequency resource block (RB) for their communications. Numerical results are presented to verify the effectiveness of the proposed user grouping method, as well as the superior performance of modular XL-MIMO over its collocated counterpart with densely distributed users.

The sixth generation (6G) mobile communication networks are expected to offer a new paradigm of cellular integrated sensing and communication (ISAC). However, due to the intrinsic difference between wireless sensing and communication in terms of coverage requirement, current cellular networks that are deliberately planned mainly for communication coverage are difficult to achieve seamless sensing coverage. Therefore, this paper studies the coverage issue for cellular ISAC systems, which aims to concurrently sense a prescribed region while serving a group of communication users equipment (UEs). Towards this end, the radar sensing signal processing procedures and communication signal models are presented in a general multi-static cellular ISAC system with coordinated multi-point joint transmission (CoMP-JT), and an optimization problem is formulated to maximize the worst-case sensing signal-to-noise ratio (SNR) in the prescribed sensing coverage region, subject to the signal-to-interference-plus-noise ratio (SINR) requirement for each communication UE. To gain useful insights, we first investigate the basic bi-static ISAC system, for which a closed form expression of the optimal beamforming is obtained for the special case with one UE and one sensing point. Then, the general case with multiple communication UEs and contiguous regional sensing coverage is further studied, in which the mesh grid approach and direction discretization approach are proposed for ease of solving the optimization problem. Afterward, we further investigate the beamforming optimization in the multi-static ISAC system to maximize the probability of detection of the prescribed sensing coverage region. We show that the problem is equivalent to maximize the sum of sensing SNR from the different transmit BSs. The formulated problems are non-convex, and we propose an efficient algorithm based on successive convex approximation (SCA) technique. Numerical results demonstrate that the proposed ISAC design is able to achieve seamless sensing coverage in the prescribed region while guaranteeing the communication requirements of the UEs.

Delay alignment modulation(DAM) is an emerging technique for achieving inter-symbol interference (ISI)-free wideband communications using spatial-delay processing, without relying on channel equalization or multi-carrier transmission. However, existing works on DAM only consider multiple-input single-output (MISO) communication systems and assume time-invariant channels. In this paper, by extending DAM to time-variant frequency-selective multiple-input multiple-output (MIMO) channels, we propose a novel technique termeddelay-Doppler alignment modulation(DDAM). Specifically, by leveragingdelay-Doppler compensationandpath-based beamforming, the Doppler effect of each multi-path can be eliminated and all multi-path signal components may reach the receiver concurrently and constructively. We first show that by applying path-based zero-forcing (ZF) precoding and receive combining, DDAM can transform the original time-variant frequency-selective channels into time-invariant ISI-free channels. The necessary and/or sufficient conditions to achieve such a transformation are derived. Then an asymptotic analysis is provided by showing that when the number of base station (BS) antennas is much larger than that of channel paths, DDAM enables time-invariant ISI-free channels with the simple delay-Doppler compensation and path-based maximal-ratio transmission (MRT) beamforming. Furthermore, for the general DDAM design with some tolerable ISI, the path-based transmit precoding and receive combining matrices are optimized to maximize the spectral efficiency. Numerical results are provided to compare the proposed DDAM technique with various benchmarking schemes, including MIMO-orthogonal time frequency space (OTFS), MIMO-orthogonal frequency-division multiplexing (OFDM) without or with carrier frequency offset (CFO) compensation, and beam alignment along the dominant path.

Delay alignment modulation (DAM) is a promising technology to achieve inter-symbol interference (ISI)-free single-carrier communication, by leveragingdelay compensationandpath-based beamforming, rather than the conventional channel equalization or multi-carrier transmission. In particular, when there exist a few strong time-dispersive channel paths, DAM is able to effectively align different propagation delays and achieve their constructive superposition, thus especially appealing for intelligent reflecting surfaces (IRSs)-aided communications with controllable multi-paths. In this paper, we apply single-carrier DAM to multi-IRS aided communication and study its design and achievable performance. We first provide an asymptotic analysis showing that when the number of base station (BS) antennas is much larger than the number of IRSs, an ISI-free channel can be established from the BS to the user with appropriate delay pre-compensation and the simple path-based maximal-ratio transmission (MRT) beamforming. We then consider the general system setup and study the problem of joint path-based beamforming design at the BS and phase shifts design at the IRSs for DAM transmission, by considering the three classical beamforming techniques on a per-path basis, namely the low-complexity path-based MRT beamforming to maximize the desired signal power, the path-based zero-forcing (ZF) beamforming for ISI-free DAM communication, and the optimal path-based minimum mean-square error (MMSE) beamforming to maximize the signal-to-interference-plus-noise ratio (SINR). As a comparison, orthogonal frequency-division multiplexing (OFDM)-based multi-IRS aided communication is considered for benchmarking. Simulation results are provided which demonstrate the significant performance gain of DAM over OFDM, in terms of spectral efficiency and bit error rate (BER), as well as its lower peak-to-average-power ratio (PAPR).

Hybrid analog/digital beamforming is a promising technique to realize millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems cost-effectively. However, existing hybrid beamforming designs mainly rely on real-time channel training or beam sweeping to find the desired beams, which incurs prohibitive overhead due to a large number of antennas at both the transmitter and receiver with only limited radio frequency (RF) chains. To resolve this challenging issue, in this paper, we propose a newenvironment-awarehybrid beamforming technique that requires only light real-time training, by leveraging the useful tool of channel knowledge map (CKM) with the user’s location information. CKM is a site-specific database, which offers location-specific channel-relevant information to facilitate or even obviate the acquisition of real-time channel state information (CSI). Two specific types of CKM are proposed in this paper for hybrid beamforming design in mmWave massive MIMO systems, namelychannel angle map(CAM) andbeam index map(BIM). It is shown that compared with existing environment-unaware schemes, the proposed environment-aware hybrid beamforming scheme based on CKM can drastically improve the effective communication rate, even under moderate user location errors, thanks to its great saving of the prohibitive real-time training overhead.

Channel knowledge map (CKM) has been recently proposed to enable environment-aware communications by utilizing historical or simulation generated wireless channel data. This paper studies the construction of one particular type of CKM, namely channel gain map (CGM), by using a finite number of measurements or simulation-generated data, with model-based spatial channel prediction. We try to answer the following question: How much data is sufficient for CKM construction? To this end, we first derive the average mean square error (AMSE) of the channel gain prediction as a function of the sample density of data collection in offline CGM construction, as well as the number of data points used in online spatial channel gain prediction. To model the spatial variation of the wireless environment within each cell, we divide the CGM into subregions and estimate the channel parameters from the local data within each subregion. The parameter estimation error and the channel prediction error based on estimated channel parameters are derived as functions of the number of data points within the subregion. The analytical results may guide the CGM construction and utilization by determining the required spatial sample density for offline data collection and the number of data points to be used for online channel prediction, so that the desired level of channel prediction accuracy is guaranteed.

For extremely large-scale array (XL-array) communications, the scatterers and/or user equipments (UEs) may be located in the near-field region and only visible to some portions of the XL-array. This paper studies the near-field spatial correlation function (S-CF) of multi-path XL-array communications with mixed line-of-sight (LoS) and non-LoS (NLoS) links. The generic near-field non-uniform spherical wave (NUSW) characteristic and the partial visibility property are considered. For the LoS link, a novel near-field S-CF is derived, which is in terms of the correlation of UE's visibility and location. It is found that the near-field S-CF depends on the UE's angle of arrival (AoA) and distance, which differs from the far-field result that only depends on the UE's AoA. For the NLoS links, we derive a novel integral expression for the near-field S-CF in terms of the correlation of the scatterers' visibility and location distribution. The near-field result is shown to depend on the scatterers' partial visibility and the power location spectrum (PLS) characterized by the AoAs and distances of scatterers, in contrast to the far-field model, which relies on the power angular spectrum (PAS). The result demonstrates that the near-field S-CF of the LoS/NLoS component no longer exhibits spatial wide-sense stationarity (SWSS) and is more generic than the far-field model. To gain further insights, we consider a specific scatterer's location distribution, namely the multi-ring scatterer model. Numerical results show the necessity of modeling near-field S-CF for XL-array communications with partial visibility.

Extremely large-scale multiple-input multiple-output (XL-MIMO) communication system with extremely large-scale antenna arrays can achieve ultra-high spectral efficiency. However, the conventional far-field beam codebooks may be mismatched with the near-field spherical-wavefront channel caused by large array aperture, which results in severe performance loss. To address this problem, we develop a criterion of code book design to maximize the worst-case beam gain within the beam coverage. Then, a closed-form expression of the near-field full dimensional (FD) codebook with non-orthogonal structure is derived, which can realize the spatial oversampling regardless of the number of antennas at the transceiver. Simulation results show that our proposed non-orthogonal codebook can potentially improve the accuracy of near-field beam training, compared with existing codebooks.

In this paper, we investigate the near-field modelling and analyze the beam focusing pattern for modular extremely large-scale array (XL-array) communications. As modular XL-array is physically and electrically large in general, the accurate characterization of amplitude and phase variations across its array elements requires the non-uniform spherical wave (NUSW) model, which, however, is difficult for performance analysis and optimization. To address this issue, we first present two ways to simplify the NUSW model by exploiting the unique regular structure of modular XL-array, termed sub-array based uniform spherical wave (USW) models with different or common angles, respectively. Based on the developed models, the near-field beam focusing patterns of XL-array communications are derived. It is revealed that compared to the existing collocated XL-array with the same number of array elements, modular XL-array can significantly enhance the spatial resolution, but at the cost of generating undesired grating lobes. Fortunately, different from the conventional far-field uniform plane wave (UPW) model, the near-field USW model for modular XL-array exhibits a higher grating lobe suppression capability, thanks to the non-linear phase variations across the array elements. Finally, simulation results are provided to verify the near-field beam focusing pattern and grating lobe characteristics of modular XL-array.

Integrated sensing and communication is a promising technology for enabling high-speed and low-latency commu-nication between connected vehicles. However, due to the unique characteristics of millimeter wave (mmWave) communication, such as severe path loss, susceptibility to blockage and beam alignment mismatch caused by positioning or sensing errors, outage probability analysis is critical to evaluate the performance of such systems. In this paper, we investigate the outage probability of mm Wave V2V communication systems in highway and urban scenarios with varying propagation environments and traffic densities. Furthermore, the optimal 3dB beamwidth is also derived for a given transmitting power and communication distance.

For integrated sensing and communication (ISAC) systems, the desired channel variables by communication and sensing tasks vary with different timescales. For sensing, one is mainly interested in the state information (e.g., delays, angles, Doppler frequencies, etc.) of individual multi-path channel components, which evolves much more slowly than the composite channel state information (CSI) required by communications. In this paper, by exploiting the double timescales for sensing and communication, a novel technique termed as delay-Doppler alignment modulation (DDAM) is investigated, which is an appealing technique for ISAC systems, since the sensing result of resolvable multi-paths can be directly exploited for delay-Doppler compensation and path-based beamforming of DDAM. We first show that with perfect CSI, as long as the number of base station (BS) antennas is no smaller than that of resolvable multi-paths, the proposed DDAM is able to transform the time-frequency double selective-fading channel into a simple additive white Gaussian noise (AWGN) channel for inter-symbol interference (ISI)-free communication without requiring the conventional channel equalization or multi-carrier transmission. We then present the DDAM-based signal processing for ISAC, and the resulting communication performance with imperfectly sensed CSI is studied. Simulation results demonstrate that the proposed DDAM-based ISAC can achieve higher communication rate compared to orthogonal frequency division multiplexing (OFDM) and DFT-spread(s)-OFDM, while guaranteeing high sensing performance.

Wireless communication using fully passive metal reflectors is a promising technique for coverage expansion, signal enhancement, rank improvement and blind-zone compensation, thanks to its appealing features including zero energy consumption, ultra low cost, signaling- and maintenance-free, easy deployment and full compatibility with existing and future wireless systems. However, a prevalent understanding for reflection by metal plates is based on Snell's Law, i.e., signal can only be received when the observation angle equals to the incident angle, which is valid only when the electrical dimension of the metal plate is extremely large. In this paper, we rigorously derive a general reflection model that is applicable to metal reflectors of any size, any orientation, and any linear polarization. The derived model is given compactly in terms of the radar cross section (RCS) of the metal plate, as a function of its physical dimensions and orientation vectors, as well as the wave polarization and the wave deflection vector, i.e., the change of direction from the incident wave direction to the observation direction. Furthermore, experimental results based on actual field measurements are provided to validate the accuracy of our developed model and demonstrate the great potential of communications using metal reflectors.

