The energy consumption of communications networks has been steadily increasing, becoming one of the key factors restricting the development of mobile communications. Cell-free massive multiple-input multiple-output (CF-mMIMO) systems offer wide coverage, high spectral efficiency (SE) and excellent energy efficiency (EE), and by integrating renewable energy sources, can greatly enhance their energy-saving capabilities. However, differences in energy harvesting (EH) efficiency and communications conditions across various deployment locations bring significant challenges. In this paper, we investigate the access point (AP) deployment strategy for CF-mMIMO systems with hybrid energy supply. We formulate an optimization problem aimed at maximizing grid EE and propose an AP deployment optimization method based on the deep deterministic policy gradient (DDPG) algorithm. By considering the large number of potential AP locations, aK-means-based preliminary AP selection method is introduced to significantly reduce the action space dimensionality in the DDPG framework. Simulation results indicate that the proposedK-means-based approach effectively reduces the action space dimensionality and enhances the training efficiency of the DDPG algorithm. Furthermore, the proposed method shows great effectiveness in improving the grid EE.

Efficient resource allocation in hardware-impaired cell-free systems is critical for achieving the stringent requirements of ultra-reliable low-latency communication (URLLC) while maintaining energy efficiency (EE). Traditional optimization-based approaches face scalability issues, while existing learning-based methods struggle to adapt to varying network structures and the complexities of hardware impairments. In this work, we address these challenges by incorporating rate-splitting multiple access (RSMA) into cell-free systems and designing a graph neural network (GNN)-based framework. First, we propose a method leveraging explicit channel state information to optimize precoding for both common and private streams under rate, power, and latency constraints. Next, we further develop an end-to-end approach that bypasses channel estimation by directly using raw pilot signals for joint feature extraction and optimization. Finally, we introduce a pilot-free method that processes distorted message-passing information from real channels, reducing communications overhead while enhancing adaptability to practical conditions. Through extensive simulations, we validate the proposed methods, demonstrating significant improvements in EE, along with insights into their computational complexity and scalability in diverse system configurations.

Future wireless networks aim to deliver ultra-reliable and low-latency services while containing their rapidly growing energy footprint. In a cell-free radio access network (CF-RAN), this objective translates into a tightly coupled optimisation over access point (AP) activation, user association, precoding design and virtual-CPU provisioning, all under finite-blocklength reliability constraints. We build a detailed power model that includes radio hardware, fronthaul and load-dependent computing, then recast the resulting energy efficiency problem as a mixed-integer second-order cone programming using a tight surrogate for decoding-error probability. A sparsity-promoting convex–concave solver can reach near-optimal solutions but must be run for every channel realisation, making real-time use impractical. To overcome this limitation, we propose a graph neural network (GNN) that represents CF-RAN as a heterogeneous AP-to-user graph, predicts precoding vectors, rates and soft association probabilities in a single forward pass, and then applies a lightweight reliability-enforcement layer to remove any residual violations. Simulation results show that, whenever the constraints are feasible, the learned solver achieves comparable energy efficiency as the optimization-based baseline while operating with only a single-pass inference step per channel realization.

This work explores the maximization of energy efficiency (EE) in cell-free ultra-reliable low-latency communication (URLLC) systems, specifically addressing the challenges presented by hardware impairments (HWIs). We introduce ratesplitting multiple access (RSMA) as an effective technique to enhance EE by managing the distortions caused by HWIs during downlink transmission. The optimization problem is formulated to maximize EE by optimizing precoding vectors for both common and private streams, while adhering to rate, power, and URLLC constraints. To address the non-convex and dynamic nature of this optimization problem, we propose a graph neural network (GNN) model that facilitates scalable and data-efficient solutions. Simulation results demonstrate that the proposed RSMA-GNN method consistently outperforms baseline approaches, including space-division multiple access (SDMA) and successive convex approximation (SCA) methods, particularly in scenarios characterized by severe HWIs.

This paper introduces a two-stage operational framework for computation offloading in the mobile edge computing (MEC)-enabled cell free massive multiple-input multiple-output (CF-mMIMO) systems. The computation offloading task is divided into data transmission and task computing sub-tasks to achieve fine-grained optimization of energy efficiency and reduce task complexity. A staged multi-agent deep deterministic policy gradient (MADDPG) algorithm is proposed to solve the energy optimization problem. In the transmission stage, user equipments (UEs) act as agents to optimize the allocation of transmission power, while in the computing stage, access point (AP) clusters act as agents to optimize the allocation of computational resources. The simulation results demonstrate that compared with the baseline algorithm, the proposed scheme can achieve a significant improvement in energy consumption during the transmission stage, and near-optimal performance with lower complexity during the computing stage.

