This paper investigates a near-field covert communication system enhanced by an absorptive reconfigurable intelligent surface (ARIS) and a fluid antenna system (FAS), enabling covert transmission to arbitrary receiver locations. By generating near-field spherical waves via large-scale antenna arrays at Alice and ARIS, covert transmission to Bob is enabled while evading detection by Willie. We jointly optimize Alice’s hybrid precoding, ARIS reflection coefficients, and Bob’s active port selection to maximize the worst-case covert transmission rate. We begin by evaluating ARIS’s suitability versus conventional RIS. We demonstrate the asymptotic orthogonality of near-field beam-focusing vectors in the 3D domain for uniform planar arrays, and characterize the beam-focusing behavior in cascaded ARIS-enabled covert transmissions. Additionally, we reveal the channel gain improvement owing to FAS over traditional antenna systems. To solve the coupled non-convex problem, we propose a low-complexity block coordinate descent algorithm. It incorporates Fibonacci search for hybrid precoding, three complexity-performance trade-off strategies for reflection coefficients optimization, and both exhaustive search and linear conic relaxation for active port selection. Finally, we recover precoding via an alternating minimization scheme. Numerical results show that (i) significant improvement of covert transmission is achieved only with both ARIS and FAS, when Bob and Willie are co-located; (ii) the proposed algorithm outperforms near-field and far-field beam alignment schemes without ARIS, as well as beam focusing of full-map zeroing with ARIS, when Bob and Willie share the same reception direction.

In this paper, we study a movable antenna (MA) empowered secure transmission scheme for reconfigurable intelligent surface (RIS) aided cell-free symbiotic radio (SR) systems. Specifically, the MAs deployed at distributed access points (APs) work collaboratively with the RIS to establish high-quality propagation links for both primary and secondary transmissions, as well as suppressing the risk of eavesdropping on confidential primary information. We consider both continuous and discrete MA position cases and maximize the secrecy rate of primary transmission under the secondary transmission constraints, respectively. For the continuous position case, we propose a two-layer iterative optimization method based on differential evolution with one-in-one representation (DEO), to find a high-quality solution with relatively moderate computational complexity. For the discrete position case, we first extend the DEO based iterative framework by introducing the mapping and determination operations to handle the characteristic of discrete MA positions. To further reduce the computational complexity, we then design a single-layer iterative framework to solve all variables alternatively. In particular, we develop an efficient strategy to derive the sub-optimal solution for the discrete MA positions, superseding the DEO-based method. Numerical results validate the effectiveness of the proposed MA empowered secure transmission scheme along with its optimization algorithms.

Fine-tuning (FT) large language models (LLMs) is crucial for adapting general-purpose models to specific tasks, enhancing accuracy and relevance with minimal resources. To further enhance generalization ability while reducing training costs, this paper proposes Federated LoRA with Dropout (FedLoDrop), a new framework that applies dropout to the rows and columns of the trainable matrix in Federated LoRA. A generalization error bound and convergence analysis under sparsity regularization are obtained, which elucidate the fundamental trade-off between underfitting and overfitting. The error bound reveals that a higher dropout rate increases model sparsity, thereby lowering the upper bound of pointwise hypothesis stability (PHS). While this reduces the gap between empirical and generalization errors, it also incurs a higher empirical error, which, together with the gap, determines the overall generalization error. On the other hand, though dropout reduces communication costs, deploying FedLoDrop at the network edge still faces challenges due to limited network resources. To address this issue, an optimization problem is formulated to minimize the upper bound of the generalization error, by jointly optimizing the dropout rate and resource allocation subject to the latency and per-device energy consumption constraints. To solve this problem, a branch-and-bound (B&B)-based method is proposed to obtain its globally optimal solution. Moreover, to reduce the high computational complexity of the B&B-based method, a penalized successive convex approximation (P-SCA)-based algorithm is proposed to efficiently obtain its high-quality suboptimal solution. Finally, numerical results demonstrate the effectiveness of the proposed approach in mitigating overfitting and improving the generalization capability.

Unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) is regarded as a key enabler for next-generation wireless systems. However, conventional fixed-position antennas limit the ability of UAVs to fully exploit their inherent potential. To overcome this limitation, we propose a UAV-enabled ISAC framework equipped with fluid antennas (FAs), where the mobility of antenna elements introduces additional spatial degrees of freedom to simultaneously enhance communication and sensing performance. A multi-objective optimization problem is formulated to maximize the communication rates of multiple users while minimizing the Cram´er-Rao bound (CRB) for the angle estimation of a single target. Due to excessively frequent updates of FA positions may lead to response delay, a three-timescale optimization framework is developed to jointly optimize transmit beamforming, FA positions, and UAV trajectory based on their characteristics. To solve the non-convexity of the problem, an alternating optimization-based algorithm is developed to obtain a sub-optimal solution. Numerical results show that the proposed scheme significantly outperforms various benchmark schemes, validating the effectiveness of integrating the FA technology into the UAV-enabled ISAC systems.

In this paper, we study efficient codebook design for limited feedback in extremely large-scale multiple-input-multiple-output (XL-MIMO) frequency division duplexing (FDD) systems. It is worth noting that existing codebook designs for XL-MIMO, such as the polar-domain codebook, have not well taken into account user (location) distribution in practice, thereby incurring excessive feedback overhead. To address this issue, we propose in this paper a novel and efficient feedback codebook tailored to the user distribution. To this end, we first consider a typical scenario where users are uniformly distributed within a specific polar-region, based on which a sum-rate maximization problem is formulated to jointly optimize angle-range samples and bit allocation among angle/range feedback. This problem is challenging to solve due to the lack of a closed-form expression for the received power in terms of angle and range samples. By leveraging a Voronoi partitioning approach, we show that uniform angle sampling is optimal for received power maximization. For the more challenging range sampling design, we obtain a tight lower bound on the received power and show thatgeometricsampling, where the ratio between adjacent samples is constant, can maximize the lower bound and thus serves as a high-quality suboptimal solution. We then extend the proposed framework to accommodate more general non-uniform user distribution via an alternating sampling method. Furthermore, theoretical analysis reveals that as the array size increases, the optimal allocation of feedback bits increasingly favors range samples at the expense of angle samples. Finally, numerical results validate the superior rate performance and robustness of the proposed codebook design under various system setups, achieving significant gains over benchmark schemes, including the widely used polar-domain codebook.

Extremely large-scale arrays (XL-arrays) have emerged as a key enabler in achieving the unprecedented performance requirements of future wireless networks, leading to a significant increase in the range of the near-field region. This transition necessitates the spherical wavefront model for characterizing the wireless propagation rather than the far-field planar counterpart, thereby introducing extra degrees-of-freedom (DoFs) to wireless system design. In this paper, we explore the beam focusing-based physical layer security (PLS) in the near field, where multiple legitimate users and one eavesdropper are situated in the near-field region of the XL-array base station (BS). First, we consider a special case with one legitimate user and one eavesdropper to shed useful insights into near-field PLS. In particular, it is shown that 1) Artificial noise (AN) is crucial to near-fieldsecurity provisioning, transforming an insecure system to a secure one; 2) AN can yield numeroussecurity gains, which considerably enhances PLS in the near field as compared to the case without AN taken into account. Next, for the general case with multiple legitimate users, we propose an efficient low-complexity approach to design the beamforming with AN to guarantee near-field secure transmission. Specifically, the low-complexity approach is conceived starting by introducing the concept ofinterference domainto capture the inter-user interference level, followed by athree-step identification frameworkfor designing the beamforming. Finally, numerical results reveal that 1) the PLS enhancement in the near field is pronounced thanks to the additional spatial DoFs; 2) the proposed approach can achieve close performance to that of the computationally-extensive conventional method yet with a significantly lower computational complexity.

rare-events, despite their infrequency, often carry critical information and require immediate attentions in mission-critical applications such as autonomous driving, healthcare, and industrial automation. The data-intensive nature of these tasks and their need for prompt responses, combined with designing edge AI (or edge inference), pose significant challenges in systems and techniques. Existing edge inference approaches often suffer from communication bottlenecks due to high-dimensional data transmission and fail to provide timely responses to rare-events, limiting their effectiveness for mission-critical applications in thesixth-generation(6G) mobile networks. To overcome these challenges, we propose a channel-adaptive, event-triggered edge-inference framework that prioritizes efficient rare-event processing. Central to this framework is a dual-threshold, multi-exit architecture, which enables early local inference for rare-events detected locally while offloading more complex rare-events to edge servers for detailed classification. To further enhance the system’s performance, we developed an online algorithm to dynamically determine the optimal confidence thresholds for controlling offloading decisions. The associated optimization problem is solved by reformulating the original non-convex function into an equivalent strongly convex one. Using deep neural network classifiers and real medical datasets, our experiments demonstrate that the proposed framework not only achieves superior rare-event classification accuracy, but also effectively reduces communication overhead, as opposed to existing edge-inference approaches.

Vision-based localization plays a critical role in ensuring the positioning continuity of vehicles when Global Navigation Satellite System (GNSS) signals are unavailable. Although visual localization provides an effective auxiliary solution under GNSS-denied conditions, its performance is often constrained by insufficient landmarks. To address these limitations, collaborative vision-based localization via vehicle-to-vehicle (V2V) communication has been introduced, enabling vehicles to exchange positioning information and mitigate localization failures caused by landmark scarcity at individual nodes. However, existing studies predominantly emphasize algorithmic design, while a unified probabilistic framework for systematic performance analysis remains largely unexplored. To bridge this gap, this paper develops a novel analytical framework for collaborative vision-based localization in vehicular networks based on stochastic geometry. Specifically, the environmental landmark distribution is modeled using a homogeneous Poisson Point Process (HPPP), while vehicle locations are characterized by a Poisson Line Cox Process (PLCP). On this basis, we first derive the successful localization probability of a single vehicle relying solely on vision in GNSS-denied conditions. We then analyze the coverage probability of V2V transmissions under a Nakagami-$m$fading channel. Leveraging the derived coverage probability, a closed-form expression for the time-of-arrival (TOA)-based multi-vehicle collaborative localization probability is obtained. Finally, we define and characterize the overall GNSS-denied localization probability, which serves as a unified system-level metric quantifying the likelihood that an arbitrary vehicle can be successfully localized without GNSS support. The proposed framework explicitly reveals the coupled impacts of environmental uncertainty, wireless channel fading, and vehicular spatial distribution, thereby providing a theoretical benchmark for performance evaluation and parameter optimization of collaborative vision-based localization in vehicular networks.

