Considering the random access behaviour of mobile devices (MDs), heterogeneous service requirements, and dynamic completion or discarding of tasks, the statistical characteristics of tasks to be scheduled, transmitted, and computed vary over time in practical multi-access edge computing (MEC) systems. Therefore, it is necessary to design a dynamic task offloading and resource allocation (TORA) strategy to adaptively match these time-varying task characteristics. To achieve the goal, we first formulate the dynamic TORA problem as a Markov decision process (MDP) with time-varying extension of state space and action space, which cannot be effectively solved by conventional deep reinforcement learning (DRL) algorithms. To address this challenge, we propose a general state-action space adaptive (SASA) DRL framework by exploiting the advantages of the Transformer architecture and its multi-head attention (MHA) mechanism. This framework facilitates the integration of available actor-critic DRL algorithms to efficiently solve MDPs with time-varying state and action spaces. Based on the proposed SASA DRL framework, we further develop the SASA-based TORA algorithm, referred to as SASA-TORA, which is adaptable to not only dynamic network conditions but also time-varying statistical characteristics of tasks. Simulation results demonstrate the superiority of SASA-TORA over baseline algorithms and highlight the limitations of conventional DRL algorithms in handling MDPs with time-varying state and action spaces.

Cascaded reconfigurable intelligent surfaces (RISs) greatly improve network coverage and reliability. This paper examines the outage probability (OP) of multi-cascaded RISs (MCRISs)-aided systems over doubly-correlated Rayleigh multiple-input multiple-output (MIMO) channels. The moment-generating function is invoked to convert the OP into a numerical inversion of Laplace integral. To capture profound insights, we conduct an in-depth asymptotic investigation into the outage behavior of MCRIS-aided MIMO communications by leveraging random matrix theory in the high-SNR regime. The asymptotic results reveal that the firsthRISs with the smallest number of reflective elements primarily dictate the bottleneck of reliability performance, wherehis influenced by the variation in the number of reflective elements across the RISs. Specifically, smaller variations result in a largerh, while greater disparities reduce it. Additionally, we identify an “unsaturation effect” that occurs when the performance margin, or residual spatial degree of freedom (DoF), after propagation through the firsthRISs cannot be evenly distributed between the transmitter and the RISs. This effect slows down the decline of the OP with increasing SNR. More cascaded RISs impair the spatial DoF of wireless communications, resulting in the loss of half of the independent fading channels compared to the system without the support of RIS as the cascaded number of RISs increases. Majorization theory is subsequently applied to unveil the negative effect of doubly-spatial correlation on system reliability. Finally, Monte Carlo simulations are carried out for validations.

Many emerging artificial intelligence-driven applications rely on the availability of large-scale, stable, and fresh sensing data, underscoring the Ultra-Reliable Low-Latency Communications (URLLC). This paper proposes to amalgamate Non-Orthogonal Multiple Access (NOMA) and Hybrid Automatic Repeat reQuest with Incremental Redundancy (HARQ-IR) to accommodate reliable and real-time wireless services. The outage probability and the Average Age of Information (AAoI) are used to evaluate the transmission reliability and data freshness for HARQ-IR-NOMA schemes. To reveal physical insights as well as ease system designs, the asymptotic outage probability in the high Signal-to-Noise Ratio (SNR) regime is derived by developing a recursive dominant term approximation method. Moreover, the AAoI is deduced in terms of the outage probability by considering an M/G/1/1 queuing model. The asymptotic AAoI at high SNR is shown to be an increasing function of the diversity order-deficiency, which theoretically justifies the tradeoff between the transmission reliability and the data freshness. Furthermore, the AAoI is minimized by optimizing the transmission powers between users as well as HARQ rounds while maintaining the outage and total power constraints. The minimal AAoI is obtained by developing Geometric Programming (GP)-based and Deep Reinforcement Learning (DRL)-based methods. Finally, the numerical results are presented for verification.

We investigate the role of channel access schemes in enhancing the timeliness of status updates in sensor networks. Specifically, we model the large-scale sensor network as a Poisson cellular network and derive the network average age of information (AoI) under five different channel access schemes: slotted ALOHA, frame slotted ALOHA, random scheduling, round robin, and channel-aware. These schemes are categorized based on random vs. scheduled access and non-channel-aware vs. channel-aware. Our goal is to investigate when the additional overhead and complexity introduced by scheduling and channel state information (CSI) are beneficial, enabling better decisions in network design. Our findings reveal that the effectiveness of these schemes is influenced by the signal-to-interference ratio (SIR) decoding threshold, which often reflects the length of communication data. For short-packet communications, the performance differences among various channel access strategies are minimal, and the gains from scheduling are limited. Additionally, the inclusion of extra CSI does not yield performance improvements; in fact, some simple scheduling strategies, along with channelaware strategy that leverage CSI, may not outperform basic random access methods. Among the protocols we examined, the round robin scheme achieves the best performance. In contrast, scheduled access schemes exhibit a clear performance advantage in long-packet communications. Furthermore, the channel-aware scheme significantly enhances the network AoI performance, particularly in networks with higher transmitter competition.

The integration of pinching antenna systems with non-orthogonal multiple access (NOMA) has emerged as a promising technique for future 6G applications. This paper is the first to investigate power minimization for NOMA-assisted pinching antenna systems utilizing multiple dielectric waveguides. We formulate a total power minimization problem constrained by each user’s minimum data requirements, addressing a classical challenge. To efficiently solve the non-convex optimization problem, we propose an iterative algorithm. Furthermore, we demonstrate that the interference function of this algorithm is standard, ensuring convergence to a unique fixed point. Numerical simulations validate that our developed algorithm converges within a few steps and significantly outperforms benchmark strategies across various data rate requirements. The results also indicate that the minimum transmit power, as a function of the interval between the waveguides, exhibits an approximately oscillatory decay with a negative trend.

The space-air-ground integrated network (SAGIN) can enable global range and seamless coverage in the future network. SAGINs consist of three spatial layer network nodes: 1) satellites on the space layer, 2) aerial vehicles on the aerial layer, and 3) ground devices on the ground layer. Data transmissions in SAGINs include six unique cross-spatial-layer scenarios, i.e., three uplink and three downlink transmissions across three spatial layers. For simplicity, we call them six cross-layer scenarios. Considering the diverse cross-layer scenarios, it is crucial to conduct a unified network modeling regarding node coverage and distributions in all scenarios. To achieve this goal, we develop a unified modeling approach of coverage regions for all six cross-layer scenarios. Given a receiver in each scenario, its coverage region on a transmitter-distributed surface is modeled as a spherical dome. Utilizing spherical geometry, the analytical models of the spherical-dome coverage regions are derived and unified for six cross-layer scenarios. We conduct extensive numerical results to examine the coverage models under varying carrier frequencies, receiver elevation angles, and transceivers' altitudes. Based on the coverage model, we develop an algorithm to generate node distributions under spherical coverage regions, which can assist in testing SAGINs before practical implementations.

