This paper explores the challenges of user equipment (UE) association and downlink power control in scalable cell-free massive multiple-input multiple-output (MIMO) systems. Initially, we develop a scalable UE association algorithm, which ensures that each UE is connected to only a small set of access points (APs). This approach effectively reduces both fronthaul requirements and the computational load on the APs. The algorithm employs a competition-based mechanism to ensure that APs efficiently distribute workloads while satisfying the quality of service (QoS) requirements of UEs, preventing them from losing network connectivity. Second, we introduce a distributed downlink power control method based on deep neural networks (DNNs) to improve the long-term downlink spectral efficiency (SE) of the entire network. This method relies solely on locally collected large-scale fading information as DNN input, making it adaptable to dynamic scenarios involving varying numbers of associated UEs. To further enhance the training efficiency of the DNN, we design a distributed training framework that fully leverages the computational resources of distributed edge processors (EPs). Simulation results show that the proposed UE association algorithm and downlink power control method exhibit significant advantages over the benchmarks.

An analytical and optimization framework on rate-Cramér-Rao bound (CRB) tradeoff is proposed in this paper for the continuous-aperture array (CAPA)-based integrated sensing and communication (ISAC) system. To evaluate the dual-functional performance, the sensing CRB and communication rate are analyzed concerning the induced electromagnetic (EM) waves of CAPAs. For rate-CRB region characterization, the spatially continuous beamforming of transmit CAPA is optimized under three cases: i) A novel closed-form expression for the optimal CAPA beamformer is derived under the single-user single-target scenario, proven to be aligned within the space spanned by the EM-based sensing and communication channels; ii) A general subspace-based beamforming design approach is proposed to address the intractable continuity, converting the continuous beamforming design in spatial domain to discrete weight design in subspace domain and resorting to the semidefinite relaxation for the globally optimal solution; iii) Moreover, the general beamforming design is specialized to both the low-complexity zero-forcing (ZF) and the conventional spatially discrete array (SPDA)-based designs. Numerical results demonstrate that: i) The proposed subspace-based approach can realize efficient and effective beamforming design for reduced mutual interference, enhanced sensing performance, and guaranteed communication rate; ii) The general CAPA beamforming design achieves broader rate-CRB region than the ZF-oriented design and reaches the ultimate performance of the SPDA-based system.

Enabled by metamaterials, continuous aperture array (CAPA) has been proven to play a crucial role in communication performance enhancement, while its potentials in integrated sensing and communication (ISAC) systems have not been investigated. This paper investigates the performance analysis and optimization of CAPA-based ISAC systems for simultaneous user communication and target sensing. To be specific, communication and sensing rates are evaluated based on electromagnetic channels and a Pareto-optimal problem is formulated for beamforming optimization. Closed-form solutions to CAPA-oriented beamforming are derived under communication-, sensing-, and Pareto-optimal cases, and the attainable ISAC rate region is obtained. Numerical results verify that CAPA-based systems can achieve the ultimate sensing and communication performance of spatially discrete array (SPDA)-based systems and significantly expand the ISAC rate region for Pareto optimality.

In this study, we investigate the resource management challenges in next-generation mobile crowdsensing networks with the goal of minimizing task completion latency while ensuring coverage performance, i.e., an essential metric to ensure comprehensive data collection across the monitored area, yet it has been commonly overlooked in existing studies. To this end, we formulate a weighted latency and coverage gap minimization problem via jointly optimizing user selection, subchannel allocation, and sensing task allocation. The formulated minimization problem is a non-convex mixed-integer programming issue. To facilitate the analysis, we decompose the original optimization problem into two subproblems. One focuses on optimizing sensing task and subband allocation under fixed sensing user selection, which is optimally solved by the Hungarian algorithm via problem reformulation. Building upon these findings, we introduce a time-efficient two-sided swapping method to refine the scheduled user set and enhance system performance. Extensive numerical results demonstrate the effectiveness of our proposed approach compared to various benchmark strategies.