Federated Dropout is an efficient technique to overcome both communication and computation bottlenecks for deploying federated learning at the network edge. In each training round, an edge device only needs to update and transmit a sub-model, which is generated by the typical method of dropout in deep learning, and thus effectively reduces the per-round latency. \textcolor{blue}{However, the theoretical convergence analysis for Federated Dropout is still lacking in the literature, particularly regarding the quantitative influence of dropout rate on convergence}. To address this issue, by using the Taylor expansion method, we mathematically show that the gradient variance increases with a scaling factor of $γ/(1-γ)$, with $γ\in [0, θ)$ denoting the dropout rate and $θ$ being the maximum dropout rate ensuring the loss function reduction. Based on the above approximation, we provide the convergence analysis for Federated Dropout. Specifically, it is shown that a larger dropout rate of each device leads to a slower convergence rate. This provides a theoretical foundation for reducing the convergence latency by making a tradeoff between the per-round latency and the overall rounds till convergence. Moreover, a low-complexity algorithm is proposed to jointly optimize the dropout rate and the bandwidth allocation for minimizing the loss function in all rounds under a given per-round latency and limited network resources. Finally, numerical results are provided to verify the effectiveness of the proposed algorithm.

Movable antenna (MA) is envisioned as a promising technique in future wireless communication systems, offering flexible antenna movement to achieve enhanced communication performance. In this paper, we propose a new and efficient position optimization framework based on differential evolution (DE) to improve the communication performance of MA-enabled wireless systems. In particular, the proposed framework addresses two key issues of the widely used particle swarm optimization (PSO)-based methods, namely, the extremely high computational cost and the vanilla fitness function. First, instead of the conventionalall-in-oneindividual representation method, where all MA positions are encoded into a single individual, we introduce a newone-in-onerepresentation method, in which each MA’s position is treated as an individual. This design significantly reduces both the dimensionality of individuals and the total number of individuals, thereby significantly reducing computational complexity. Second, we propose anadaptive penalty mechanismthat imposes larger penalties/weights on constraints encountered stronger violations, in contrast to traditionally used uniform penalties. These two ideas are integrated into our proposed framework, referred to asDE with one-in-one representation (DEO). In addition, to further improve search capabilities, we extend our approach to a variant calledDE with both all-in-one and one-in-one representations (DEAO), which combines the strengths of both representations. This method balances exploration and exploitation by alternately identifying and refining promising solution regions. Then, we evaluate the effectiveness of DEO and DEAO in a typical MA-enabled multiuser downlink communication system, where a weighted sum-rate optimization problem is formulated and solved using atwo-layerapproach. Finally, numerical results demonstrate that our methods can achieve over 95% reduction in computational cost compared to PSO-based methods, while delivering superior performance.

Extremely large-scale arrays (XL-arrays) have emerged as a promising technology for enablingnear-fieldcommunications in future wireless systems. However, the huge number of antennas deployed pose demanding challenges on the hardware cost and power consumption, especially when the antennas employ high-resolution phase shifters (PSs). To address this issue, in this paper, we consider low-resolutiondiscretePSs at the XL-array which are practically more energy efficient, and investigate the impact of PS resolution on the near-fieldbeam-focusingeffect. To this end, we propose a newFourier series expansionmethod to efficiently tackle the difficulty in characterizing the beam pattern properties under phase quantization. Interestingly, we analytically show, for the first time, that 1) discrete PSs introduce additionalgratinglobes; 2) the main lobe still exhibits the beam-focusing property with its beam power increasing with PS resolution; and 3) there are two types of grating lobes, featured by the beam-focusing and beam-steering properties, respectively. In addition, we provide intuitive understanding for the appearance of grating lobes under discrete PSs from anarray-of-subarraysperspective. Finally, numerical results demonstrate that the grating lobes generally degrade communication rate performance. However, a low-resolution of 3-bit PSs can achieve similar beam pattern and rate performance with the continuous PS counterpart, while it attains much higher energy efficiency.

Prior studies on mixed near-field and far-field communications have focused exclusively onsingle-cellscenarios, where both near-field and far-field users are served by the same base station (BS), leading tointra-cellmixed-field interference. In this paper, we consider a more general and practicalmulti-cell mixed-fieldscenario consisting of multiple cells, each serving multiple users, thus resulting in more complexinter-cellmixed-field interference. To address this new challenge, we propose leveragingrotatable antenna(RA) technology to enhance multi-cell mixed-field communication performance by exploiting the additional spatial degree-of-freedom (DoF) introduced by RA rotation to mitigate interference in an efficient way. Specifically, we study an RA-enabled multi-cell mixed-field communication system in which each BS is equipped with an RA array to serve its associated users. We formulate a network-wide sum-rate maximization problem that jointly optimizes the transmit beamforming and the rotation angles of the RA arrays, subject to per-BS power constraints and admissible array rotation limits. To gain useful insights into the role of RAs in multi-cell mixed-field communications, we first analyze a special case with a single user per cell. For this case, we obtain a closed-form expression for therotation-awareinter-cell mixed-field interference using the Fresnel integrals and analytically show that RA rotation can effectively mitigate such interference, thereby substantially improving system performance. For the general case with multiple users per cell, we develop an efficientdouble-layeralgorithm: the inner layer optimizes the transmit beamforming at each BS via semidefinite relaxation (SDR) and successive convex approximation (SCA); while the outer layer determines the rotation angles of the RA arrays using particle swarm optimization (PSO). Numerical results demonstrate that RA-enabled multi-cell systems achieve significant performance gains over conventional fixed-antenna systems, and the proposed joint design consistently outperforms various benchmark schemes.

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

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

In this paper, we studyrobustbeamforming design fornear-fieldphysical-layer-security (PLS) systems, where a base station (BS) equipped with an extremely large-scale array (XL-array) serves multiple near-field legitimate users (Bobs) in the presence of multiple near-field eavesdroppers (Eves). Unlike existing works that mostly assume perfect channel state information (CSI) or location information of Eves, we consider a more practical and challenging scenario in this paper, where the locations of Bobs are perfectly known, while onlyimperfect location informationof Eves is available at the BS. We first formulate a robust optimization problem to maximize the sum-rate of Bobs while guaranteeing a worst-case limit on the eavesdropping rate under location uncertainty. By transforming Cartesian position errors into the polar domain, we reveal an important near-fieldangular-error amplification effect, i.e., under the same location error, the closer the Eve, the larger the angle error, which severely degrades the performance of conventional robust beamforming methods based on imperfect channel state information. To address this issue, we first establish the conditions for which the first-order Taylor approximation of the near-field channel steering vector under location uncertainty is largely accurate. Then, we propose atwo-stagerobust beamforming method, which first partitions the uncertainty region into multiple fan-shaped sub-regions, followed by the second stage to formulate and solve a refined linear-matrix-inequality (LMI)-based robust beamforming optimization problem. In addition, the proposed method is further extended to scenarios with multiple Bobs and multiple Eves. Finally, numerical results validate that the proposed method achieves a superior trade-off between rate performance and secrecy robustness, hence significantly outperforming existing benchmarks under Eve location uncertainty.

The prior works on near-field target localization have mostly assumed ideal hardware model and thus suffer two limitations in practice. First, extremely large-scale arrays (XL-arrays) usually face a variety of hardware impairments (HIs) that may introduce unknown phase/amplitude errors. Second, the existing block coordinate descent (BCD) methods for the joint HIs indicator, angle, and range estimation may suffer considerable estimation errors when the target is very close to the XL-array. To address the above issues, we propose in this paper a new three-phase HI-aware near-field localization method, by jointly detecting faulty antennas and estimating the locations of targets. Specifically, we first determine the faulty antennas by using compressed sensing (CS) methods and improve the detection accuracy using estimated coarse targets’ locations. Then, an effective phase calibration method is proposed to correct phase errors induced by detected faulty antennas. Subsequently, an efficient near-field localization method is devised to accurately estimate the locations of targets based on fully XL-array with phase-calibration. Numerical results demonstrate that our proposed method significantly reduces the localization errors as compared to various benchmark schemes, especially when there is a high faulty-antenna probability.

In this paper, we study efficient limited feedback design for near-field frequency division duplex (FDD) systems, where an extremely large-scale array (XL-array) serves multiple single-antenna users. Although the existing polar-domain codebook caters to the near-field spherical wavefronts, it is designed based on inter-codeword coherence minimization for the entire space. Thus, it suffers considerable performance loss in the achievable rate when the users are randomly located in a specific region. To address this issue, we propose in this paper a new and efficient codebook for limited feedback in near-field FDD systems to facilitate the codebook-based analog beamforming at the BS, while the digital beamforming is designed according to the feedback CSI based on zero-forcing (ZF) technique. Specifically, an optimization problem is formulated to maximize the expected beamforming gain (or equivalent received signal power) at the individual user by optimizing the angle and range sampling in the near-field codebook, given a finite number of feedback bits. Interestingly, we show that when the users are randomly and uniformly located in a polar subspace, the angle domain should be uniformly sampled, while the range samples should be sampled in a new geometric form, with the ratio of adjacent range samples being a constant. Finally, numerical results are presented to validate the significant rate performance gain of the proposed near-field codebook over the existing polar-domain codebook.

This paper introduces the Federated LoRA with Dropout (FedLoDrop) framework, designed to enhance generalization performance for downstream tasks at the network edge while simultaneously reducing overhead. Within this framework, we derive a generalization error bound under sparsity regularization, elucidating the theoretical principles that balance underfitting and overfitting. Our analysis shows that a higher dropout rate increases sparsity, lowering the Pointwise Hypothesis Stability (PHS) upper bound and narrowing the gap between empirical and generalization errors. However, this also leads to a higher empirical error, which, together with the gap, contributes to the total generalization error. Consequently, we formulate an optimization problem that jointly considers dropout rate and resource allocation, aiming to minimize the upper bound of the generalization error. Finally, numerical results demonstrate the effectiveness of the proposed approach in mitigating overfitting and enhancing generalization capabilities.

In this paper, we develop a scalable framework for asynchronous cell-free (CF) massive multiple-input multiple-output (mMIMO) systems with partially coherent transmission. To address the scalability issue, we exploit the dynamic cooperation cluster concept and propose a novel scalable clustering algorithm. Specifically, in the first stage, the users are connected to a subset of access points (APs), aiming to make the users and APs establish the best connection in terms of large-scale fading. Next, the connected APs with the same quantized phase shift are grouped into a set of coherent clusters. By AP clustering, the APs in the same coherent cluster transmit data coherently, while the APs in different clusters transmit data in a non-coherent fashion. This can effectively improve the spectral efficiency of the asynchronous CF mMIMO system while ensuring the requirement of scalability. Finally, numerical results verified the effectiveness of the proposed method.

Extremely large-scale arrays (XL-arrays) have emerged as a promising technology for enabling near-field communications in future wireless systems. However, the huge num-ber of antennas pose demanding challenges on the hardware cost and energy consumption, especially when the antennas employ high-resolution phase shifters (PSs). To address this issue, in this paper, we consider discrete PSs at the XL-array which are practically more energy efficient, and investigate the impact of PS resolution on the near-field beam-focusing effect. To this end, we propose a new Fourier series expansion method to efficiently tackle the difficulty in characterizing the beam pattern properties under phase quantization. Interestingly, we analytically show, for the first time, that 1) discrete PSs introduce additional grating lobes; 2) the main lobe still exhibits the beam-focusing effect with its beam power increasing with PS resolution; and 3) there are two types of grating lobes, featured by the beam-focusing and beam-steering effects, respectively. Finally, numerical results demonstrate that the grating lobes generally degrade the communication performance. However, a low-resolution of 3-bit PSs can achieve similar beam pattern and rate performance with the continuous PS counterpart, while it attains much higher energy efficiency.

