Network-assisted full-duplex (NAFD) cell-free massive MIMO (CF-mMIMO) systems constitute a promising enabler for supporting dynamic downlink (DL) and uplink (UL) traffic demands in forthcoming sixth-generation (6G) wireless networks. In this paper, we analyze channel aging effects on NAFD CF-mMIMO systems. First, we propose an extended beamforming training scheme to relieve heavy pilot overhead for high-mobility channel estimation, significantly enhancing system performance through cross-link interference (CLI) cancellation. Then, we derive novel closed-form expressions for the UL/DL achievable SEs, enabling a comprehensive analysis of channel aging impacts on spectral efficiency (SE) and energy efficiency (EE) across diverse normalized Doppler shiftfDTsscenarios. Furthermore, we develop a joint optimization framework of transmission interval and power control to balance SE-EE tradeoffs while alleviating channel aging effects. We formulate a mixed-integer multi-objective optimization problem (MOOP), which is subsequently transformed into a tractable single-objective formulation via a weighted ℓpscalarizing method. Based on this analytical foundation, we propose a constrained deep reinforcement learning (DRL) algorithm that integrates a primal-dual optimization strategy with multi-agent deep deterministic policy gradient (MADDPG) for safe policy exploitation. Simulation results validate the accuracy of analytical expressions and demonstrate the superiority of the proposed algorithm over the conventional non-dominated sorting genetic algorithm-II (NSGA-II) in achieving Pareto-optimal SE-EE tradeoffs under channel aging.

Reconfigurable intelligent surfaces (RISs) have been extensively applied in integrated sensing and communication (ISAC) systems due to the capability of enhancing physical layer security (PLS). However, conventional static RIS architectures lack the flexibility required for adaptive beam control in multi-user and multifunctional scenarios. To address this issue without introducing additional hardware complexity and power consumption, in this paper, we exploit a movable RIS (MRIS) architecture, which consists of a large fixed sub-surface and a smaller movable sub-surface that slides on the fixed sub-surface to achieve dynamic beam reconfiguration with static phase shifts. This paper investigates an MRIS-assisted ISAC system under imperfect sensing estimation, where dedicated radar signals serve as artificial noise to enhance secure transmission against potential eavesdroppers (Eves). The transmit beamforming vectors, MRIS phase shifts, and relative positions of the two sub-surfaces are jointly optimized to maximize the minimum secrecy rate, ensuring robust secrecy performance for the weakest user under the uncertainty of the Eves’ channels. To handle the non-convexity, a convex bound is derived for the Eve channel uncertainty, and the$\mathcal {S}$-procedure is employed to reformulate semi-infinite constraints as linear matrix inequalities. An efficient alternating optimization and penalty dual decomposition-based algorithm is developed. Simulation results demonstrate that the proposed MRIS architecture substantially improves secrecy performance, especially when only a small number of elements are allocated to the movable sub-surface.

Wi-Fi fingerprint-based indoor positioning has been a widely studied research topic. However, the single fingerprint database model fails to effectively establish a complete mapping of dynamic spatial correlations between access points and reference points in complex indoor environments, thereby limiting positioning performance. To address the challenge of adapting to dynamic changes in complex indoor environments, this paper proposes a multi-fingerprint database construction strategy based on Gaussian Mixture Model (GMM) clustering. For each reference point and its associated access points (AP), GMM clustering is applied to identify all possible states within the AP data. By establishing a mapping between different environmental characteristics and fingerprint states, the proposed approach enhances the dynamic representation capability of the database, thereby improving the overall performance of the positioning system. Simulation and experimental results demonstrate that various fingerprint-based positioning algorithms achieve higher accuracy with a multi-fingerprint database compared to a single fingerprint database.