A continuous-aperture array (CAPA)-based integrated sensing and communication (ISAC) framework is proposed in this paper, where CAPA transceivers are optimized to enhance both target sensing and user communication performance. Novel expressions for achievable communication and sensing rates are derived and CAPA-oriented beamforming is designed to balance the dual-functional Pareto-optimal tradeoff in two scenarios: i) For the single-user single-target case, closed-form continuous beamformers are derived based on communication-, sensing-, and Pareto-optimal criteria to reveal the interrelation of the ISAC rate region with the antenna aperture and channel gains; ii) For the multi-user multi-target case, a general CAPA-ISAC beamforming design algorithm is developed to achieve the Pareto optimality. Beamformer design in the continuous spatial domain is transformed into weight design in the discrete wavenumber domain using Fourier series expansions. Furthermore, alternating optimization, successive convex approximation, and difference of convex techniques are employed to tackle the coupling and non-convexity issues. Numerical results demonstrate that: i) The proposed CAPA-ISAC framework significantly improves both sensing and communication performance and expands the ISAC Pareto rate region; ii) CAPAs exhibit superior beamforming capabilities and reach the ultimate performance limits of spatially discrete arrays (SPDAs).

Integrated sensing and communication (ISAC) enables the next-generation network to possess networked sensing capability, propelling the proliferation of various intelligent applications but introducing complex sensing and communication interference. To this end, this paper studies the cooperative transceiver beamforming design for a multi-cell anti-unmanned aerial vehicle (UAV) ISAC system, where multiple base stations (BSs) collaboratively perform joint UAV sensing. Specifically, to ensure reliable detection, we jointly optimize the ISAC transmit and receive beamformers at BSs and downlink users via maximizing the signal-to-clutter-plus-noise ratio of sensing, taking into account the communication requirements and power constraints. To handle the nonconvex fractional problem, we first propose a centralized beamforming algorithm resorting to alternating optimization, successive convex approximation, and Dinkelbach methods. Then, to alleviate heavy backhaul overhead, a distributed algorithm is put forward, adopting the primal decomposition technique to decouple the inter-cell interference. Numerical results verify that: i) Compared with the standalone sensing by a single BS, the proposed cooperative beamforming design achieves notable enhancement in sensing performance; ii) The designed transceiver beamforming is constructive for interference and clutter suppression in multi-cell ISAC systems.

The growing reliance on mobile crowdsensing net-works for real-time data collection in various applications, from urban infrastructure monitoring to environmental sensing, ne-cessitates the reduction of latency for enhanced efficiency. In this work, we address this critical challenge by focusing on the joint subband and sensing task allocation for next-generation mobile crowdsensing networks, emphasizing the minimization of latency. To achieve this goal, an optimization problem is formulated to minimize the system's total latency, taking various practical constraints into account. Therein, the latency is comprised of sensing delay and transmission delay. The considered problem is a mixed-integer programming problem, which is also non-convex. To facilitate the analysis, we utilize the underlying structural properties of the problem and derive the optimal sensing task allocation strategy under a given sub band allocation scheme. With the discussions, we show that the original optimization problem can be transformed into a maximum weighted matching problem in a bipartite graph. This problem can be optimally solved by the Hungarian algorithm in a cubic time complexity. Building upon these analyses, we further approximate the optimal network delay in closed form under certain circumstances. Extensive simulation results validate that our proposed joint optimization method outperforms various benchmark strategies in terms of latency saving under comprehensive system settings.

The next-generation wireless network is perceived to integrate with sensing capability and evolve into the perceptive mobile network (PMN), enabling massive sensing-intensive applications. However, the sensing function will affect the communication performance in cellular networks. To study the sensing and communication performance of PMNs and their interactions, this paper investigates a millimeter-wave PMN with dual-functional base stations (BSs) for simultaneous detection of unauthorized unmanned aerial vehicles (UAVs) and user communication via the unified transmit signal and beamforming. We develop a system-level theoretical framework to investigate the sensing and communication performance of PMNs based on stochastic geometry, which captures the mutual interference and resource contention between the two functions and builds a foundation for the optimization of network configurations. In addition, by leveraging the collaboration of multiple BSs in PMNs, we propose a cooperative sensing strategy combining the monostatic and bistatic sensing processes to enhance the reliability of UAV surveillance. Simulation results verify the effectiveness of the proposed theoretical framework and demonstrate the benefits of cooperative sensing in UAV detection and communication performance, as compared with the standalone sensing by individual BSs.

