Visual Object Tracking (VOT) remains challenging under occlusion scenarios, where traditional trackers often suffer from feature degradation and target loss. To address this issue, we propose OASAMT, an occlusion-aware tracking framework that equips SAM2 with explicit temporal occlusion reasoning via two Temporal Convolutional Networks (TCNs) and a Dual-Memory Bank (DMB). Specifically, two TCN-based modules are designed to model temporal occlusion dynamics: the Temporal Occlusion Classifier (TOC) for inferring target occlusion states using confidence scores, mask IoU, and area ratio; and the Temporal Occlusion Predictor (TOP) for forecasting target bounding boxes during occlusion. The proposed DMB consists of a Non-Occlusion Memory Bank (N-OMB) and an Occlusion Memory Bank (OMB), explicitly decoupling reliable and occluded representations to prevent memory contamination and improve re-detection after occlusion. Additionally, to facilitate systematic evaluation under occlusion scenarios, we construct OccTrack, a dedicated occlusion-oriented dataset derived from four UAV-view benchmarks. Extensive experiments were conducted on the OccTrack, LaSOT, LaSOT ext , and GOT-10k datasets. The results demonstrate that OASAMT consistently outperforms SAM2.1 and other advanced trackers in both occlusion-specific and general tracking scenarios. The code and the dataset are available at https://github.com/ChaseFalcon99/OASAMT .

This paper investigates the delay minimization problem for multi-task federated learning (MTFL) systems at the network edge. We develop a novel MTFL framework, based on which the divergence bounds are derived for both task-related and task-unrelated scenarios, systematically quantifying the effects of user importance, user participation, and inter-task correlations on convergence behavior. Building upon these insights, a long-term joint optimization problem is formulated to minimize the overall training delay under the long-term divergence bounds and energy constraints. To address the coupling in multi-slot user scheduling, the optimization problem is decomposed into a single-slot joint resource allocation and task scheduling subproblem and a cross-slot user scheduling subproblem. The former is solved using block coordinate descent (BCD) combined with Johnson’s rule, while the latter is modeled as a constrained Markov decision process (CMDP) and addressed via a dueling double deep Q-network (D3QN) with cost shaping and prioritized experience replay. Numerical results verify the effectiveness of the proposed framework and convergence analysis, demonstrating its significant improvements over baseline schemes in terms of convergence and delay reduction.

The deployment of extremely large antenna arrays (ELAAs) in extremely large-scale multiple-input multiple-output (XL-MIMO) systems introduces significant near-field effects, such as spherical wavefront propagation and spatially non-stationary (SnS) properties. When combined with the dual-wideband effects inherent to wideband systems, these phenomena fundamentally alter the channel’s sparsity patterns in the angular-delay domain, rendering existing estimation methods insufficient. To address these challenges, this paper reconsiders the channel estimation problem for wideband XL-MIMO systems. Leveraging the spatial-chirp property of array responses, we first quantitatively characterize the angular-delay domain sparsity of wideband XL-MIMO channels, revealing both global block sparsity and local common-delay sparsity. To effectively capture this structured sparsity, we then propose a novel column-wise hierarchical prior model that integrates a precision sharing mechanism and a Markov random field (MRF) structure. Building on this prior model, the channel estimation task is formulated as a multiple measurement vector (MMV)-based Bayesian inference problem. Tailored to the complex factor graph induced by this hierarchical prior, we develop a MMV-based hybrid message passing (MMV-HMP) algorithm. This algorithm performs message updates along the edges of the factor graph, and selectively applies either the variational message passing (VMP) or sum-product (SP) rules, depending on the factor-node structure and message tractability. Simulation results validate the effectiveness of the proposed column-wise hierarchical prior model through ablation studies and demonstrate that the MMV-HMP algorithm, while maintaining moderate computational complexity, consistently outperforms existing baselines which fail to capture the structured sparsity of wideband XL-MIMO channels.

This paper analyzes the spatial multiplexing capability in the line-of-sight (LoS) extremely large-scale multiple-input multiple-output (XL-MIMO) systems, where the impacts of array geometry (such as the shape, size, position, and orientation) on spatial degrees of freedom (DoF) is presented, resulting the validation of promising performance gain of the fluid antenna system (FAS). To this end, we first provide an exact closed-form expression for the local spatial bandwidth at the center of the receive array. Then, we analyze the maximum local spatial bandwidth at different spatial positions. An approximate closed-form expression for the achievable spatial DoF is obtained based on the derived local spatial bandwidth. Simulation results are presented for validation.

Existing channel estimation algorithms for ex-tremely large-scale multiple-input multiple-output (XL-MIMO) systems are predominantly designed for single-user scenarios and often overlook inter-user correlations. To address this limitation, this paper reformulates the joint multiuser channel estimation problem as a multiple-measurement vector (MMV)-based sparse signal recovery task. To solve this, we propose a novel row-wise hierarchical prior model that captures the structured sparsity of the joint multiuser channel in the angular-delay domain. Specifically, shared precision parameters for each row of the angular-delay domain channel are introduced to model common-row sparsity, while a Markov random field (MRF) is employed to encourage cluster sparsity. Building on this structured prior, we develop a computationally effi-cient channel estimation algorithm using variational message passing. Simulation results demonstrate that the proposed method significantly outperforms existing single-user-based approaches.