During Unmanned Aerial Vehicle (UAV) missions, sudden weather changes like strong winds can destabilize UAV flight, making it essential to quickly transmit weather information to improve safety and efficiency. However, these abrupt data surges can lead to network congestion, affecting transmission timeliness and reliability. Current congestion control schemes often prioritize network efficiency but overlook user fairness. This paper introduces an Adaptive Explicit Congestion Control algorithm based on Fairness (AECCF) for handling Microburst traffic, balancing congestion management with fair resource distribution. The AECCF algorithm works as follows: First, the router node monitors its outgoing packet queue and assesses local link congestion state using a double delay threshold. It then calculates and attaches congestion probability, congestion state, and sojourn time to packets, enabling complete congestion feedback to downstream nodes. In cases of burst traffic, the router labels the traffic as heavily congested and activates flow shaping, transferring some packets to a cache queue for staggered forwarding. The consumer then adjusts its interest packet rate based on congestion markings and fair bandwidth allocation, preventing throughput loss from over-adjustment and maintaining network stability and fairness. Finally, the router forwards interest packets based on the proportion of consumer request prefixes on available interfaces, improving data transmission efficiency under sudden traffic. Simulations on ndnSIM show that AECCF achieves higher throughput and fairness in different scenarios compared to existing methods. For dumbbell networks, AECCF reaches 18.64 Mbps throughput with a fairness index of 0.97, and in complex network scenarios, the throughput is 24.9% higher than that of the PCON protocol, and the fairness index is 0.86.

Unmanned aerial vehicles (UAVs)-assisted multi-access edge computing (MEC) platforms are becoming an increasingly popular solution for infrastructure-less Internet of Vehicles (IoVs) due to their mobility and flexibility. To address the challenges of uneven task offloading and vehicle mobility, in this paper, we propose a mobility-aware service offloading and migration scheme for UAV-assisted IoVs. We formulate the service placement, service migration, and UAV deployment as an optimization problem to minimize the serving delay of task addressing for IoVs, under a predefined long-term migration cost budget. To solve the problem, we use the Lyapunov optimization method to transform the long-term optimization into a real-time optimization problem. Additionally, we design a multi-agent deep deterministic policy gradient (MADDPG) algorithm to solve the problem. Compared with traditional central optimization methods, the proposed algorithm can achieve a near-global optimal policy by leveraging only local observation information. Simulation results show that the proposed MADDPG algorithm can achieve good convergence performance, and the proposed scheme can achieve quasi-optimal performance in terms of serving delay, service offloading rate, and service migration cost.

The forthcoming wireless network promises revolutionary advancements with significantly higher peak data rates, reduced latency, and vastly improved reliability. Among pivotal technologies, the design of novel multiple access schemes, particularly rate-splitting multiple access (RSMA), holds significant importance. In this article, we focus on the joint optimization of user scheduling, rate allocation, and beamforming for downlink multiple-input single-output communication networks under RSMA finite blocklength (FBL) transmission. The difficulty of the formulated optimization problem lies on the achievable rate function with FBL transmission and the joint design of user scheduling and beamforming. In order to solve the formulated problem, we first analyze the convexity and feasibility of the achievable rate function and further provide an efficient algorithm by cooperatively using strong Lagrangian duality, the difference of convex functions programming, the big-M method, and the alternating optimization algorithm for the joint optimization process. Numerical simulations validate the effectiveness of the proposed approach, offering promising insights for the future of 6G wireless networks.

Current mobile networks are facing dramatic growth in wireless traffics due to the prosperity of streaming media services. Cooperative edge caching, enabling multiple edge nodes to cache and share contents by exploiting the spatial/temporal user request differentiation, is regarded as a promising method to enhance Quality of Experience (QoE). However, frequent content sharing between BSs consumes operation cost such as the usage of cross-edge bandwidth and energy consumption. Therefore, new challenges incurred by performance-cost trade-off arise. In this paper, we propose a user preference-aware content caching and migration (PACM) scheme for video content delivery in a cost-efficient edge network. In this scheme, the dynamic user request preference and the long-term content migration cost budget are considered for content placement and delivery. To navigate a good performance-cost trade-off, we formulate the content caching and migration to be a long-term optimization problem. Then, the Lyapunov optimization method is used to decompose the problem into a series of real-time optimizations. As the decomposed problem is NP-hard, we design a novel collective reinforcement learning (CRL) algorithm that can realize online efficient decision-making by interacting with training experience. Simulation results show that the CRL algorithm has a high convergence rate and the proposed scheme can achieve quasi-optimal performance in terms of user-perceived latency, cache hit rate, and video stalling rate.