Mobile crowdsensing (MCS) networks enable largescale data collection by leveraging the ubiquity of mobile devices. However, frequent sensing and data transmission can lead to significant resource consumption. To mitigate this issue, edge caching has been proposed as a solution for storing recently collected data. Nonetheless, this approach may compromise data freshness. In this paper, we investigate the trade-off between re-using cached task results and re-sensing tasks in cacheenabled MCS networks, aiming to minimize system latency while maintaining information freshness. To this end, we formulate a weighted delay and age of information (AoI) minimization problem, jointly optimizing sensing decisions, user selection, channel selection, task allocation, and caching strategies. The problem is a mixed-integer non-convex programming problem which is intractable. Therefore, we decompose the long-term problem into sequential one-shot sub-problems and design a framework that optimizes system latency, task sensing decision, and caching strategy subproblems. When one task is re-sensing, the one-shot problem simplifies to the system latency minimization problem, which can be solved optimally. The task sensing decision is then made by comparing the system latency and AoI. Additionally, a Bayesian update strategy is developed to manage the cached task results. Building upon this framework, we propose a lightweight and time-efficient algorithm that makes real-time decisions for the long-term optimization problem. Extensive simulation results validate the effectiveness of our approach.

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.

Due to their flexibility, aerial vehicles (AVs), such as unmanned aerial vehicles and airships, are widely employed as relays to assist communications between massive ground users (GUs) and satellites, forming an AV-relayed ground-air-satellite solution (GASS). In GASS, the deployment of AVs is crucial to ensure overall performance from GUs to satellites. This paper develops a stochastic geometry-based analytical model for GASS under Matérn hard-core point process (MHCPP) distributed AVs. The 3D distributions of AVs and GUs are modeled by considering their locations on spherical surfaces in the presence of high-altitude satellites. Accordingly, we derive an overall connectivity analytical model for GASS, which includes the average performance of AV-relayed two-hop transmissions. Extensive numerical results validate the accuracy of the connectivity model and provide essential insights for configuring AV deployments.

With the advancement of intelligent driving technology, vehicular networks generate vast amounts of decentralized data that need to be processed. As a distributed paradigm, Federated Learning (FL) enables data integration and processing across various vehicles. However, traditional FL methods face significant challenges in vehicular networks, including high communication overhead and the difficulty of meeting strict latency and reliability requirements. To address these challenges, we propose a Multi-scale Semantic Selection-based FL (MSSFL) scheme, which integrates multi-scale semantic parameter selection and transmission optimization to reduce the system's communication cost. The proposed scheme selects parameters with high semantic importance and allocates bandwidth proportionally based on their quantity to enhance communication efficiency. We further formulate an optimization problem to minimize both parameters' transmission cost and upload delay. To solve this problem, we develop an alternating iterative solution using the block coordinate descent (BCD) method, which alternately optimizes the semantic parameter selection and bandwidth allocation strategy. Experimental results validate the effectiveness of the proposed framework in enhancing both communication efficiency and model accuracy.

In a practical Mobile Edge Computing (MEC) system, the stochastic arrival and departure of heterogeneous Mobile Devices (MDs) can cause fluctuations in the number of generated tasks to be scheduled over time, with the generated tasks differing in data size, complexity, and delay constraint. In this work, we consider a dynamic Task Offloading (TO) problem aiming at maximizing the long-term average system utility jointly defined by task completion and energy consumption in the MEC system, where the number of MDs to be served varies over time, and the processing of generated tasks may span multiple time slots. Particularly, we first transform the TO problem into a Markov Decision Process (MDP) with time-varying state and action spaces, which cannot be effectively solved by conventional Deep Reinforcement Learning (DRL) algorithms. Then, we propose a state and action spaces adaptive DRL algorithm to efficiently solve the formulated MDP by leveraging the Transformer model. Finally, simulation results demonstrate the superiority of our proposed algorithm over baseline algorithms and emphasize the limitation of the conventional DRL algorithm in handling time-varying state and action spaces.

We investigate the role of channel access schemes in enhancing the timeliness of status updates in sensor networks. Specifically, we model the large-scale sensor network as a Poisson cellular network and derive the network average Age of Information (AoI) under two channel access schemes: random and scheduled access. Our findings reveal that the effectiveness of these schemes is influenced by the signal-to-interference ratio (SIR) decoding threshold, which often reflects the length of communication data. For short-packet communications, performance differences among various channel access strategies are minimal, and the gains from scheduling are limited; in fact, some simple scheduling strategies may not outperform basic random strategies. Conversely, scheduled access schemes demonstrate a distinct performance advantage for long-packet communications. The round robin scheme consistently yields the best performance among the four protocols we examined-slotted ALOHA, frame slotted ALOHA, random scheduling, and round robin scheduling. This is due to its ability to mitigate intra-cell interference and regularize both status updates and channel access periods for each sensor, which is particularly beneficial in reducing AoI.

In this work, we investigate a task-driven delay minimization problem for autonomous aerial vehicle (AAV) enabled mobile crowdsensing (MCS) networks. Our focus is to reduce overall latency and improve data collection efficiency for delay-sensitive tasks through jointly optimizing the sensing data size, bandwidth allocation, and AAV hovering position. The formulated problem is a mixed-integer programming problem, which is also nonconvex. We introduce an efficient alternating optimization algorithm to address this challenge. Specifically, the original minimization problem is decomposed into two subproblems. The first subproblem focuses on bandwidth and sensing data allocation. By leveraging the latent structure property of the problem, we derive the optimal sensing data allocation given a bandwidth allocation policy. Based on it, we reveal that the first optimization subproblem can be converted into a maximum weighted matching problem in a bipartite graph, which can be optimally solved using the Hungarian algorithm. To address the optimization of the AAV’s hovering position subproblem, we employ the successive convex approximation (SCA) technique, which transforms it into a convex problem that can be efficiently solved by standard convex optimization solvers. We also analyze the convergence and the time complexity for the developed joint optimization algorithm. Afterward, we approximate the global optimal solutions in closed form for several specific cases of the problem. Extensive simulations confirm the superior performance of our proposed scheme compared to various benchmark strategies in terms of both delay and energy consumption.

With the rapid development and widespread deployment of Internet of Things devices, existing networks face significant challenges in meeting the demands of emerging large-scale applications. In this article, we propose a novel paradigm to address these challenges by decomposing large applications/services into lightweight microservices (MSs) distributed among small base stations (SBSs), each responsible for specific functions. Upon receiving a service request, a macro base station (MBS) invokes a series of SBSs that cache the required MSs to execute the associated computational tasks. The computed results are then returned to the MBS, which integrates and delivers the final result to the user. Under this framework, we investigate the joint problem of MS caching, computation task assignment, and computing resource allocation, aiming to minimize the total energy consumption. Various practical constraints, such as users’ latency requirements, and the limited caching and computing resources of SBSs are taken into account. To facilitate the analysis, we transform the original minimization problem into an equivalent problem focusing on MS computation task assignment and computing resource allocation, which remains NP-hard. To tackle this challenge efficiently, we devise a two-stage method. In the first stage, we derive a closed-form expression for the computing resource allocation policy based on the MS computation task assignment. Subsequently, we introduce a two-side swapping oriented approach to explore an improved MS computation task assignment strategy. In addition, we propose the use of exhaustive and simulated annealing algorithms to approach the optimal and near-optimal solutions, respectively. Extensive simulation results demonstrate that our proposed algorithm achieves close-to-optimal performance and outperforms benchmark schemes significantly.