The ultra-dense access point (AP) deployment in cell-free massive multiple-input multiple-output (CF-mMIMO) networks significantly escalates the energy burden. Equipping APs with energy harvesting (EH) capabilities can reduce grid energy comsumption by leveraging renewable resources. And strategic AP selection can further enhance energy savings without compromising quality of service (QoS) during low-traffic periods. Therefore, we formulate a problem of minimizing grid energy consumption under stochastic EH patterns and different QoS requirements, where AP selection and power allocation (PA) are taken into consideration jointly. Then, we reformulate it as a mixed-integer second-order cone programming (MISOCP) problem for optimal solutions. Given the prohibitive complexity for large-scale networks, we propose: 1) A sparsity-enhanced iterative optimization algorithm employing reweighted L1-norm relaxation to overcome the non-convexity of L0-norm optimization, and 2) A deep reinforcement learning (DRL)-based AP selection algorithm that circumvents combinatorial complexity. Simulation results show that compared to baseline algorithms, the proposed algorithms can significantly save energy consumption, which approximate the optimal solution with dramatically lower complexity.

In this paper, we investigate a cooperative edge caching problem in the fog radio access networks (F-RANs). In order to obtain the globally optimal caching strategy that minimizes the content transmission delay and maximizes communication efficiency, we propose a multi-agent reinforcement learning based cooperative caching policy with low entropy communications. First, we propose a double deep Q network (DDQN) based caching policy by taking into account the non-deterministic polynomial hard (NP-hard) aspect of this cooperative caching optimization problem. Then, we extend the state transition model of Markov Decision Process (MDP) under the single agent system into the Stochastic Game (SG) one under the multi-agent system. By employing the DDQN in each agent, the agents can learn and make the global decision for caching. For utilizing the cooperation resources of fog access points (F-APs), the interaction of information is introduced to exchange the historical cache records of cooperative F-APs. However, the information in the interaction may require lower entropy in the fiber link. Therefore, the information entropy is largely reduced to improve the communication efficiency by quantifying the information. Finally, due to the non-computable gradient of information entropy, we apply a pseudo gradient descent method to approximate the gradient descent in the local model. Simulation results show that our policy achieves better performance in terms of reducing the transmission delay and improving the cooperation among F-APs compared to the benchmark policies. Additionally, it is demonstrated that the proposed policy improves communication efficiency without compromising the performance of cooperative caching.

Ultra-Reliable and Low-Latency Communications (URLLC) is essential for sixth generation communications, with Cell-Free massive Multiple-input-Multiple-Output (CF mMIMO) being a promising architecture to support these demands. This paper addresses the challenge of optimizing energy efficiency in CF mMIMO systems for URLLC, focusing on the probabilistic delay bounds and finite blocklength communications. We propose a theoretical framework that considers tail distributions to evaluate extreme reliability and latency requirements, instead of relying on asymptotic analysis. In particular, a closed-form expression for the signal-to-interference-plus-noise ratio (SINR) distribution is derived, accommodating imperfections in channel state information caused by pilot contamination. Then, the paper also presents a comprehensive reliability analysis, incorporating both delay violation probability and average decoding error probability, utilizing stochastic network calculus for accurate statistical modeling. Finally, an innovative power control algorithm is proposed to maximize effective energy efficiency (EEE), the ratio of the effective data rate to total power consumption, while meeting stringent Quality-of-Service (QoS) constraints and power limits. Extensive simulations validate the theoretical framework and the efficacy of the proposed algorithm, demonstrating its ability to enhance EEE in various scenarios and providing insights into the interplay between EEE, delay, and reliability metrics.

Task offloading is a promising approach to efficiently realize delay-sensitive, computation-intensive applications in Internet of Vehicles (IoVs). However, task allocation and scheduling pose great challenges in Vehicular Fog Computing (VFC) environment due to resource heterogeneity, workload unpredictability, fixed Fog Access Points (F-APs), and the dynamic nature of fog environment. This paper investigates the delay- and energy-efficient task offloading strategy in Cell Free massive MIMO (CF-mMIMO)-enabled VFC network. CF-mMIMO system is integrated into the VFC network so that task transfer among F-APs is enabled. A Long Short Term Memory (LSTM)-based algorithm is designed to predict the workload of F-APs. Based on the result, the delay and energy consumption of a task if it is offloaded on a F-AP can be calculated. After that, a Multi-Agent Deep Deterministic Policy Gradient (MADDPG)-based algorithm is developed to explore the best combination of task offloading and resource allocation strategies to reduce the overhead of each vehicle, and to minimize the long-term system cost, eventually. Simulation results show that the proposed strategy not only exhibits good convergence performance in scenario which involves a mixture of continuous-discrete action spaces, but also achieves satisfying performance in terms of average cost under varied circumstances.