As an evolution of cloud radio access network (C-RAN), fog radio access network (F-RAN) becomes promising for future mobile communications by enabling processing and caching at fog access points (FAPs). Different from the centralised C-RAN, F-RAN has a semi-distributed architecture, aiming to alleviate traffic load on the fronthaul links in C-RAN. Under the semi-distributed architecture in F-RAN, which employs a cell-free multiple input multiple output (MIMO) access technique, decisions on the joint user-FAP association and transmit power allocation are made at individual FAPs. To mitigate strong interference, FAPs will need to exchange cooperative status information, such as CSI, user association details or transmission power levels. However, this can lead to significant communication overhead within the network and introduce high complexity in the decision-making process. In this paper, accounting for the semi-distributed nature of the F-RAN architecture, reinforcement learning is leveraged as a potential solution to this kind of problem, and a novel multi-agent dual deep Q-network (MA-DDQN) algorithm is proposed by introducing experience exchange in partially observable Markov decision process environments. The simulation results show that the proposed reinforcement learning based algorithm outperforms the DDQN algorithm as well as the existing low-complexity algorithms.

This paper investigates the spatial bandwidth of line-of-sight (LoS) channels in extra-large MIMO (XL-MIMO) systems. For linear large-scale antenna arrays (LSAAs) with transceivers randomly positioned in 3D space, a simple but accurate closed-form expression is derived to characterize the local spatial bandwidth. Based on this analysis, we examine the properties of local spatial bandwidth and further derive expressions for the effective spatial bandwidth and the achievable degrees of freedom (i.e., theKnumber) for LSAAs. We also conduct case studies for both coplanar and non-coplanar transmitting and receiving arrays, providing more concise and intuitive expressions for local spatial bandwidth and achievable spatial degrees of freedom. Finally, the impact of array geometry on LoS XL-MIMO channel capacity is explored. When the transmitting and receiving arrays are coplanar and perpendicular to the line connecting their centers, the effective degree of freedom of the LoS channel is found to be approximately maximized. This orientation also maximizes the channel capacity in near-field high-SNR scenarios.

With the growing demand for virtual reality (VR) applications, mobile wireless networks should support the connections of a massive number of VR users. To support ultra-high data rates of multiple simultaneously transmitted VR streamings, we propose a mobile edge computing (MEC)-assisted rate splitting (RS) VR streaming transmission system. In the proposed system, RS technology exploits the shared interests of multiple VR users and MEC offloads the partial rendering tasks to achieve a better quality of experience (QoE) for VR users. Aiming to minimize the total weighted energy consumption, we formulate a joint communication and computing resource optimization problem while ensuring the required distortion and latency of VR users. To deal with the formulated intractable problem, we propose a joint rendering offloading and resource allocation algorithm that alternately solves the subproblems of quantization parameters selection, rendering offloading decision, transmit precoding design, rate allocation of RS transmission, and computing resource allocation. The simulation results demonstrate the effectiveness of the proposed algorithm in saving energy consumption. Specially, the performance of the proposed algorithm is 22.1% higher than that of the multicast-unicast scheme and can achieve 95.9% of the exhaustive search based algorithm.

In this paper, we investigate the channel estimation problem for extremely large-scale multiple-input multiple-output (XL-MIMO) systems with a hybrid analog-digital architecture, implemented within a decentralized baseband processing (DBP) framework with a star topology. Existing centralized and fully decentralized channel estimation methods face limitations due to excessive computational complexity or degraded performance. To overcome these challenges, we propose a novel two-stage channel estimation scheme that integrates local sparse reconstruction with global fusion and refinement. Specifically, in the first stage, by exploiting the sparsity of channels in the angular-delay domain, the local reconstruction task is formulated as a sparse signal recovery problem. To solve it, we develop a graph neural networks-enhanced sparse Bayesian learning (SBL-GNNs) algorithm, which effectively captures dependencies among channel coefficients, significantly improving estimation accuracy. In the second stage, the local estimates from the local processing units (LPUs) are aligned into a global angular domain for fusion at the central processing unit (CPU). Based on the aggregated observations, the channel refinement is modeled as a Bayesian denoising problem. To efficiently solve it, we devise a variational message passing algorithm that incorporates a Markov chain-based hierarchical sparse prior, effectively leveraging both the sparsity and the correlations of the channels in the global angular-delay domain. Simulation results show the effectiveness and superiority of the proposed SBL-GNNs algorithm over existing methods, demonstrating improved estimation performance and reduced computational complexity.