The novel notion of Edge Computing Power Networks (ECPN) has recently been proposed to provide highly flexible matching strategies among edge servers and user devices to facilitate seamless computing power and network architectures. However, recent work about ECPN is still in its infancy, and almost all work has ignored the tidal phenomenon of computing power requests from mobile user devices, including temporal characteristic of the quantity and spatial characteristic of the distribution in different periods of one day, which leads to the energy waste of idle edge servers for always keeping active. To deal with this problem, we propose the ECPN model in this article, taking into account the tidal phenomenon of mobile user devices to develop energy-efficient computing power matching strategies. Specifically, we formulate the optimization problem that encompasses computing power allocation and task matching for user devices as well as dynamic on-off for edge servers. Our main objective is to maximize the quality of service (QoS) for user devices while minimizing the energy consumption for task execution with respect to resource limitations and task requirements. To solve the formulated problem efficiently, we propose a distributed algorithm based on matching theory to determine the optimal computing power allocation, task matching and on-off strategies. Extensive numerical results show that the proposed scheme can reduce energy consumption while ensuring the QoS.

In this paper, we exploit an emergency mobile crowdsensing (MCS) framework that utilizes unmanned aerial vehicles (UAVs) in collaboration with uncrashed base stations (BSs) to enhance sensing and communication efficiency. In the proposed framework, mobile users (MUs) equipped with sensors collect data, while UAVs, deployed as aerial relays, collaborate with uncrushed BSs to facilitate the transmission and aggregation of the sensed data from all MUs. However, the limited resources significantly affect the deployment of UAVs and the design of the UAV-relay-assisted MCS system. Moreover, selecting MUs for sensing tasks and allocating bandwidth among them are crucial factors that determine MUs’ sensing capabilities and the data transmitting policies. Incorporating with foregoing essential factors, we formulate a comprehensive problem that jointly optimizes the MU selection, bandwidth allocation, UAV deployment, as well as strategies for sensing and transmitting data, aiming to improve the total reward of agent. The formulated problem poses high challenges due to the coupling between the sensing, transmission, as well as the UAVs deployment policies. To deal with this problem, we first derive the optimal transmission power and sensing data size under given MU selection, bandwidth allocation, and UAVs deployment strategy. The original optimization problem is subsequently decomposed into three folds, corresponding to finding the optimal MU selection, bandwidth allocation solution, as well as the deployment of UAVs. Meanwhile, a joint dynamic programming and a swap-then-compare enabled algorithm is proposed to obtain the optimal MU selection and bandwidth allocation policies. Next, the successive convex approximation (SCA) techniques are used to find the optimal locations for the UAVs. Extensive numerical results verify that the proposed joint algorithm can significantly outperform several benchmark approaches.

Caching recommended contents at the network edge can effectively alleviate the traffic pressure of the backbone network and significantly improve user experience. However, highly personalized and precise recommendations often rely on leveraging more user request records, raising serious privacy concerns. Existing recommendation-aware edge caching mechanisms typically apply a fixed level of privacy protection, without considering the personalized privacy of users. This one-size-fits-all approach often introduces significant noise, adversely impacting cache hit ratio (CHR). In this work, we propose a differential privacy-based edge caching framework supporting personalized privacy-preserving to address these challenges. We formulate a CHR maximization problem under personalized privacy constraints and reveal the NP-completeness of the problem with a rigorous mathematical proof. Subsequently, we mathematically model the relationship between personalized privacy and user preference distortion, analyzing its impact on recommendations and user requests. To solve it, we introduce an efficient heuristic algorithm named the Backhaul Traffic-Aware Caching Algorithm. This algorithm utilizes backhaul traffic as a feedback signal to make accurate caching decisions, enabling adaptive optimization of caching decisions by perceiving the impact of noise and low-quality recommendations. Extensive experiments on two typical real-world datasets validate the effectiveness of our framework, demonstrating its ability to enhance privacy protection while simultaneously improving CHR.

Vehicular edge computing (VEC) is critical for the safe and efficient driving of intelligent vehicles, by which they can offload computation-intensive tasks (such as driving environment perception) to edge servers to overcome the limitations of onboard computational resources and cooperate with others. One of the major challenges faced by VEC is that the offloaded intelligent driving tasks generally generate large amounts of data, which can easily stretch and congest the vehicle communication channels. To address the above challenges, we first propose a novel semantic VEC (SVEC) architecture, which can extract the semantic information of tasks and offload them to edge servers, thereby achieving reliable and efficient offloaded task communication and computation adaptively. Considering the scarce channel resources of vehicles and the intelligent tasks with different priorities and modalities, we define a novel user utility model for SVEC and transform the problem of maximizing user utility into a joint optimization problem of semantic feature extraction, task offloading and resource allocation. Furthermore, to cope with the complexity of the solution space of the optimization problem, we propose a diffusion-based multi-agent reinforcement learning algorithm, which improves the ability of agents to explore the solution space through the diffusion process, thereby achieving optimal decisions for semantic feature extraction, task offloading and resource allocation. Simulation results show that the proposed scheme improves the overall performance of SVEC while reducing offload latency and average system cost.

In this paper, we study the intricate realm of digital twin synchronization and deployment in multi-access edge computing (MEC) networks, with the aim of optimizing and balancing the two performance metrics Age of Information (AoI) and energy efficiency. We jointly consider the problems of edge association, power allocation, and digital twin deployment. However, the inherent randomness of the problem presents a significant challenge in identifying an optimal solution. To address this, we first analyze the feasibility conditions of the optimization problem. We then examine a specific scenario involving a static channel and propose a cyclic scheduling scheme. This enables us to derive the sum AoI in closed form. As a result, the joint optimization problem of edge association and power control is solved optimally by finding a minimum weight perfect matching. Moreover, we examine the one-shot optimization problem in the contexts of both frequent digital twin migrations and fixed digital twin deployments, and propose an efficient online algorithm to address the general optimization problem. This algorithm effectively reduces system costs by balancing frequent migrations and fixed deployments. Numerical results demonstrate the effectiveness of our proposed scheme in terms of low cost and high efficiency.

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.

Unmanned aerial vehicles (UAV) assisted mobile crowdsensing (MCS) is an emerging paradigm that utilizes mobile user (MU) collaboration to complete sensing tasks. However, little attention has been paid to the issue of how to solve the resource allocation of sensing, communication, and computing processes, as well as the trajectory planning of UAV. Therefore, this paper focuses on maximizing the total completion of sensed bits in the UAV-assisted MCS network by jointly optimizing MU selection, resource allocation, and UAV trajectory planning. Considering the random mobility of MUs and the limited communication, computation, and energy resources, we formulate a nonconvex optimization problem that necessitates real-time decision-making. To tackle this demanding problem, we approach it by formulating it as a Markov decision process (MDP). In response, we propose a real-time solution based on proximal policy optimization (PPO) to obtain an approximate suboptimal solution for the problem. Numerical results show that the proposed PPO-based method yields a noteworthy improvement in the completion of sensed bits within when compared to other benchmark schemes.