In this paper, we investigate the energy efficiency (EE) optimization in the user-centric Cell-Free massive MIMO (CF mMIMO) system for Ultra-Reliable Low-Latency Communications (URLLC), where access points (APs) use maximum ratio transmission for downlink transmission. We first formulate an optimization problem to maximize the system EE while taking the finite blocklength achievable rate in URLLC into consideration. To deal with the intractable achievable rate in the objective function and the constraint, we derive a convex lower bound of it using successive convex approximation (SCA), and then reformulate the original problem into a second-order cone programming (SOCP). Next, we propose a low-complexity iterative algorithm to solve the SOCP by applying SCA. Simulation results show that the proposed method provides near-optimal performance in terms of Branch-and-Bound (BnB), and provide insights into the influences of blocklength, system parameters and AP clustering schemes on system EE.

In this paper, the cooperative edge caching problem is studied in fog radio access networks (F-RANs). Given the non-deterministic polynomial hard (NP-hard) nature of the problem, a dueling deep Q network (Dueling DQN) based caching update algorithm is proposed to make an optimal caching decision by learning the dynamic network environment. In order to protect user data privacy and solve the problem of slow convergence of the single deep reinforcement learning (DRL) model training, we propose a communication-efficient federated deep reinforcement learning (CE-FDRL) method to implement cooperative training of models from multiple fog access points (F-APs) in F-RANs. To address the excessive consumption of communication resources caused by model transmission, we propose to prune and quantize the shared DRL models to reduce the number of transferred model parameters. The communication interval is increased and the communication round is reduced by periodic model aggregation. The global convergence and computational complexity of our proposed method are also analyzed. Simulation results verify that our proposed method can offer better performance in reducing user request delay and improving cache hit rate and the transmitted parameters of our proposed method can drop to 60% compared to the existing benchmark schemes. Our proposed method is also shown to have faster training speed and higher communication efficiency.

In this paper, the computation offloading problem in reconfigurable intelligent surface (RIS)-aided mobile edge computing (MEC)-enabled cell-free radio access network (CF-RAN) is investigated. To minimize the average task execution delay, we propose to formulate a joint optimization problem of computation offloading and RIS phase shifts. Considering the non-deterministic polynomial hard (NP-hard) property of this problem and time-varying network environment, we further propose a meta reinforcement learning (meta-RL)-based computation offloading policy, which can adapt to new environment quickly with only a few gradient updates. By aggregating powerful decision-making ability of conventional RL and rapid environment learning ability of meta-learning, our proposed policy can find the optimal strategy in very fast speed. Simulation results show that our proposed meta-RL-based computation offloading policy reduces the average task execution delay by 25% compared to the considered two state-of-the-art benchmark policies.

As an important part of beyond the fifth-generation (B5G) and the sixth-generation (6G) mobile communication systems, ultra-reliable and low latency communications (uRLLC) puts forward strict requirements for delay and reliability (e.g., 99.9999% reliability and$500 \mu \text{s}$latency). At present, the evaluation measures of delay and reliability are usually based on infinite block length and rely on long-term statistics, which cannot meet the requirement of low latency. The cell-free system, with a very large number of distributed antennas, has the characteristics of macro-diversity and spatial sparsity, which can further enhance the performance of uRLLC. In this paper, the downlink multidevice cell-free system with hard deadlines is considered and analyzed in the finite block length (FBL) regime. The communication’s delay and reliability are described based on two instantaneous evaluation measures: transmission error (TE) and time overflow (TO) probability. From the perspective of information theory, this paper analyzes the analytic expression of TE probability for a single device and the performance impact of FBL on the traditional channel capacity analysis. Considering the multidevice TO probability in a cell-free system, the closed-form expressions of upper and lower bounds are derived and compared with the gamma approximation results. This paper further provides three methods, namely, transmission rate selection, device grouping and space division multiplexing, to balance the delay and reliability of the system and analyzes the performance.

In this paper, we investigate the content popularity prediction problem in cache-enabled fog radio access networks (F-RANs). In order to predict the content popularity with high accuracy and low complexity, we propose a Gaussian process based regressor to model the content request pattern. Firstly, the relationship between content features and popularity is captured by our proposed model. Then, we utilize Bayesian learning to train the model parameters, which is robust to overfitting. However, Bayesian methods are usually unable to find a closed-form expression of the posterior distribution. To tackle this issue, we apply a stochastic variance reduced gradient Hamiltonian Monte Carlo (SVRG-HMC) method to approximate the posterior distribution. To utilize the computing resource of fog access points (F-APs) and also reduce the communication overhead, we propose a quantized federated learning (FL) framework combining with Bayesian learning. The proposed quantized federated Bayesian learning framework allows each F-AP to send gradients to the cloud server after quantizing and encoding. It can achieve a tradeoff between prediction accuracy and communication overhead effectively. Simulation results show that the performance of our proposed policy outperforms the considered baseline policies.