To cope with the unendurable delay of maritime wireless networks (MWNs), this paper proposes a collaborative transmission framework utilizing a multi-antenna uncrewed surface vessel (USV) and multiple cache-aided buoys to satisfy the on-demand file requirements for remote users (RUs). Specifically, a direct transmission scheme is adopted for hit-requested files and a multi-hop transmission scheme is devised to handle cache misses. To fully exploit the local cache and signal processing capabilities, we integrate two schemes into a collaborative transmission framework, where the USV dynamically supports buoys in uncached file fetching, and buoys collaborate to forward both cached and fetched files to RUs through a cooperative beamforming policy. We aim to minimize the overall transmission completion time by jointly optimizing the USV trajectory, cooperative beamforming, and transmission duration under the constraints of USV kinetic, transmit power, and file requirements. By leveraging the completion condition analysis, the original problem is transformed into a sequence of one-slot problems and a finite-horizon problem, where the closed-form solution for the local caching beamforming at each buoy is derived. Due to the complexity of the multivariable coupling, we propose an equivalent rate transformation method for transmission strategy design. Numerical results validate the effectiveness of the proposed scheme and algorithm.

In this paper, we introduce a novel uplink semanticaware wireless communication system, catering to multiple users by leveraging a shared probability graph between an access point (AP) and a base station (BS). Under imperfect channel state information (CSI), all users transmit data information to the AP through conventional bit transmission and the lower bound of signal estimation error is derived. An edge server equipped with the AP and a cloud server equipped with the BS execute information compression and recovery based on the shared probability graph, respectively. While semantic information compression incurs computational resource consumption, it significantly reduces communication resource usage. This paper addresses the challenge of minimizing system latency through jointly optimizing communication resource allocation, channel truncation threshold, and data compression ratio, considering limited wireless resources, signal estimation error, and the system’s energy budget. To solve the formulated non-convex time minimization problem, we decompose the optimization problem into four subproblems and solve each of them iteratively. In particular, the power allocation subproblem is transformed into a convex time allocation optimization problem. The bandwidth allocation subproblem is proven to be convex. The optimal channel truncation threshold is obtained through a bisection search. The data compression ratio optimization subproblem is a non-convex integer programming problem solved by the Gurobi optimizer. Numerical results show the effectiveness of the proposed algorithm, validating the necessity to consider imperfect CSI and semantic transmission and the superior performance of semantic communication compared to conventional bit transmission.

This paper presents a comprehensive framework for jointly analyzing the angle estimation error and designing a three-dimensional (3D) positioning algorithm for an Internet of Things (IoT) millimeter wave (mmWave) positioning system. Initially, the azimuth and elevation angles of arrival (AoAs) at the anchors are estimated by applying the two-dimensional discrete Fourier transform (2D-DFT) algorithm. The angle estimation error is then analyzed in terms of probability density functions (PDF) by utilizing the properties of the 2D-DFT algorithm and employing challenging derivations and linear approximations. The analysis reveals that the resulting angle estimation error is non-Gaussian, distinguishing it from previous studies. Next, the complex expression of the PDF for the AoA estimation error is simplified using the first-order linear approximation of triangle functions. Subsequently, a complex expression for the variance is derived based on the obtained PDF. Specifically, the variance for the azimuth estimation error is integrated separately according to the different non-zero intervals of the obtained PDF. Additionally, the closed-form expressions of the variances are formulated using generalized hypergeometric series. Finally, the two-stage weighted least square (TSWLS) algorithm is employed to estimate the 3D position of the mobile user (MU) using the estimated AoAs and the obtained non-Gaussian variance. Extensive simulation results confirm the non-Gaussian nature of the derived angle estimation error and demonstrate the superiority of the proposed framework.

In this paper, the problem of joint user scheduling and computing resource allocation in asynchronous mobile edge computing (MEC) networks is studied. In such networks, edge devices will offload their computational tasks to an MEC server, using the energy they harvest from this server. To get their tasks processed on time using the harvested energy, edge devices will strategically schedule their task offloading, and compete for the computational resource at the MEC server. Then, the MEC server will execute these tasks asynchronously based on the arrival of the tasks. This joint user scheduling, time and computation resource allocation problem is posed as an optimization framework whose goal is to find the optimal scheduling and allocation strategy that minimizes the energy consumption of these mobile computing tasks. To solve this mixed-integer non-linear programming problem, the general benders decomposition method is adopted which decomposes the original problem into a primal problem and a master problem. Specifically, the primal problem is related to computation resource and time slot allocation, of which the optimal closed-form solution is obtained. The master problem regarding discrete user scheduling variables is constructed by adding optimality cuts or feasibility cuts according to whether the primal problem is feasible, which is a standard mixed-integer linear programming problem and can be efficiently solved. By iteratively solving the primal problem and master problem, the optimal scheduling and resource allocation scheme is obtained. Simulation results demonstrate that the proposed asynchronous computing framework reduces 87.17% energy consumption compared with conventional synchronous computing counterpart.

With the combination of extra-large arrays and high frequencies, near-field transmissions have become prevalent, challenging the validity of classical channel representations typically derived under the plane wavefront assumption. In this paper, we investigate the angular-domain representation of line-of-sight (LoS) extra-large MIMO (XL-MIMO) channels, considering the impact of spherical wavefront effects. First, we demonstrate the structured sparsity of LoS XL-MIMO channels in the angular domain. Leveraging this sparsity, we propose an effective spatial bandwidth channel representation method, which characterizes near-field LoS XL-MIMO channels as a superposition of multiple plane wave components, enabling us to capture the spherical wavefront effect in a low-dimensional angular channel. Subsequently, we introduce an angular-domain transceiver architecture based on this low-dimensional channel representation. This architecture could significantly facilitate the implementation of LoS XL-MIMO systems. Finally, simulation results confirm the effectiveness of the effective spatial bandwidth identification method and analyze the impact of various array geometries on the effective spatial bandwidth. Additionally, the availability of the angular-domain processing architecture is validated.