This paper analyzes the block error rate (BLER) of hybrid automatic repeat request with incremental redundancy (HARQ-IR) assisted short packet communications. The small packet size leads to the occurrence of the time correlation among HARQ rounds. To accurately capture the effect of time correlation, a general correlated Nakagami-m fading channel model is first developed. However, the channel correlation, multiple transmissions, and finite blocklength information theory extremely challenge the analysis of BLER. To confront this, the average BLER is derived in a compact form by capitalizing on linearization approximation and conditional Laplace transform. To reveal more insights, the asymptotic BLER in high signal-to-noise ratio is obtained in a simple form. It is disclosed that full time diversity can be achieved by the proposed HARQ-IR-aided scheme. This justifies the feasibility of using retransmissions to offer reliable short packet communications. The numerical results finally corroborate the validity of our analysis.

Abstract Cache‐enabled device‐to‐device (D2D) network has been deemed as an effective technique to offload the data traffic. However, the gain of the caching schemes is closely related to the homogeneity among users' preference distribution. To tackle this issue, recommendation is a promising proactive approach. It increases the request probability of recommended contents, reshaping users' contents demand patterns, and improving caching performance. Moreover, considering the heterogeneous network settings, i.e. content retrieval costs vary, the routing design becomes a non‐negligible factor on caching performance optimization. On these grounds, the average system cost of D2D‐enabled wireless caching networks with multiple BSs is first described. Then the routing strategies are designed together with caching and recommendation policies by minimizing the average cost of these networks. The optimization problem is proven as NP‐hard. To facilitate the analysis, the original problem is decoupled into two sub‐problems and solve them respectively. Afterwards, all the variables are optimized in an alternating manner until the convergence is achieved. The proposed algorithm's convergence performance and benefits over benchmark strategies in terms of total cost and cache hit ratio are supported by Monte‐Carlo simulation results.

Wireless cellular networks currently face constantly growing data demands, which lead to network congestion and high latency. Cache-aware recommendation can reshape users’ request behavior and improve cache efficiency in wireless caching systems, thus alleviating network congestion and shorting transmission delay. However, existing cache-aware recommendations serve the caching system by reducing the quality of recommendations. Specifically, it usually provides only limited and similar content items and lacks diversified recommendation services, which severely reduces users’ satisfaction. To tackle this challenge, we propose a diversified recommendation mechanism-based solution that aims to simultaneously improve the performance of wireless caching systems and the quality of recommendation to improve users’ satisfaction to a greater extent. To this end, we propose a quantitative model that captures the impact of recommendation decisions on the diversity of recommendation sets. This model enables us to formulate a joint cache hit ratio and recommendation diversity maximization problem, taking into account each user’s recommendation size and cache capacity requirements. Since this problem is a non-convex integer programming problem, we decompose it into two subproblems, i.e., the cache placement problem and the diversified recommendation problem. Then we design tabu search-assisted and simulated annealing-oriented algorithms to solve these two subproblems, respectively, and perform iterative alternating optimization for the whole problem. Monte-Carlo simulation validates the effectiveness of our method in terms of cache hit ratio and recommendation diversity compared to various benchmarks.

By integrating the merits of aerial, terrestrial, and satellite communications, the space-air-ground integrated network (SAGIN) is an emerging solution that can provide massive access, seamless coverage, and reliable transmissions for global-range applications. In SAGINs, the uplink connectivity from ground users (GUs) to the satellite is essential because it ensures global-range data collections and interactions, thereby paving the technical foundation for practical implementations of SAGINs. In this article, we aim to establish an accurate analytical model for the uplink connectivity of SAGINs in consideration of the global distributions of both GUs and aerial vehicles (AVs). Particularly, we investigate the uplink path connectivity of SAGINs, which refers to the probability of establishing the end-to-end path from GUs to the satellite with or without AV relays. However, such an investigation on SAGINs is challenging because all GUs and AVs are approximately distributed on a spherical surface (instead of the horizontal surface), resulting in the complexity of network modeling. To address this challenge, this paper presents a new analytical approach based on spherical stochastic geometry. Based on this approach, we derive the analytical expression of the path connectivity in SAGINs. Extensive simulations confirm the accuracy of the analytical model.

In wireless content caching networks (WCCNs), a user's content consumption crucially depends on the assortment offered. Here, the assortment refers to the recommendation list. An appropriate user choice model is essential for greater revenue. Therefore, in this paper, we propose a practical multinomial logit choice model to capture users' content requests. Based on this model, we first derive the individual demand distribution per user and then investigate the effect of the interplay between the assortment decision and cache planning on WCCNs' achievable revenue. A revenue maximization problem is formulated while incorporating the influences of the screen size constraints of users and the cache capacity budget of the base station (BS). The formulated optimization problem is a non-convex integer programming problem. For ease of analysis, we decompose it into two folds, i.e., the personalized assortment decision problem and the cache planning problem. By using structure-oriented geometric properties, we design an iterative algorithm with examinable quadratic time complexity to solve the non-convex assortment problem in an optimal manner. The cache planning problem is proved to be a 0-1 Knapsack problem and thus can be addressed by a dynamic programming approach with pseudo-polynomial time complexity. Afterwards, an alternating optimization method is used to optimize the two types of variables until convergence. It is shown by simulations that the proposed scheme outperforms various existing benchmark schemes.

A variable-length cross-packet hybrid automatic repeat request (VL-XP-HARQ) is proposed to boost the spectral efficiency (SE) and the energy efficiency (EE) of communications. The SE is firstly derived in terms of the outage probabilities, with which the SE is proved to be upper bounded by the ergodic capacity (EC). Moreover, to facilitate the maximization of the SE, the asymptotic outage probability is obtained at high signal-to-noise ratio (SNR), with which the SE is maximized by properly choosing the number of new information bits while guaranteeing outage requirement. By applying Dinkelbach’s transform, the fractional objective function is transformed into a subtraction form, which can be decomposed into multiple sub-problems through alternating optimization. By noticing that the asymptotic outage probability is a convex function, each sub-problem can be easily relaxed to a convex problem by adopting successive convex approximation (SCA). Besides, the EE of VL-XP-HARQ is also investigated. An upper bound of the EE is found and proved to be attainable. Furthermore, by aiming at maximizing the EE via power allocation while confining outage within a certain constraint, the methods to the maximization of SE are invoked to solve the similar fractional problem. Finally, numerical results are presented for verification.

Digital twins (DTs) have emerged as a promising enabler for representing the real-time states of physical worlds and realizing self-sustaining systems. In practice, DTs of physical devices, such as mobile users (MUs), are commonly deployed in multi-access edge computing (MEC) networks for the sake of reducing latency. To ensure the accuracy and fidelity of DTs, it is essential for MUs to regularly synchronize their status with their DTs. However, MU mobility introduces significant challenges to DT synchronization. Firstly, MU mobility triggers DT migration which could cause synchronization failures. Secondly, MUs require frequent synchronization with their DTs to ensure DT fidelity. Nonetheless, DT migration among MEC servers, caused by MU mobility, may occur infrequently. Accordingly, we propose a two-timescale DT synchronization and migration framework with reliability consideration by establishing a non-convex stochastic problem to minimize the long-term average energy consumption of MUs. We use Lyapunov theory to convert the reliability constraints and reformulate the new problem as a partially observable Markov decision-making process (POMDP). Furthermore, we develop a heterogeneous agent proximal policy optimization with Beta distribution (Beta-HAPPO) method to solve it. Numerical results show that our proposed Beta-HAPPO method achieves significant improvements in energy savings when compared with other benchmarks.