In this paper, the cooperative edge caching problem in fog radio access networks (F-RANs) is investigated. To minimize the content transmission delay, we formulate the cooperative caching optimization problem to find the globally optimal caching strategy. By considering the non-deterministic polynomial hard (NP-hard) property of this problem, a Multi Agent Reinforcement Learning (MARL)-based cooperative caching scheme is proposed. Our proposed scheme applies a double deep Q-network (DDQN) in every fog access point (F-AP), and introduces the communication process in a multi-agent system. Every F-AP records the historical caching strategies of its associated F-APs as the observations of communication procedure. By exchanging the observations, F-APs can leverage the cooperation and make the globally optimal caching strategy. Simulation results show that the proposed MARL-based cooperative caching scheme has remarkable performance compared with the benchmark schemes in minimizing the content transmission delay.

In this paper, the cooperative caching problem in fog radio access networks (F-RANs) is investigated to jointly optimize the transmission delay and energy consumption. Exploiting the potential social relationships among fog access points (F-APs), we firstly propose a clustering scheme based on hedonic coalition game (HCG) to improve the potential cooperation gain. Then, considering that the optimization problem is non-deterministic polynomial hard (NP-hard), we further propose an improved firefly algorithm (FA) based cooperative caching scheme, which utilizes a mutation strategy based on local content popularity to avoid pre-mature convergence. Simulation results show that our proposed scheme can effectively reduce the content transmission delay and energy consumption in comparison with the baselines.

In this paper, we investigate maximum distance separable (MDS) codes based group coded caching in fog radio access networks (F-RANs). The goal is to minimize the average fronthaul rate under nonuniform file popularity. Firstly, an MDS codes and file grouping based coded placement scheme is proposed to provide coded packets and allocate more cache to the most popular files simultaneously. Next, a fog access point (F-AP) grouping based coded delivery scheme is proposed to meet the requests for files from different groups. Furthermore, a closed-form expression of the average fronthaul rate is derived. Finally, the parameters related to the proposed coded caching scheme are optimized to fully utilize the gains brought by MDS codes and file grouping. Simulation results show that our proposed scheme obtains significant performance improvement over several existing caching schemes in terms of fronthaul rate reduction.

In this paper, the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. Based on clustered federated learning, we propose a novel mobility-aware popularity prediction policy, which integrates content popularities in terms of local users and mobile users. For local users, the content popularity is predicted by learning the hidden representations of local users and contents. Initial features of local users and contents are generated by incorporating neighbor information with self information. Then, dual-channel neural network (DCNN) model is introduced to learn the hidden representations by producing deep latent features from initial features. For mobile users, the content popularity is predicted via user preference learning. In order to distinguish regional variations of content popularity, clustered federated learning (CFL) is employed, which enables fog access points (F-APs) with similar regional types to benefit from one another and provides a more specialized DCNN model for each F-AP. Simulation results show that our proposed policy achieves significant performance improvement over the traditional policies.

In this paper, the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. In order to obtain accurate prediction with low complexity, we propose a novel context-aware popularity prediction policy based onfederated learning(FL). Firstly, user preference learning is applied by considering that users prefer to request the contents they are interested in. Then, users’ context information is utilized to cluster users efficiently by adaptive context space partitioning. After that, we formulate a popularity prediction optimization problem to learn the local model parameters by using the stochastic variance reduced gradient (SVRG) algorithm. Finally, FL based model integration is proposed to learn the global popularity prediction model based on local models using the distributed approximate Newton (DANE) algorithm with SVRG. Our proposed popularity prediction policy not only can predict content popularity accurately, but also can significantly reduce computational complexity. Moreover, we theoretically analyze the convergence bound of our proposed FL based model integration algorithm. Simulation results show that our proposed policy increases the cache hit rate by up to 21.5 % compared to existing policies.

In this paper, we investigate maximum-distance separable (MDS) codes and weighted graph based coded caching in fog radio access networks (F-RANs). In the placement phase, the redundant MDS based coded placement scheme is used to provide redundant coded packets and homogeneous cached contents. The redundant coded packets can be used to construct multicast opportunities for similar requests. In the delivery phase, the weighted graph based coded delivery scheme is conducted based on homogeneous cached contents, which can induce considerable multicast opportunities. By integrating the above two schemes, a joint MDS codes and weighted graph based coded caching policy is proposed to minimize the fronthaul load. Finally, we theoretically analyze the performance of the proposed policy by deriving the lower and upper bounds of the fronthaul load. Simulation results show that our proposed policy can provide 44% savings in the fronthaul load compared to the MDS-based uncoded delivery policy.