Predictive millimeter-wave (mmWave) beamforming is a promising technique to enable low-latency and high-rate ground-air communications for cellular-connected unmanned aerial vehicles (UAVs). However, the high vulnerability of mmWave to blockages poses practical challenges to the implementation of such a technology. In this paper, we tackle the challenges by proposing a channel knowledge map (CKM)-assisted predictive beamforming approach based on the echoed joint communication and sensing signal, whereby the line-of-sight (LoS) link identification is performed via hypothesis testing using prior information provided by CKM. Depending on the identification result, extended Kalman filtering (EKF) is adopted to reliably track the target UAV. Furthermore, if the non-line-of-sight (NLoS) state is identified, the target UAV will be immediately connected to a candidate base station (BS), namely a handover will be triggered to alleviate the communication outage. The simulation results show that the proposed method can significantly enhance the UAV tracking and mmWave communication performance compared to the benchmarking schemes without using CKM or LoS identification.

Sensing-assisted predictive beamforming is a promising technique for reducing the communication overhead and latency in millimeter wave (mmWave) communication systems. In this paper, we propose a robust sensing-assisted predictive beamforming scheme that performs non-line-of-sight (NLoS) identification based on the reflected joint communication and sensing signal. Specifically, the time delay, Doppler shift and reflection coefficient are estimated from the echo signal, based on which a hypothesis test problem is formulated to determine whether the echo signal is reflected by the target vehicle or obstacles. Besides, based on the estimated channel parameters, extended Kalman filtering (EKF) is employed to estimate and predict the motion parameters of the target vehicle. If the echo signal is reflected by obstacles, it implies that the LoS link between the base station (BS) and the target vehicle is blocked. In this case, the proposed scheme can adjust the communication mode and vehicle tracking mechanism accordingly to enhance link reliability. Numerical results demonstrate that the proposed predictive beamforming scheme with NLoS identification can substantially enhance the achievable communication rate as compared to the existing techniques without taking blockage into account.

Integrated sensing and communication (ISAC) is a promising technology to simultaneously provide high performance wireless communication and radar sensing services in future networks. In this paper, we propose the concept of integrated super-resolution sensing and communication (ISSAC), which uses super-resolution algorithms in ISAC systems to achieve extreme sensing performance for those critical parameters, such as delay, Doppler, and angle of the sensing targets. Based on practical fifth generation (5G) New Radio (NR) wave forms, the signal processing techniques of ISSAC are investigated and prototyping experiments are performed to verify the achievable performance. To this end, we first study the effect of uneven cyclic prefix (CP) lengths of 5G NR orthogonal frequency division multiplexing (OFDM) waveforms on various sensing algorithms. Specifically, the performance of the standard Periodogram based radar processing method, together with the two classical super resolution algorithms, namely, MUltiple SIgnal Classification (MUSIC) and Estimation of Signal Parameter via Rotational Invariance Techniques (ESPRIT) are analyzed in terms of the delay and Doppler estimation. To resolve the uneven CP issue, a new structure of steering vector for MUSIC and a new selection of submatrices for ESPRIT are proposed. Furthermore, an ISSAC experimental platform is setup to validate the theoretical analysis, and the experimental results show that the performance degradation caused by unequal CP length is insignificant and high-resolution delay and Doppler estimation of the target can be achieved with 5G NR waveforms.

SUMMARY: Cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) has emerged as a promising liquid biopsy technology to detect cancers and monitor treatments. While several bioinformatics tools for DNA methylation analysis have been adapted for cfMeDIP-seq data, an end-to-end pipeline and quality control framework specifically for this data type is still lacking. Here, we present the MEDIPIPE, which provides a one-stop solution for cfMeDIP-seq data quality control, methylation quantification, and sample aggregation. The major advantages of MEDIPIPE are: (i) ease of implementation and reproducibility with Snakemake containerized execution environments that will be automatically deployed via Conda; (ii) flexibility to handle different experimental settings with a single configuration file; and (iii) computationally efficiency for large-scale cfMeDIP-seq profiling data analysis and aggregation. AVAILABILITY AND IMPLEMENTATION: This pipeline is an open-source software under the MIT license and it is freely available at https://github.com/pughlab/MEDIPIPE.

Cell-free massive MIMO and symbiotic radio communication have been recently proposed as the promising beyond fifth-generation (B5G) networking architecture and transmission technology, respectively. To reap the benefits of both, this paper studies cell-free symbiotic radio communication systems, where a number of cell-free access points (APs) cooperatively send primary information to a receiver, and simultaneously support the passive backscattering communication of the secondary backscatter device (BD). We first derive the achievable communication rates of the active primary user and passive secondary user under the assumption of perfect channel state information (CSI), based on which the transmit beamforming of the cell-free APs is optimized to characterize the achievable rate-region of cell-free symbiotic communication systems. Furthermore, to practically acquire the CSI of the active and passive channels, we propose an efficient channel estimation method based on two-phase uplink-training, and the achievable rate-region taking into account CSI estimation errors is further characterized. Simulation results are provided to show the effectiveness of our proposed beamforming and channel estimation methods.

The evolution of mobile communication networks has always been accompanied by the advancement of inter-symbol interference (ISI) mitigation techniques, from equalization in the second-generation (2G), spread spectrum and RAKE receiver in the third generation (3G), to orthogonal frequency-division multiplexing (OFDM) in the fourth-generation (4G) and fifth-generation (5G). Looking forward towards the sixth-generation (6G), by exploiting the high spatial resolution brought by large antenna arrays and the multi-path sparsity of millimeter wave (mmWave) and Terahertz channels, a novel ISI mitigation technique termed delay alignment modulation (DAM) was recently proposed. However, existing works only consider the single-carrier perfect DAM, which is feasible only when the number of base station (BS) antennas is no smaller than that of channel paths, so that all multi-path signal components can be aligned for arriving at the receiver simultaneously and constructively. This imposes stringent requirements on the number of BS antennas and multi-path sparsity. In this paper, we propose a generic DAM technique to manipulate the channel delay spread via spatial-delay processing, thus providing a flexible framework to combat channel time dispersion for efficient single- or multi-carrier transmissions. To gain some insights, we first show that when the number of BS antennas is much larger than that of channel paths, perfect delay alignment can be achieved to transform the time-dispersive channel to time non-dispersive channel with the simple delay pre-compensation and path-based maximal-ratio transmission (MRT) beamforming. When perfect DAM is infeasible or undesirable, the proposed generic DAM technique can be applied to significantly reduce the channel delay spread. Based on such results, we further propose the novel DAM-OFDM technique, which is able to save the cyclic prefix (CP) overhead or mitigate the peak-to-average-power ratio (PAPR) issue suffered by conventional OFDM. We show that the proposed DAM-OFDM involves joint frequency- and time-domain beamforming optimization, for which a closed-form solution is derived. Simulation results show that the proposed DAM-OFDM achieves significant performance gains over the conventional OFDM, in terms of spectral efficiency, bit error rate (BER) and PAPR.

Symbiotic radio has emerged as a promising technology for spectrum- and energy-efficient wireless communications, where the passive secondary backscatter devices (BDs) reuse not only the spectrum but also the power of the active primary users to transmit their own information. In return, the primary communication links can be enhanced by the additional multipaths created by the BDs. This is known as the mutualism relationship of symbiotic radio. However, due to the severe double-fading attenuation of the passive backscattering links, the enhancement of the primary link provided by one single BD is extremely limited. To address this issue and enable full mutualism of symbiotic radio, in this paper, we study multiple-input multiple-output (MIMO) symbiotic radio communication systems with massive BDs. We first derive the achievable rates of the primary active communication and secondary passive communication, and then consider the asymptotic regime as the number of BDs goes large, for which closed-form expressions are derived to reveal the relationship between the primary and secondary communication rates. Furthermore, the precoding optimization problem is studied to maximize the primary communication rate while guaranteeing that the secondary communication rate is no smaller than a certain threshold. Simulation results are provided to validate our theoretical studies.

Delay alignment modulation (DAM) has been recently proposed to enable manipulable channel delay spread for efficient single- or multi-carrier communications. In particular, with perfect delay alignment, inter-symbol interference (ISI) can be eliminated even with single-carrier (SC) transmission, without relying on sophisticated channel equalization. The key ideas of DAM aredelay pre-compensationandpath-based beamforming, so that all multi-path signal components may arrive at the receiver simultaneously and be superimposed constructively, rather than causing the detrimental ISI. Compared to the classic orthogonal frequency division multiplexing (OFDM) transmission, DAM-enabled SC communication has several appealing advantages, including low peak-to-average-power ratio (PAPR) and high tolerance for Doppler frequency shift, which renders DAM also appealing for radar sensing. Therefore, in this paper, DAM is investigated for integrated sensing and communication (ISAC) systems. We first study the output signal-to-noise ratios (SNRs) for ISI-free SC communication and radar sensing, respectively, and then derive the closed-form expressions for DAM-based sensing in terms of the ambiguity function (AF) and integrated sidelobe ratio (ISR). Furthermore, we study the beamforming design problem for DAM-based ISAC to maximize the communication SNR while guaranteeing the sensing performance in terms of the sensing SNR and ISR. Finally, we provide performance comparison between DAM and OFDM for ISAC, and it is revealed that DAM signal may achieve better communication and sensing performance, thanks to its low PAPR, reduced guard interval overhead, as well as higher tolerance for Doppler frequency shift. Simulation results are provided to show the great potential of DAM for ISAC.

The performance of wireless localization critically depends on the actual radio propagation environment. This paper proposes a novel framework towards environment-aware wireless localization, enabled by the emerging concept known as channel knowledge map (CKM). Specifically, we propose a line-of-sight (LoS) map-enabled environment-aware anchor selection scheme to minimize the Bayesian Cramer-Rao lower bound (BCRLB) of the positioning error. As the formulated problem is combinatorial, we propose an efficient greedy-based algorithm, which selects the best anchor node sequentially by ensuring that each newly selected anchor leads to the maximum reduction to the BCRLB. Simulation results show that the proposed environment-aware anchor selection can significantly outperform the benchmarking environment-ignorant schemes, including the min-distance based selection or simply activating all anchors.

Effective beamforming is essential for multi-antenna based integrated sensing and communication (ISAC), where multiple beams are usually required to concurrently direct signal power towards both the communication user equipment (UE) and the sensing target. This paper studies millimeter wave (mmWave) ISAC system, where an ISAC node with large antenna arrays wishes to simultaneously communicate with an UE and sense a target using the cost-effective analog beamforming. We first propose a subarray-based double-beam codebook design, which includes the conventional single-beam codebook as a special case. With the proposed codebook design, for all possible combinations of the UE and target directions, signal power can be effectively directed towards them concurrently, as long as the beam is appropriately selected. To this end, we further propose a novel beam sweeping protocol, for which the beam searching processes for communication and sensing are carried out simultaneously, with either single-beam or double-beam sweeping that achieves different balances between the required sweeping time and beamforming gain. Simulation results are provided to show the effectiveness of the proposed codebook design and simultaneous beam sweeping methods.

Delay alignment modulation (DAM) has been recently proposed to enable inter-symbol interference (ISI)-free single-carrier (SC) communication without relying on sophisticated channel equalization. The key idea of DAM is to pre-introduce deliberate symbol delays at the transmitter side, so that all multi-path signal components may arrive at the receiver simultaneously and be superimposed constructively, rather than causing the detrimental ISI. Compared to the classic orthogonal frequency division multiplexing (OFDM) transmission, DAM has several appealing advantages, including low peak-to-average-power ratio (PAPR) and high tolerance for Doppler frequency shift, which makes DAM also appealing for radar sensing. Therefore, in this paper, DAM is investigated for the emerging integrated sensing and communication (ISAC) setup. We first derive the output signal-to-noise ratios (SNRs) for ISI-free communication and radar sensing, respectively, and then propose an efficient beamforming design for DAM-ISAC to maximize the communication SNR while guaranteeing the sensing performance. The comparison analysis of DAM versus OFDM for ISAC is developed, and it is revealed that DAM enables higher sensing SNR and larger Doppler frequency estimation. Simulation results are provided to show the great potential of DAM for ISAC.