In wireless caching networks, users' content request behavior is a compelling aspect for maximizing the achievable revenue. Multinomial logit (MNL) choice model is commonly used to characterize the relationship between users' request behavior and the assortment decision. However, conventional MNL model assumes that the systems show an assortment of content items to users, and a user can purchase the items among the assortment or leave without consuming anything. Yet in most cases, users tend to observe the assorted items, and select within the list in accordance with their personal preference or just search for the items they are interested in by closing the assortment set directly. To address this issue, a revised MNL (RevMNL) choice model is proposed in this paper, wherein we presume that all the remaining items will be shown to the user if the pre-determined assortment set is unsatisfactory. Under which, we mathematically derive the content demanding probability distribution per user. Thereafter, the assortment decision-making problem is studied to maximize system's revenue, which is a non-convex integer programming problem. By using structure-oriented geometric properties, we design an iterative algorithm with quadratic time complexity to obtain the globally optimal solution to the formulated optimization problem. Extensive simulation results validate the superiority of our devised scheme in terms of system revenue and cache hit ratio when compared against various baselines under the conventional MNL model.

Both intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) are considered as potential techniques for the next-generation mobile communication system. In this paper, we concentrate on the power minimization problem for a multi-antenna IRS-NOMA uplink system, which is addressed by alternatively optimizing transceivers and phase shifts. Particularly, the users are divided into one central group and one edge group according to their propagation distances to the base station, where the data transmission of the edge group is assisted by the IRS. Besides, from the perspective of practice, a non-ideal group-based successive interference cancellation (GSIC) is taken into account. To proceed, an iterative algorithm with the minimum mean square error equalizer is leveraged to design the transceivers and a sequential rotation algorithm is developed to optimize the phase shifts. Simulation results indicate the superiority of the proposed scheme compared with various benchmarks in terms of transmit power consumption.

This letter focuses on the robust low-rank matrix recovery (RLRMR) in the presence of gross sparse outliers. Instead of using$\ell _{1}$-norm to reduce or suppress the influence of anomalies, we aim to eliminate their impact. To this end, we model the RLRMR as a mixed integer programming (MIP) problem based on the$\ell _{0}$-norm. Then, a block coordinate descent (BCD) algorithm is developed to iteratively solve the resultant MIP. At each iteration, the proposed approach first utilizes the$\ell _{0}$-norm optimization theory to assign binary weights to all entries of the residual between the known and estimated matrices. With these binary weights, the optimization over the bilinear term is reduced to a weighted extension of the Frobenius norm. As a result, the optimization problem is decomposed into a group of row-wise and column-wise subproblems with closed-form solutions. Additionally, the convergence of the proposed algorithm is studied. Simulation results demonstrate that the proposed method is superior to five state-of-the-art RLRMR algorithms.

In this paper, reconfigurable intelligent surface (RIS) and hybrid automatic repeat request with incremental redundancy (HARQ-IR) are amalgamated to lower power expenditure, shorten latency and strengthen reliability of the internet of things (IoT) communications. By considering Rician fading channels and multiple RISs, the outage probability of single-input single-output (SISO) HARQ-IR-RIS aided IoT networks is derived in closed-form, with which the asymptotic outage analysis is carried out. The asymptotic results are further extended to single-input multiple-output (SIMO)/multiple-input multiple-output (MIMO) HARQ-IR-RIS aided IoT networks by using random matrix theory. Thereafter, the asymptotic expressions are invoked to reduce the design complexity of the phase shifts, transmit powers, and rate, which aims at minimizing the age of information (AoI) while ensuring the power and outage constraints. Particularly, the optimal phase shifts are firstly determined. The alternating optimization technique is then applied to solve the transmit rate and powers, which are iteratively updated based on majorization-minimization (MM) principle and geometric programming (GP) approximation, respectively. The numerical results consequently corroborate our theoretical analysis. More interestingly, the HARQ-IR-aided scheme with fixed power is found to provide a comparable performance as the proposed scheme with variable power especially for numerous reflecting elements at RIS.

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.

Caching at the wireless edge is recognized as a promising solution to accommodate the explosive growth of traffic demand. However, the gain of edge caching is only pronounced given homogeneous user preference. To reap the full potential of caching, recommendation mechanism has emerged as an attractive technology due to its capability of reshaping users’ request distribution. In this work, we propose a joint user-side recommendation and device-to-device (D2D)-assisted offloading strategy, aiming to maximize the operator’s utility. Specifically, we consider that users can recommend their cached contents to encountered users. This strategy takes into account users’ personalized preferences and relative locations, and hence can directly offload the recommended contents through D2D links without burdening cellular links. We then develop a theoretical framework to evaluate the subsequent content transmission, accounting for the randomness of spatial deployment, user mobility, individual delay requirement, incentive, and protection mechanism for existing links. Based on the analytical results, we design a D2D-assisted offloading strategy, which allows the requester to postpone data reception in exchange for discounted service fees. Simulation results show that the operator’s utility can be significantly improved. Particularly, it is found that user mobility facilitates the above process.

Federated learning (FL) is an emerging machine learning method that can be applied in mobile edge systems, in which a server and a host of clients collaboratively train a statistical model utilizing the data and computation resources of the clients without directly exposing their privacy-sensitive data. We show that running stochastic gradient descent (SGD) in such a setting can be viewed as adding a momentum-like term to the global aggregation process. Based on this finding, we further analyze the convergence rate of a federated learning system by accounting for the effects of parameter staleness and communication resources. These results advance the understanding of the Federated SGD algorithm, and also forges a link between staleness analysis and federated computing systems, which can be useful for systems designers.

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.

To accommodate the stringent requirements of massive connectivity and ultra-high throughput, both reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) have been perceived as the key techniques for future communication networks. In this paper, a versatile framework is conceived to boost transmit power efficiency for RIS-enabled multi-group NOMA networks in the presence of coordinated multi-point (CoMP) reception and imperfect successive interference cancellation (SIC). Particularly, a group-level SIC (GSIC) method is proposed to eliminate the decoded group’s interference together with the well-designed transceivers to mitigate the aggregated interference, including the intra-group interference, the residual interference caused by imperfect SIC, and the interference of NOMA users decoded later. According to the novel framework, a power minimization problem is formulated by collaboratively optimizing the transmit power and the phase shifts. To render the problem tractable, an alternating scheme is developed to optimize the transmit power and the phase shifts iteratively. Specifically, the transmit powers for the users in the same group are devised by a parallel iteration algorithm, whilst the phase shifts are optimized by a sequential rotation method. In simulations, it is shown that the proposed scheme requires less transmit power than various benchmark methods under the constraint of each user’s quality of service.