In this paper, the content popularity prediction problem in cache-enabled fog radio access networks (F-RANs) is investigated. In order to predict the content popularity with high accuracy and low complexity, we propose a Gaussian process based Poisson regressor to model the content request pattern. Firstly, the relationship between content features and popularity is captured by our developed model. Then, we utilize Bayesian learning to learn the model parameters, which are robust to over-fitting. However, Bayesian methods are usually unable to find a closed-form expression of the posterior distribution. To tackle this issue, we apply a Stochastic Variance Reduced Gradient Hamiltonian Monte Carlo (SVRG-HMC) to approximate the posterior distribution. Two types of predictive content popularity are formulated for the requests of existing contents and newly-added contents. Simulation results show that the performance of our proposed policy outperforms the policy based on other Monte Carlo based method.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

In this article, delay and energy efficiency (EE) are investigated in fog radio access networks (F-RANs) with hybrid caching. With multiple caching and transmission strategies, hybrid caching offers great flexibility for file placement and file fetching. By using tools from stochastic geometry, we firstly derive tractable expressions of delay for coded cached, non-partitioned cached and uncached files. Then, we derive tractable expressions of EE by jointly considering power consumed in circuits, transmissions and fronthaul links. To balance delay and EE, the corresponding multi-objective optimization problem is formulated to obtain the optimal hybrid caching strategy. Furthermore, considering the NP-hard complexity of the problem, we first theoretically analyze the optimal structure of the caching result. Then, we convert the original problem into a classification problem. We further propose a gradual-replacement greedy algorithm to obtain a near optimal hybrid caching strategy, which ensures high accuracy with low complexity. Numerical results show a significant performance gain of the proposed near optimal hybrid caching strategy over baselines and flexibility in delay-sensitive and EE-sensitive scenarios.

In this paper, the cooperative caching problem in fog radio access networks (F-RANs) is investigated. To minimize the content request delay, we formulate the hierarchical cooperative caching optimization problem to find the optimal caching policy. Considering the non-deterministic polynomial hard (NP-hard) property of this problem, we propose a brain storm optimization (BSO) approach which utilizes the penaltybased fitness function in individuals evaluation to meet the storage capacity constraint and the genetic algorithm (GA) in new individuals generation to meet the integer constraint, respectively. To further reduce the computational complexity, we propose to implement the convergent operation in the objective space via individuals classification. Simulation results show that our proposed BSO-based hierarchical cooperative caching policy achieves remarkable performance in minimizing the content request delay.

In this paper, the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. In order to obtain accurate prediction with low complexity, we propose a novel context-aware popularity prediction policy based on federated learning. Firstly, user preference learning is applied by considering that users prefer to request the contents they are interested in. Then, users' context information is utilized to cluster users efficiently by adaptive context space partitioning. After that, we formulate a popularity prediction optimization problem to learn the local model parameters using the stochastic variance reduced gradient (SVRG) algorithm. Finally, federated learning based model integration is proposed to construct the global popularity prediction model based on local models by combining the distributed approximate Newton (DANE) algorithm with SVRG. Our proposed popularity prediction policy not only predicts content popularity accurately, but also significantly reduces computational complexity. Simulation results show that our proposed policy increases the cache hit rate by up to 21.5 % compared to the traditional policies.

In this paper, the edge caching optimization problem in fog radio access networks (F-RANs) is investigated. Taking into account time-variant user requests and ultra-dense deployment of fog access points (F-APs), we propose a distributed edge caching scheme to jointly minimize the request service delay and fronthaul traffic load. Considering the interactive relationship among F-APs, we model the optimization problem as a stochastic differential game (SDG) which captures the dynamics of F-AP states. To address both the intractability problem of the SDG and the caching capacity constraint, we propose to solve the optimization problem in a distributive manner. Firstly, a mean field game (MFG) is converted from the original SDG by exploiting the ultra-dense property of F-RANs, and the states of all F-APs are characterized by a mean field distribution. Then, an iterative algorithm is developed that enables each F-AP to obtain the mean field equilibrium and caching control without extra information exchange with other F-APs. Secondly, a fractional knapsack problem is formulated based on the mean field equilibrium, and a greedy algorithm is developed that enables each F-AP to obtain the final caching policy subject to the caching capacity constraint. Simulation results show that the proposed scheme outperforms the baselines.

In this paper, we investigate the problem of asynchronous coded caching in fog radio access networks (F-RANs). To minimize the fronthaul load, the encoding set collapsing rule and encoding set partition method are proposed to establish the relationship between the coded-multicasting contents for asynchronous and synchronous coded caching. Furthermore, a decentralized asynchronous coded caching scheme is proposed, which provides asynchronous and synchronous transmission methods for different delay requirements. The closed-form expression of the fronthaul load is established for the special case where the number of requests during each time slot is fixed, and the upper and lower bounds of the fronthaul load are given for the general case where the number of requests during each time slot is random. The simulation results show that our proposed scheme can create considerable coded-multicasting opportunities in asynchronous request scenarios.

In this article, the edge caching policy in fog radio access networks (F-RANs) is optimized via deep learning. Considering that it is hard for fog access points (F-APs) to collect sufficient data of massive content features, our proposed edge caching policy only utilizes the number of requests and user location. In an offline phase, we propose to learn the corresponding popularity prediction model for every content popularity trend class and user location prediction models to make the popularity prediction accurate, adaptive and targeted. Moreover, we develop a loss function to avoid overfitting and increase sensitivity to high popularity for popularity prediction models. In an online phase, we propose a reactive caching scheme to react to user requests. In order to guarantee that classification can improve the popularity prediction accuracy in both phases, deep learning and k-Nearest Neighbor (kNN) are combined to classify popularity trends. Besides, a joint proactive-reactive caching policy is proposed to maximize the cache hit rate. The proposed policy is able to promptly track the various popularity trends with spatial-temporal popularity, trend and user dynamics with a low computational complexity. Extensive performance evaluation results show that the cache hit rate of our proposed policy approaches that of the optimal policy.