In this paper, we propose a super-resolution wideband beam training method for near-field communications, which is able to achieve ultra-low overhead. To this end, we first study the multi-beam characteristic of a sparse uniform linear array (S-ULA) in the wideband. Interestingly, we show that this leads to a new beam pattern property, called rainbow blocks, where the S-ULA generates multiple grating lobes and each grating lobe is further splitted into multiple versions in the wideband due to the well-known beam-split effect. As such, one directional beamformer based on S-ULA is capable of generating multiple rainbow blocks in the wideband, hence significantly extending the beam coverage. Then, by exploiting the beam-split effect in both the frequency and spatial domains, we propose a new three-stage wideband beam training method for extremely large-scale array (XL-array) systems. Specifically, we first sparsely activate a set of antennas at the central of the XL-array and judiciously design the time-delay (TD) parameters to estimate candidate user angles by comparing the received signal powers at the user over subcarriers. Next, to resolve the angular ambiguity introduced by the S-ULA, we activate all antennas in the central subarray and design an efficient subcarrier selection scheme to estimate the true user angle. In the third stage, we resolve the user range at the estimated user angle with high resolution by controlling the splitted beams over subcarriers to simultaneously cover the range domain. Finally, numerical results are provided to demonstrate the effectiveness of proposed wideband beam training scheme, which only needs three pilots in near-field beam training, while achieving near-optimal rate performance.

International audience

International audience

To accommodate new applications such as extended reality, fully autonomous vehicular networks and the metaverse, next generation wireless networks are going to be subject to much more stringent performance requirements than the fifth-generation (5G) in terms of data rates, reliability, latency, and connectivity. It is thus necessary to develop next generation advanced transceiver (NGAT) technologies for efficient signal transmission and reception. In this tutorial, we explore the evolution of NGAT from three different perspectives. Specifically, we first provide an overview of new-field NGAT technology, which shifts from conventional far-field channel models to new near-field channel models. Then, three new-form NGAT technologies and their design challenges are presented, including reconfigurable intelligent surfaces, flexible antennas, and holographic multi-input multi-output (MIMO) systems. Subsequently, we discuss recent advances in semantic-aware NGAT technologies, which can utilize new metrics for advanced transceiver designs. Finally, we point out other promising transceiver technologies for future research.

Large-aperturecoprime arrays(CAs) are expected to achieve higher sensing resolution than conventional dense arrays (DAs), yet with lower hardware and energy cost. However, existing CA far-field localization methods cannot be directly applied to near-field scenarios due to channel model mismatch. To address this issue, in this paper, we propose an efficient near-field localization method for CAs. Specifically, we first construct an effective covariance matrix, which allows to decouple the target angle-and-range estimation. Then, a customized two-phase multiple signal classification (MUSIC) method for CAs is proposed, which first detects all possible angles of targets by using an angular-domain MUSIC method, followed by a second phase to resolve the true angles of targets and their ranges by devising a range-domain MUSIC method. We show that the proposed method can achieve near-optimal multi-target localization performance as conventional two-dimensional (2D)-MUSIC method with much lower computational complexity. Additionally, we characterize the Cramér-Rao bounds for symmetric CAs and provide interesting insights. Finally, numerical results demonstrate that our proposed method is able to localize more targets than the existing subarray-based method as well as achieve lower root mean square error than DAs.

In the double-sided near-field extremely large-scale multi-input multi-output (XL-MIMO) systems, due to the spherical-wavefront propagation, the line-of-sight (LoS) path exhibits multiple independent propagation components, leading to a channel rank greater than one. In contrast, the non-line-of-sight (NLoS) path is typically dominated by a single propagation component. Consequently, the unified modeling of mixed LoS and NLoS paths remains unresolved, particularly when with non-parallel and non-coplanar uniform linear arrays (ULAs) at the transceivers. Furthermore, there exist bottlenecks in the beamforming codeword design and low-overhead estimation in double-sided near-field communications. In this paper, we present a unified channel model to characterize both LoS and NLoS paths in extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Apart from the transmitter (Tx)-side and receiver (Rx)-side separate response vectors, an additional Tx/Rx-coupled term with a Vandermonde windowing pattern is studied for the XL-MIMO LoS path. Two codebook-based beamfocusing schemes are proposed, which are termed as the beamspace-projection and diagonal-decomposition schemes. The achievable spectral efficiency and average power are theoretically analyzed in closed form. Under this framework, we further propose a low-overhead unified LoS/NLoS orthogonal matching pursuit (UOMP) algorithm for XL-MIMO channel estimation, which is then extended via 3-stage multiple-measurement-vector (3S-MMV) for complexity reduction. Last, simulation results demonstrate the superiority of the proposed strategies in both beamforming codeword selection and sparse channel estimation.

Extremely large-scale arrays (XL-arrays) have emerged as one promising technology to improve the spectral efficiency and spatial resolution in future sixth generation (6G) wireless systems. The drastic increase in the number of antennas renders the communication users more likely to be located in the near-field region, which requires a more accurate spherical (instead of planar) wavefront propagation modeling. However, this also inevitably incurs unaffordable beam training overhead when performing a two-dimensional (2D) beam-search in both the angular and range domains. To address this issue, we first introduce in this paper a new sparse discrete Fourier transform (DFT) codebook, which exhibits the angular periodicity in the received beam pattern at the user. This thus motivates us to propose a three-phase beam training scheme. Specifically, in the first phase, we utilize the sparse DFT codebook for beam sweeping in an angular subspace and estimate candidate user angles according to the received beam pattern. Then, a central subarray is activated to scan specific candidate angles for resolving the issue of angular ambiguity for identifying the user angle. In the third phase, the polar-domain codebook is applied in the estimated angle to search the best effective user range. Finally, numerical results show that our proposed beam training scheme enabled by the sparse DFT codebook achieves 98.67% beam training overhead reduction as compared to the exhaustive-search scheme, yet without compromising rate performance in the high signal-to-ratio (SNR) regime.

Integrated sensing and communication (ISAC) based on coordinated multi-point (CoMP) can provide enhanced sensing and communication capabilities. However, existing CoMP ISAC designs are mostly based on sensing and communication with the same symbol duration and waveform, which greatly restricts the range resolution of sensing and the separation of multiple echoes of the target. In this paper, we propose a new CoMP ISAC design employing distinct symbol durations and waveforms. It can achieve high-resolution range sensing of the target with short sensing symbol durations and distinguish target echoes from different transmitting stations (TXs) with orthogonal sensing waveforms. First, we derive the average spectral efficiency of communication and the detection probability of sensing as the performance metrics of the two functionalities, respectively. Then, an efficient CoMP beamforming design is proposed to maximize the communication performance under the constraints on the maximum transmit power of each TX and the required sensing performance. Moreover, a distributed implementation of the above beamforming design is proposed to reduce the computational burden of the central controller. Finally, a low-complexity design based on linear beamforming structures is presented. Numerical results verifies the effectiveness of the proposed designs.

Extremely large-scale arrays (XL-arrays) have emerged as a promising technology to enablenear-fieldcommunications for achieving enhanced spectrum efficiency and spatial resolution, by drastically increasing the number of antennas. However, this also inevitably incurs higher hardware and energy cost, which may not be affordable in future wireless systems. To address this issue, we propose in this paper two types ofsparse arrays(SAs) for enabling near-field communications. Specifically, we first consider thelinear sparse array(LSA) and characterize its near-field beam pattern. It is shown that LSAs can achieve the near-field beam-focusing gain with lower hardware cost and energy consumption, while it introduces several undesiredgrating-lobes, which are focused on specific regions exhibiting comparable beam power with the main-lobe. An efficient hybrid beamforming design is then proposed for the LSA to deal with the potential strong inter-user interference (IUI). Next, we further consider another form of SA, calledextended coprime array(ECA), which is composed of two LSA subarrays with different (coprime) inter-antenna spacing. By characterizing the ECA near-field beam pattern, we show that compared with the LSA of the same array sparsity, ECAs can greatly suppress the beam power of near-field grating-lobes thanks to theoffseteffect of the two subarrays, albeit generating more low-power grating-lobes. This thus motivates us to propose a customized two-phase hybrid beamforming design for ECAs. Finally, numerical results are presented to demonstrate the energy-efficiency gain of the proposed two SAs over dense uniform linear arrays.

Federated dropout on the weight is an efficient technique to overcome both communication and computation bottlenecks for deploying federated learning at the network edge. However, the theoretical analysis for Federated Dropout is still lacking in the literature, due to the challenge arising from the gradient bias. To address this issue, by using the Taylor expansion method, we mathematically show that the gradient vector with dropout can be approximated as an unbiased estimation of that without dropout; while its gradient variance increases with a scaling factor of γ/(1 − γ), with γ ∈ [0,θ) denoting the dropout rate and θ being the maximum dropout rate ensuring the loss function reduction. Based on the above approximation, we provide the loss function analysis for Federated Dropout. Specifically, it is shown that a larger dropout rate of each device leads to a slower convergence rate. Finally, numerical results are provided to verify the effects of dropout rate on convergence in both underfitting and overfitting scenarios.

In this paper, we introduce a novel uplink semantic relay (SemRelay)-aided wireless communication system, catering to multiple users by leveraging a shared probability graph between the SemRelay and the base station (BS). In this system, users transmit text information to the SemRelay through conventional bit transmission, and the SemRelay compresses this information using a knowledge based characterized by probability graph before transmitting it to the BS through semantic communication. Then, the BS recovers the information based on the shared probability graph. While the semantic information compression incurs computational resource consumption, it significantly reduces communication resource usage. This paper addresses the challenge of minimizing overall system latency through jointly optimizing communication and computation re-source allocation, considering limited wireless resources and the system's energy budget. To address this problem, we introduce an efficient iterative algorithm, which employs block coordinate descent for communication resource allocation and exhaustive searching for determining the optimal data compression scheme. In particular, both power allocation subproblem and bandwidth allocation subproblem are proved to be convex. The complexity analysis of the proposed algorithm are also provided. Numerical results validate the effectiveness of the proposed algorithm and the superior performance of semantic communication compared to the conventional bit transmission.