We thoroughly investigate the outage performance of reconfigurable intelligent surface (RIS) aided multi-input multi-output (MIMO) communications by exploiting statistical channel state information (CSI). Kornecker channel model is adopted to characterize the impact of spatial correlations among MIMO antennas and reconfigurable reflectors. Mellin transform and random matrix theory are then utilized to derive the outage probability, with which we further conduct the asymptotic outage analysis to obtain insightful findings. In particular, the asymptotic analysis reveals that the number of reflecting elements at the RIS should not be smaller than the total number of MIMO transmit and receive antennas to get rid of the rank deficiency of the cascaded MIMO channels. Moreover, the asymptotic outage probability is a monotonically increasing and convex function with respect to the transmission rate. The numerical outcomes not only corroborate our analytical results, but also demonstrate the negative impact of the spatial correlation and the benefit of increasing the number of reconfigurable reflectors. Finally, we apply the asymptotic results to optimally devise the phase shifts with a low computational complexity.

Reconfigurable intelligent surface (RIS) has recently attracted a spurt of interest due to its innate advantages over massive MIMO on power consumption. In this paper, we study the outage performance of multi-RIS system with the help of hybrid automatic repeat request (HARQ) to improve the RIS system reliability, where the fading channels are modeled by Rician fading and the phase shift setting only depends on the line-of-sight (LoS) component. Both the exact and asymptotic outage probabilities under Type-I HARQ and HARQ with chase combining (HARQ-CC) schemes are derived. Particularly, the tractable asymptotic results empower us to derive meaningful insights for HARQ-aided multi-RIS systems. On the one hand, we find that both the Type-I and the HARQ-CC schemes can achieve full diversity that is equal to the maximal number of HARQ rounds. On the other hand, the closed-form expression of the optimal phase shift setting with respect to outage probability minimization is obtained. The optimal solution indicates that the reflecting link direction should be consistent with the direct link LoS component. Finally, the analytical results are validated by Monte-Carlo simulations.

Content caching is recognized as a promising solution to release the heavy burden of backhaul links and decrease the content transmission latency in Fog radio access networks (Fog-RANs). However, the content caching design is still a challenging problem with considering the user request patterns, the content delivery strategies, and the limited caching capacity. Recommendation has the capability of reshaping users' content requests for further prompting caching gain. The joint recommendation, caching, beamforming holds the potential to improve the system performance of Fog-RANs. In this paper, a joint recommendation, caching, and beamforming scheme is proposed for multi-cell multi-antenna recommendation aware Fog-RANs. Aiming at minimizing the content transmission latency, we formulate a joint recommendation, caching, and beamforming optimization problem. The minimization problem is a very challenging two-timescale mixed integer nonlinear programming problem, which is hard to solve in general. By exploring structural properties of the problem, we propose an alternative optimization algorithm with low complexity through decomposing the original problem into three sub-problems. Extensive simulations show that our proposed method can significantly reduce the content transmission delay.

Edge side caching assisted device-to-device (D2D) communication has been acknowledged as a promising technique to alleviate the heavy burden of backhaul transmission link and to reduce the network latency. However, the effectiveness of caching strategies at the network edge is highly dependent on the distribution of individual user’s content preference. To fully attain the benefits of edge caching, some proactive mechanisms shall be considered. Among which, recommendation performs noticeably well due to its capability of reshaping the content request probabilities of different users, which in turn affects the cache decision significantly. In this work, we quantitatively investigate how recommendation can be applied to enhance the caching efficiency of D2D enabled wireless content caching networks. And for that, the cache hit ratio maximization problem for a generic network model is formulated taking into account the requirements of each user’s personalized recommendation quality, recommendation quantity and cache capacity. Then, we show that the optimal recommendation and caching policies which jointly maximize the cache efficiency is NP-hard to compute. Further, a time-efficient sub-optimal algorithm is designed, which works in an iterative manner and has provable convergence guarantee as well as polynomial time complexity. Monte-Carlo simulation results demonstrate the convergence performance of our proposed joint decision algorithm and its cache efficiency improvements compared to extensive benchmarks.

To maintain reliability of content-oriented wireless caching networks (CWCNs), repair mechanism is of necessity to be considered due to the natural that storage entities are individually unreliable and thus subject to failure on account of hardware error, network congestion or software updating. Meanwhile, recommendation is tunable for edge caching performance improvement. In this paper, we study the revenue maximization problem for CWCNs with both repair and recommendation considerations. The formulated problem is an integer non-convex and non-linear problem, and thus is difficult to be solved. The difficulties are intrinsically derived from the implicit weighted sum costs (WSCs) as regards storage and repair of each content and the coupling among the Boolean variables. For the sake of analytical tractability, a two-step methodology is developed. Specifically, we first explore the optimal storage and repair amount among the content providers to minimize the WSCs in terms of successfully fixing any occurred data corruption for the stored contents. Thereof, an explicit instance is provided to show how the contents can be coded, stored and then repaired in our network given that an error occurs. Based on the obtained storage and repair amount vectors, we solve the resultant joint caching and recommendation decision making problem (DMP). To be more specific, we decouple the DMP into a pair of subproblems, namely a cache placement and a recommendation optimization subproblems. For each subproblem, a globally optimal and a time-efficient suboptimal solutions are developed, respectively. Later, a versatile iterative paradigm is devised to do the decision making jointly. The convergence performance and the complexity analysis of the proposed algorithms are rigorously analyzed. Numerical results confirm the convergence performance of our iterative algorithms and illustrate their revenue improvements compared to various baseline schemes.

Caching popular contents at the network edge has been considered as a promising enabler to relieve the pressure on networks due to the fact that a substantial portion of global data traffic is repeatedly requested by many subscribers and thus redundantly generated. Recommendation, on the other hand, has attracted spiraling attention for its capability of reshaping users’ contents demand patterns. In this paper, we examine the practicability of recommendation in boosting the gains of edge caching with uncharted users’ feature information. To this end, we first characterize the average system cost for a generic network model, disclosing its dependence on the recommendation and caching strategies. Then, we formulate the joint caching and recommendation decision oriented cost minimization problem, taking the constraints on each content provider’s cache capacity budget, each individual user’s recommendation size and recommendation quality into account. However, the implicit information regarding users’ preference makes the problem inextricable. To address this issue, a versatile long short term memory (LSTM) network assisted prediction paradigm is proposed to attain the preference schema of users with the assistance of their historical behavior data. Based on that, we rigorously prove the NP-hardness of obtaining the optimal recommendation and caching policies that jointly minimize the system cost. Therewith, an iterative suboptimal algorithm is developed, which has provable polynomial time complexity and convergence guarantee. Extensive simulation results validate the effectiveness of our proposed LSTM enabled feature information prediction approach and the convergence performance of the devised joint decision making methodology. In addition, it is shown that the proposed scheme outperforms numerous benchmarks significantly.

In this paper, a full-stack perspective that involves content coding, caching and recommendation is presented for wireless edge caching networks. Therein, content coding and recommendations are utilized to ensure the reliability of the network and to boost the gain of edge caching, respectively. Specifically, with a well-designed coding pattern, the concurrently occurred data corruptions of multiple access points (APs) can be successfully recovered. Whilst, the successful content retrieval is warranted with highly selective retrieval sets. Further, by offering personalized recommendations, the cache hitting at network edges can be promoted. On these grounds, a cache hit ratio maximization problem for reliability-aware wireless edge caching system is formulated, considering the successful content retrieval and repair, total repair cost requirements, cache capacity budget of each AP, and recommendation quality and quantity per user. To solve this intractable problem, a framework with two steps is developed, which leads to construction of a suitable coding schema with the minimum repair cost per content to satisfy the retrieval and repair criteria, thus achieving a stabilized optimal solution regarding the joint cache placement and recommendation decisions from a game-theoretical viewpoint. Finally, extensive numerical simulations are performed to confirm the superior performance of our devised algorithms compared with benchmark baselines.