Channel knowledge map (CKM) is an emerging technique to enable environment-aware wireless communications, in which databases with location-specific channel knowledge are used to facilitate or even obviate real-time channel state information acquisition. One fundamental problem for CKM-enabled communication is how to efficiently construct the CKM based on finite measurement data points at limited user locations. Towards this end, this paper proposes a novel map construction method based on the expectation maximization (EM) algorithm, by utilizing the available measurement data, jointly with the expert knowledge of well-established statistic channel models. The key idea is to partition the available data points into different groups, where each group shares the same modelling parameter values to be determined. We show that determining the modelling parameter values can be formulated as a maximum likelihood estimation problem with latent variables, which is then efficiently solved by the classic EM algorithm. Compared to the pure data-driven methods such as the nearest neighbor based interpolation, the proposed method is more efficient since only a small number of modelling parameters need to be determined and stored. Furthermore, the proposed method is extended for constructing a specific type of CKM, namely, the channel gain map (CGM), where closed-form expressions are derived for the E-step and M-step of the EM algorithm. Numerical results are provided to show the effectiveness of the proposed map construction method as compared to the benchmark curve fitting method with one single model.

This paper investigates the tradeoff between data freshness and transmission efficiency for packet delivery over erasure channels with delayed feedback. First, we show that the transmission efficiency of the preemptive last-generated-first-served (pLGFS) policy degrades drastically with the feedback delay. To address this issue, we propose a new coding scheme by incorporating the pLGFS policy with a selective packet encoding strategy. Specifically, the transmitter estimates the packet reception status based on the delayed feedback information, and then decides whether to remain silent, or to send the latest information packet or a coded packet. The proposed transmission policy can achieve flexible tradeoff between data freshness and transmission efficiency by adjusting the decision and coding parameters. In particular, compared with the pLGFS policy, the proposed policy can greatly enhance the transmission efficiency, with only a slight degradation of data freshness.

Integrated sensing and communication (ISAC) is a promising technology to fully utilize the precious spectrum and hardware in wireless systems, which has attracted significant attentions recently. This paper studies ISAC for the important and challenging monostatic setup, where one single ISAC node wishes to simultaneously sense a radar target while communicating with a communication receiver. Different from most existing schemes that rely on either radar-centric half-duplex (HD) pulsed transmission with information embedding that suffers from extremely low communication rate, or communication-centric waveform that suffers from degraded sensing performance, we propose a novel full-duplex (FD) ISAC scheme that utilizes the waiting time of conventional pulsed radars to transmit communication signals. Compared to radar-centric pulsed waveform with information embedding, the proposed design can drastically increase the communication rate, and also mitigate the sensing eclipsing and near-target blind range issues, as long as the self-interference (SI) is effectively suppressed. On the other hand, compared to communication-centric ISAC waveform, the proposed design has better auto-correlation property as it preserves the classic radar waveform for sensing. Performance analysis is developed by taking into account the residual SI, in terms of the probability of detection and ambiguity function for sensing, as well as the spectrum efficiency for communication. Numerical results are provided to show the significant performance gain of our proposed design over benchmark schemes.

This paper studies the three-dimensional (3D) trajectory optimization problem for unmanned aerial vehicle (UAV) aided wireless communication. Existing works mainly rely on the kinematic equations for UAV’s mobility modeling, while its dynamic equations are usually missing. As a result, the planned UAV trajectories are piece-wise line segments in general, which may be difficult to implement in practice. By leveraging the concept of state-space model, a control-based UAV trajectory design is proposed in this paper, which takes into account both of the UAV’s kinematic equations and the dynamic equations. Consequently, smooth trajectories that are amenable to practical implementation can be obtained. Moreover, the UAV’s controller design is achieved along with the trajectory optimization, where practical roll angle and pitch angle constraints are considered. Furthermore, a new energy consumption model is derived for quad-rotor UAVs, which is based on the voltage and current flows of the electric motors and thus captures both the consumed energy for motion and the energy conversion efficiency of the motors. Numerical results are provided to validate the derived energy consumption model and show the effectiveness of our proposed algorithms.

Wireless communications with extremely large-scale array (XL-array) correspond to systems whose antenna sizes are so large that conventional modeling assumptions, such as uniform plane wave (UPW) impingement, are no longer valid. This paper studies the mathematical modeling and performance analysis of XL-array communications. By deviating from the conventional modeling approach that treats the array elements as sizeless points, we explicitly model their physical area/aperture, which enables a unified modeling for the classical discrete antenna arrays and the emerging active continuous surfaces. As such, a generic array/surface model that accurately takes into account the variations of signal phase, amplitude and projected aperture across array elements is proposed. Based on the proposed model, a closed-form expression of the resulting signal-to-noise ratio (SNR) with the optimal single-user maximum ratio combining/transmission (MRC/MRT) beamforming is derived. The expression reveals that instead of scaling linearly with the antenna number$M$as in conventional UPW modeling, the SNR with the more generic model increases with$M$with diminishing return, which is governed by the collective properties of the array, such as thearray occupation ratioand the physical sizes of the array along each dimension, while irrespective of the properties of the individual array element. In addition, we have derived an alternative insightful expression for the optimal SNR in terms of theverticalandhorizontal angular spans, which are fully determined by the geometric angles formed by the array/surface and user location. Furthermore, we also show that our derived results include the far-field UPW modeling as a special case. One important finding during the study of far-field approximation is the necessity to introduce a new distance criterion to complement the classical Rayleigh distance, termeduniform-power distance(UPD), which concerns the signal amplitude/power variations across array elements, instead of phase variations as for Rayleigh distance. Extensive numerical results are provided to demonstrate the necessity of proper modeling for XL-array communications by comparing the proposed model with various benchmark models.

Cellular-connected unmanned aerial vehicles (UAVs) are expected to become integral components of future cellular networks. To this end, one of the important problems to address is how to support energy-efficient UAV operation while maintaining reliable connectivity between those aerial users and cellular networks. In this paper, we aim to minimize the energy consumption of cellular-connected UAV via jointly designing the mission completion time and UAV trajectory, as well as communication base station (BS) associations, while ensuring a satisfactory communication connectivity with the ground cellular network during the UAV flight. An optimization problem is formulated by taking into account the UAV’s flight energy consumption and various practical aspects of the air-ground communication models, including BS antenna pattern, interference from non-associated BSs and local environment. The formulated problem is difficult to tackle due to the lack of closed-form expressions and non-convexity nature. To this end, we first assume that thechannel knowledge map(CKM) or radio map for the considered area is available, which contains rich information about the relatively stable (large-scale) channel parameters. By utilizingpath discretizationtechnique, we obtain a discretized equivalent problem and develop an efficient solution based on graph theory by employing convex optimization technique and a dynamic-weight shortest path algorithm over graph. Next, we study the more practical case that the CKM is unavailable initially. By transforming the optimization problem to a Markov decision process (MDP), we develop a deep reinforcement learning (DRL) algorithm based on multi-step learning and double Q-learning over a dueling Deep Q-Network (DQN) architecture, where the UAV acts as an agent to explore and learn its moving policy according to its local observations of the measured signal samples. Extensive simulations are carried out and the results show that our proposed designs significantly outperform baseline schemes. Furthermore, our results reveal new insights of energy-efficient UAV flight with connectivity requirements and unveil the tradeoff between UAV energy consumption and time duration along line segments.

Symbiotic radio is a promising technology to achieve spectrum-and energy-efficient wireless communications, where the secondary backscatter device (BD) leverages not only the spectrum but also the power of the primary signals for its own information transmission. In return, the primary communication link can be enhanced by the additional multipaths created by the BD. This is known as the mutualism relationship of symbiotic radio. However, as the backscattering link is much weaker than the direct link due to double attenuations, the improvement of the primary link brought by one single BD is extremely limited. To address this issue and enable full mutualism of symbiotic radio, in this paper, we study symbiotic radio with massive number of BDs. For symbiotic radio multiple access channel (MAC) with successive interference cancellation (SIC), we first derive the achievable rate of both the primary and secondary communications, based on which a receive beamforming optimization problem is formulated and solved. Furthermore, considering the asymptotic regime of massive number of BDs, closed-form expressions are derived for the primary and the secondary communication rates, both of which are shown to be increasing functions of the number of BDs. This thus demonstrates that the mutualism relationship of symbiotic radio can be fully exploited with massive BD access.

Extremely large-scale multiple-input multiple-output (XL-MIMO) communications correspond to systems whose antenna size is so large that conventional assumptions, such as uniform plane wave (UPW) impingement, are no longer valid. This paper studies the channel modelling and performance analysis of XL-MIMO communication based on the generic spherical wavefront propagation model. First, for the single-user uplink/downlink communication with the optimal maximum ratio combining/transmission (MRC/MRT), we rigorously derive a new closed-form expression for the resulting signal-to-noise ratio (SNR), which includes the conventional SNR expression based on UPW assumption as a special case. Our result shows that instead of scaling linearly with the base station (BS) antenna number M, the SNR with the more generic spherical wavefront model increases with M with diminishing return, governed by a new parameter called angular span. One important finding from our derivation is the necessity to introduce a new distance criterion, termed critical distance, to complement the classical Rayleigh distance for separating the near- and far-field propagation regions. While Rayleigh distance is based on the phase difference across array elements and hence depends on the electrical size of the antenna, the critical distance cares about the amplitude/power difference and only depends on its physical size. We then extend the study to the multi-user XL-MIMO communication system, for which we demonstrate that inter-user interference (IUI) can be mitigated not just by angle separation, but also by distance separation along the same direction. This offers one new degree of freedom (DoF) for interference suppression with XL-MIMO.

Wireless communication is an essential technology to unlock the full potential of unmanned aerial vehicles (UAVs) in numerous applications and has thus received unprecedented attention recently. Although technologies such as direct link, WiFi, and satellite communications are still useful in some remote scenarios where cellular services are unavailable, it is believed that exploiting the thriving 5G and beyond cellular networks to support UAV communications is the most promising and cost-effective approach, especially when the number of UAVs grows dramatically. On the one hand, to guarantee safe and efficient flight operations of multiple UAVs, it is of paramount importance to provide secure and ultra-reliable communication links between the UAVs and their ground pilots or control stations for conveying command and control signals, especially in beyond-visual-line-of-sight (BVLOS) scenarios. On the other hand, because of advances in communication equipment miniaturization as well as UAV manufacturing, mounting compact and lightweight base stations (BSs) or relays on UAVs becomes increasingly feasible. This has led to two promising research paradigms for UAV communications, namely, UAV-assisted cellular communications and cellular-connected UAVs, where UAVs are integrated into cellular networks as aerial communication platforms and aerial users, respectively. As such, integrating UAVs into cellular networks is believed to be a win-win technology for both UAV-related industries and cellular network operators, which not only creates plenty of new business opportunities but also benefits the communication performance of 3-D wireless networks. In addition, UAV related sensing and computing are also helpful for achieving efficient and reliable communication (e.g., in avoiding coverage holes) as well as smart UAV coordination, positioning, and trajectory design. However, 5G and beyond wireless networks with UAVs significantly differs from traditional communication systems, because of the high altitude and high maneuverability of UAVs, the unique UAV-ground channels, the diversified quality of service (QoS) requirements for downlink command and control (C&C) and uplink mission-related data transmission, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the new design degrees of freedom enabled by joint UAV mobility control and communication resource allocation.

Due to the advancements in cellular technologies and the dense deployment of cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the fifth-generation (5G) and beyond cellular networks is a promising solution to achieve safe UAV operation as well as enabling diversified applications with mission-specific payload data delivery. In particular, 5G networks need to support three typical usage scenarios, namely, enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine-type communications (mMTC). On the one hand, UAVs can be leveraged as cost-effective aerial platforms to provide ground users with enhanced communication services by exploiting their high cruising altitude and controllable maneuverability in three-dimensional (3D) space. On the other hand, providing such communication services simultaneously for both UAV and ground users poses new challenges due to the need for ubiquitous 3D signal coverage as well as the strong air-ground network interference. Besides the requirement of high-performance wireless communications, the ability to support effective and efficient sensing as well as network intelligence is also essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting aerial and ground users. In this paper, we provide a comprehensive overview of the latest research efforts on integrating UAVs into cellular networks, with an emphasis on how to exploit advanced techniques (e.g., intelligent reflecting surface, short packet transmission, energy harvesting, joint communication and radar sensing, and edge intelligence) to meet the diversified service requirements of next-generation wireless systems. Moreover, we highlight important directions for further investigation in future work.