In this paper, we investigate a novel intelligent reflecting surface (IRS)-aided multiuser communication system, where a multi-antenna base station (BS) integrated with an IRS simultaneously serves multiple low-mobility and high-mobility users via transmit diversity and active/passive precoding, respectively. Specifically, we exploit IRS's common phase shift to help achieve transmit diversity for high-mobility users without any channel state information (CSI), while incorporating the active/passive precoding design into the IRS-integrated BS to serve low-mobility users with known CSI. Then, we formulate and solve a new problem to minimize the total transmit power at the BS by jointly optimizing the reflect precoding at the IRS and the transmit precoding at the BS to cope with interference among different users. Simulation results validate the performance superiority of our proposed IRS-aided multiuser communication.

Large-aperture coprime arrays (CAs) are expected to achieve higher sensing resolution than conventional dense arrays (DAs), yet with lower hardware and energy cost. However, existing CA far-field localization methods cannot be directly applied to near-field scenarios due to channel model mismatch. To address this issue, in this paper, we propose an efficient near-field localization method for CAs. Specifically, we first construct an effective covariance matrix, which allows to decouple the target angle-and-range estimation. Then, a customized two-phase multiple signal classification (MUSIC) algorithm for CAs is proposed, which first detects all possible targets' angles by using an angular-domain MUSIC algorithm, followed by the second phase to resolve the true targets' angles and ranges by devising a range-domain MUSIC algorithm. Finally, we show that the proposed method is able to locate more targets than the subarray-based method as well as achieve lower root mean square error (RMSE) than DAs.

Cell-free (CF) massive multiple-input multiple-output (mMIMO) is a promising technology for enhancing the spectrum efficiency in future wireless systems. It leverages distributed access points (APs) to jointly serve multiple users over the same time-frequency resource. In this paper, we study the uplink performance of CF mMIMO orthogonal frequency division multiplexing (OFDM) wireless systems under asynchronous signal reception. First, we provide a complete and detailed frequency-domain uplink asynchronous signal reception model for CF mMIMO-OFDM systems, which shows that asynchronous reception introduces phase shift, inter-carrier interference (ICI), and inter-symbol interference (ISI) at the receivers. Then, we design the minimum mean square error channel estimation under asynchronous reception. The results indicate that the phase shift, ICI, and ISI destroy the orthogonality of pilot sequences at APs, leading to significant channel estimation error. Furthermore, the FDM pilot sequences prove to be more effective in mitigating the impact of asynchronous reception on channel estimation compared to the CDM pilot sequences. Given the imperfect channel state information caused by pilot contamination and asynchronous reception, we characterize the uplink achievable spectral efficiency under asynchronous reception. The results indicate that severe uplink rate loss mainly arises from phase shift caused by asynchronous reception. Numerical simulation results corroborate our analyses.

In this paper, we investigate codebook-based near-field beam training for extremely large-scale intelligent reflecting surface (XL-IRS). Compared with the conventional far-field beam training method that only searches for the best beam direction, the near-field beam training is more challenging since it requires a beam search over both the angular and distance domains due to the spherical wavefront propagation model. To reduce the near-field beam-training overhead of two-dimensional exhaustive search, we propose a novel two-layer codebook-based near-field beam training scheme that decomposes the two-dimensional search into two sequential phases. Specifically, the layer-l codebook designed based on the omnidirectivity of random-phase beam pattern is firstly employed to estimate the user distance. Then, given the estimated user distance of the layer-1, a customized layer-2 codebook is employed to scan the candidate locations of the user. Numerical results demonstrate that the proposed scheme can achieve more accurate estimation of the user distance and angle, as well as higher data rate with smaller training overhead, compared with benchmarks.

Prior works on near-field beam training mostly assume dedicated polar-domain codebooks and on-grid range estimation, however, this may incur large training overhead and deteriorated estimation accuracy. In this paper, we propose a new and efficient beam training scheme with off-grid range esti-mation based on conventional discrete Fourier transform (DFT) codebook, which greatly reduces the beam training overhead. In particular, we first analyze the received beam pattern at the user when far-field beamforming vectors are used for beam scanning, and reveal an interesting result that this beam pattern contains useful user angle and range information. Then, an efficient scheme was proposed to jointly estimate the user angle and range using DFT codebook. This scheme estimates the user angle based on a defined angular support and resolves the user range by leveraging an approximated angular support width. Finally, numerical simulations show that our proposed scheme significantly reduces the near-field beam training overhead and improves the range estimation accuracy compared with various benchmark schemes.

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

Cell-free massive multiple-input–multiple-output (CF mMIMO) is a promising technology to enhance the spectrum efficiency in future wireless systems by leveraging distributed access points (APs) to jointly serve the users over the same time-frequency resource. However, for uplink communications under orthogonal frequency division multiplexing (OFDM), the signals received at different APs from different users are generally asynchronous, due to the varying distances between the APs and users. In this article, we study the performance of CF mMIMO-OFDM wireless system under asynchronous signal reception in terms of uplink channel estimation and achievable spectral efficiency. In particular, we devise the minimum mean square error (MMSE) channel estimation under asynchronous reception and reveal how the phase shift, intersymbol interference, and intercarrier interference introduced by asynchronous reception destroy the orthogonality between different pilot sequences at the APs. In addition, given the imperfect channel state information caused by pilot contamination and asynchronous reception, we obtain closed-form expressions for the uplink achievable rate for both centralized and distributed operations. We show that for both operations, the uplink achievable rates under asynchronous reception are much smaller than those under synchronous reception due to the phase shift caused by asynchronous reception. Last, numerical results demonstrate that under asynchronous reception, the maximum sum-rate loss with MMSE and maximum ratio (MR) combining in centralized and distributed operations reach 29.5%, 4%, 45.4%, and 8%, respectively. This indicates that while MMSE combining is much more sensitive to asynchronous reception than MR combining, it still achieves a higher achievable rate.

Positioning and sensing over wireless networks are imperative for many emerging applications. However, since traditional wireless channel models over-simplify the user equipment (UE) as a point target, they cannot be used for sensing the attitude of the UE, which is typically described by the spatial orientation. In this paper, a comprehensive electromagnetic propagation modeling (EPM) based on electromagnetic theory is developed to precisely model the near-field channel. For the noise-free case, the EPM model establishes the non-linear functional dependence of observed signals on both the position and attitude of the UE. To address the difficulty in the non-linear coupling, we first propose to divide the distance domain into three regions, separated by the defined Phase ambiguity distance and Spacing constraint distance. Then, for each region, we obtain the closed-form solutions for joint position and attitude estimation with low complexity. Next, to investigate the impact of random noise on the joint estimation performance, the Ziv-Zakai bound (ZZB) is derived to yield useful insights. The expected Cramér-Rao bound (ECRB) is further provided to obtain the simplified closed-form expressions for the performance lower bounds. Our numerical results demonstrate that the derived ZZB can provide accurate predictions of the performance of estimators in all signal-to-noise ratio (SNR) regimes. More importantly, we achieve the millimeter-level accuracy in position estimation and attain the 0.1-level accuracy in attitude estimation.

Extremely large-scale array (XL-array) has emerged as a promising technology to enhance the spectrum efficiency and spatial resolution in future wireless networks by exploiting massive number of antennas for generating pencil-like beamforming. This also leads to a fundamental paradigm shift from conventional far-field communications towards the new near-field communications. In contrast to the existing works that mostly considered simultaneous wireless information and power transfer (SWIPT) in the far field, we consider in this paper a new and practical scenario, calledmixed near- and far-fieldSWIPT, where energy harvesting (EH) and information decoding (ID) receivers are located in the near- and far-field regions of the XL-array base station (BS), respectively. Specifically, we formulate an optimization problem to maximize the weighted sum-power harvested at all EH receivers by jointly designing the BS beam scheduling and power allocation, under the constraints on the maximum sum-rate and BS transmit power. First, for the general case with multiple EH and ID receivers, we propose an efficient algorithm to obtain a suboptimal solution by utilizing the binary variable elimination and successive convex approximation methods. To obtain useful insights, we then study the joint design for special cases. In particular, we show that when there are multiple EH receivers and one ID receiver, in most cases, the optimal design is allocating a portion of power to the ID receiver for satisfying the rate constraint, while the remaining power is allocated to one EH receiver with the highest EH capability. This is in sharp contrast to the conventional far-field SWIPT case, for which all powers should be allocated to ID receivers. Numerical results show that our proposed joint design significantly outperforms other benchmark schemes without the optimization of beam scheduling and/or power allocation.

Intelligent surfaces (ISs) have emerged as a key technology to empower a wide range of appealing applications for wireless networks, due to their low cost, high energy efficiency, flexibility of deployment, and capability of constructing favorable wireless channels/radio environments. Moreover, the recent advent of several new IS architectures further expanded their electromagnetic functionalities from passive reflection to active amplification, simultaneous reflection, and refraction, as well as holographic beamforming. However, the research on ISs is still in rapid progress and there have been recent technological advances in ISs and their emerging applications that are worthy of a timely review. Thus, in this article, we provide a comprehensive survey on the recent development and advances of ISs-aided wireless networks. Specifically, we start with an overview on the anticipated use cases of ISs in future wireless networks such as 6G, followed by a summary of the recent standardization activities related to ISs. Then, the main design issues of the commonly adopted reflection-based IS and their state-of-the-art solutions are presented in detail, including reflection optimization, deployment, signal modulation, wireless sensing, and integrated sensing and communications. Finally, recent progress and new challenges in advanced IS architectures are discussed to inspire future research.

Extremely large-scale array (XL-array) and Terahertz (THz) band have emerged as key research areas in the sixth generation (6G) network. As such, the near-field communication becomes more important since users are more likely to be located in the near-field. Different from their far-field counterparts, the near-field focusing beams utilize additional distance information to focus beam energy at specific spatial-locations. In this letter, we consider a uniform circular array (UCA) system and propose a fast distance sampling method for UCA and design its codebook. Specifically, we propose the non-uniform distance sampling and codebook for UCA by utilizing the zero-order Bessel function, and taking into account the effect of the distance information on the correlation between the near-field beam vectors of UCA. Based on the proposed non-uniform distance sampling and codebook, a two-stage searching scheme for three-dimensional (3D) beam focusing is designed. Simulation results verify that the designed beam focusing scheme can achieve comparable accuracy and rate performance as compared to the 3D exhaustive search.

In wireless networks with distributed computing, the computational performance is limited by stragglers. To mitigate the stragglers’ effect, coded computation is adopted through computational redundancy. Moreover, in wireless transmission, transmission errors may occur due to noise, channel fading and so on. Existing works design coded computation and error detection separately. However, this leads to frequent encoding and inefficient allocation. In this paper, we propose a joint computation and transmission coding (JCTC) scheme to design coded computation and error detection jointly. The coded computation is based on Luby transform (LT) code and linear error-detecting codes are applied for the re-transmission mechanism. To achieve the low dynamic encoding, two-layer encoding is adopted. Then, the performances of JCTC scheme are analyzed in terms of latency and computation reliability. Finally, in order to achieve efficient task and redundancy allocation, the wireless LT coded computation with error detection (WLTCC-ED) algorithm is given from both iterative and low-complexity perspectives respectively. Through theoretical analysis and numerical simulation, it shows that our proposed JCTC scheme has significant advantages over separate designs.