In this work, we investigate the joint visibility region (VR) detection and channel estimation (CE) problem for extremely large-scale multiple-input-multiple-output (XL-MIMO) systems considering both the spherical wavefront effect and spatial non-stationary (SnS) property. Unlike existing SnS CE methods that rely on the statistical characteristics of channels in the spatial or delay domain, we propose an approach that simultaneously exploits the antenna-domain spatial correlation and the wavenumber-domain sparsity of SnS channels. To this end, we introduce a two-stage VR detection and CE scheme. In the first stage, the belief regarding the visibility of antennas is obtained through a VR detection-oriented message passing (VRDO-MP) scheme, which fully exploits the spatial correlation among adjacent antenna elements. In the second stage, leveraging the VR information and wavenumber-domain sparsity, we accurately estimate the SnS channel employing the belief-based orthogonal matching pursuit (BB-OMP) method. Simulations show that the proposed algorithms lead to a significant enhancement in VR detection and CE accuracy as compared to existing methods, especially in low signal-to-noise ratio (SNR) scenarios.

In this paper, a mobile edge computing (MEC) assisted multi-layer architecture is proposed to support the implementation of federated learning in Internet of Things (IoT) networks. In this architecture, when performing a federated learning based task, data samples can be partially offloaded to MEC servers and cloud server rather than only processing the task at the IoT devices. After collecting local model parameters from devices and MEC servers, cloud server makes an aggregation and broadcasts it back to all devices. An optimization problem is presented to minimize the total federated training latency by jointly optimizing decisions on data offloading ratio, computation resource allocation and bandwidth allocation. To solve the formulated NP hard problem, the optimization problem is converted into quadratically constrained quadratic program (QCQP) and an efficient algorithm is proposed based on semidefinite relaxation (SDR) method. Furthermore, the scenario with the constraint of indivisible tasks in devices is considered and an applicable algorithm is proposed to get effective offloading decisions. Simulation results show that the proposed solutions can get effective resource allocation strategy and the proposed multi-layer federated learning architecture outperforms the conventional federated learning scheme in terms of the learning latency performance.

In this paper, we investigate the energy minimization problem in cell-free mobile edge computing (MEC) networks under dynamic channel conditions and random arrival tasks. We aim to minimize the total energy consumption of user equipment (UEs) and MEC servers (MECSs) by jointly optimizing MECS active/sleep mode selection, UE-MECS association decision (MSAD), and task offloading, communication, and computation resource allocation (TORA) across two different timescales. Considering that the involved optimization variables affect the system performance in different timescales, we decouple the formulated stochastic optimization problem into two subproblems: MSAD operating in the large timescale and TORA in the small timescale. Leveraging the Lyapunov method, the stochastic TORA problem is decoupled into a series of deterministic problems, of which the closed-form solutions are presented. Furthermore, the MSAD problem is reformulated as a constrained Markov decision process (MDP). Then, we propose a double dueling deep Q-network (D3QN) to learn the optimal MSAD based on the TORA results. Numerical results demonstrate that the proposed online TORA-assisted MSAD learning algorithm has effective convergence and achieves substantial energy reductions for the MEC networks compared with the benchmark schemes.

In this paper, we investigate the power allocation problem to maximize the long-term average downlink sum-rate for orthogonal frequency division multiplexing (OFDM) based multi-cell networks. The traditional centralized training and centralized execution (CTCE) scheme is impractical to solve this complex problem, because it is hard for training center to obtain the global information, and the signaling overhead is also unbearable. To this end, a centralized training and distributed execution (CTDE) framework for wireless power control based on deep reinforcement learning (DRL) is proposed. Specifically, we first transform this problem into a sequential decision problem by designing appropriate state, action and reward. Then, a CTDE scheme, where each agent only requires its own local observations for power allocation, is devised through combining QMIX algorithm and Normalized Advantage Functions (NAF) algorithm. In particular, QMIX network is used to fit the total Q-value function and NAF network is deployed to handle continuous power output. Simulation results show that the proposed scheme converges fast and achieves better rate performance compared with conventional CTDE algorithms.

Unmanned aerial vehicles (UAVs) have improved the capacity and coverage of wireless networks. Mobile edge computing (MEC) has provided substantial computation capability to user equipment (UEs). The integration of UAV and MEC can take advantages of both to provide flexible computation service. In UAV assisted MEC networks, delay and energy consumption are two main concerns, which are conflicting to a certain extent. This paper investigates delay and energy consumption jointly in a multiple UAVs assisted MEC network. A cost function is defined to balance the delay and the energy consumption. The user access, task offloading, and computational resource allocation are jointly considered to minimize the long-term cost. To tackle this difficult problem, we formulate the long-term problem into sequential decision problem and treat all UEs as intelligent agents. Each UE decides its access UAV, task offloading proportion, and required edge computation resource to minimize the its own cost. Moreover, the optimal task offloading proportion and required computation resource can be obtained in closed-form given user access so that the action space can be significantly reduced. Then, an adversarial multi-armed bandit based algorithm is employed at each UE and a distributed scheme is proposed to solve the joint optimization problem. Simulation results validate the effectiveness and robustness of the distributed scheme and show its superiority to benchmarks.