Wireless communication is an essential technology to unlock the full potential of unmanned aerial vehicles (UAVs) in numerous applications and has thus received unprecedented attention recently. Although technologies such as direct link, WiFi, and satellite communications are still useful in some remote scenarios where cellular services are unavailable, it is believed that exploiting the thriving 5G and beyond cellular networks to support UAV communications is the most promising and cost-effective approach, especially when the number of UAVs grows dramatically. On the one hand, to guarantee safe and efficient flight operations of multiple UAVs, it is of paramount importance to provide secure and ultra-reliable communication links between the UAVs and their ground pilots or control stations for conveying command and control signals, especially in beyond-visual-line-of-sight (BVLOS) scenarios. On the other hand, because of advances in communication equipment miniaturization as well as UAV manufacturing, mounting compact and lightweight base stations (BSs) or relays on UAVs becomes increasingly feasible. This has led to two promising research paradigms for UAV communications, namely, UAV-assisted cellular communications and cellular-connected UAVs, where UAVs are integrated into cellular networks as aerial communication platforms and aerial users, respectively. As such, integrating UAVs into cellular networks is believed to be a win-win technology for both UAV-related industries and cellular network operators, which not only creates plenty of new business opportunities but also benefits the communication performance of 3-D wireless networks. In addition, UAV related sensing and computing are also helpful for achieving efficient and reliable communication (e.g., in avoiding coverage holes) as well as smart UAV coordination, positioning, and trajectory design. However, 5G and beyond wireless networks with UAVs significantly differs from traditional communication systems, because of the high altitude and high maneuverability of UAVs, the unique UAV-ground channels, the diversified quality of service (QoS) requirements for downlink command and control (C&C) and uplink mission-related data transmission, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the new design degrees of freedom enabled by joint UAV mobility control and communication resource allocation.

Intelligent reflecting surface (IRS) is a promising technology to reconfigure wireless channels, which brings a new degree of freedom for the design of future wireless networks. This article proposes a new three-dimensional (3D) wireless system architecture enabled by aerial IRS (AIRS). Compared to the conventional terrestrial IRS, AIRS enjoys more deployment flexibility as well as wider-view signal reflection, thanks to its high altitude and thus more likelihood of establishing line-of-sight (LoS) links with ground source/destination nodes. We aim to maximize the worst-case signal-to-noise ratio (SNR) over all locations in a target area by jointly optimizing the transmit beamforming for the source node, as well as the placement and 3D passive beamforming for the AIRS. The formulated problem is non-convex and difficult to solve. To gain useful insights, we first consider the special case of maximizing the SNR at a given target location, for which the optimal solution is obtained in closed-form. The result shows that the optimal horizontal AIRS placement only depends on the ratio between the source-destination distance and the AIRS altitude. Then for the general case of AIRS-enabled area coverage, we propose an efficient solution by decoupling the AIRS passive beamforming design to maximize the worst-case array gain, from its placement optimization by balancing the resulting angular span and the cascaded channel path loss. Our proposed solution is based on a novel 3D beam broadening and flattening technique, where the passive array of the AIRS is divided into sub-arrays of appropriate size, and their phase shifts are designed to form a flattened beam pattern with adjustable beamwidth catering to the size of the coverage area. Both uniform linear array (ULA)-based and uniform planar array (UPA)-based AIRSs are considered in our design, which enable two-dimensional (2D) and 3D passive beamforming, respectively. Numerical results show that the proposed designs achieve significant performance gains over the benchmark schemes.

Reconfigurable intelligent surfaces (RISs) comprised of tunable unit cells have recently drawn significant attention due to their superior capability in manipulating electromagnetic waves. In particular, RIS-assisted wireless communications have the great potential to achieve significant performance improvement and coverage enhancement in a cost-effective and energy-efficient manner, by properly programming the reflection coefficients of the unit cells of RISs. In this article, free-space path loss models for RIS-assisted wireless communications are developed for different scenarios by studying the physics and electromagnetic nature of RISs. The proposed models, which are first validated through extensive simulation results, reveal the relationships between the free-space path loss of RIS-assisted wireless communications and the distances from the transmitter/receiver to the RIS, the size of the RIS, the near-field/far-field effects of the RIS, and the radiation patterns of antennas and unit cells. In addition, three fabricated RISs (metasurfaces) are utilized to further corroborate the theoretical findings through experimental measurements conducted in a microwave anechoic chamber. The measurement results match well with the modeling results, thus validating the proposed free-space path loss models for RISs, which may pave the way for further theoretical studies and practical applications in this field.

Achieving high-rate communication with accurate localization and wireless environment sensing has emerged as an important trend of beyond-fifth and sixth generation cellular systems. Extension of the antenna array to an extremely large scale is a potential technology for achieving such goals. However, the super massive operating antennas significantly increases the computational complexity of the system. Motivated by the inherent advantages of lens antenna arrays in reducing system complexity, we consider communication and localization problems with an extremely large lens antenna array, which we call “ExLens”. Since radiative near-field property emerges in the setting, we derive the closed-form array response of the lens antenna array with spherical wave, which includes the array response obtained on the basis of uniform plane wave as a special case. Our derivation result reveals a window effect for energy focusing property of ExLens, which indicates that ExLens has great potential in position sensing and multi-user communication. We also propose an effective method for location and channel parameters estimation, which is able to achieve the localization performance close to the Cramér-Rao lower bound. Finally, we examine the multi-user communication performance of ExLens that serves coexisting near-field and far-field users. Numerical results demonstrate the effectiveness of the proposed channel estimation method and show that ExLens with a minimum mean square error receiver achieves significant spectral efficiency gains and complexity-and-cost reductions compared with a uniform linear array.

Cellular-connected unmanned aerial vehicle (UAV) is a promising technology to unlock the full potential of UAVs in the future by reusing the cellular base stations (BSs) to enable their air-ground communications. However, how to achieve ubiquitous three-dimensional (3D) communication coverage for the UAVs in the sky is a new challenge. In this paper, we tackle this challenge by a new coverage-aware navigation approach, which exploits the UAV's controllable mobility to design its navigation/trajectory to avoid the cellular BSs' coverage holes while accomplishing their missions. To this end, we formulate an UAV trajectory optimization problem to minimize the weighted sum of its mission completion time and expected communication outage duration, which, however, cannot be solved by the standard optimization techniques due to the lack of an accurate and tractable end-to-end communication model in practice. To overcome this difficulty, we propose a new solution approach based on the technique of deep reinforcement learning (DRL). Specifically, by leveraging the state-of-the-art dueling double deep Q network (dueling DDQN) with multi-step learning, we first propose a UAV navigation algorithm based on direct RL, where the signal measurement at the UAV is used to directly train the action-value function of the navigation policy. To further improve the performance, we propose a new framework called simultaneous navigation and radio mapping (SNARM), where the UAV's signal measurement is used not only for training the DQN directly, but also to create a radio map that is able to predict the outage probabilities at all locations in the area of interest. This enables the generation of simulated UAV trajectories and predicting their expected returns, which are then used to further train the DQN via Dyna technique, thus greatly improving the learning efficiency.

This paper considers an unmanned aerial vehicle (UAV)-enabled uplink non-orthogonal multiple-access (NOMA) system, where multiple users on the ground send independent messages to a UAV via NOMA transmission. We aim to design the UAV's dynamic maneuver in real time for maximizing the sum-rate throughput of all ground users over a finite time horizon. Different from conventional offline designs considering static user locations under deterministic or stochastic channel models, we consider a more challenging scenario with mobile users and segmented channel models, where the UAV only causally knows the users' (moving) locations and channel state information (CSI). Under this setup, we first propose a new approach for UAV dynamic maneuver design based on reinforcement learning (RL) via Q-learning. Next, in order to further speed up the convergence and increase the throughput, we present an enhanced RL-based approach by additionally exploiting expert knowledge of well-established wireless channel models to initialize the Q-table values. Numerical results show that our proposed RL-based and enhanced RL-based approaches significantly improve the sum-rate throughput, and the enhanced RL-based approach considerably speeds up the learning process owing to the proposed Q-table initialization.

Software-defined networking (SDN) is a cutting-edge technology, featuring a centralized control that facilitates, e.g., flexible deployment of unmanned aerial vehicles (UAVs). On the other hand, UAV-enabled communication is a promising technology due to its numerous traits such as the ability of on-demand deployment and the high likelihood of strong line-of-sight (LoS) communication links. However, UAV-enabled communication suffers non-perpetual nodes and intermittent communication links. Fortunately, SDN provisions an unparalleled global vision on such dynamic network architecture to overcome the challenges of loose links and disappearing nodes. On the other hand, there are catastrophic or remote regions where a UAV-mounted base station (BS) potentially functions without a terrestrial BS, albeit a WiFi access point (AP) may still exist. Therefore, this paper considers software-defined coexisting UAV-mounted BS (UBS) and WiFi AP, and investigates the queuing delay behavior via the UBS positioning and the AP traffic offloading. The subscribers (users) are divided into cellular subscribers (CSs) and WiFi subscribers (WSs). A CS is connected to the UBS and is possibly granted simultaneous access to the AP, leading to WiFi traffic offloading. Leveraging the software-defined global view, the objective of a software-defined controller (SDC) is to minimize the average M/M/1 queuing delay of the CSs, while guaranteeing the delay performance for the WSs, via optimizing the spectrum allocation, the UBS position, the CS association to the AP, and the CS traffic offloading. The optimization problem is non-convex, comprising binary variables, for which we use the block coordinate descent and successive convex approximation methods to find a high-quality solution. Numerical results verify that our solution achieves significant performance gains over benchmark schemes.

This paper studies an unmanned aerial vehicle (UAV)-enabled two-user interference channel for wireless powered communication networks (WPCNs). In this system, two UAVs wirelessly charge two low-power Internet-of-things (IoT)-devices on the ground and collect information from them. We consider two scenarios when both UAVs cooperate in energy transmission and/or information reception via interference coordination and coordinated multi-point (CoMP), respectively. For both scenarios, the UAVs' trajectories are designed to not only enhance the wireless power transfer (WPT) efficiency in the downlink, but also mitigate the co-channel interference for wireless information transfer (WIT) in the uplink. In particular, the objective is to maximize the uplink common (minimum) throughput of the two IoT-devices over a finite UAV mission period, by jointly optimizing the trajectories of both UAVs and the downlink/uplink wireless resource allocation, subject to the maximum flying speed and collision avoidance constraints for UAVs, as well as the individual energy neutrality constraints at IoT-devices. Under both scenarios with interference coordination and CoMP, we first obtain the optimal solutions to the two common-rate maximization problems for the special case with sufficiently long UAV mission duration. Next, we obtain high-quality solutions for the practical case with finite UAV mission duration by using the alternating optimization and successive convex approximation (SCA). Numerical results show that the proposed designs significantly outperform benchmark schemes, and the utilization of CoMP achieves much higher uplink throughput than interference coordination.

Unmanned aerial vehicle (UAV)-enabled communication has emerged as an appealing technology for efficient data collection in wireless sensor networks (WSNs). This paper considers a scenario where multiple UAVs collect data from a group of sensor nodes (SNs) on the ground. We study the fundamental tradeoff between the aerial cost, which is defined by the propulsion energy consumption and operation costs of all UAVs, and the ground cost, which is defined as the energy consumption of all SNs. To characterize such a tradeoff, an optimization problem is formulated to minimize the weighted sum of the above two costs, by optimizing the UAV trajectory jointly with wake-up time allocation, as well as the transmit power of all SNs. As the formulated problem is non-convex, it is difficult to be optimally solved in general. To tackle this issue, we decouple it into two sub-problems: UAV trajectory and wake-up time allocation optimization, as well as SN transmit power optimization. We propose an iterative algorithm to solve the two sub-problems by leveraging successive convex approximation and alternating optimization techniques. In addition, a new approach is proposed to design the UAV initial trajectory with multiple travelling salesman problem (MTSP) technique. Simulations are conducted to corroborate our study and show the flexible tradeoff achieved by the proposed design for cost balance between UAVs and SNs.