We consider a multi-user multi-server mobile edge computing (MEC) network with time-varying fading channels and formulate an offloading decision and resource allocation problem. To solve this mixed-integer non-convex problem, we propose two hybrid approaches that learn offloading strategy with DQN (opt-DQN) or Q-table (opt-QL) at each user equipment (UE). The communication resources are allocated with an optimization algorithm at each computational access point (CAP). We also propose a pure DQN method that learns both the offloading strategy and resource allocation via Q-learning (QL). We analyze the convergence behavior of the QL-based algorithms from a game-theoretical perspective and demonstrate the performance of the proposed hybrid approaches for different network sizes. The simulation results show that the hybrid approaches reach lower costs than other baseline algorithms and the pure-DQN approach. Moreover, the performance of the pure-DQN approach degrades severely as the network size increases, while opt-DQN still performs the best, followed by opt-QL. These observations demonstrate that the hybrid approach that combines the advantages of both QL and convex optimization is a promising design for a multi-user MEC network, wherein complicated offloading and resource allocation strategies need to be determined in a timely and accurate fashion.

This paper is concerned with slicing a radio access network (RAN) for simultaneously serving two 5G-and-Beyond typical use cases, i.e., enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC). Although many researches have been conducted to tackle this issue, few of them have considered the impact of bursty URLLC. The bursty characteristic of URLLC traffic may significantly increase the difficulty of RAN slicing in terms of ensuring an ultra-low packet blocking probability. To reduce the probability, we re-visit the structure of physical resource blocks orchestrated for URLLC traffic based on theoretical results. Meanwhile, we formulate the problem of slicing a RAN enabling coordinated multi-point (CoMP) transmissions for multicast eMBB and bursty URLLC service multiplexing as a multi-timescale optimization problem aiming at maximizing eMBB and URLLC slice utilities, subject to physical resource constraints. To mitigate this problem, we transform it into multiple single timescale problems by exploring sample average approximations. An iterative algorithm with provable performance guarantees is developed to obtain solutions to these single timescale problems and aggregate obtained solutions into those of the multi-timescale problem. We also design a CoMP-enabled RAN slicing system prototype and compare the iterative algorithm with the state-of-the-art algorithm to verify its effectiveness.

To support massive connectivity and boost spectral efficiency for Internet of Things (IoT), a downlink scheme combining virtual multiple-input-multiple-output (MIMO) and nonorthogonal multiple access (NOMA) is proposed. All the single-antenna IoT devices in each cluster cooperate with each other to establish a virtual MIMO entity, and multiple independent data streams are requested by each cluster. NOMA is employed to superimpose all the requested data streams, and each cluster leverages zero-forcing detection to demultiplex the input data streams. Only statistical channel state information (CSI) is available at the base station to avoid the waste of the energy and bandwidth on frequent CSI estimations. The outage probability and goodput of the virtual MIMO-NOMA system are thoroughly investigated by considering the Kronecker model, which embraces both the transmit and receive correlations. Furthermore, the asymptotic results facilitate not only the exploration of physical insights but also the goodput maximization. In particular, the asymptotic outage expressions provide quantitative impacts of various system parameters and enable the investigation of diversity-multiplexing tradeoff (DMT). Moreover, power allocation coefficients and/or transmission rates can be properly chosen to achieve the maximal goodput. By favor of the Karush-Kuhn-Tucker conditions, the goodput maximization problems can be solved in closed form, with which the joint power and rate selection is realized by using alternately iterating optimization. Besides, the optimization algorithms tend to allocate more power to clusters under unfavorable channel conditions and support clusters with a higher transmission rate under benign channel conditions.

Future wireless communication systems have been imposed high requirement on power efficiency for operator's profitability as well as to alleviate information and communication technology (ICT) global carbon emission. To meet these challenges, the power consumption minimization problem for a generic multi-cell multiple input and single output non-orthogonal multiple access (MISO-NOMA) system is studied in this work. The associated joint user grouping, beamforming (BF) and power control problem is a mixed integer non-convex programming problem, which is tackled by an iterative distributed methodology. Towards this end, the near-optimal zero-forcing (ZF) BF is leveraged, wherein the semiorthogonal user selection (SUS) strategy is applied to select BF users. Based on these, the BF vectors and BF users are determined for each cell using only local information. Then, two distributed user grouping strategies are proposed. The first one, called channel condition based user clustering (CCUC), performs user grouping in each cell based on the channel conditions. This is conducted independently of the power control part and has low computational complexity. Another algorithm, called power consumption based user clustering (PCUC), uses both the channel conditions and inter-cell interference information to minimize each cell's power consumption. In contrary to CCUC, PCUC is optimized jointly with the power control. Finally, with the obtained user grouping and BF vectors, the resultant power allocation problem is optimally solved via an iterative algorithm, whose convergence is mathematically proven given that the problem is feasible. We perform Monte-Carlo simulation and numerical results show that the proposed resource management methods outperform various conventional MISO schemes and the non-clustered MISO-NOMA strategy in several aspects, including power consumption, outage probability, energy efficiency, and connectivity efficiency.

In this paper, we propose a novel secure random caching scheme for large-scale multi-antenna heterogeneous wireless networks, where the base stations (BSs) deliver randomly cached confidential contents to the legitimate users in the presence of passive eavesdroppers as well as active jammers. In order to safeguard the content delivery, we consider that the BSs transmits the artificial noise together with the useful signals. By using tools from stochastic geometry, we first analyze the average reliable transmission probability (RTP) and the average confidential transmission probability (CTP), which take both the impact of the eavesdroppers and the impact of the jammers into consideration. We further provide tight upper and lower bounds on the average RTP. These analytical results enable us to obtain rich insights into the behaviors of the average RTP and the average CTP with respect to key system parameters. Moreover, we optimize the caching distribution of the files to maximize the average RTP of the system, while satisfying the constraints on the caching size and the average CTP. Through numerical results, we show that our proposed secure random caching scheme can effectively boost the secrecy performance of the system compared to the existing solutions.

The combination between non-orthogonal multiple access (NOMA) and hybrid automatic repeat request (HARQ) is capable of realizing ultra-reliability, high throughput and massive concurrent connections particularly for emerging communication systems. This paper focuses on characterizing the asymptotic scaling law of the outage probability of the HARQ with chase combining (HARQ-CC)-aided downlink NOMA scheme with respect to the transmit power, i.e., diversity order. The diversity order of the two- user HARQ-CC-aided NOMA system is derived in closed-form, where an integration domain partition trick is developed to obtain the upper and lower bounds of the outage probability. The analytical results show that the diversity order is a decreasing step function of transmission rate given the ratio between the transmit powers allocated to the two users. Moreover, full time diversity can only be achieved under a sufficiently low transmission rate. Additionally, the users' diversity orders follow a descending order according to their respective average channel gains. Monte Carlo simulations finally confirm the analysis.