In this paper, we consider a cooperative mobile edge computing (MEC) network where both the user equipments (UEs) and the MEC server can help with the task computing. Multiple heterogeneous tasks of different sizes for each UE are separately offloaded to the nearby UEs and MEC server’s central processing unit and graphics processing unit for processing. Tasks of the same type are transmitted sequentially so that the waiting latency is required for offloading transmission and computation. We aim to minimize the maximum latency of UEs for processing all the tasks while ensuring that all tasks are successfully transmitted and processed. To solve the formulated non-convex problem, an iterative algorithm named directed mutation process based on discrete differential evolution is proposed. Simulation results are presented to verify the performance gain provided by the proposed algorithm.

With the increasing demand of virtual reality (VR) applications, wireless systems need to provide ultra-high data rate to support VR streaming for multiple users simultaneously. In this paper, we propose a mobile edge computing-assisted rate splitting (RS) VR streaming transmission scheme to pursue better quality of experience (QoE) and alleviate the computing burden of VR users (VUs). We formulate an optimization problem to minimize the weighted energy consumption while ensuring the required QoE. The quantization parameters selection, rendering offloading decision, transmit precoding, rate allocation, and computing resource allocation are optimized and a joint Wrendering offloading and resource allocation algorithm is proposed. Simulation results validate the efficiency of the proposed algorithm and reveal the performance gain obtained from RS.

With the combination of extra-large arrays and high frequencies, near-field transmissions have become increasingly prevalent. In this paper, we investigate the angular-domain representation of near-field line-of-sight (LoS) extra-large multiple-input-multiple-output (XL-MIMO) channels. Specifically, we first demonstrate the structured sparsity of the near-field LoS channel in the angular domain. By leveraging this sparsity property, we propose an effective spatial bandwidth channel representation method. This method characterizes near-field LoS XL-MIMO channels as a superposition of multiple plane wave components within the effective spatial band between transceiver arrays. Finally, simulation results validate the equivalence between the proposed representation and the existing antenna domain channel model and demonstrate the effects of array geometries on the effective spatial bandwidth.

In this work, we aim to minimize the deployment cost of active simultaneously transmitting and reflecting RISs (STAR-RISs) with the constraints of users’ communication quality requirements. To address this problem, we decouple the optimization problem into a transmission optimization subproblem and a deployment optimization subproblem. The transmission scheme is obtained by leveraging fractional programming (FP). In addition, we propose two approaches to efficiently obtain the deployment scheme of active STAR-RIS, namely a penalty-majorization-minimization (MM) method and a heuristic binary search method. Simulation results validated the effectiveness of the proposed algorithm in terms of deployment cost.

The low hardware cost makes ultra-large (XL) reconfigurable intelligent surfaces (RIS) an attractive solution for enabling the intelligent electromagnetic environment, but it brings the challenge of near-field propagation channels. In this paper, we consider the propagation feature of the spherical wavefront in the near field of the millimeter-wave/sub Terahertz (mmWave/subTHz) localization system with the assistance of a RIS. The localization problem is investigated based on the derived second-order Fresnel approximation of the near-field channel model. In addition, the RIS training phase shifts and pilots are carefully designed to increase the channel rank so that the channel covariance matrix can be efficiently estimated. Simulation results validate the proposed near-field channel approximation and the localization algorithm.

This paper investigates the lower bounds on the location estimation error for multiple reconfigurable intelligent surfaces (RISs)-aided millimeter-wave (mmWave) positioning systems. The error lower bound is quantified by Cramer-Rao lower bounds (CRLB), of which two are decisive, namely, the position error bound (PEB), and the rotation error bound (REB). This paper begins by deriving the analytical expressions of the PEB and REB as functions of the RIS phase shifts. Then, the lowest achievable PEB and REB are obtained by optimizing the phase shifts of all RISs using the particle swarm optimization (PSO) algorithm. Numerical results have shown that a three-RIS-aided system generates 38.6% lower PEB and REB with the most basic beam-alignment phase shifts strategy compared to the single-RIS system. With the RIS phase shifts optimized by the PSO algorithm, the PEB and REB can be further reduced by another 41.2%.

In this paper, the sum rate is maximized for the intelligent reflective surface (IRS) assisted terahertz (THz) non-orthogonal multiple access (NOMA) communication system. A novel algorithm is proposed to alternatively optimize the IRS phase shift, the sub-band allocation, and power control. To tackle the formulated non-convex problem, we utilize the auxiliary variables to find the feasible initialization solution meanwhile guarantee the individual rate requirements. The decoding order of successive interference cancellation (SIC) is determined according to channel gain maximization, and the IRS phase is further adjusted to improve the sum rate. A long-distance priority (LDP) algorithm is then proposed to compensate for the distance-dependent THz pathloss attenuation, and a blocking pair eliminating (BPE) algorithm is proposed to obtain a stable THz sub-band allocation. Simulation results show that the proposed scheme significantly enhances the sum-rate performance of the IRS-assisted THz NOMA networks.