In this paper, we consider the packet-level forward error correction (FEC) code design, without feedback or with delayed feedback, for achieving the minimum end-to-end latency, i.e., the latency between the time that packet is generated at the source and its in-order delivery to the application layer of the destination. We first show that the minimum-latency FEC design problem can be modeled as a partially observable Markov decision process (POMDP), and hence the optimal code construction can be obtained by solving the corresponding POMDP. However, solving the POMDP optimally is in general difficult unless its state and action space is very small. To this end, we propose an efficient heuristic algorithm, namely the majority vote policy, for obtaining a high quality approximate solution. We also derive the tight lower and upper bounds of the optimal state values of this POMDP, based on which a more sophisticated D-step search algorithm can be implemented for obtaining near-optimal solutions. The simulation results show that the proposed code designs via solving the POMDP, either with the majority vote policy or the D-step search algorithm, strictly outperform the existing schemes, for both cases, without or with only delayed feedback.

Integrating unmanned aerial vehicles (UAVs) into cellular networks offers a promising solution to support their efficient operations and achieve high-quality communication with the ground. In this paper, we consider a cellular-connected UAV communication system, in which one energy-constrained UAV flies from a given initial location to a final location while uploading data to the ground base stations (GBSs) along its flight. We study the joint design of UAV operation time, communication scheduling, as well as UAV trajectory and transmit power to maximize the data uploading throughput, subject to the communication quality of service (QoS) requirement and UAV energy budget constraints. We first consider an offline design approach by utilizing only the channel distribution information (CDI) that is available prior to the UAV's flight, which is formulated as a non-convex optimization problem and challenging to solve. By using path disretization and successive convex approximation (SCA) techniques, an efficient alternating optimization algorithm is proposed, which can converge to a solution that satisfies the Karush-Kuhn-Tucker (KKT) conditions. Then, we further study the online design approach by utilizing the instantaneous channel state information (CSI) that is available to the UAV in real time along its flight. As the online design problem has similar structure as that of the offline design, an adaptive online optimization algorithm is proposed. To further reduce the computational complexity, a low-complexity online algorithm based on receding horizon optimization (RHO) is developed by utilizing a combined offline and online design approach. Simulations are conducted to corroborate our study and the results demonstrate the performance gain of proposed designs as compared to various baseline schemes. Furthermore, our results unveil the tradeoff between system throughput and UAV endurance in the considered system.

This paper studies the path design problem for cellular-connected unmanned aerial vehicle (UAV), which aims to minimize its mission completion time while maintaining good connectivity with the cellular network. We first argue that the conventional path design approach via formulating and solving optimization problems faces several practical challenges, and then propose a new reinforcement learning-based UAV path design algorithm by applying temporal-difference method to directly learn the state-value function of the corresponding Markov Decision Process. The proposed algorithm is further extended by using linear function approximation with tile coding to deal with large state space. The proposed algorithms only require the raw measured or simulation-generated signal samples as the input and are suitable for both online and offline implementations. Numerical results show that the proposed path designs can successfully avoid the coverage holes of cellular networks even in the complex urban environment.

Minimizing energy consumption while providing quality of service (QoS) is of paramount importance for energy-constrained networks, such as Internet of Things (IoT). The emergence of fog computing in IoT has the great potential to reduce the energy consumption of the IoT nodes, which also known as terminal nodes (TNs), and also minimizing the task delays. However, this in general comes at the price of the higher energy consumption of the fog nodes (FNs). This paper aims to study the energy consumption tradeoff between the TNs and FNs in Fog-IoT system. To this end, we first propose a new protocol, where the TNs immediately broadcast their data with certain transmission rate to potentially all FNs without the need of firstly determining which FN to associate with. Upon receiving the data, FNs determine whether to process the data locally or forward to the cloud center, depending on whether they are overloaded or not. By considering the fading channels between TNs and FNs, we mathematically characterize the energy consumption tradeoff between TNs and FNs by varying the broadcasting rate of the TNs.

Wireless powered communication (WPC), where the required energy for communication is obtained via radio frequency (RF) energy harvesting, is a promising technology for the upcoming self-sustainable Internet of Things (IoT) networks. In this paper, a multiuser IoT network is considered, where an energy-constrained relay assists the information transmission of a number of IoT devices to the access point (AP) using WPC. In particular, the relay acquires the energy needed for information forwarding from the RF energy transfer of the AP, which serves as a dedicated energy transmitter for the relay. The objective is to maximize the total network throughput by jointly optimizing the wireless energy transfer (WET) duration and the relay's energy expenditure in each time slot, subject to the energy causality constraint, for both amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols. We show that the optimization problems for both the AF- and DF-based systems are convex and thus can be solved using convex optimization techniques. Our analysis shows that the solution of the sum-throughput maximization problems depends on the scheduling order of the IoT devices and the optimal solution is derived for different ordering scenarios. Finally, numerical simulations are presented to corroborate our analysis.

Unmanned aerial vehicles (UAVs) have found numerous applications and are expected to bring fertile business opportunities in the next decade. Among various enabling technologies for UAVs, wireless communication is essential and has drawn significantly growing attention in recent years. Compared to the conventional terrestrial communications, UAVs' communications face new challenges due to their high altitude above the ground and great flexibility of movement in the 3-D space. Several critical issues arise, including the line-of-sight (LoS) dominant UAV-ground channels and induced strong aerial-terrestrial network interference, the distinct communication quality-of-service (QoS) requirements for UAV control messages versus payload data, the stringent constraints imposed by the size, weight, and power (SWAP) limitations of UAVs, as well as the exploitation of the new design degree of freedom (DoF) brought by the highly controllable 3-D UAV mobility. In this article, we give a tutorial overview of the recent advances in UAV communications to address the above issues, with an emphasis on how to integrate UAVs into the forthcoming fifth-generation (5G) and future cellular networks. In particular, we partition our discussion into two promising research and application frameworks of UAV communications, namely UAV-assisted wireless communications and cellular-connected UAVs, where UAVs are integrated into the network as new aerial communication platforms and users, respectively. Furthermore, we point out promising directions for future research.

This letter investigates a multiple-input multiple-output (MIMO) wireless power transfer system under practical nonliner energy harvesting (EH) models. We propose a new generic energy receiver (ER) architecture consisting of N receive antennas and L rectifiers, for which one power splitter is inserted after each antenna to adaptively split the received radio frequency (RF) signals among the L rectifiers for efficient nonlinear RF-to-direct current (dc) conversion. With the proposed architecture, we maximize the total harvested dc power at the ER, by jointly optimizing the transmit energy beamforming at the energy transmitter and the power splitting ratios at the ER. Numerical results show that our proposed design by exploiting the nonlinearity of EH significantly improves the harvested dc power at the ER, as compared to two conventional designs.

Integrating the unmanned aerial vehicles (UAVs) into the cellular network is envisioned to be a promising technology to significantly enhance the communication performance of both UAVs and existing terrestrial users. In this paper, we first provide an overview on the two main research paradigms in cellular UAV communications, namely, cellular-enabled UAV communication with UAVs as new aerial users served by the ground base stations (GBSs), and UAV-assisted cellular communication with UAVs as new aerial communication platforms serving the terrestrial users. Then, we focus on the former paradigm and study a new UAV trajectory design problem subject to practical communication connectivity constraints with the GBSs. Specifically, we consider a cellular-connected UAV in the mission of flying from an initial location to a final location that are given, during which it needs to maintain reliable communication with the cellular network by associating with one of the available GBSs at each time instant that has the best line-of-sight channel (or shortest distance) with it. We aim to minimize the UAV's mission completion time by optimizing its trajectory, subject to a quality-of-connectivity constraint of the GBS-UAV link specified by a minimum receive signal-to-noise ratio target, which needs to be satisfied throughout its mission. To tackle this challenging non-convex optimization problem, we first propose an efficient method to verify its feasibility via checking the connectivity between two given vertices on an equivalent graph. Next, by examining the GBS-UAV association sequence over time, we obtain useful structural results on the optimal UAV trajectory, based on which two efficient methods are proposed to find high-quality approximate trajectory solutions by leveraging the techniques from graph theory and convex optimization. The proposed methods are analytically shown to be capable of achieving a flexible tradeoff between complexity and performance, and yielding a solution in polynomial time with the performance arbitrarily close to that of the optimal solution. Numerical results further validate the effectiveness of our proposed designs against benchmark schemes. Finally, we make concluding remarks and point out promising directions for future work.

This paper studies unmanned aerial vehicle (UAV)-enabled wireless communication, where a rotary-wing UAV is dispatched to communicate with multiple ground nodes (GNs). We aim to minimize the total UAV energy consumption, including both propulsion energy and communication related energy, while satisfying the communication throughput requirement of each GN. To this end, we first derive a closed-form propulsion power consumption model for rotary-wing UAVs, and then formulate the energy minimization problem by jointly optimizing the UAV trajectory and communication time allocation among GNs, as well as the total mission completion time. The problem is difficult to be optimally solved, as it is non-convex and involves infinitely many variables over time. To tackle this problem, we first consider the simple fly-hover-communicate design, where the UAV successively visits a set of hovering locations and communicates with one corresponding GN while hovering at each location. For this design, we propose an efficient algorithm to optimize the hovering locations and durations, as well as the flying trajectory connecting these hovering locations, by leveraging the travelling salesman problem with neighborhood and convex optimization techniques. Next, we consider the general case, where the UAV also communicates while flying. We propose a new path discretization method to transform the original problem into a discretized equivalent with a finite number of optimization variables, for which we obtain a high-quality suboptimal solution by applying the successive convex approximation technique. The numerical results show that the proposed designs significantly outperform the benchmark schemes.

Energy consumption is one of the important design aspect for data collection in wireless sensor networks (WSNs). This paper studies data collection from a set of sensor nodes (SNs) in WSNs enabled by multiple unmanned aerial vehicles (UAVs). We aim to minimize the maximum mission completion time among all UAVs by jointly optimizing the UAV trajectory, as well as the wake-up scheduling and association for SNs, while ensuring that each SN can successfully upload the targeting amount of data with a given energy budget. The formulated problem is a non-convex problem which is difficult to be solved directly. To tackle this problem, we first propose a simple scheme that each UAV only collects data while hovering, termed as hovering mode (Hmode). For this mode, in order to find the optimized hovering locations for each SN and the serving order among all locations, we propose an efficient algorithm by leveraging the min-max multiple Traveling Salesman Problem (min-max m-TSP) and convex optimization techniques. Furthermore, we propose the more general scheme that enables continuous data collection even while flying, termed as flying mode (Fmode). By leveraging bisection method and time discretization technique, the original problem is transformed into a discretized equivalent with a finite number of optimization variables, based on which a Karush-Kuhn-Tucker (KKT) solution is obtained by applying the successive convex approximation (SCA) technique. The simulation results show that the proposed multi-UAV enabled data collection with joint trajectory and communication design achieves significant performance gains over the benchmark schemes.

This paper studies a wireless communication system with a rotary-wing unmanned aerial vehicle (UAV) dispatched to communicate with multiple ground nodes (GNs). With limited on-board energy available at the UAV, we aim to maximize the weighted minimum of the communication throughput of the GNs, by jointly designing the UAV trajectory and communication resource allocation. The formulated problem is difficult to be directly solved, as it is non-convex and involves infinitely many variables over time. To tackle this problem, we first propose a simple fly-hover-communicate protocol, where the UAV successively visits a set of hovering locations and at each of them communicates with one corresponding GN. By leveraging the classic travelling salesman problem (TSP) and convex optimization techniques, we propose an efficient algorithm to optimize the hovering locations and durations, as well as the flying trajectory connecting these hovering locations. To further improve the performance, we consider the general scenario where the UAV also communicates while flying, and under a given UAV path, we find the optimal time allocation by solving a linear programming (LP) problem. Numerical results show the significant performance gains of the proposed designs over benchmark schemes.