In this paper, the content popularity prediction problem in cache-enabled fog radio access networks (F-RANs) is investigated. In order to make the popularity prediction accurate and adaptive, we propose to learn the corresponding popularity prediction model for every content class from the preprocessed popularity series by training a simplified bidirectional long short-term memory (Bi-LSTM) network, and further use it to help build a content classifier in terms of content popularity trend in the training phase. Then, content popularity can be predicted by the right prediction model with respect to the corresponding content class in the predicting phase. Considering that it is hard to collect enough data about numerous content features through F-APs, we propose to only use the number of requests. Our proposed content popularity prediction policy offers a high prediction accuracy with low computational complexity by transferring the high complexity tasks from the predicting phase to the training phase. Simulation results show that the cache hit rate of our proposed policy approaches the optimal performance.

In this paper, the cooperative edge caching problem in fog radio access networks (F-RANs) is investigated to minimize the average download delay. Considering the non-linear and coupled multi-variable nature of the original optimizing problem, we transform it into an equivalent integer linear programming problem with decoupled variables. Then, we decomposed the transformed problem into two subproblems which can be solved separately by each fog access point (F-AP). Considering the non-deterministic polynomial hard (NP-hard) nature of the two decomposed subproblems, we propose an improved pigeon inspired optimization (PIO) based cooperative edge caching scheme, which utilizes Cauchy perturbation and self-adaptive factor to avoid pre-mature convergence and achieve a better search performance, respectively. Our proposed scheme not only allows F-APs to make cache decisions with low computational complexity, but also has very low message passing overhead. Simulation results show that our proposed scheme can greatly decrease the average download delay.

In this paper, the distributed edge caching problem in fog radio access networks (F-RANs) is investigated. By considering the unknown spatio-temporal content popularity and user preference, a user request model based on hidden Markov process is proposed to characterize the fluctuant spatio-temporal traffic demands in F-RANs. Then, the Q-learning method based on the reinforcement learning (RL) framework is put forth to seek the optimal caching policy in a distributed manner, which enables fog access points (F-APs) to learn and track the potential dynamic process without extra communications cost. Furthermore, we propose a more efficient Q-learning method with value function approximation (Q-VFA-learning) to reduce complexity and accelerate convergence. Simulation results show that the performance of our proposed method is superior to those of the traditional methods.

In this study, cooperative caching is investigated in fog radio access networks. To maximise the offloaded traffic, a cooperative caching optimisation problem is formulated. By analysing the relationship between clustering and cooperation and utilising the solutions of the knapsack problems, the above challenging optimisation problem is transformed into a clustering subproblem and a content placement subproblem. To further reduce complexity, the authors propose an effective graph‐based approach to solve the two subproblems. In the graph‐based clustering approach, a node graph and a weighted graph are constructed. By setting the weights of the vertices of the weighted graph to be the incremental offloaded traffics of their corresponding complete subgraphs, the objective cluster sets can be readily obtained by using an effective greedy algorithm to search for the max‐weight independent subset. In the graph‐based content placement approach, a redundancy graph is constructed by removing the edges in the complete subgraphs of the node graph corresponding to the obtained cluster sets. Furthermore, they enhance the caching decisions to ensure each duplicate file is cached only once. Compared with traditional approximate solutions, their proposed graph‐based approach has lower complexity. Simulation results show remarkable improvements in terms of offloaded traffic by using the proposed approach.

In this paper, the edge caching problem in fog radio access network (F-RAN) is investigated. By maximizing the overall cache hit rate, the edge caching optimization problem is formulated to find the optimal policy. Content popularity in terms of time and space is considered from the perspective of regional users. We propose an online content popularity prediction algorithm by leveraging the content features and user preferences, and an offline user preference learning algorithm by using the online gradient descent (OGD) method and the follow the (proximally) regularized leader (FTRL-Proximal) method. Our proposed edge caching policy not only can promptly predict the future content popularity in an online fashion with low complexity, but also can track the content popularity with spatial and temporal popularity dynamic in time without delay. Furthermore, we design two learning-based edge caching architectures. Moreover, we theoretically derive the upper bound of the popularity prediction error, the lower bound of the cache hit rate, and the regret bound of the overall cache hit rate of our proposed edge caching policy. Simulation results show that the overall cache hit rate of our proposed policy is superior to those of the traditional policies and asymptotically approaches the optimal performance.