In this article, we investigate the energy minimization problem of an unmanned-aerial-vehicle (UAV)-assisted data collection sensor network. We jointly optimize the trajectory of the UAV and the power consumption of the sensors for data uploading with the power and energy constraints of sensors. The trajectory design consists of two parts: 1) the serving orders for sensors and 2) the UAV’s hovering positions, where the latter is highly coupled with the power consumption of the sensors. To find the optimal serving orders of sensors, we formulate the problem as a standard traveling salesman problem (TSP), which can be optimally solved by the efficient Cutting-Plane method. To solve the UAV position and sensor uploading power optimization problem, we propose the PSPSCA algorithm that optimizes the transmit power by the pattern search method, while the UAV’s hovering positions are optimized by the successive-convex-approximation (SCA) method in the inner loop. To deal with the high computational complexity of the PSPSCA algorithm, we analyze the analytical relationship between optimal sensor uploading power and the UAV’s hovering positions, based on which we simplify the optimization problem and propose the AQSCA algorithm as an alternative approach. Simulation results have validated that the proposed algorithm outperforms the existing benchmark schemes.

This letter proposes a nonparametric detector, termed as Gini Correlation (GC), to solve the classical problem of detecting deterministic signals buried in impulsive noise. With the help of the popular Middleton’s Class-A impulsive noise (MCAN) model, we derive the expectation and variance of GC under alternative hypothesis and null hypothesis, which, along with the central limit theorem, are further employed for determining the detection probability and the detection threshold. The results show that the proposed detector possesses a constant false alarm rate property. Monte Carlo simulations verify not only the correctness of our theoretical findings but also the superiority of GC to other state-of-the-art methods in terms of receiver operating characteristic (ROC) curves and asymptotic relative efficiency (ARE) curves.

This paper proposes a new simultaneous terahertz (THz) information and power transfer (STIPT) system, which utilizes a reconfigurable intelligent surface (RIS) for both the data and power transmission. We aim to maximize the information users’ (IUs’) sum data rate while guaranteeing the power harvesting requirements of energy users (EUs) and RIS. To solve the formulated non-convex problem, the block coordinate descent (BCD) based algorithm is adopted to alternately optimize the transmit precoding of IUs, RIS’s reflecting coefficients, and the position of RIS. Additionally, the penalty constrained convex approximation (PCCA) algorithm is proposed to optimize the deployment of the RIS, where the introduced penalties ensure that the solution is always feasible. The simulation results show that the proposed solution outperforms the benchmark schemes, and the proposed BCD algorithm can greatly improve the performance of the STIPT system.

This letter proposes to apply two rank correlations, namely, Kendalls tau (KT) and Spearmans rho (SR), to the fundamental problem of detecting known signals in impulsive noise. Under the popular Middletons Class-A impulsive noise model, we derived the expectations and variances of KT and SR, which, along with the central limit theorem, are further employed to determining the detection threshold and the detection probability. Monte Carlo simulations not only verified the correctness of our theoretical findings but also demonstrated the superiority of KT and SR to the other five state-of-the-art methods in terms of detection probability.

In this paper, a cooperative task computation framework exploits the computation resource in user equipments (UEs) to accomplish more tasks meanwhile minimizes the power consumption of UEs. The system cost includes the cost of UEs' power consumption and the penalty of unaccomplished tasks, and the system cost is minimized by jointly optimizing binary offloading decisions, the computational frequencies, and the offloading transmit power. To solve the formulated mixed-integer non-linear programming problem, three efficient algorithms are proposed, i.e., integer constraints relaxation-based iterative algorithm (ICRBI), heuristic matching algorithm, and the decentralized algorithm. The ICRBI algorithm achieves the best performance at the cost of the highest complexity, while the heuristic matching algorithm significantly reduces the complexity while still providing reasonable performance. As the previous two algorithms are centralized, the decentralized algorithm is also provided to further reduce the complexity, and it is suitable for the scenarios that cannot provide the central controller. The simulation results are provided to validate the performance gain in terms of the total system cost obtained by the proposed cooperative computation framework.

Fog-computing network combines the cloud computing and fog access points (FAPs) equipped with mobile edge computing (MEC) servers together to support computation-intensive tasks for mobile users. In this paper, we investigate the delay minimization problem for task offloading in a hierarchical fog-computing C-RAN network, which consists of three tiers of computational services: MEC server in radio units, MEC server in distributed units, and the cloud computing in central units. The receive beamforming vectors, task allocation, computing speed for offloaded tasks in each server and the transmission bandwidth split of fronthaul links are optimized by solving the formulated mixed integer programming problem. The simulation results validate the superiority of the proposed hierarchical fog-computing C-RAN network in terms of the delay performance.