In this paper, we study a cellular-enabled unmanned aerial vehicle (UAV) communication system consisting of one UAV and multiple ground base stations (GBSs). The UAV has a mission of flying from an initial location to a final location, during which it needs to maintain reliable wireless connection with the cellular network by associating with one of the GBSs at each time instant. We aim to minimize the UAV mission completion time by optimizing its trajectory, subject to a quality of connectivity constraint of the GBS-UAV link specified by a minimum received signal-to-noise ratio (SNR) target, which needs to be satisfied throughout the mission. This problem is non-convex and difficult to be optimally solved. We first propose an effective approach to check its feasibility based on graph connectivity verification. Then, by examining the GBS-UAV association sequence during the UAV mission, we obtain useful insights on the optimal UAV trajectory, based on which an efficient algorithm is proposed to find an approximate solution to the trajectory optimization problem by leveraging techniques in convex optimization and graph theory. Numerical results show that our proposed trajectory design achieves near-optimal performance.

In this paper, we investigate the optimal scheduling schemes for multi-hop video streaming with generation-based random linear network coding in vehicular networks. With the proposed scheduling algorithm, each vehicle in the network is guaranteed to decode the video block from the dedicated transmissions and the overheard packets with high probability. We derive explicit expression for the number network coded packets that need to be generated at each vehicle in order to ensure successful decoding at its downstream vehicles, by considering the overheard packets due to wireless broadcasting. Furthermore, the distance beyond which the channel can be reused is optimized to maximize the achievable video generating rate at the source node. Simulation results show that the proposed coding and scheduling scheme achieves higher end-to-end throughput than the conventional packet erasure correction coding scheme that is only optimized for one-hop communications.

Wireless communication enabled by unmanned aerial vehicles (UAVs) has emerged as an appealing technology for many application scenarios in future wireless systems. However, the limited endurance of UAVs greatly hinders the practical implementation of UAV-enabled communications. To overcome this issue, this paper proposes a novel scheme for UAV-enabled communications by utilizing the promising technique of proactive caching at the users. Specifically, we focus on content-centric communication systems, where a UAV is dispatched to serve a group of ground nodes (GNs) with random and asynchronous requests for files drawn from a given set. With the proposed scheme, at the beginning of each operation period, the UAV pro-actively transmits the files to a subset of selected GNs that cooperatively cache all the files. As a result, when requested, a file can be retrieved by each GN either directly from its local cache or from its nearest neighbor that has cached the file via device-to-device communications. It is revealed that there exists a fundamental trade-off between the file caching cost, which is the total time required for the UAV to transmit the files to their designated caching GNs, and the file retrieval cost, which is the average time required for serving one file request. To characterize this trade-off, we formulate an optimization problem to minimize the weighted sum of the two costs, via jointly designing the file caching policy, the UAV trajectory, and communication scheduling. As the formulated problem is NP-hard in general, we propose efficient algorithms to find high-quality approximate solutions for it. Numerical results are provided to corroborate our study and show the great potential of proactive caching for overcoming the endurance issue in UAV-enabled communications.

In this paper, we study the achievable throughput of batched temporal network coding in multi-hop erasure channels, where network coding is applied only within small coding blocks and each communication hop is modeled as a Gilbert-Elliott (GE) packet erasure channel. The GE channel is a 2-state Markov model that is commonly used for channels with memory. While channel memory does not affect the end-to-end capacity of multi-hop erasure channels, we show that it degrades the end-to-end throughput, when batched network coding with finite batch size is applied, due to the higher variance in erasures within one coding block. On the other hand, if the initial channel state information is available, the channel variance can be significantly reduced. We show that this fact can be utilized for improving the efficiency of the recoding operations at the intermediate nodes, and hence improve the end-to-end throughput of batched network coding schemes. Specifically, we propose adaptive recoding operations, where the network coded packets are adaptively generated based on the number of received packets and the initial channel state for each coding block. The simulation results show that the proposed adaptive recoding scheme significantly enhances the end-to-end throughput of batched network coding over multi-hop GE channels.

In this paper we consider scalable video multicasting over erasure networks with heterogeneous video quality requirements. With random linear network coding (RLNC) applied at the intermediate nodes the information received by the destinations is determined by the associated channel rank distributions based on which we obtain the optimal achievable code rate at the source node. We show that although a concatenation of priority encoded transmission (PET) with RLNC achieves the optimal code rate it incurs prohibitive high coding complexity. On the other hand batched sparse (BATS) code has been recently proposed for unicast networks which has low coding complexity with near-optimal overhead. However the existing BATS code design cannot be applied for multicast networks with heterogeneous channel rank distributions at different destinations. To this end we propose a novel expanding window BATS (EW-BATS) code where the input symbols are grouped into overlapped windows according to their importance levels. The more important symbols are encoded with lower rate and hence they can be decoded by more destinations while the less important symbols are encoded with higher rate and are only decoded by the destinations with high throughput for video quality enhancement. Based on asymptotical performance analysis we formulate the linear optimization problems to jointly optimize the degree distributions for each window and the window selection probabilities. Simulation results show that the proposed EW-BATS code satisfies the decoding requirements with much lower transmission overhead compared with separate BATS code where the degree distributions are separately optimized for each destination.

For millimeter wave (mm-wave) communication systems, a lens antenna array with single-carrier transmission and path delay compensation is a promising technique for realizing cost-effective large multiple-input multiple-output communications with limited number of radio frequency chains. In this paper, we study the multi-user mm-wave downlink lens antenna array system for the general frequency-selective channels. By leveraging the angle-dependent energy focusing property of the lens antenna array and the angular sparsity of mm-wave channels, we investigate the low-complexity single-carrier transmission scheme with path delay pre-compensation applied at the base station (BS). The resulting signal-to-interference-plus-noise ratio (SINR) is derived by taking into account both the residual inter-symbol interference and inter-user interference. Based on the derived SINR expression, we propose an effective joint antenna selection and beamforming scheme by utilizing the group sparse optimization to accommodate for the limited number of RF chains at the BS. Thus, the proposed scheme can obtain the approximate performance with the fully digital case and has a better performance than the conventional orthogonal frequency-division multiplexing mode for the frequency-selectivity channels. Numerical results are provided to verify the effectiveness of the proposed schemes.

In conventional terrestrial cellular networks, mobile terminals (MTs) at the cell edge often pose a performance bottleneck due to their long distances from the serving ground base station (GBS), especially in the hotspot period when the GBS is heavily loaded. This paper proposes a new hybrid network architecture that leverages use of unmanned aerial vehicle (UAV) as an aerial mobile base station, which flies cyclically along the cell edge to offload data traffic for cell-edge MTs. We aim to maximize the minimum throughput of all MTs by jointly optimizing the UAV's trajectory, bandwidth allocation, and user partitioning. We first consider orthogonal spectrum sharing between the UAV and GBS, and then extend to spectrum reuse where the total bandwidth is shared by both the GBS and UAV with their mutual interference effectively avoided. Numerical results show that the proposed hybrid network with optimized spectrum sharing and cyclical multiple access design significantly improves the spatial throughput over the conventional GBS-only network; while the spectrum reuse scheme provides further throughput gains at the cost of slightly higher complexity for interference control. Moreover, compared with the conventional small-cell offloading scheme, the proposed UAV offloading scheme is shown to outperform in terms of throughput, besides saving the infrastructure cost.

Due to the high maneuverability, flexible deployment, and low cost, unmanned aerial vehicles (UAVs) have attracted significant interest recently in assisting wireless communication. This paper considers a multi-UAV enabled wireless communication system, where multiple UAV-mounted aerial base stations are employed to serve a group of users on the ground. To achieve fair performance among users, we maximize the minimum throughput over all ground users in the downlink communication by optimizing the multiuser communication scheduling and association jointly with the UAV's trajectory and power control. The formulated problem is a mixed integer nonconvex optimization problem that is challenging to solve. As such, we propose an efficient iterative algorithm for solving it by applying the block coordinate descent and successive convex optimization techniques. Specifically, the user scheduling and association, UAV trajectory, and transmit power are alternately optimized in each iteration. In particular, for the nonconvex UAV trajectory and transmit power optimization problems, two approximate convex optimization problems are solved, respectively. We further show that the proposed algorithm is guaranteed to converge. To speed up the algorithm convergence and achieve good throughput, a low-complexity and systematic initialization scheme is also proposed for the UAV trajectory design based on the simple circular trajectory and the circle packing scheme. Extensive simulation results are provided to demonstrate the significant throughput gains of the proposed design as compared to other benchmark schemes.

This paper studies a new unmanned aerial vehicle (UAV)-enabled wireless power transfer system, where a UAV-mounted mobile energy transmitter is dispatched to deliver wireless energy to a set of energy receivers (ERs) at known locations on the ground. We investigate how the UAV should optimally exploit its mobility via trajectory design to maximize the amount of energy transferred to all ERs during a finite charging period. First, we consider the maximization of the sum energy received by all ERs by optimizing the UAV's trajectory subject to its maximum speed constraint. Although this problem is non-convex, we obtain its optimal solution, which shows that the UAV should hover at one single fixed location during the whole charging period. However, the sum-energy maximization incurs a “near-far” fairness issue, where the received energy by the ERs varies significantly with their distances to the UAV's optimal hovering location. To overcome this issue, we consider a different problem to maximize the minimum received energy among all ERs, which, however, is more challenging to solve than the sum-energy maximization. To tackle this problem, we first consider an ideal case by ignoring the UAV's maximum speed constraint, and show that the relaxed problem can be optimally solved via the Lagrange dual method. The obtained trajectory solution implies that the UAV should hover over a set of fixed locations with optimal hovering time allocations among them. Then, for the general case with the UAV's maximum speed constraint considered, we propose a new successive hover-and-fly trajectory motivated by the optimal trajectory in the ideal case and obtain efficient trajectory designs by applying the successive convex programing optimization technique. Finally, numerical results are provided to evaluate the performance of the proposed designs under different setups, as compared with benchmark schemes.

Wireless power transfer (WPT) via dedicated radio frequency (RF) transmission is an appealing technology to provide cost-effective energy supply to low-power devices in the future era of Internet of Things. To achieve efficient power delivery over moderate distance, WPT usually relies on highly directional power transmission from the energy transmitter (ET) to the energy receiver (ER). To this end, the ET needs to be equipped with a large number of antennas and employ adaptive energy beamforming to flexibly control the energy focusing directions to the ER based on the multipath channels between them. However, this renders the conventional fully digital beamforming with one dedicated RF chain for each transmit antenna too costly, in terms of both hardware implementation and energy consumption. To overcome this issue, we study in this paper, a new WPT system based on the hybrid analog or digital beamforming technique, where the number of RF chains is in general significantly less than that of transmit antennas. We first show that for a general point-to-point multiple-input multiple-output WPT system over frequency-selective channels, hybrid beamforming is able to achieve the optimal performance as the fully digital beamforming, as long as the number of RF chains at the ET is no less than twice the number of sub-bands used or twice the number of channel paths. Furthermore, for the special cases of line-of-sight channel or multiple-input single-output WPT, the required number of RF chains can be further reduced to equal the number of channel paths only. Finally, for the scenarios when the given number of RF chains is insufficient to achieve the fully digital beamforming performance, we propose efficient algorithms for the hybrid beamforming design to maximize its efficiency. Numerical results are provided to validate our analytical results and demonstrate the effectiveness of the proposed hybrid beamforming design.

Mobile-edge computation offloading (MECO) is an emerging technology for enhancing mobiles' computation capabilities and prolonging their battery lifetime by offloading intensive computation from mobiles to nearby servers, such as base stations. In this paper, we study the energy-efficient resource-management policy for the asynchronous MECO system, where the mobiles have heterogeneous input-data arrival time instants and computation deadlines. First, we consider the general case with arbitrary arrival-deadline orders. Based on the monomial energy-consumption model for data transmission, an optimization problem is formulated to minimize the total mobile-energy consumption under the time-sharing and computation-deadline constraints. The optimal resource-management policy for data partitioning (for offloading and local computing) and time division (for transmissions) is obtained in (semi-)closed-form expression by using the block coordinate decent method. To gain further insight, we study the optimal resource-management design for two special cases. First, consider the case of identical arrival-deadline orders, i.e., a mobile with input data arriving earlier also needs to complete computation earlier. The optimization problem is reduced to two sequential problems corresponding to the optimal scheduling order and joint data-partitioning and time-division given the optimal order. It is found that the optimal time-division policy tends to equalize the defined effective computing power among offloading mobiles via time sharing. Furthermore, this solution approach is extended to the case of reverse arrival-deadline orders. The corresponding time-division policy is derived by a proposed transformation-and-scheduling approach that first determines the total offloading duration and data size for each mobile in the transformation phase and then specifies the offloading intervals for each mobile in the scheduling phase.