The performance of multiple-input multiple-output (MIMO) integrated sensing and communication systems (ISAC) can be evaluated from the perspectives of information theory and estimation theory to provide more fundamental insights. In this paper, we study the relationship between mutual information (MI) and minimum mean square error (MMSE) by characterizing the Pareto boundary for a general ISAC scenario, a dual-functional BS simultaneously estimates the target response matrix while communicating with a user. First, optimization problems are formulated to achieve MI Pareto boundary and MMSE Pareto boundary, respectively. Then, we show that under the same maximum transmit power constraint and set of transmit filters, MI Pareto bounary can be transformed to MMSE Pareto boundary with optimized MSE-weights in ISAC with colored Gaussian noise. Subsequently, based on unified MI and MMSE performance, we propose Data-dependent alternate algorithm (DDA) to obtain the MI Pareto boundary with colored Gaussian noise. In order to reduce complexity, we propose Data-independent alternate algorithm (DIA) when noise degenerates into white Gaussian noise. Finally, simulation results show DDA almost achieves the MI Pareto boundary with colored Gaussian noise and DIA achieves almost the same performance as DDA with white Gaussian noise at a lower cost to implement.

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) have emerged as a promising technology to reconfigure the radio propagation environment in the full space. Prior works on STAR-RISs have mostly considered the energy splitting operation protocol, which has high hardware complexity in practice. Moreover, the full and instantaneous channel state information (CSI) is always assumed available for designing the STAR-RIS nearly passive beamforming, which, however, is practically difficult to obtain due to the large number of STAR-RIS elements. To address these issues, we study the mode switching design in STAR-RIS aided non-orthogonal multiple access (NOMA) communication systems. Moreover, two efficient two-timescale (TTS) transmission protocols are proposed for different channel setups to maximize the respective average achievable sum-rate. Specifically, 1) for the case of line-of-sight (LoS) dominant channels, we propose the beamforming-then-estimate (BTE) protocol, where the long-term STAR-RIS transmission and reflection coefficients are optimized based on the statistical CSI only, while the short-term power allocation at the base station (BS) is designed based on the estimated effective fading channels of all the users; 2) for the case of rich scattering environments, we propose an alternative partition-then-estimate (PTE) protocol, where the BS first determines the long-term STAR-RIS surface-partition strategy based on the path-loss information only, with each subsurface being assigned to one user; and then the BS estimates the instantaneous subsurface channels associated with the users and designs its power allocation and STAR-RIS phase-shifts accordingly. For the two proposed transmission protocols, we further propose efficient algorithms to solve the respective long-term and short-term optimization problems. Moreover, we show that both proposed transmission protocols substantially reduce the channel estimation overhead as compared to the existing schemes based on full instantaneous CSI. Last, simulation results validate the superiority of our proposed transmission protocols as compared to various benchmarks. It is shown that the BTE protocol outperforms the PTE protocol when the number of STAR-RIS elements is large and/or the LoS channel components are dominant, and vice versa.

High-speed train (HST) communication plays a crucial role in providing reliable data services to passengers inside the trains. However, due to the train’s high mobility, a fast time-varying channel generally exists between the static BS and high-speed users, resulting in severe communication performance degradation. To address this issue, we propose in this paper an intelligent reflecting surface (IRS)-aided HST relaying system, where an IRS is integrated with the relay to aid the data transmission from the BS to the relay. Specifically, an optimization problem is formulated to maximize the received signal-to-noise ratio (SNR) at the relay by jointly optimizing the active transmit beamforming at the BS, the active receive beamforming at the relay, and the passive reflect beamforming at the IRS. To solve this problem, we first decouple it into two simpler sub-problems by leveraging the low-dimensional channel decomposition of the high-dimensional BS-relay channel matrix, and then solve them in closed-form with low complexity. Additionally, an efficient transmission protocol tailored for HST communication systems is proposed to implement channel estimation and beam tracking with low complexity. Simulation results verify the performance gains of the proposed IRS-aided HST relaying system, compared with the traditional relaying scheme without IRS and other benchmark schemes.

Intelligent reflecting surface (IRS) has emerged as a promising technology to enhance the wireless communication network coverage and capacity by dynamically controlling the radio signal propagation environment. In contrast to the existing works that considered active or passive IRS only, we propose in this paper a new hybrid active-passive IRS architecture that consists of both active and passive reflecting elements, thus achieving their combined advantages flexibly. Under a practical channel setup with Rician fading where only the statistical channel state information (CSI) is available, we study the hybrid IRS design in a multi-user communication system. Specifically, we formulate an optimization problem to maximize the achievable ergodic capacity of the worst-case user by designing the hybrid IRS beamforming and active/passive elements allocation based on the statistical CSI, subject to various practical constraints on the active-element amplification factor and amplification power consumption, as well as the total active and passive elements deployment budget. To solve this challenging problem, we first approximate the ergodic capacity in a simpler form and then propose an efficient algorithm to solve the problem optimally. Moreover, we show that for the special case with all channels to be line-of-sight (LoS), only active elements need to be deployed when the total deployment budget is sufficiently small, while both active and passive elements should be deployed with a decreasing number ratio when the budget increases and exceeds a certain threshold. Finally, numerical results are presented which demonstrate the performance gains of the proposed hybrid IRS architecture and its optimal design over the conventional schemes with active/passive IRS only under various practical system setups.

Prior works on near-field beam training have mostly assumed dedicated polar-domain codebook and on-grid range estimation, which, however, may suffer long training overhead, high codebook storage requirement, and degraded estimation accuracy. To address these issues, we propose in this paper new and efficient beam training schemes with off-grid range estimation by using conventional discrete Fourier transform (DFT) codebook. Specifically, we first analyze the received beam pattern at the user when far-field beamforming vectors are used for beam scanning, and show an interesting result that this beam pattern contains useful user angle and range information. Then, we propose two efficient schemes to jointly estimate the user angle and range with the DFT codebook. The first scheme estimates the user angle based on a defined angular support and resolves the user range by leveraging an approximated angular support width, while the second scheme estimates the user range by minimizing a power ratio mean square error (MSE) to improve the range estimation accuracy. Finally, numerical simulations show that our proposed schemes greatly reduce the near-field beam training overhead and improve the range estimation accuracy as compared to various benchmark schemes.

Intelligent reflecting surface (IRS) has become a cost-effective solution for constructing a smart and adaptive radio environment. Most previous works on IRS have jointly designed the active and passive precoding based on perfectly or partially known channel state information (CSI). However, in delay-sensitive or high-mobility communications, it is imperative to explore more effective methods for leveraging IRS to enhance communication reliability without the need for any CSI. In this paper, we investigate an innovative IRS-aided multiuser communication system, which integrates an IRS with its aided multi-antenna base station (BS) to simultaneously serve multiple high-mobility users through transmit diversity and multiple low-mobility users through active/passive precoding. In specific, we first reveal that when dynamically tuning the IRS’s common phase-shift shared with all reflecting elements, its passive precoding gain to any low-mobility user remains unchanged. Inspired by this property, we utilize the design of common phase-shift at the IRS for achieving transmit diversity to serve high-mobility users, yet without requiring any CSI at the BS. Meanwhile, the active/passive precoding design is incorporated into the IRS-integrated BS to serve low-mobility users (assuming the CSI is known). Then, taking into account the interference among different users, we formulate and solve a joint optimization problem of the IRS’s reflect precoding and the BS’s transmit precoding, with the aim of minimizing the total transmit power at the BS. Simulation results demonstrate that our proposed co-design of transmit diversity and active/passive precoding in IRS-aided multiuser systems can achieve superior and desirable performance compared to other benchmarks.

Reconfigurable intelligent surface (RIS) is a promising technology for future wireless communication systems. Channel estimation (CE) of RIS device is a critical but also challenging issue for its development. The mainstream of existing CE methods is confined to the so-called cascaded channel (CscdChn) estimation scheme, which treats the multiplicative two-hop RIS channels as an effective one and measures it as a whole. This CscdChn training method suffers from severe double-fading attenuation loss, which significantly degrades the CE accuracy. In this paper, we propose a novel RIS-transmitting (RIS-TX) based CE scheme, which has lower pilot overhead than CscdChn scheme and effectively overcomes the double-fading curse via incorporating only one single transmit radio frequency (RF)-chain into RIS. We develop highly efficient gradient descent (GD) and penalty duality decomposition (PDD)-based solutions to resolve the pilot design task for the RIS-TX CE scheme, which is a difficult quartic optimization problem. Our designed pilot signal outperforms the discrete Fourier transform (DFT) sequence, which is reported to be optimal for CscdChn scheme. Besides, both theoretical analysis and numerical results demonstrate that our proposed RIS-TX scheme exhibits distinct performance characteristics as opposed to its CscdChn counterpart and yields superior accuracy when RIS device is not extremely large.

Extremely large-scale array (XL-array) has emerged as a promising technology to enhance the spectrum efficiency and spatial resolution in future wireless networks, leading to a fundamental paradigm shift from conventional far-field communications towards the near-field communications. Different from the existing works that mostly considered simultaneous wireless information and power transfer (SWIPT) in the far field, we consider in this paper a new and practical scenario, called mixed near- and far-field SWIPT, in which energy harvesting (EH) and information decoding (ID) receivers are located in the near- and far-field regions of the XL-array base station (BS), respectively. Specifically, we formulate an optimization problem to maximize the weighted sum-power harvested at all EH receivers by jointly designing the BS beam scheduling and power allocation, under the constraints on the ID sum-rate and BS transmit power. To solve this non-convex optimization problem, an efficient algorithm is proposed to obtain a suboptimal solution by leveraging the binary variable elimination and successive convex approximation methods. Numerical results demonstrate that our proposed joint design achieves substantial performance gain over other benchmark schemes.

In this paper, we propose a semi-federated learning (SemiFL) framework that allows computing-limited clients to collaboratively train a shared model with resource-abundant clients. Specifically, by supporting the coexistence of model-updating and data-offloading, the SemiFL framework enables both centralized and federated learning in a hybrid fashion. Due to the decoding error, we consider the practical case with residual interference. To improve uplink throughput for centralized learning while reducing aggregation distortion for federated learning, we formulate a non-convex optimization problem to jointly optimize the transmit power and receive strategy. Then, we propose an efficient algorithm to solve the challenging problem by using successive convex approximation. Simulation results demonstrate the effectiveness of our SemiFL framework for heterogeneous networks, and reveal the impact of imperfect signal decoding on communication rates.

In this paper, we propose an efficient channel estimation algorithm for orthogonal time frequency space (OTFS) systems in the presence of fractional Doppler. The proposed algorithm first employs the well-known threshold-based estimator to obtain the effective channel response. With the effective channel matrix in hand, we then utilize the linear system to recover the Doppler shifts and channel gains of different resolvable paths. The interference between different paths is also considered. Our simulation results verify that, by selecting appropriate samples in the effective channel matrix, the Doppler shifts and channel gains can be estimated robustly even in poor signal-to-noise ratio (SNR) conditions.