High energy costs and carbon-neutral targets emphasize the economics and sustainability of mobile communications. This paper studies a long-term average energy transaction expenditure minimization problem for smart-grid powered heterogeneous cellular networks (SG-HCNs) where renewable energy is introduced. Renewable energy and wireless channel dynamics evolve over different timescales. Thus, we seek a two-timescale dynamic resource management solution in SG-HCNs, where the real-time joint issue of flow control, power allocation, and energy sharing of renewable energy, and the ahead-of-time two-way energy trading are considered. Based on a two-layer Lyapunov framework, a two-timescale dynamic optimization (TTDO) algorithm is developed for the proposed problem. Specifically, the real-time joint issue is decoupled into two subproblems, addressed by linear programming and successive convex approximation methods. An approximate solution for ahead-of-time two-way energy trading is achieved via the stochastic subgradient approach, where past data of related random events is referred to as prior knowledge that is required but difficult to acquire. Theoretically, the proposed TTDO algorithm can attain an asymptotic optimum and ensure queue stability. Simulation results verify the theoretical analysis and reveal that the proposed TTDO algorithm can obtain lower energy transaction expenditure than benchmarks. Besides, the proposed TTDO algorithm presents an energy-saving property.

This paper proposes a two-timescale online resource management solution for smart-grid supplied heterogeneous cel-lular networks, where grid-energy pre-ordering in advance and bidirectional energy trading in real time are performed. We for-mulate a long-term average energy transaction cost minimization problem considering grid-energy pre-ordering, power allocation, and energy sharing. Leveraging the Lyapunov technique, succes-sive convex approximation method, and stochastic subgradient approach, we develop a two-timescale dynamic optimization (TTDO) algorithm to make online decisions on two time scales. It is theoretically proved that the proposed TTDO algorithm can asymptotically achieve optimality via tuning a control parameter. Numerical tests verify the theoretical results.

Vehicular edge computing is an emerging enabler to support vehicular-based computation-intensive tasks. By reason of the time-varying vehicular wireless environments and the stochastic task generation, the dynamically unbalanced task load distribution among resource-constrained edge infrastructures leads to the performance bottleneck and low efficiency of computation resource utilization. We employ an aerial relay station that can establish relay connections between vehicles and nearby heterogeneous edge infrastructures to relieve this situation. The computation offloading strategy design in the multivehicle multi-edge infrastructure scenario that is closely linked to system latency performance will be particularly complicated. To address this issue, a model-free multi-agent reinforcement learning is adopted, and we propose a practical constraint in the problem formulation. Simulation experiments show that the proposed strategy can guarantee load balancing among edge infrastructures.

This paper investigates a long-term average total energy cost minimization problem via resource management, including admission control, power allocation, and Energy Sharing (ES) of renewable energy in Heterogeneous Cellular Networks powered by Grid-connected Smart Micro Grids (GSMG-HCNs). In GSMG-HCNs, both renewable and grid energy power the base stations. Unlike existing works, we consider the cost of both renewable and grid energy and formulate the power line loss process caused by ES into our model. To solve the proposed problem, we transform it into a real-time issue by the Lyapunov technique. The proposed Cost-Aware Online Resource Management (CAORM) algorithm decouples the real-time issue into two sub-problems, one of which is linear and the other is addressed based on the successive convex approximation approach. We theoretically prove the asymptotic optimality of the CAORM algorithm and a tradeoff between the average total energy cost and the average queue length. Simulation results reveal that the CAORM algorithm outperforms benchmarks in reducing total energy cost and can make appropriate decisions according to different unit costs of renewable energy. Besides, the designed distance-related ES loss rate can help obtain better solutions with lower ES losses.