This paper studies an unmanned aerial vehicle (UAV)-enabled multicasting system, where a UAV is dispatched to disseminate a common file to a set of ground terminals (GTs). We aim to design the UAV trajectory to minimize its mission completion time, while ensuring that each GT successfully recovers the file with a desired high probability. The formulated problem is nonconvex and difficult to be solved in its original form. Therefore, we first derive an effective lower bound for the success file recovery probability of each GT. The problem is then reformulated in a more tractable form, where the UAV trajectory only needs to be designed to ensure the minimum connection time constraint with each GT, during which their distance is below a certain threshold. We show that without loss of optimality, the UAV trajectory consists of connected line segments only, which can be obtained by determining the optimal set of waypoints as well as the UAV speed along the path connecting the waypoints. We propose efficient schemes for the waypoint design based on a novel concept of virtual base station placement and by applying convex optimization. Furthermore, for fixed waypoints, the optimal UAV speed is efficiently obtained by solving a linear programming problem. Numerical results show that the proposed UAV-enabled multicasting with optimized trajectory design achieves significant performance gains over other benchmark schemes.

Millimeter wave (mm-wave) communication using lens antenna arrays is a promising technique for realizing cost-effective large multiple-input multiple-output (MIMO) systems with only limited radio frequency chains. This paper studies a multi-user mm-wave single-sided lens MIMO system, where the base station (BS) is equipped with a full-dimensional lens antenna array and each mobile station employs the conventional antenna arrays. By exploiting the angle-dependent energy focusing property of lens antenna array and the multi-path sparsity of mm-wave channels, we propose a low-complexity single-carrier (SC)-based path-division multiple access (PDMA) scheme for the general wide-band frequency-selective channels. To this end, a new technique called path delay compensation is proposed at the BS to transform the multi-user frequency-selective MIMO channels to parallel frequency-flat small-size MIMO channels. In addition, we propose an efficient channel estimation scheme tailored for the SC-based PDMA, which requires negligible training overhead in practical mm-wave systems and yet leads to comparable performance as that with perfect channel state information. Numerical results show that the proposed design achieves comparable performance as the state-of-the-art benchmark systems in terms of spectrum efficiency, but with significantly reduced hardware/power consumption cost and signal processing complexity.

This paper investigates an unmanned aerial vehicle (UAV)-enabled multiuser wireless power transfer (WPT) system, where a UAV-mounted energy transmitter (ET) is dispatched to broadcast wireless energy to charge multiple energy receivers (ERs) on the ground. To ensure efficient and fair WPT, we maximize the minimum of the energy harvested by all ERs during a given charging period, by optimizing the UAV's trajectory subject to its maximum speed constraints. Such a min-energy maximization problem, however, is non-convex, and thus is challenging to be directly solved. To tackle this problem, we first consider an ideal case by ignoring the UAV's maximum speed constraint, and show that the relaxed problem can be optimally solved via the Lagrange dual method. The obtained trajectory solution implies that the UAV should hover over a set of fixed locations with optimal allocation of the hovering time among them. Then, for the general case with the UAV's maximum speed constraint considered, we propose a new successive hover-and-fly trajectory motivated by the optimal trajectory in the ideal case, and obtain efficient trajectory designs by applying the successive convex programing (SCP) optimization technique. Numerical results show that our proposed trajectory designs significantly improve the min-energy transferred to all ERs, as compared to other benchmark schemes.

In conventional terrestrial cellular systems, mobile terminals (MTs) at the cell edge often pose the performance bottleneck due to their long distance from the ground base station (GBS), especially in hotspot areas. This paper proposes a new hybrid network architecture by leveraging the use of unmanned aerial vehicle (UAV) as an aerial mobile base station, which flies cyclically along the cell edge to serve the cell-edge MTs and help offloading the traffic from the GBS. To achieve user fairness, we aim to maximize the minimum throughput of all MTs in a single cell by jointly optimizing the UAV's trajectory, as well as the bandwidth allocation and user partitioning between the UAV and GBS. Numerical results show that the proposed hybrid network with optimized spectrum sharing and cyclical multiple access design significantly improves the spatial throughput over the conventional cellular network with the GBS only.

Unmanned aerial vehicles (UAVs) have attracted significant interest recently in wireless communication due to their high maneuverability, flexible deployment, and low cost. This paper studies a UAV-enabled wireless network where the UAV is employed as an aerial mobile base station (BS) to serve a group of users on the ground. To achieve fair performance among users, we maximize the minimum throughput over all ground users by jointly optimizing the multiuser communication scheduling and UAV trajectory over a finite horizon. The formulated problem is shown to be a mixed integer non-convex optimization problem that is difficult to solve in general. We thus propose an efficient iterative algorithm by applying the block coordinate descent and successive convex optimization techniques, which is guaranteed to converge. To achieve fast convergence and stable throughput, we further propose a low-complexity initialization scheme for the UAV trajectory design based on the simple circular trajectory. Extensive simulation results are provided which show significant throughput gains of the proposed design as compared to other benchmark schemes.

Millimeter wave (mmWave) communication with advanced lens antenna arrays is a promising technology for achieving cost-effective 5G wireless systems. This paper studies a multi-user mmWave single-sided lens multiple-input multiple-output (MIMO) system in the uplink communication, where the base station (BS) is equipped with a full-dimensional (FD) lens antenna array with both elevation and azimuth angle resolution capabilities, and each mobile station (MS) has the conventional uniform planar array (UPA). With limited radio frequency (RF) chains at the BS and one single RF chain at each MS, we propose a low-complexity path division multiple access (PDMA) scheme to enable virtually interference-free multiuser communications, by exploiting the angle-dependent energy focusing property of the lens antenna array at the BS as well as the multi-path sparsity of mmWave channels. Besides, a new technique called path delay compensation is proposed at the BS to effectively transform the frequency-selective MIMO channel to parallel frequency-flat small-size MIMO channels, for each of which the low-complexity single-carrier (SC) transmission can be applied. Numerical results show significant sum-rate gain with the proposed design over benchmark systems.

Wireless communication by leveraging the use of low-altitude unmanned aerial vehicles (UAVs) has received significant interests recently due to its low-cost and flexibility in providing wireless connectivity in areas without infrastructure coverage. This paper studies a UAV-enabled mobile relaying system, where a high-mobility UAV is deployed to assist in the information transmission from a ground source to a ground destination with their direct link blocked. By assuming that the UAV adopts the energy-efficient circular trajectory and employs time-division duplexing (TDD) based decode-and-forward (DF) relaying, we maximize the spectrum efficiency (SE) in bits/second/Hz as well as energy efficiency (EE) in bits/Joule of the considered system by jointly optimizing the time allocations for the UAV's relaying together with its flying speed and trajectory. It is revealed that for UAV-enabled mobile relaying with the UAV propulsion energy consumption taken into account, there exists a trade-off between the maximum achievable SE and EE by exploiting the new degree of freedom of UAV trajectory design.

Radiative wireless power transfer (WPT) is a promising technology to provide cost-effective and real-time power supplies to wireless devices. Although radiative WPT shares many similar characteristics with the extensively studied wireless information transfer or communication, they also differ significantly in terms of design objectives, transmitter/receiver architectures and hardware constraints, and so on. In this paper, we first give an overview on the various WPT technologies, the historical development of the radiative WPT technology and the main challenges in designing contemporary radiative WPT systems. Then, we focus on the state-of-the-art communication and signal processing techniques that can be applied to tackle these challenges. Topics discussed include energy harvester modeling, energy beamforming for WPT, channel acquisition, power region characterization in multi-user WPT, waveform design with linear and non-linear energy receiver model, safety and health issues of WPT, massive multiple-input multiple-output and millimeter wave enabled WPT, wireless charging control, and wireless power and communication systems co-design. We also point out directions that are promising for future research.

Wireless communication with unmanned aerial vehicles (UAVs) is a promising technology for future communication systems. In this paper, assuming that the UAV flies horizontally with a fixed altitude, we study energy-efficient UAV communication with a ground terminal via optimizing the UAV's trajectory, a new design paradigm that jointly considers both the communication throughput and the UAV's energy consumption. To this end, we first derive a theoretical model on the propulsion energy consumption of fixed-wing UAVs as a function of the UAV's flying speed, direction, and acceleration. Based on the derived model and by ignoring the radiation and signal processing energy consumption, the energy efficiency of UAV communication is defined as the total information bits communicated normalized by the UAV propulsion energy consumed for a finite time horizon. For the case of unconstrained trajectory optimization, we show that both the rate-maximization and energy-minimization designs lead to vanishing energy efficiency and thus are energy-inefficient in general. Next, we introduce a simple circular UAV trajectory, under which the UAV's flight radius and speed are jointly optimized to maximize the energy efficiency. Furthermore, an efficient design is proposed for maximizing the UAV's energy efficiency with general constraints on the trajectory, including its initial/final locations and velocities, as well as minimum/maximum speed and acceleration. Numerical results show that the proposed designs achieve significantly higher energy efficiency for UAV communication as compared with other benchmark schemes.

This paper studies a new active eavesdropping technique via the so-called spoofing relay attack, which could be launched by the eavesdropper to significantly enhance the information leakage rate from the source over conventional passive eavesdropping. With this attack, the eavesdropper acts as a relay to spoof the source to vary transmission rate in favor of its eavesdropping performance by either enhancing or degrading the effective channel of the legitimate link. The maximum information leakage rate achievable by the eavesdropper and the corresponding optimal operation at the spoofing relay are obtained. It is shown that such a spoofing relay attack could impose new challenges from a physical-layer security perspective since it leads to significantly higher information leakage rate than conventional passive eavesdropping.

In this paper, an efficient channel estimation scheme is proposed for the point-to-point lens-antenna-array enabled millimeter wave (mmWave) multiple-input multiple-output (MIMO) system with limited number of radio frequency (RF) chains at both source and destination. By exploiting the energy focusing property of lens antenna arrays at both the transmitter and receiver, as well as the multi-path sparsity of mmWave channels, the proposed scheme employs two-way training to achieve energy-based antenna selections at both the source and destination, thus significantly reducing the number of RF chains required. The proposed scheme has low complexity for implementation and yet does not require any prior knowledge of the channel. It is shown by simulations that compared to the traditional channel training method for MIMO systems without lens antenna, the effective data rate achieved by the lens array enabled mmWave MIMO system can be significantly improved, thanks to the energy focusing capability of lens antenna arrays and the matching efficient channel estimation scheme proposed.

Millimeter wave (mmWave) communication is a promising technology for future wireless systems, while one practical challenge is to achieve its large-antenna gains with only limited radio frequency (RF) chains for cost-effective implementation. To this end, we study in this paper a new lens antenna array enabled mmWave multiple-input multiple-output (MIMO) communication system. We first show that the array response of lens antenna arrays follows a “sinc” function, where the antenna element with the peak response is determined by the angle of arrival (AoA)/departure (AoD) of the received/transmitted signal. By exploiting this unique property along with the multi-path sparsity of mmWave channels, we propose a novel low-cost and capacity-achieving spatial multiplexing scheme for both narrow-band and wide-band mmWave communications, termed path division multiplexing (PDM), where parallel data streams are transmitted over different propagation paths with simple per-path processing. We further propose a simple path grouping technique with group-based small-scale MIMO processing to effectively mitigate the inter-stream interference due to similar AoAs/AoDs. Numerical results are provided to compare the performance of the proposed mmWave lens MIMO against the conventional MIMO with uniform planar arrays (UPAs) and hybrid analog/digital processing. It is shown that the proposed design achieves significant throughput gains as well as complexity and cost reductions, thus leading to a promising new paradigm for mmWave MIMO communications.