In this article, we study the uplink transmission scheme and channel estimation design for reconfigurable intelligent surfaces (RIS)-aided orthogonal time–frequency space (OTFS) systems in high-mobility scenarios. To this end, we first propose an efficient and reliable transmission scheme that utilizes the delay-Doppler (DD) information in OTFS to facilitate the configuration of RIS. Specifically, the proposed scheme exploits the estimated delay and Doppler shifts of the cascaded channel to sense the channel parameters, and the sensing parameters are then used for RIS passive beamforming. It is noteworthy that we estimate the channel state information (CSI) by employing only one OTFS frame and configure the RIS based on the predicted channel parameters, leading to substantially reduced channel training overhead and more real-time RIS configuration. To obtain the essential information for channel information sensing, we then propose a low-complexity algorithm which determines the Doppler and delay shifts of the channel between the user and RIS based on linear systems and the mapping relationship of the DD pairs, respectively. With the DD information in hand, a user localization algorithm constructed by the least square (LS) and a channel tracking method relying on extended Kalman filter (EKF) are then presented to obtain the spatial angle information. By making use of the channel parameters acquired at the base station (BS), the RIS reflection vector is designed to maximize the achievable rate. The results obtained from the simulation experiments affirm the efficacy of the proposed scheme, thereby confirming its capability to attain efficient communications under high Doppler channels.

In wireless sensor networks, unmanned aerial vehicles (UAVs) can be employed to collect data from sensor nodes (SNs) efficiently. In this article, we consider a dual-UAV-enabled (long-distance) data collection system, where one UAV is dispatched to collect data from distributed SNs, while the other UAV is employed to relay data from the data-collection UAV to a fusion center (FC) that locates far from the SNs. To shorten the time duration for the FC to collect all data, we propose to minimize the completion time of the data collection task by jointly optimizing the transmit power and bandwidth of all SNs and the UAVs, as well as the three-dimensional trajectories of the two UAVs. Instead of assuming the simplified line-of-sight UAV-ground channel model as in most existing works, we model the channels between the UAVs and SNs as well as that between the UAVs and FC by applying the practically more accurate elevation-angle-dependent Rician fading channel model. The resulting optimization problem is nonconvex and thus is difficult to solve in general. Nevertheless, we propose an algorithm to solve it efficiently by using the techniques of block coordinate descent, slack variable substitution, and successive convex approximation. Simulation results show that our proposed algorithm can achieve higher communication efficiency than other benchmark schemes and greatly reduce the task completion time for data collection.

Prior studies on intelligent reflecting surface (IRS) have mostly considered wireless communication systems aided by a single passive IRS, which, however, has limited control over wireless propagation environment and suffers severe product-distance path-loss. To address these issues, we propose in this paper a new multi-active multi-passive (MAMP) -IRS aided wireless communication system, where a number of active and passive IRSs are deployed to assist the communication between a base station (BS) and a remote user in complex environment, by establishing a multi-hop reflection path across active and passive IRSs. In particular, the active IRSs enable to opportunistically amplify the reflected signal along the multi-reflection link, thus effectively compensating for the severe product-distance path-loss. For the new MAMP-IRS aided system, an optimization problem is formulated to maximize the achievable rate of a typical user by designing the active-and-passive IRS routing path as well as the joint beamforming of the BS and selected active/passive IRSs. To draw useful insights into the optimal design, we first consider a special case of the single-active multi-passive (SAMP) -IRS aided system. For this case, we propose an efficient algorithm to obtain its optimal solution by first optimizing the joint beamforming given any SAMP-IRS routing path, and then optimizing the routing path by using a new path decomposition method and graph theory. Moreover, we show that the active IRS should be selected to establish the beam routing path when its amplification power and/or number of active reflecting elements are sufficiently large. Next, for the general MAMP-IRS aided system, we show that its challenging beam routing optimization problem can be efficiently solved by a new two-phase approach. Its key idea is to first optimize the inner passive-IRS beam routing between each two active IRSs for effective channel power gain maximization, followed by an outer active-IRS beam routing optimization for rate maximization. Last, numerical results are provided to validate our analytical results and demonstrate the effectiveness of the proposed MAMP-IRS beam routing scheme as compared to various benchmark schemes.

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

Prior studies on intelligent reflecting surface (IRS) have mostly considered wireless communication systems aided by a single passive IRS, which, however, has limited control over wireless propagation environment and suffers product-distance path-loss. To address these issues, we propose in this paper a new hybrid active/passive IRS aided wireless communication system, where an active IRS and multiple passive IRSs are deployed to assist the communication between a base station (BS) and a remote user in complex environment, by establishing a multi-hop reflection path across active/passive IRSs. In particular, the active IRS enables signal reflection with power amplification, thus effectively compensating the severe path-loss in the multi-reflection path. To maximize the achievable rate at the user, we first design the optimal beamforming of the BS and selected (active/passive) IRSs for a given multi-reflection path, and then propose an efficient algorithm to obtain the optimal multi-reflection path by using the path decomposition method and graph theory. We show that the active IRS should be selected to establish the beam routing path when its amplification power and/or number of active reflecting elements are sufficiently large. Last, numerical results demonstrate the effectiveness of the proposed hybrid active/passive IRS beam routing design as compared to the benchmark scheme with passive IRSs only.

In this paper, we consider a new unmanned aerial vehicle (UAV)-assisted oblique image acquisition system where a UAV is dispatched to take images of multiple ground targets (GTs). To study the three-dimensional (3D) UAV trajectory design for image acquisition, we first propose a novel UAV-assisted oblique photography model, which characterizes the image resolution with respect to the UAV’s 3D image-taking location. Then, we formulate a 3D UAV trajectory optimization problem to minimize the UAV’s traveling distance subject to the image resolution constraints. The formulated problem is shown to be equivalent to a modified 3D traveling salesman problem with neighbourhoods, which is NP-hard in general. To tackle this difficult problem, we propose an iterative algorithm to obtain a high-quality suboptimal solution efficiently, by alternately optimizing the UAV’s 3D image-taking waypoints and its visiting order for the GTs. Numerical results show that the proposed algorithm significantly reduces the UAV’s traveling distance as compared to various benchmark schemes, while meeting the image resolution requirement.

Intelligent reflecting surface (IRS) has emerged as a promising technology to reconfigure the radio propagation environment by dynamically controlling wireless signal’s amplitude and/or phase via a large number of reflecting elements. In contrast to the vast literature on studying IRS’s performance gains in wireless communications, we study in this paper a new application of IRS for sensing/localizing targets in wireless networks. Specifically, we propose a newself-sensing IRSarchitecture where the IRS controller is capable of transmitting probing signals that are not only directly reflected by the target (referred to as the direct echo link), but also consecutively reflected by the IRS and then the target (referred to as the IRS-reflected echo link). Moreover, dedicated sensors are installed at the IRS for receiving both the direct and IRS-reflected echo signals from the target, such that the IRS can sense the direction of its nearby target by applying a customized multiple signal classification (MUSIC) algorithm. However, since the angle estimation mean square error (MSE) by the MUSIC algorithm is intractable, we propose to optimize the IRS passive reflection for maximizing the average echo signals’ total power at the IRS sensors and derive the resultant Cramer-Rao bound (CRB) of the angle estimation MSE. Last, numerical results are presented to show the effectiveness of the proposed new IRS sensing architecture and algorithm, as compared to other benchmark sensing systems/algorithms.

Intelligent reflecting surface (IRS) has emerged as a promising technique for wireless communication networks. By dynamically tuning the reflection amplitudes/phase shifts of a large number of passive elements, IRS enables flexible wireless channel control and configuration and thereby enhances the wireless signal transmission rate and reliability significantly. Despite the vast literature on designing and optimizing assorted IRS-aided wireless systems, prior works have mainly focused on enhancing wireless links with single signal reflection only by one or multiple IRSs, which may be insufficient to boost the wireless link capacity under some harsh propagation conditions (e.g., indoor environment with dense blockages/obstructions). This issue can be tackled by employing two or more IRSs to assist each wireless link and jointly exploiting their single as well as multiple signal reflections over them. However, the resultant double-/multi-IRS-aided wireless systems face more complex design issues as well as new practical challenges for implementation compared to the conventional single-IRS counterpart, in terms of IRS reflection optimization, channel acquisition, as well as IRS deployment and association/selection. As such, a new paradigm for designing multi-IRS cooperative passive beamforming and joint active/passive beam routing arises, which calls for innovative design approaches and optimization methods. In this article, we give a tutorial overview of multi-IRS-aided wireless networks, with an emphasis on addressing the new challenges due to multi-IRS signal reflection and routing. Moreover, we point out important directions worthy of research and investigation in the future.

To achieve the more promising passive beamforming gains in the double-intelligent reflecting surface (IRS) assisted system over the conventional single-IRS system, channel estimation is practically indispensable but also a more challenging problem to tackle, due to the presence of not only the single-but also double-reflection links that are intricately coupled. In this paper, we propose a new and efficient channel estimation scheme for the double-IRS assisted uplink multiple-input multiple-output (MIMO) communication system to resolve the cascaded channel state information (CSI) of both its single- and double-reflection links. First, for the single-user case, the higher-dimensional double-reflection channel is efficiently estimated at the multi-antenna base station (BS) with low training overhead by exploiting the fact that its cascaded channel coefficients are scaled versions of those of a lower-dimensional single-reflection channel. Then, the proposed channel estimation scheme is extended to the multi-user case, where given an arbitrary user’s cascaded channel estimated as in the single-user case, the other users’ cascaded channels are scaled versions of it and thus can be estimated with reduced training overhead. Simulation results verify the effectiveness of the proposed channel estimation scheme as compared to the benchmark scheme.

The performance optimization of UAV communication systems requires the joint design of UAV trajectory and communication efficiently. To tackle the challenge of infinite design variables arising from the continuous-time UAV trajectory optimization, a commonly adopted approach in the existing literature is by approximating the UAV trajectory with piecewise-linear path segments connected via a finite number of waypoints in three-dimensional (3D) space. However, this approach may still incur prohibitive computational complexity in practice when the UAV flight period/distance becomes long, as the distance between consecutive waypoints needs to be kept sufficiently small to retain high approximation accuracy. To resolve this fundamental issue, we propose in this paper anewandgeneralframework for UAV trajectory and communication co-design with flexible number of waypoint optimization variables (calleddesignablewaypoints) or theirsub-pathrepresentations. First, we propose aflexible path discretizationscheme that optimizes only a number of selected waypoints (designable waypoints) along the UAV path for complexity reduction, while all the designable and non-designable waypoints are used in calculating the approximated communication utility along the UAV trajectory for ensuring high trajectory discretization accuracy. Next, we propose a novelpath compressionscheme, which treats the UAV trajectory as a signal and compresses its path representation based on the basis decomposition. Specifically, the UAV 3D path is first decomposed into three one-dimensional (1D) sub-paths and each sub-path is then approximated by superimposing a number of selected basis paths (which are generally less than the number of designable waypoints) weighted by their corresponding path coefficients, thus further reducing the path design complexity. Finally, we provide a case study on UAV trajectory design for aerial data harvesting from distributed sensors, and numerically show that the proposed flexible path discretization and path compression schemes can significantly reduce the UAV trajectory design complexity yet achieve favorable rate performance as compared to conventional path/time discretization schemes.