This paper studies the downlink spectral efficiency of a two-way cell-free massive multiple-input multiple-output system with few-bit analog-to-digital converts (ADCs). By utilizing minimum mean squared error channel estimation method and maximal-ratio transmission precoder, we derive a closed-form expression for the effective downlink signal-to-interference-plus-noise ratio (SINR), which applies to any number of access point (AP) antennas M and distortion factors of ADCs in both AP and user pair sides. Additionally, the obtained analytical expression specializes to the conventional one when the ideal ADCs are adopted. Moreover, the asymptotic performance and power scaling law of the effective SINR in high M regime are studied. The corresponding analysis indicates that increasing M can provide considerable gains to compensate the rate loss caused by non-ideal ADCs and also, appropriately cutting down the pilot power and transmission power will not affect the SINR performance in high M region. Finally, all the theoretical results are verified via simulations.

In a wireless device-to-device (D2D) network, mobile edge caching can reduce transmission cost for network traffic, but it may also cause outdated caching information. How to reduce the transmission cost and improve the freshness of information becomes an important issue. A dedicated caching device called helper which has a large cache can be used in a wireless D2D network, which can significantly improve the performance of network. In this paper, to better model the file-centric data transmission, we proposed a concept called age of file (AoF), which is defined as the duration from the latest updating of the file. We analyzed the AoF, energy cost and updating cost of the files in the network level. We comprehensively consider the AoF and energy cost through the maximum and minimum normalization methods, and proposed an AoF-based accessing strategy. In the strategy, users can adjust the access of files according to their demand for file freshness and improve the quality of service. The results indicate that this strategy can reduce the energy cost for file transmission and reduce the total cost of the entire network while satisfying the freshness of files.

In this paper, we explore the interplay between personalized bundle recommendation and cache decision on the performance of wireless edge caching networks. A revenue maximization perspective is provided. To this end, we first examine the quantitative impact of bundle recommendation on the content request probability of different users. We then specify the definition of system revenue, showing its dependence on bundle recommendation and caching policies. With that, a joint bundling, caching and recommendation decision problem is formulated to maximize the achievable system revenue, taking into account the constraints of user-distinguished recommendation quality, recommendation amount, and the cache capacity budget. To solve this non-tractable optimization problem, a divide-then-conquer methodology is adopted. Specifically, we first determine the bundle state per user, on which basis we perform the joint bundle recommendation and caching decision-making, wherein several bundling strategies with different time-complexity are devised. Last but not least, we provide detailed properties analysis for our proposed bundling and joint optimization algorithms. Comprehensive numerical simulations validate the performance enhancement of the designed solutions compared to extensive conventional single-item recommendation oriented benchmarks.

Carrier Sense Multiple Access (CSMA) has been widely applied to various kinds of wireless networks, such as Wi-Fi, to serve portable devices which are usually greedy in terms of throughput, but with finite battery budget. Accordingly, how to optimize the usage of finite battery budget to get the best possible throughput performance is of great importance. This paper aims to address this issue by focusing on a saturated CSMA network. Explicit expressions of maximum energy efficiency and the corresponding optimal backoff parameter with or without throughput constraint are derived. It is revealed that optimizing the energy efficiency leads to throughput performance degradation. With a stringent throughput constraint, the energy efficiency has to be sacrificed. The energy efficiency and the throughput can be optimized at the same time only in special cases, e.g., the network size is large. The analysis is verified by simulations and sheds important light on performance optimization of practical CSMA-based networks such as Wi-Fi 6 networks.

With the deluge of wireless data, unmanned aerial vehicles (UAVs) are expected to be deployed as aerial small base stations (SBSs) to relieve the load of ground macro base stations by establishing wireless backhaul connections with them and providing high-quality service to users. Thanks to the emergence of integrated access and backhaul (IAB), the access and backhaul communication links can work on the same millimeter wave (mmWave) band with huge available bandwidth. This paper studies the quality-of-service (QoS) performance of heterogeneous traffic under equal partition and average load partition spectrum allocation strategies for mmWave UAV-assisted IAB cellular networks. Specifically, we develop a theoretical framework to analyze the mean packet throughput (MPT) of users based on stochastic geometry and queueing theory. Simulation results demonstrate that the deployment of UAVs can promote MPT performance compared to ground SBSs and appropriate UAV height, UAV density, and spectrum allocation play significant roles in improving QoS performance of heterogeneous traffic in the network.