Device-to-Device (D2D) communications incorporated with content caching have been regarded as a promising way to offload the cellular traffic data. In this paper, the caching strategy is investigated to maximize the D2D offloading gain with the comprehensive consideration of user collaborative characteristics as well as the physical transmission conditions. Specifically, for a given content, the number of interested users in different groups is different, and users always ask the most trustworthy user in proximity for D2D transmissions. An analytical expression of the D2D success probability is first derived, which represents the probability that the received signal to interference ratio is no less than a given threshold. As the formulated problem is nonconvex, the optimal caching strategy for the special unbiased case is derived in a closed form, and a numerical searching algorithm is proposed to obtain the globally optimal solution for the general case. To reduce the computational complexity, an iterative algorithm based on the asymptotic approximation of the D2D success probability is proposed to obtain the solution that satisfies the Karush-Kuhn-Tucker conditions. The simulation results verify the effectiveness of the analytical results and show that the proposed algorithm outperforms the existing schemes in terms of offloading gain.

To support millimeter-wave (mmWave) communications successfully, a large number of antennas (in the order of hundreds or thousands) must be implemented to mitigate significant propagation and scattering losses. Designing phased arrays for carrier frequency is a great method for narrowband systems, but the performance degrades significantly with larger bandwidth. We show that as the system bandwidth increases, the beams steer away from the focus direction, which is an effect known as beam squint in wideband systems. In this paper, at first, analysis of capacity is performed for an increasing number of antennas, to show the significance of beam squint. Then, a solution in digital domain is proposed. Conventionally, a single beam would be allocated to a user. By exploiting beam squint effect, in this solution more than one beam can carry a single-user’s data, which improves the system’s performance significantly, especially when the number of antennas in an array is large and there are multiple users.

In this paper, the user cooperative task computation is explored by sharing the computing capability of the user equipments (UEs) so as to enhance the performance of mobile edge computing (MEC) networks. The number of completed tasks is maximized while minimizing the total power consumption of the UEs by jointly optimizing the user task offloading decision, the computational speed for the offloaded task and the transmit power for task offloading. An iterative algorithm based on the linear programming relaxation is proposed to solve the formulated mixed integer non-linear problem. The simulation results show that the proposed user cooperative computation scheme can achieve a higher completed tasks ratio than the non-cooperative scheme.

This paper studies energy efficient resource allocation for a machine-to-machine enabled cellular network with nonlinear energy harvesting, especially focusing on two different multiple access strategies, namely nonorthogonal multiple access (NOMA) and time division multiple access (TDMA). Our goal is to minimize the total energy consumption of the network via joint power control and time allocation while taking into account circuit power consumption. For both NOMA and TDMA strategies, we show that it is optimal for each machine type communication device (MTCD) to transmit with the minimum throughput, and the energy consumption of each MTCD is a convex function with respect to the allocated transmission time. Based on the derived optimal conditions for the transmission power of MTCDs, we transform the original optimization problem for NOMA to an equivalent problem which can be solved suboptimally via an iterative power control and time allocation algorithm. Through an appropriate variable transformation, we also transform the original optimization problem for TDMA to an equivalent tractable problem, which can be iteratively solved. Numerical results verify the theoretical findings and demonstrate that NOMA consumes less total energy than TDMA at low circuit power regime of MTCDs, while at high circuit power regime of MTCDs TDMA achieves better network energy efficiency than NOMA.

In this paper, we aim to minimize the average file transmission delay via bandwidth allocation and cache placement in two-tier heterogeneous networks with limited storage capacity, which consists of cache capacity and buffer capacity. For average delay minimization problem with fixed bandwidth allocation, although this problem is nonconvex, the optimal solution is obtained in closed form by comparing all locally optimal solutions calculated from solving the Karush-Kuhn-Tucker conditions. To jointly optimize bandwidth allocation and cache placement, the optimal bandwidth allocation is first derived and then substituted into the original problem. The structure of the optimal caching strategy is presented, which shows that it is optimal to cache the files with high popularity instead of the files with big size. Based on this optimal structure, we propose an iterative algorithm with low complexity to obtain a suboptimal solution, where the closed-from expression is obtained in each step. Numerical results show the superiority of our solution compared with the conventional cache strategy without considering cache and buffer tradeoff in terms of delay.

In this paper, we consider the sum α-fair utility maximization problem for joint downlink (DL) and uplink (UL) transmissions of a wireless powered communication network via time and power allocation. In the DL, the users with energy harvesting receiver architecture decode information and harvest energy based on simultaneous wireless information and power transfer. While in the UL, the users utilize the harvested energy for information transmission, and harvest energy when other users transmit UL information. We show that the general sum α-fair utility maximization problem can be transformed into an equivalent convex one. Trade-offs between sum rate and user fairness can be balanced via adjusting the value of α. In particular, for zero fairness, i.e., α = 0, the optimal allocated time for both DL and UL is proportional to the overall available transmission power. Trade-offs between sum rate and user fairness are presented through simulations.