In this paper, we consider an unmanned aerial vehicle (UAV) enabled relaying system where multiple UAVs are deployed as aerial relays to support simultaneous communications from a set of source nodes to their destination nodes on the ground. An optimization problem is formulated under practical channel models to maximize the minimum achievable expected rate among all pairs of ground nodes by jointly designing UAVs' three-dimensional (3D) placement as well as the bandwidth-and-power allocation. This problem, however, is non-convex and thus difficult to solve. As such, we propose a new method, called iterative Gibbs-sampling and block- coordinate-descent (IGS-BCD), to efficiently obtain a high-quality suboptimal solution by synergizing the advantages of both the deterministic (BCD) and stochastic (GS) optimization methods. Specifically, our proposed method alternates between two optimization phases until convergence is reached, namely, one phase that uses the BCD method to find locally-optimal UAVs' 3D placement and the other phase that leverages the GS method to generate new UAVs' 3D placement for exploration. Moreover, we present an efficient method for properly initializing UAVs' placement that leads to faster convergence of the proposed IGS-BCD algorithm. Numerical results show that the proposed IGS-BCD and initialization methods outperform the conventional BCD or GS method alone in terms of convergence-and-performance trade-off, as well as other benchmark schemes.

Intelligent reflecting surface (IRS) is an enabling technology to engineer the radio signal propagation in wireless networks. By smartly tuning the signal reflection via a large number of low-cost passive reflecting elements, IRS is capable of dynamically altering wireless channels to enhance the communication performance. It is thus expected that the new IRS-aided hybrid wireless network comprising both active and passive components will be highly promising to achieve a sustainable capacity growth cost-effectively in the future. Despite its great potential, IRS faces new challenges to be efficiently integrated into wireless networks, such as reflection optimization, channel estimation, and deployment from communication design perspectives. In this paper, we provide a tutorial overview of IRS-aided wireless communications to address the above issues, and elaborate its reflection and channel models, hardware architecture and practical constraints, as well as various appealing applications in wireless networks. Moreover, we highlight important directions worthy of further investigation in future work.

To achieve the more significant passive beamforming gain in the double-intelligent reflecting surface (IRS) aided system over the conventional single-IRS counterpart, channel state information (CSI) is indispensable in practice but also more challenging to acquire, due to the presence of not only the single- but also double-reflection links that are intricately coupled and also entail more channel coefficients for estimation. In this paper, we propose a new and efficient channel estimation scheme for the double-IRS aided multi-user multiple-input multiple-output (MIMO) communication system to resolve the cascaded CSI of both its single- and double-reflection links. First, for the single-user case, the single- and double-reflection channels are efficiently estimated at the multi-antenna base station (BS) with both the IRSs turned ON (for maximal signal reflection), by exploiting the fact that their cascaded channel coefficients are scaled versions of their superimposed lower-dimensional CSI. Then, the proposed channel estimation scheme is extended to the multi-user case, where given an arbitrary user's cascaded channel (estimated as in the single-user case), the other users' cascaded channels can also be expressed as lower-dimensional scaled versions of it and thus efficiently estimated at the BS. Simulation results verify the effectiveness of the proposed channel estimation scheme and joint training reflection design for double IRSs, as compared to various benchmark schemes.

Intelligent reflecting surface (IRS) has emerged as an enabling technology to achieve smart and reconfigurable wireless communication environment cost-effectively. Prior works on IRS mainly consider its passive beamforming design and performance optimization without the inter-IRS signal reflection, which thus do not unveil the full potential of multi-IRS assisted wireless networks. In this paper, we study a double-IRS assisted multi-user communication system with the cooperative passive beamforming design that captures the multiplicative beamforming gain from the inter-IRS channel. Under the general channel setup with the co-existence of both double- and single-reflection links, we jointly optimize the (active) receive beamforming at the base station (BS) and the cooperative (passive) reflect beamforming at the two distributed IRSs (deployed near the BS and users, respectively) to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of all users. Moreover, for the single-user and multi-user setups, we analytically show the superior performance of the double-IRS cooperative system over the conventional single-IRS system in terms of the maximum signal-to-noise ratio (SNR) and multi-user effective channel rank, respectively. Simulation results validate our analytical results and show the practical advantages of the proposed double-IRS system with cooperative passive beamforming designs.

In this paper, we consider a multi-unmanned aerial vehicle (UAV) relaying system where the UAVs are deployed to assist data relaying from multiple ground source nodes to their corresponding destination nodes. An optimization problem is formulated to maximize the minimum achievable rate among all pairs of nodes, by jointly designing the UAV placement and communication resource allocation. To solve this non-convex problem, we first reformulate it into two sub-problems, corresponding to a slave problem for the resource allocation given fixed UAV placement and a master problem for the UAV placement optimization. Then for the non-convex slave problem, we sub-optimally solve it by using the successive convex approximation method. The master problem, however, is intractable due to the lack of a closed-form expression for the max-min rate with respect to the UAV placement. We thus propose a new solution approach, called Gibbs-sampling-based (GSB) placement learning, to gradually learn a sub-optimal UAV placement by generating a sequence of samples for the UAV placement that constitute a Markov chain, where the transition probabilities are determined by the max-min rates of different configurations of UAV placement. Furthermore, a high-quality UAV-placement initialization scheme is proposed to accelerate the convergence speed of the proposed GSB algorithm. Numerical results are presented to demonstrate the significant rate improvement and fast convergence speed of the proposed scheme as compared to various benchmark schemes.

In this paper, we consider an intelligent reflecting surface (IRS)-aided single-user system where an IRS with discrete phase shifts is deployed to assist the uplink communication. A practical transmission protocol is proposed to execute channel estimation and passive beamforming successively. To minimize the mean square error (MSE) of channel estimation, we first formulate an optimization problem for designing the IRS reflection pattern in the training phase under the constraints of unit-modulus, discrete phase, and full rank. This problem, however, is NP-hard and thus difficult to solve in general. As such, we propose a low-complexity yet efficient method to solve it sub-optimally, by constructing a near-orthogonal reflection pattern based on either discrete Fourier transform (DFT)-matrix quantization or Hadamard-matrix truncation. Based on the estimated channel, we then formulate an optimization problem to maximize the achievable rate by designing the discrete-phase passive beamforming at the IRS with the training overhead and channel estimation error taken into account. To reduce the computational complexity of exhaustive search, we further propose a low-complexity successive refinement algorithm with a properly-designed initialization to obtain a high-quality suboptimal solution. Numerical results are presented to show the significant rate improvement of our proposed IRS training reflection pattern and passive beamforming designs as compared to other benchmark schemes.

Prior studies on intelligent reflecting surface (IRS) have mostly assumed perfect channel state information (CSI) available for designing the IRS passive beamforming as well as the continuously adjustable phase shift at each of its reflecting elements, which, however, have simplified two challenging issues for implementing IRS in practice, namely, its channel estimation and passive beamforming designs both under the constraint of discrete phase shifts. To address them, we consider in this paper an IRS-aided single-user communication system and design the IRS training reflection matrix for channel estimation as well as the passive beamforming for data transmission, both subject to the new constraint of discrete phase shifts. We show that the training reflection matrix design with discrete phase shifts greatly differs from that with continuous phase shifts, and the corresponding passive beamforming design should take into account the correlated IRS channel estimation errors due to discrete phase shifts. Moreover, a novel hierarchical training reflection design is proposed to progressively estimate IRS elements' channels over multiple time blocks by exploiting the IRS-elements grouping and partition. Based on the resolved IRS channels in each block, we further design the progressive passive beamforming at the IRS with discrete phase shifts to improve the achievable rate for data transmission over the blocks. Extensive numerical results are presented, which demonstrate the significant performance improvement of proposed channel estimation and passive beamforming designs as compared to various benchmark schemes.

This paper considers an unmanned aerial vehicle (UAV)-enabled wireless sensor network (WSN) in urban areas, where a UAV is deployed to collect data from distributed sensor nodes (SNs) within a given duration. To characterize the occasional building blockage between the UAV and SNs, we construct the probabilistic line-of-sight (LoS) channel model for a Manhattan-type city by using the combined simulation and data regression method, which is shown in the form of a generalized logistic function of the UAV-SN elevation angle. We assume that only the knowledge of SNs' locations and the probabilistic LoS channel model is known a priori, while the UAV can obtain the instantaneous LoS/Non-LoS channel state information (CSI) with the SNs in real time along its flight. Our objective is to maximize the minimum (average) data collection rate from all the SNs for the UAV. To this end, we formulate a new rate maximization problem by jointly optimizing the UAV three-dimensional (3D) trajectory and transmission scheduling of SNs. Although the optimal solution is intractable due to the lack of complete UAV-SNs CSI, we propose in this paper a novel and general design method, called hybrid offline-online optimization, to obtain a suboptimal solution to it, by leveraging both the statistical and real-time CSI. Essentially, our proposed method decouples the joint design of UAV trajectory and communication scheduling into two phases: namely, an offline phase that determines the UAV path prior to its flight based on the probabilistic LoS channel model, followed by an online phase that adaptively adjusts the UAV flying speeds along the offline optimized path as well as communication scheduling based on the instantaneous UAV-SNs CSI and SNs' individual amounts of data received accumulatively. Extensive simulation results are provided to show the significant rate performance improvement of our proposed design as compared to various benchmark schemes.

To achieve the full passive beamforming gains of intelligent reflecting surface (IRS), accurate channel state information (CSI) is indispensable but practically challenging to acquire, due to the excessive amount of channel parameters to be estimated which increases with the number of IRS reflecting elements as well as that of IRS-served users. To tackle this challenge, we propose in this paper two efficient channel estimation schemes for different channel setups in an IRS-assisted multi-user broadband communication system employing the orthogonal frequency division multiple access (OFDMA). The first channel estimation scheme, which estimates the CSI of all users in parallel simultaneously at the access point (AP), is applicable for arbitrary frequency-selective fading channels. In contrast, the second channel estimation scheme, which exploits a key property that all users share the same (common) IRS-AP channel to enhance the training efficiency and support more users, is proposed for the typical scenario with line-of-sight (LoS) dominant user-IRS channels. For the two proposed channel estimation schemes, we further optimize their corresponding training designs (including pilot tone allocations for all users and IRS time-varying reflection pattern) to minimize the channel estimation error. Moreover, we derive and compare the fundamental limits on the minimum training overhead and the maximum number of supportable users of these two schemes. Simulation results verify the effectiveness of the proposed channel estimation schemes and training designs, and show their significant performance improvement over various benchmark schemes.