We exploit a behavior-shaping proactive mechanism, namely, recommendation, in cache-assisted non-orthogonal multiple access (NOMA) networks, aiming at minimizing the average system’s latency. Thereof, the considered latency consists of two parts, i.e., the backhaul link transmission delay and the content delivery latency. Towards this end, we first examine the expression of system latency, demonstrating how it is critically determined by content cache placement, personalized recommendation, and delivery associated NOMA user pairing and power control strategies. Thereafter, we formulate the minimization problem mathematically taking into account the cache capacity budget, the recommendation-oriented requirements, and the total transmit power constraint, which is a non-convex, multi-timescale, and mixed-integer programming problem. To facilitate the process, we put forth an entirely new paradigm nameddivide-and-rule. Specifically, we first solve the short-term optimization problem regarding user pairing as well as power allocation and the long-term decision-making problem with respect to recommendation and caching, respectively. On this basis, an iterative algorithm is developed to optimize all the optimization variables alternately. Particularly, for solving the short-timescale problem, graph theory enabled NOMA user grouping and efficient inter-group power control manners are invoked. Meanwhile, a dynamic programming approach and a complexity-controllable swap-then-compare method with convergence insurance are designed to derive the caching and recommendation policies, respectively. From Monte-Carlo simulation, we show the superiority of the proposed joint optimization method in terms of both system latency and cache hit ratio when compared to extensive benchmark strategies.

In this paper, by considering the average mean squared error (AMSE) of channel estimation, we primarily obtain the closed-from expressions of the probability density function (PDF) and cumulative distribution function of AMSE for the least squares (LS)/minimum mean squared error (MMSE) estimation method as the line-of-sight (LOS) component is known, where the asymptotic analysis is executed in Rayleigh fading and strong LOS conditions. Secondly, the closed-form expressions for the expectation of AMSE ( Expamse) and variance of AMSE ( Varamse) are acquired, where Varamseis inversely proportional to the number of antennas ( M). As M becomes infinite, the PDF of AMSE at Expamsehas an order of root M. When the pilot power decreases with M in a power law, the LS case keeps deteriorating while the MMSE case converges to a constant which basically depends on the Rician K-factor. Next, the spectral efficiency is investigated by considering AMSE. When Expamseaccelerates, the spectral efficiency of the LS method keeps dropping and that of the MMSE method firstly is degraded and then is improved to a constant except Rayleigh fading. Finally, all results are validated via simulations.

Although millimeter wave (mmWave) communications can well support high-data-rate transmissions, the inherent shortcomings, e.g., high path loss and sensitivity to blockage, may cause severe outage problems if the network is not configured properly. This paper aims to minimize the long-term outage probability of an mmWave communication network by optimizing the base station (BS) deployment and user association. For the BS deployment problem, existing works usually assumed that the positions of users are fixed and formulated it as a deterministic optimization problem. With the time-varying nature of positions of user equipments (UEs) taken into account, we establish a stochastic optimization framework for BS deployment optimization. The objective is to maximize the average number of physically accessible BSs of each UE under an inaccessible probability constraint, and a cooperative stochastic approximation (CSA)-based algorithm is developed to effectively search the optimal positions of BSs. For user association, our focus is to properly associate UEs with BSs to minimize the outage probability with balanced workloads among BSs. Combined with the proposed user association scheme, the proposed BS deployment scheme can significantly improve the network outage probability in the long term, especially when the aggregation degree of UEs is large.

Although many efforts have been devoted to the access point (AP) placement problem in IEEE 802.11 networks, most of them assumed that the positions of users are fixed and formulated it as a deterministic optimization problem. In this paper, with the time-varying nature of users' positions taken into account, we establish a stochastic optimization framework to jointly optimize the positions and coverage radius of APs. The aim is to maximize the average network throughput under an outage probability constraint. With both the objective and constraint functions in the form of expectation, a novel algorithm is developed based on the recently proposed cooperative stochastic approximation (CSA). Simulation results show that the proposed algorithm can significantly improve the average network throughput, especially when the outage constraint becomes loose or the aggregation degree of users increases.