In this paper, a joint transmitter and receiver design for pattern division multiple access (PDMA) is proposed. At the transmitter, pattern mapping utilizes power allocation to improve the overall sum rate, and beam allocation to enhance the access connectivity. At the receiver, hybrid detection utilizes a spatial filter to suppress the inter-beam interference caused by beam-domain multiplexing, and successive interference cancellation to remove the intra-beam interference caused by power-domain multiplexing. Furthermore, we propose a PDMA joint design approach to optimize pattern mapping based on both the power domain and beam domain. The optimization of power allocation is achieved by maximizing the overall sum rate, and the corresponding optimization problem is shown to be convex theoretically. The optimization of beam allocation is achieved by minimizing the maximum of the inner product of any two beam allocation vectors, and an effective dimension reduction method is proposed through the analysis of pattern structure and proper mathematical manipulations. Simulation results show that the proposed PDMA approach outperforms the orthogonal multiple access and power-domain non-orthogonal multiple access approaches even without any optimization of pattern mapping, and the optimization of beam allocation yields a significant performance improvement than the optimization of power allocation.

In this paper, power control in the uplink for two-tier small-cell networks is investigated. We formulate the power control problem as a Stackelberg game, where the macrocell user equipment (MUE) acts as the leader and the small-cell user equipment (SUE) acts as the follower. To reduce the cross-tier and cotier interferences and the power consumption of both the MUE and SUE, we propose optimizing not only the transmit rate but also the transmit power. The corresponding optimization problems are solved through a two-layer iteration. In the inner iteration, the SUE items (SUEs) compete with each other, and their optimal transmit powers are obtained through iterative computations. In the outer iteration, the optimal transmit power of the MUE is obtained in a closed form based on the transmit powers of the SUEs through proper mathematical manipulations. We prove the convergence of the proposed power control scheme, and we also theoretically show the existence and uniqueness of the Stackelberg equilibrium (SE) in the formulated Stackelberg game. The simulation results show that the proposed power control scheme provides considerable improvements, particularly for the MUE.

In this paper, the edge caching problem in ultra-dense fog radio access networks (F-RAN) is investigated. Taking into account time-variant user requests and ultra-dense deployment of fog access points (F-APs), we propose a dynamic distributed edge caching scheme to jointly minimize the request service delay and fronthaul traffic load. Considering the interactive relationship among F-APs, we model the caching optimization problem as a stochastic differential game (SDG) which captures the temporal dynamics of F-AP states and incorporates user requests status. The SDG is further approximated as a mean field game (MFG) by exploiting the ultra-dense property of F-RAN. In the MFG, each F-AP can optimize its caching policy independently through iteratively solving the corresponding partial differential equations without any information exchange with other F-APs. The simulation results show that the proposed edge caching scheme outperforms the baseline schemes under both static and time-variant user requests.

In this paper, we investigate asynchronous coded caching in fog radio access networks (F-RAN). To minimize the fronthaul load, the encoding set collapsing rule and encoding set partition method are proposed to establish the relationship between the coded-multicasting contents in asynchronous and synchronous coded caching. Furthermore, a decentralized asynchronous coded caching scheme is proposed, which provides asynchronous and synchronous transmission methods for different delay requirements. The simulation results show that our proposed scheme creates considerable coded-multicasting opportunities in asynchronous request scenarios.

In this paper, to make better use of file diversity provided by random caching and improve the successful transmission probability (STP) of a file, we consider retransmissions with random discontinuous transmission (DTX) in a large-scale cache-enabled heterogeneous wireless network (HetNet) employing random caching. We analyze the STP in two mobility scenarios, i.e., the high mobility scenario and the static scenario. In each scenario, by using tools from stochastic geometry and series expansion of some special functions, we obtain the closed-form expressions for the STP in the general and low signal-to-interference ratio (SIR) threshold regimes, respectively. It shows that a larger caching probability corresponds to a higher STP in both scenarios; random DTX can improve the STP in the static scenario and its benefit gradually diminishes when mobility increases. In addition, the asymptotic analysis shows that the diversity gain is jointly affected by random caching and random DTX in both scenarios.

Base station cooperation in heterogeneous wireless networks (HetNets) is a promising approach to improve the network performance, but it also imposes a significant challenge on backhaul. On the other hand, caching at small base stations (SBSs) is considered as an efficient way to reduce backhaul load in HetNets. In this paper, we jointly consider SBS caching and cooperation in a downlink large-scale HetNet. We propose two SBS cooperative transmission schemes under random caching at SBSs with the caching distribution as a design parameter. Using tools from stochastic geometry and adopting appropriate integral transformations, we first derive a tractable expression for the successful transmission probability under each scheme. Then, under each scheme, we consider the successful transmission probability maximization by optimizing the caching distribution, which is a challenging optimization problem with a non-convex objective function. By exploring optimality properties and using optimization techniques, under each scheme, we obtain a locally optimal solution in the general case and a globally optimal solution in some special cases. Compared with some existing caching designs in the literature, e.g., the most popular caching, the i.i.d. caching and the uniform caching, the optimal random caching under each scheme achieves a promising successful transmission probability. The analysis and optimization results provide valuable design insights for practical HetNets.