In this paper, we consider a UAV-enabled wireless sensor network (WSN), where a UAV is dispatched to collect data from multiple sensor nodes (SNs) at known locations within a given duration. In urban areas, the signal propagation between the UAV and SNs can be occasionally blocked (or at least severely attenuated) by high and dense buildings. To address this issue, we first establish a probabilistic line-of-sight (LoS) channel model for a Manhattan-type city by using simulation and data regression methods, which is shown in the form of a generalized logistic function of the UAV-SN elevation angle. Based on the obtained channel model, an off-line optimization problem is then formulated to maximize the minimum expected (average) data-collection rate from all SNs by jointly designing the UAV three-dimensional (3D) trajectory and transmission scheduling of SNs. Since the expected rate is a highly complex function of the 3D UAV trajectory, we approximate it by a tractable lower bound based on the dominant rate in the LoS channel state. The resultant optimization problem is still non-convex and thus difficult to solve. As such, we further propose an efficient algorithm to solve it sub- optimally by applying the techniques of block coordination descent and successive convex approximation. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm and reveal useful properties of the optimized 3D UAV trajectory for data harvesting in WSNs.

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

Due to densification of wireless networks, there exist abundance of idling computation resources at (network) edge helpers (e.g., base stations and handheld computers). These resources can be scavenged by offloading heavy computation tasks from small Internet-of-Things (IoT) devices (e.g., sensors and wearable computing devices) in proximity, thereby overcoming their limitations and lengthening their battery lives. However, unlike dedicated servers, the spare resources offered by edge helpers are random and intermittent. Thus, it is essential to intelligently control a user (IoT device) the amounts of data for offloading and local computing so as to ensure that a computation task can be finished in time-consuming minimum energy. In this paper, we design energy-efficient control policies in a computation offloading system with a random channel and a helper with a dynamically loaded CPU (due to the primary service). Specifically, the policy adopted by the helper aims at determining the sizes of offloaded and locally computed data for a given task in different slots such that the total energy consumption for transmission and local CPU is minimized under a task-deadline constraint. As the result, the polices endow an offloading user robustness against channel-and-helper randomness besides balancing offloading and local computing. By modeling the channel and helper CPU as Markov chains, the problem of offloading control is converted into a Markov decision process. Though dynamic programming (DP) for numerically solving the problem does not yield the optimal policies in closed form, we leverage the procedure to quantify the optimal policy structure and apply the result to design optimal or sub-optimal policies. For three cases ranging from zero, small to large helper buffers, the low complexity of the policies overcomes the “curse of dimensionality” in DP arising from joint consideration of channel, helper CPU, and buffer states.

Dispatching unmanned aerial vehicles (UAVs) to harvest sensing-data from distributed sensors is expected to significantly improve the data collection efficiency in conventional wireless sensor networks (WSNs). In this paper, we consider a UAV-enabled WSN, where a flying UAV is employed to collect data from multiple sensor nodes (SNs). Our objective is to maximize the minimum average data collection rate from all SNs subject to a prescribed reliability constraint for each SN by jointly optimizing the UAV communication scheduling and three-dimensional (3D) trajectory. Different from the existing works that assume the simplified line-of-sight (LoS) UAV-ground channels, we consider the more practically accurate angle-dependent Rician fading channels between the UAV and SNs with the Rician factors determined by the corresponding UAV-SN elevation angles. However, the formulated optimization problem is intractable due to the lack of a closed-form expression for a key parameter termed effective fading power that characterizes the achievable rate given the reliability requirement in terms of outage probability. To tackle this difficulty, we first approximate the parameter by a logistic (“S” shape) function with respect to the 3D UAV trajectory by using the data regression method. Then, the original problem is reformulated to an approximate form, which, however, is still challenging to solve due to its non-convexity. As such, we further propose an efficient algorithm to derive its suboptimal solution by using the block coordinate descent technique, which iteratively optimizes the communication scheduling, the UAV's horizontal trajectory, and its vertical trajectory. The latter two subproblems are shown to be non-convex, while locally optimal solutions are obtained for them by using the successive convex approximation technique. Finally, extensive numerical results are provided to evaluate the performance of the proposed algorithm and draw new insights on the 3D UAV trajectory under the Rician fading as compared to conventional LoS channel models.

Scavenging the idling computation resources at the enormous number of mobile devices, ranging from small IoT devices to powerful laptop computers, can provide a powerful platform for local mobile cloud computing. The vision can be realized by peer-to-peer cooperative computing between edge devices, referred to as co-computing. This paper exploits the non-causal helper's CPU-state information to design energy-efficient co-computing policies for scavenging time-varying spare computation resources at peer mobiles. Specifically, we consider a co-computing system where a user offloads computation of input data to a helper. The helper controls the offloading process for the objective of minimizing the user's energy consumption based on a predicted helper's CPU-idling profile that specifies the amount of available computation resource for co-computing. Consider the scenario that the user has one-shot input-data arrival and the helper buffers offloaded bits. The problem for energy-efficient co-computing is formulated as two sub-problems: the slave problem corresponding to adaptive offloading and the master one to data partitioning. Given a fixed offloaded data size, the adaptive offloading aims at minimizing the energy consumption for offloading by controlling the offloading rate under the deadline and buffer constraints. By deriving the necessary and sufficient conditions for the optimal solution, we characterize the structure of the optimal policies and propose algorithms for computing the policies. Furthermore, we show that the problem of optimal data partitioning for offloading and local computing at the user is convex, admitting a simple solution using the sub-gradient method. Finally, the developed design approach for co-computing is extended to the scenario of bursty data arrivals at the user accounting for data causality constraints. Simulation results verify the effectiveness of the proposed algorithms.

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.

Mobile-edge computation offloading (MECO) off-loads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we study resource allocation for a multiuser MECO system based on time-division multiple access (TDMA) and orthogonal frequency-division multiple access (OFDMA). First, for the TDMA MECO system with infinite or finite cloud computation capacity, the optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under the constraint on computation latency. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function, which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Moreover, for the cloud with finite capacity, a sub-optimal resource-allocation algorithm is proposed to reduce the computation complexity for computing the threshold. Next, we consider the OFDMA MECO system, for which the optimal resource allocation is formulated as a mixed-integer problem. To solve this challenging problem and characterize its policy structure, a low-complexity sub-optimal algorithm is proposed by transforming the OFDMA problem to its TDMA counterpart. The corresponding resource allocation is derived by defining an average offloading priority function and shown to have close-to-optimal performance in simulation.

Mobile-edge computation offloading (MECO) offloads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we consider resource allocation in a MECO system comprising multiple users that time share a single edge cloud and have different computation loads. The optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under constraint on computation latency and for both the cases of infinite and finite edge cloud computation capacities. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function, which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Computing the threshold requires iterative computation. To reduce the complexity, a sub-optimal resource-allocation algorithm is proposed and shown by simulation to have close-to-optimal performance.

Achieving long battery lives or even self sustainability has been a long standing challenge for designing mobile devices. This paper presents a novel solution that seamlessly integrates two technologies, mobile cloud computing and microwave power transfer (MPT), to enable computation in passive low-complexity devices such as sensors and wearable computing devices. Specifically, considering a single-user system, a base station (BS) either transfers power to or offloads computation from a mobile to the cloud; the mobile uses harvested energy to compute given data either locally or by offloading. A framework for energy efficient computing is proposed that comprises a set of policies for controlling CPU cycles for the mode of local computing, time division between MPT and offloading for the other mode of offloading, and mode selection. Given the CPU-cycle statistics information and channel state information (CSI), the policies aim at maximizing the probability of successfully computing given data, called computing probability, under the energy harvesting and deadline constraints. The policy optimization is translated into the equivalent problems of minimizing the mobile energy consumption for local computing and maximizing the mobile energy savings for offloading which are solved using convex optimization theory. The structures of the resultant policies are characterized in closed form. Furthermore, given non-causal CSI, the said analytical framework is further developed to support computation load allocation over multiple channel realizations, which further increases the computing probability. Last, simulation demonstrates the feasibility of wirelessly powered mobile cloud computing and the gain of its optimal control.

Flexible-grid elastic optical networks (EONs) have attracted intensive research interests for the agile spectrum management in the optical layer. Meanwhile, due to the relatively small spectrum allocation granularity, spectrum fragmentation has been commonly recognized as one of the key factors that can deteriorate the performance of EONs. To alleviate spectrum fragmentation, various defragmentation (DF) schemes have been considered to consolidate spectrum utilization in EONs through connection reconfigurations. However, most of the previous approaches operate in the sequential manner (Seq-DF), i.e., involving a sequence of reconfigurations to progressively migrate highly fragmented spectrum utilization to consolidated state. In this paper, we propose to perform the DF operations in a parallel manner (Par-DF), i.e., conducting all the DF-related connection reconfigurations simultaneously. We first provide a detailed analysis on the latency and disruption of Seq-DF and Par-DF in EONs, and highlight the benefits of Par-DF. Then, we study two types of Par-DF approaches in EONs, i.e., reactive Par-DF (re-Par-DF) and proactive Par-DF (pro-Par-DF). We perform hardness analysis on them, and prove that the problem of re-Par-DF is NP-hard in the strong sense while pro-Par-DF is an APX-hard problem. Next, we focus on pro-Par-DF and propose a Lagrangian-relaxation (LR) based heuristic to solve it time-efficiently. The proposed algorithm decomposes the original problem into several independent subproblems and ensures that each of them can be solved efficiently. The LR based approach informs us the proximity of current feasible solution to the optimal one constantly, and offers a near-optimal performance (relative dual gap 5%) within 500 iterations in most simulations. Extensive simulations also verify that the proposed pro-Par-DF approach outperforms Seq-DF in terms of the DF Latency, Disruption and Cost.

Achieving long battery lives or even self sustainability has been a long standing challenge for designing mobile devices. This paper seamlessly integrates two promising energy-saving technologies, namely mobile computation offloading (MCO) and microwave power transfer (MPT), and proposes a novel design framework of wirelessly powered MCO. Consider a single-user system where a base station (BS) either transfers power to or offloads computation from a mobile. Two mobile operation modes, namely local computation and offloading, are optimized separately for maximizing the mobile energy savings. For local computation, the non-convex problem of optimizing the CPU-cycle frequencies under the deadline and energy causality constraints is solved via convex relaxation. The optimal CPU-cycle frequencies are shown to have different forms depending on the BS transmission power. For offloading, the time duration before the deadline is divided for separate MPT and offloading and the optimal division is derived in a closed form. By combining above results, the optimal offloading decision is analyzed with respect to the deadline, data-input size and BS transmission power and validated by simulation.