In this paper, we investigate the problems of sum power minimization and sum rate maximization for multi-cell networks with non-orthogonal multiple access. Considering the sum power minimization, we obtain closed-form solutions to the optimal power allocation strategy and then successfully transform the original problem to a linear one with a much smaller size, which can be optimally solved by using the standard interference function. To solve the nonconvex sum rate maximization problem, we first prove that the power allocation problem for a single cell is a convex problem. By analyzing the Karush-Kuhn-Tucker conditions, the optimal power allocation for users in a single cell is derived in closed form. Based on the optimal solution in each cell, a distributed algorithm is accordingly proposed to acquire efficient solutions. Numerical results verify our theoretical findings showing the superiority of our solutions compared with the orthogonal frequency division multiple access and broadcast channel.

As a promising solution to offload cellular traffic, device-to-device (D2D) communication has been adopted to help disseminate contents. In this paper, the D2D offloading utility is maximized by proposing an optimal content pushing strategy based on the user interests and sharing willingness. Specifically, users are classified into groups by their interest probabilities and carry out D2D communications according to their sharing willingness. Although the formulated optimization problem is nonconvex, the optimal solution is obtained in closed-form by applying Karush-Kuhn-Tucker conditions. The theoretical and simulation results show that more contents should be pushed to the user group that is most willing to share, instead of the group that has the largest number of interested users.

Device to Device (D2D) communication with content caching has recently been proposed as an exciting and innovative technology for next generation cellular networks. This paper presents a novel content caching and delivery method which allows multiple devices caching the same content and the content being sent via multiple devices to single device (MDSD) communication using D2D links to enhance user experience in terms of outage probability. A closed form expression of the outage probability is derived by considering how the content caching popularity affects achievable signal to interference plus noise ratio (SINR). The simulation and theoretical results show that as the popularity of the video increases, the outage probability of the system decreases substantially. On the other hand, the results show using MDSD enhances the SINR performance and decreases the outage probability of the system significantly.

Device-to-Device (D2D) communication has been proposed as a promising way to offload traffic from the cellular network. In this paper, with the joint impact of user preference and selfishness, the D2D assisted content dissemination process is investigated in order to maximize the offloading gain via D2D communications. An alternative group pushing optimization (AGPO) algorithm is proposed to solve the formulated nonconvex problem. In addition, for the special case of two groups, the optimal solution is derived in closed-form to help validate the algorithm. Finally, the simulation results show that the AGPO algorithm converges to the global optimum and has a much lower complexity compared to exhaustive search.

We consider the problem of minimizing the total transmission power for a OFDM network where mutual interference exists among cells, with the power and load constraints for each base station (BS) and the rate demand constraint for every user. To solve the power minimization problem, we develop a distributed resource allocation and power control algorithm with low complexity. The complexity of the proposed algorithm is also analyzed. Numerical results show that the proposed algorithm is superior to the conventional schemes in terms of power consumption.

In this paper, we investigate the sum energy minimization problem in an uplink machine-to- machine (M2M) system with energy harvesting. To solve this nonconvex sum energy minimization problem, we first transform it into an equivalent convex problem and provide the optimal condition of the original problem. Then, we propose a low- complexity iterative scheme, which yields the optimal solution. The complexity of the proposed scheme is also analyzed. Numerical results show that the proposed scheme can achieve good performance.

Device-to-device (D2D) assisted offloading heavily depends on the participation of human users. The content preference and sharing willingness of human users are two crucial factors in the D2D assisted offloading. In this paper, with consideration of these two factors, the optimal content pushing strategy is investigated by formulating an optimization problem to maximize the offloading gain measured by the offloaded traffic. Users are placed into groups according to their content preferences and share content with intergroup and intragroup users at different sharing probabilities. Although the optimization problem is nonconvex, the closed-form optimal solution for a special case is obtained, when the sharing probabilities for intergroup and intragroup users are the same. Furthermore, an alternative group optimization (AGO) algorithm is proposed to solve the general case of the optimization problem. Finally, simulation results are provided to demonstrate the offloading performance achieved by the optimal pushing strategy for the special case and AGO algorithm. An interesting conclusion drawn is that the group with the largest number of interested users is not necessarily given the highest pushing probability. It is more important to give high pushing probability to users with high sharing willingness.

In this paper, we consider the problem of maximizing the sum energy efficiency (EE) for LTE networks where the interferences occur between cells. Cell load, transmit rate and transmit power, where cell load demonstrates the average resource usage in a cell, are considered in the signal-to- interference-and-noise-ratio (SINR) model. Exploiting the properties of sum EE, we prove that operating at full load is optimal and provide a distributed power control algorithm. With all other powers fixed, we transform the original nonconvex optimization problem in fractional form into an equivalent optimization problem in subtractive form. In high SINR situation, the transformed problem in subtractive form is proved a convex problem. Numerical results demonstrate the remarkable improvements in terms of EE.

This paper exploits the noncentral communication architecture for increasing system throughput with popular video files cached in user equipments, and transmitted through D2D links under control of base station. A cell is divided into equilateral hexagon clusters among which frequency resources are reused. In order to reduce inter-cluster interference, we propose to limit the transmit power of each user and adopt relay-assisted D2D communication when direct D2D link can not be established in a cluster. Simulation results show that the proposed scheme can achieve higher spectral efficiency without much degradation in system throughput when compared with the scenario where different frequency resources are used by different clusters.