Heterogeneous cellular networks with hybrid energy supplies can relieve traffic pressure and reduce grid energy consumption. In heterogeneous cellular networks, rational resource management can help improve system performances. In general, more than one performance is expected to do well, but there can exist a trade-off among different performance metrics, thus making resource management a multi-objective problem. The existing solution usually transforms a multi-objective problem into another single-objective problem by assigning weights for various objectives. However, it is difficult to know the exact weights in advance, and different systems call for different requirements for objectives. Hence, a multi-objective optimization approach based on the gravitational search algorithm (GSA) is proposed to find a series of Pareto optimal solutions. The decision-makers can select an appropriate solution according to the system requirement. In this work, three different multi-objective GSA-based algorithms are proposed to determine user association and power control, with the goal to optimize the traffic load balancing among small base stations and grid energy consumption per unit throughput simultaneously. The complexity of the proposed algorithms is analyzed, and simulations compare the performances of the proposed algorithms and the benchmark algorithm. Experimental results reveal the feasibility and effectiveness of this approach.

Network virtualization enables cloud service providers sharing various substrate resources and serving end users with heterogeneous virtual network (VN) applications. As more and more Internet applications migrate to the cloud, the survivability of VNs, particularly for disaster survivability, has become a crucial issue. In this article, we study the VN survivability problem and propose an early-warning-time-based VN live evacuation (EVLE) scheme to combat against disaster threats. Aiming at an upcoming disaster destruction, EVLE tries to evacuate as many VNs as possible from the disaster risk zone (DRZ) before the given deadline, while sustaining their online services. The evacuation mainly includes two processes, viz., VN reconfiguration and virtual machine (VM) live migration. For a threatened VN, EVLE first remaps it at the outside of the DRZ with minimal resource cost, then exploits the postcopy technique to migrate the impacted VM to the corresponding reconfigured virtual node. During the VM migration process, the operations of basic bandwidth deployment and bandwidth upgradation are implemented, so as to encourage parallel migrations and maximize the resource utilization within the period of early-warning time. In our simulations, the EVLE and a baseline scheme, as well as a counterpart named as best-effort VN live evacuation (BVLE), are tested with different disaster scenarios. Numerical results illustrate that the EVLE, even if with a longer average evacuation time, can outperform the baseline scheme and BVLE in terms of the evacuation completion ratio under different early-warning times, and particularly can achieve distinct dominance under small early-warning times.

The energy detection technology is adopted as the detection method of the unlicensed channel in LTE release 13. However, the detection may be imperfect in actual scenario due to the simplicity of the detection method. In order to eliminate the controversy over LTE licensed assisted access (LAA) scheme, the paper deeply investigates the performance of the scheme under imperfect spectrum sensing (ISS). Considering possible ISS during the enhanced clear channel assessment (eCCA), the LAA scheme is modeled as a new two dimensional discrete time markov chain. In order to capture ISS, different from the definition in Bianchi's model, the discrete time scale is defined as the end of backoff slot time in the proposed model. The definition enables LAA small base stations to enter the defer state from backoff state when the channel is detected to be busy, and further possible ISS in the defer period and backoff slot time (BST) of eCCA can be considered. Based on the proposed model, the expressions of collision probabilities and throughput under three backoff mechanisms based LAA schemes are derived to deeply analyze their performance by comparisons. A large number of experimental and analytical results prove the validity of the proposed model. The analytical results also show that not only the backoff mechanisms but also ISS can greatly affect the network performance. In the respect of fairness (i.e. the impact on WiFi users), the WiFi collision probability always increases with the decrease of the false alarm probability (FAP). This means the WiFi collision probabilities under three LAA schemes may be higher than the baseline level for the graceful coexistence when FAP is very little. In this sense, the LAA schemes cannot be referred to as being graceful though they have been proven to be deterministic graceful coexistence schemes under perfect spectrum sensing (PSS). In the respect of throughput, LAA throughput always decreases and WiFi throughput increases with the growth of FAP, and total throughput always decreases. Therefore, based on the fixed SNR and sampling rate in actual scenario, the network performance can be tuned by energy threshold, as is consistent with the viewpoint in other literatures.