In this paper, we consider a novel mobile relaying technique, where the relay nodes are mounted on unmanned aerial vehicles (UAVs) and hence are capable of moving at high speed. Compared with conventional static relaying, mobile relaying offers a new degree of freedom for performance enhancement via careful relay trajectory design. We study the throughput maximization problem in mobile relaying systems by optimizing the source/relay transmit power along with the relay trajectory, subject to practical mobility constraints (on the UAV's speed and initial/final relay locations), as well as the information-causality constraint at the relay. It is shown that for the fixed relay trajectory, the throughput-optimal source/relay power allocations over time follow a “staircase” water filling structure, with non-increasing and non-decreasing water levels at the source and relay, respectively. On the other hand, with given power allocations, the throughput can be further improved by optimizing the UAV's trajectory via successive convex optimization. An iterative algorithm is thus proposed to optimize the power allocations and relay trajectory alternately. Furthermore, for the special case with free initial and final relay locations, the jointly optimal power allocation and relay trajectory are derived. Numerical results show that by optimizing the trajectory of the relay and power allocations adaptive to its induced channel variation, mobile relaying is able to achieve significant throughput gains over the conventional static relaying.

This paper studies the optimized training design for multiple-input single-output (MISO) wireless energy transfer (WET) systems in frequency-selective channels, where the frequency-diversity and energy-beamforming gains can be both reaped by properly learning the channel state information (CSI) at the energy transmitter (ET). By exploiting channel reciprocity, a two-phase channel training scheme is proposed to achieve the diversity and beamforming gains, respectively. In the first phase, pilot signals are sent from the energy receiver (ER) over a selected subset of the available frequency sub-bands, through which the sub-band that exhibits the largest sum-power over all the antennas at the ET is determined and its index is sent back to the ER. In the second phase, the selected sub-band is further trained by the ER, so that the ET obtains an estimation for the MISO channel and implement energy beamforming. We propose to maximize the net energy harvested at the ER, which is the total harvested energy offset by that used for the two-phase channel training. The optimal training design, including the number of sub-bands trained and the energy allocated for each of the two training phases, is derived.

Radio-frequency (RF) enabled wireless energy transfer (WET), as a promising solution to provide cost-effective and reliable power supplies for energy-constrained wireless networks, has drawn growing interests recently. To overcome the significant propagation loss over distance, employing multi-antennas at the energy transmitter (ET) to more efficiently direct wireless energy to desired energy receivers (ERs), termed energy beamforming, is an essential technique for enabling WET. However, the achievable gain of energy beamforming crucially depends on the available channel state information (CSI) at the ET, which needs to be acquired practically. In this paper, we study the design of an efficient channel acquisition method for a point-to-point multiple-input multiple-output (MIMO) WET system by exploiting the channel reciprocity, i.e., the ET estimates the CSI via dedicated reverse-link training from the ER. Considering the limited energy availability at the ER, the training strategy should be carefully designed so that the channel can be estimated with sufficient accuracy, and yet without consuming excessive energy at the ER. To this end, we propose to maximize the net harvested energy at the ER, which is the average harvested energy offset by that used for channel training. An optimization problem is formulated for the training design over MIMO Rician fading channels, including the subset of ER antennas to be trained, as well as the training time and power allocated. Closed-form solutions are obtained for some special scenarios, based on which useful insights are drawn on when training should be employed to improve the net transferred energy in MIMO WET systems.

This paper studies the training design problem for multiple-input single-output (MISO) wireless energy transfer (WET) systems in frequency-selective channels, where the frequency-diversity and energy-beamforming gains can be both reaped to maximize the transferred energy by efficiently learning the channel state information (CSI) at the energy transmitter (ET). By exploiting channel reciprocity, a new two-phase channel training scheme is proposed to achieve the diversity and beamforming gains, respectively. In the first phase, pilot signals are sent from the energy receiver (ER) over a selected subset of the available frequency sub-bands, through which the ET determines a certain number of “strongest” sub-bands with largest antenna sum-power gains and sends their indices to the ER. In the second phase, the selected sub-bands are further trained by the ER, so that the ET obtains a refined estimate of the corresponding MISO channels to implement energy beamforming for WET. A training design problem is formulated and optimally solved, which takes into account the channel training overhead by maximizing thenetharvested energy at the ER, defined as the average harvested energy offset by that consumed in the two-phase training. Moreover, asymptotic analysis is obtained for systems with a large number of antennas or a large number of sub-bands to gain useful insights on the optimal training design. Finally, numerical results are provided to corroborate our analysis and show the effectiveness of the proposed scheme that optimally balances the diversity and beamforming gains in MISO WET systems with limited-energy training.

Wireless energy transfer (WET) is potentially a promising solution to provide convenient and reliable energy supplies for energy-constrained networks, and has drawn growing interests recently. To overcome the significant propagation loss over distance, employing multi-antennas at the energy transmitter (ET) to more efficiently direct wireless energy to desired energy receivers (ERs), termed energy beamforming, is an essential technique for WET. However, the achievable gain of energy beamforming crucially depends on the available channel state information (CSI) at the ET, which needs to be acquired practically. In this paper, we study the optimal design of one efficient channel-acquisition method for a point-to-point multiple-input multiple-output (MIMO) WET system, by exploiting the channel reciprocity based on which the ET estimates the CSI via dedicated reverse-link training from the ER. Considering the limited energy availability at the ER, the training strategy should be carefully designed so that the channel can be estimated with sufficient accuracy, and yet without consuming excessive energy at the ER. To this end, we propose to maximize the net energy at the ER, which is the total energy harvested offset by that used for channel training. The optimal training design, including the number of receive antennas to be trained, as well as the training time and power allocated, is derived. Our result shows that training helps only when the following conditions are satisfied: (i) the channel coherence time is sufficiently large; (ii) the number of antennas at the ET is large enough; and (iii) the effective signal-to-noise ratio (ESNR) is sufficiently high; otherwise, no training should be applied and isotropic energy transmission is optimal.

Massive multiple-input-multiple-output (MIMO) techniques have been recently advanced to tremendously improve the performance of wireless communication networks. However, the use of very large antenna arrays at the base stations brings new issues, such as the significantly increased hardware and signal processing costs. In order to reap the performance gains of massive MIMO and yet reduce its cost, this paper proposes a novel system design by integrating an electromagnetic (EM) lens with the large antenna array, termed the EM-lens enabled MIMO. The EM lens has the capability of focusing the power of an incident wave to a small area of the antenna array, whereas the location of the focal area varies with the angle of arrival (AoA) of the wave. Hence, in scenarios where the arriving signals from geographically separated users have different AoAs, the EM-lens enabled receiver provides two new benefits, namely, energy focusing and spatial interference rejection. By taking into account the effects of imperfect channel estimation via pilot-assisted training, in this paper, we analytically show that the average received signal-to-noise ratio in both the single-user and multiuser uplink transmissions can be improved by the EM-lens enabled system. Furthermore, we demonstrate that the proposed design makes it possible to considerably reduce the hardware and signal processing costs with only slight degradations in performance. To this end, two complexity/cost reduction schemes are proposed, which are small-MIMO processing with parallel receiver filtering applied over subgroups of antennas to reduce the computational complexity, and channel covariance based antenna selection to reduce the required number of radio frequency chains. Numerical results are provided to corroborate our analysis and show the great potential advantages of our proposed EM-lens enabled MIMO system for next generation cellular networks.

In this paper, we present an achievable rate region for double-unicast networks by assuming that the intermediate nodes perform random linear network coding, and the source and sink nodes optimize their strategies to maximize the achievable region. Such a setup can be modeled as a deterministic interference channel, whose capacity region is known. For the particular class of linear deterministic interference channels of our interest, in which the outputs and interference are linear deterministic functions of the inputs, we show that the known capacity region can be achieved by linear strategies. As a result, for a given set of network coding coefficients chosen by the intermediate nodes, the proposed linear precoding and decoding for the source and sink nodes will give the maximum achievable rate region for double-unicast networks. We further derive a suboptimal but easy-to-compute rate region that is independent of the network coding coefficients used at the intermediate nodes, and is instead specified by the min-cuts of the network. It is found that even this suboptimal region is strictly larger than the existing achievable rate regions in the literature.

We provide the degrees of freedom (DoF) characterization for the three-user MT× MRmultiple-input-multipleoutput (MIMO) interference channel (IC) with rank-deficient channel matrices, where each transmitter is equipped with MTantennas and each receiver with MRantennas, and the interfering channel matrices from each transmitter to the other two receivers are of ranks D1and D2, respectively. One important intermediate step for both the converse and achievability arguments is to convert the fully-connected rank-deficient channel into an equivalent partially-connected full-rank MIMO-IC by invertible linear transformations. As such, existing techniques developed for full-rank MIMO-IC can be incorporated to derive the DoF outer and inner bounds for the rank-deficient case. Our result shows that when the interfering links are weak in terms of the channel ranks, i.e., D1+ D2≤ min(MT, MR), zero forcing is sufficient to achieve the optimal DoF. On the other hand, when D1+ D2> min(MT, MR), a combination of zero forcing and interference alignment is in general required for DoF optimality. The DoF characterization obtained in this paper unifies several existing results in the literature.

This paper studies the achievable rate region of the K-user Gaussian multiple-input single-output interference channel (MISO-IC) with interference treated as noise, when improper or circularly asymmetric complex Gaussian signaling is applied. By exploiting the separable rate expression with improper Gaussian signaling, we propose a separate covariance and pseudo-covariance optimization algorithm, which is guaranteed to improve the users' rates over the conventional proper or circularly symmetric complex Gaussian signaling. In particular, for the pseudo-covariance optimization, the semidefinite relaxation (SDR) technique is applied to provide a high-quality approximate solution. For the special case of two-user MISO-IC, the SDR technique yields the optimal pseudo-covariance solution.

We study the achievable degrees of freedom (DoF) of the 3-user multiple-input multiple-output (MIMO) interference channel (IC), with possibly rank-deficient channel matrices. A two-layered linear processing scheme is proposed, with the inner layer zero-forcing part of the interfering links by applying a technique known as change of basis, and the outer layer performing interference alignment for the remaining interfering links. The achievable DoF of the proposed scheme is derived. In many special cases of the problem considered in this paper, our proposed two-layered scheme is DoF optimal.

This paper studies the transmit optimization for the K-user Gaussian single-input single-output interference channel (SISO-IC), with the interference treated as Gaussian noise and by applying improper or circularly asymmetric complex Gaussian signaling. The transmit optimization with improper Gaussian signaling involves not only the signal covariance as in the conventional proper or circularly symmetric complex Gaussian signaling, but also the signal pseudo-covariance, which is conventionally set to zero in proper Gaussian signaling. By utilizing the rate-profile method, the achievable rate region of the K-user SISO-IC is characterized by solving a sequence of minimum-weighted-rate maximization (MinWR-Max) problems, which are non-convex and thus difficult to be solved globally optimally. By applying the semidefinite relaxation (SDR) technique, we propose an efficient approximate solution, which jointly optimizes the covariance and pseudo-covariance of the transmitted signals. Simulation results demonstrate the effectiveness of the proposed algorithm for the K-user SISO-IC with improper Gaussian signaling.

This paper studies the achievable rate region of the K-user Gaussian multiple-input single-output interference channel (MISO-IC) with the interference treated as noise, when improper or circularly asymmetric complex Gaussian signaling is applied. The transmit optimization with improper Gaussian signaling involves not only the signal covariance matrix as in the conventional proper or circularly symmetric Gaussian signaling, but also the signal pseudo-covariance matrix, which is conventionally set to zero in proper Gaussian signaling. By exploiting the separable rate expression with improper Gaussian signaling, we propose a separate transmit covariance and pseudo-covariance optimization algorithm, which is guaranteed to improve the users' achievable rates over the conventional proper Gaussian signaling. In particular, for the pseudo-covariance optimization, we establish the optimality of rank-1 pseudo-covariance matrices, given the optimal rank-1 transmit covariance matrices for achieving the Pareto boundary of the rate region. Based on this result, we are able to greatly reduce the number of variables in the pseudo-covariance optimization problem and thereby develop an efficient solution by applying the celebrated semidefinite relaxation (SDR) technique. Finally, we extend the result to the Gaussian MISO broadcast channel (MISO-BC) with improper Gaussian signaling or so-called widely linear transmit precoding.