Recently, Mobile-Edge Computing (MEC) has been considered as a powerful supplement to a wireless network by processing computationally intensive tasks for resource-limited mobile devices. However, despite saving computational energy at User Equipment (UE), there is additional transmission energy consumption. As a result, the joint offloading strategy should be carefully selected to save energy and computational time. In this work, we investigated a sum cost minimization problem in a multi-UE multi-computing access point (CAP) system with time-varying channels. Our approach combines the optimization-based resource allocation algorithm with a Q-learning-based strategy selection mechanism. Without the need for communication overhead for CSI and inter- neighborhood cost value exchange, our algorithm shows prominent performance over the benchmark schemes with moderate assumptions.

The user pairing problem for cache-based timeslotted non-orthogonal multiple access (NOMA) system with index coding is investigated. During each time slot, the packets of two users are scheduled at the base station. In accordance with different cache information of the scheduled users, either superposition coding or index coding is applied for base station transmission. For some specific case, the superior performance on the aspect of power consumption of index coding compared to that of superposition coding is analyzed. Besides, the power saving of our design system when compared to that of the NOMA system with pure superposition coding is also demonstrated in a mathematical way. Subsequently, we show that the original user scheduling problem can be transformed in quadratic time into a minimum weight perfect matching problem of an undirected graph, which can be solved with time complexity O(K3), where K is the number of users. Based on this transformation, the feasibility of any given system is analyzed. Furthermore, we formulate the minimum weight perfect matching problem as an integer linear problem and solve it by integer linear programming. Numerical results validate the performance gains of our proposed system from the aspects of total transmit power and outage probability.

Both small cells and non-orthogonal multiple access (NOMA) are cutting-edge technologies to boost the system capacity for the future wireless communications. In this paper, an analytical framework for small cell uplinks using NOMA transmissions is developed. To facilitate the detection of both near and far user signals, dual path loss compensation factors (PLCFs) are exploited for uplink power control. With the aid of stochastic geometry, the average successful transmission probability are derived for small cell networks with opportunistic NOMA transmissions and dual PLCFs. Based on the derived results, optimal PLCFs are obtained by two-dimension search methods. It is shown by simulation that the proposed NOMA scheme with optimal dual PLCFs outperforms the existing NOMA scheme with a unique PLCF in terms of both average successful transmission probability and spectrum efficiency.

This paper investigates the performance of Multipleinput multiple-output non-orthogonal multiple access (MIMONOMA) systems with randomly deployed users, where the randomly deployed NOMA users follow Poisson point process (PPP), the spatial correlation between MIMO channels are characterized by using Kronecker model, and the composite channel model is used to capture large-scale fading as well as small-scale fading. The spatial randomness of users' distribution, the spatial correlation among antennas and large-scale fading will severally impact the system performance, but they are seldom considered in prior literature for MIMO-NOMA systems, and the consideration of all these impact factors challenges the analysis. Based on zeroforcing (ZF) detection, the exact expressions for both the average outage probability and the average goodput are derived in closedform. Moreover, the asymptotic analyses are conducted for both high signal-to-noise ratio (SNR) (/small cell radius) and low SNR (/large cell radius) to gain more insightful results. In particular, the diversity order is given by δ = Nr- M + 1, the average outage probability of k-th nearest user to the base station follows a scaling law of O (Dα(Nr-M+1)+2k), the average goodput scales as O(D2) and O(D-2) as D → 0 and D → ∞, respectively, where Nr, M, α and D stand for the number of receive antennas, the total number of data streams, the path loss exponent and the cell radius, respectively. The analytical results are finally validated through the numerical analysis.

In this paper, we investigate the distributed power allocation algorithm for the downlink of a two-cell multiple input and single output non- orthogonal multiple access (MISO-NOMA) system. The problem targets at minimizing the total power consumption of the base stations (BSs) while taking into consideration each user's data rate requirement. A distributed power control algorithm is devised. During each iteration, the BS updates the transmit power of its attached users according to the link gain vector and the inter-cell interference plus noise value at the users. For some special cases, we show that the proposed algorithm is guaranteed to converge to a unique fixed point that could be an optimal solution based on Yate's power control framework. Furthermore, some modifications are made for the iterative algorithm to enhance the convergence performance of the instances with feasible solutions. Simulation results demonstrate that the designed power allocation strategy can significantly improve system performance over conventional orthogonal multiple access (OMA) counterpart in terms of total transmit power and outage probability.

International audience

This paper investigates the power control problem for the downlink of a multi-cell non-orthogonal multiple access system. The problem, called P-OPT, aims to minimize the total transmit power of all the base stations subject to the data rate requirements of the users. The feasibility and optimality properties of P-OPT are characterized through a related optimization problem, called Q-OPT, which is constituted by some relevant power control subproblems. First, we characterize the feasibility of Q-OPT and prove the uniqueness of its optimal solution. Next, we prove that the feasibility of P-OPT can be characterized by the Perron-Frobenius eigenvalues of the matrices arising from the power control subproblems. Subsequently, the relationship between the optimal solutions to P-OPT and that to Q-OPT is presented, which motivates us to obtain the optimal solution to P-OPT through solving the corresponding Q-OPT. Furthermore, a distributed algorithm to solve Q-OPT is designed, and the underlying iteration is shown to be a standard interference function. According to Yates's power control framework, the algorithm always converges to the optimal solution if exists. Numerical results validate the convergence of the distributed algorithm and quantify the improvement of our proposed method over fractional transmit power control and orthogonal multiple access schemes in terms of power consumption and outage probability.

This paper investigates the power control problem for the downlink of a non-orthogonal multiple access (NOMA) system with two cells. The problem, called p-Opt, aims to minimizes the total transmit power of the base stations subject to the data rate requirements of the users. The feasibility and optimality properties of p-Opt is first characterized. It is proved that the feasible power region of p-Opt can be represented by the feasible regions of four power control subproblems that constitute a related optimization problem called q-Opt. Furthermore, the optimal solution to p-Opt can be obtained by solving the corresponding instance of q-Opt. A distributed algorithm to solve q-Opt is designed and the underlying iteration is shown to be a standard interference function. According to Yates's power control framework, the algorithm always converges to the optimal solution if exists. Numerical results validate the convergence of the distributed algorithm and quantify the improvement of NOMA over its orthogonal multiple access counterparts in terms of power consumption and outage probability.

This paper investigates the power control problem for the uplink of a non-orthogonal multiple access (NOMA) system with two cells. The game-theoretic approach is used to study the stability of distributed power control algorithms. It is shown that a unique Nash equilibrium exists if the Perron-Frobenius eigenvalue of a certain link gain matrix is less than one. A distributed power control algorithm is constructed, which is guaranteed to converge to the Nash equilibrium. Furthermore, the optimality property of the Nash equilibrium is studied. It is shown that the equilibrium is globally optimal in minimizing total power consumption, provided that some technical conditions are satisfied. Numerical results show that the power-controlled NOMA system outperforms its orthogonal counterparts.