To make better use of file diversity provided by random caching and improve the successful transmission probability (STP) of a file, we consider retransmissions with random discontinuous transmission (DTX) in a large-scale cache-enabled heterogeneous wireless network employing random caching. We analyze and optimize the STP in two mobility scenarios, i.e., the high mobility scenario and the static scenario. First, in each scenario, by using tools from stochastic geometry, we obtain a closed-form expression for the STP in the general signal-to-interference ratio (SIR) threshold regime. The analysis shows that a larger caching probability corresponds to a higher STP in both scenarios; random DTX can improve the STP in the static scenario and its benefit gradually diminishes when mobility increases. In each scenario, we also derive a closed-form expression for the asymptotic outage probability in the low SIR threshold regime. The asymptotic analysis shows that the diversity gain is jointly affected by random caching and random DTX in both scenarios. Then, in each scenario, we consider the maximization of the STP with respect to the caching probability and the BS activity probability, which is a challenging non-convex optimization problem with a complex objective function. In particular, in the high mobility scenario, we obtain a globally optimal solution. In the static scenario, we develop a low-complexity iterative algorithm to obtain a stationary point. Finally, numerical results show that the proposed solutions achieve significant gains over existing baseline schemes and can well adapt to the changes of the system parameters to wisely utilize storage resources and transmission opportunities.

In this paper, pattern division multiple access with large-scale antenna array (LSA-PDMA) is proposed as a novel non-orthogonal multiple access (NOMA) scheme. In the proposed scheme, pattern is designed in both beam domain and power domain in a joint manner. At the transmitter, pattern mapping utilizes power allocation to improve the system sum rate and beam allocation to enhance the access connectivity and realize the integration of LSA into multiple access spontaneously. At the receiver, hybrid detection of spatial filter (SF) and successive interference cancellation (SIC) is employed to separate the superposed multiple-domain signals. Furthermore, we formulate the sum rate maximization problem to obtain the optimal pattern mapping policy, and the optimization problem is proved to be convex through proper mathematical manipulations. Simulation results show that the proposed LSA-PDMA scheme achieves significant performance gain on system sum rate compared to both the orthogonal multiple access scheme and the power-domain NOMA scheme.

In this paper, energy efficient power allocation for downlink massive MIMO systems is investigated.A constrained non-convex optimization problem is formulated to maximize the energy efficiency (EE), which takes into account the quality of service (QoS) requirements.By exploiting the properties of fractional programming and the lower bound of the user data rate, the non-convex optimization problem is transformed into a convex optimization problem.The Lagrangian dual function method is utilized to convert the constrained convex problem into an unconstrained convex one.Due to the multi-variable coupling problem caused by the intra-user interference, it is intractable to derive an explicit solution to the above optimization problem.Exploiting the standard interference function, we propose an implicit iterative algorithm to solve the unconstrained convex optimization problem and obtain the optimal power allocation scheme.Simulation results show that the proposed iterative algorithm converges in just a few iterations, and demonstrate the impact of the number of users and the number of antennas on the EE.

In this paper, energy efficient power allocation for the uplink of a multi-cell massive MIMO system is investigated. With the simplified power consumption model, the problem of power allocation is formulated as a constrained Markov decision process (CMDP) framework with infinite-horizon expected discounted total reward, which takes into account different quality of service (QoS) requirements for each user terminal (UT). We propose an offline solution containing the value iteration and Q-learning algorithms, which can obtain the global optimum power allocation policy. Simulation results show that our proposed policy performs very close to the ergodic optimal policy.

In this paper, energy efficient power control for the uplink two-tier networks where a macrocell tier with a massive multiple-input multiple-output (MIMO) base station is overlaid with a small cell tier is investigated. We propose a distributed energy efficient power control algorithm which allows each user in the two-tier network taking individual decisions to optimize its own energy efficiency (EE) for the multi-user and multi-cell scenario. The distributed power control algorithm is implemented by decoupling the EE optimization problem into two steps. In the first step, we propose to assign the users on the same resource into the same group and each group can optimize its own EE, respectively. In the second step, multiple power control games based on evolutionary game theory (EGT) are formulated for each group, which allows each user optimizing its own EE. In the EGT-based power control games, each player selects a strategy giving a higher payoff than the average payoff, which can improve the fairness among the users. The proposed algorithm has a linear complexity with respect to the number of subcarriers and the number of cells in comparison with the brute force approach which has an exponential complexity. Simulation results show the remarkable improvements in terms of fairness by using the proposed algorithm.

Decode-and-forward physical-layer network coding (PLNC) is one of the promising high-performance techniques for wireless relay networks, but little has been reported on the case of asynchronous scenarios using practical pulse shaping waveforms. When signals arrive at the relay with symbol level fractional delay, inter-symbol interference (ISI) will occur. This paper presents an iteration-based ISI cancelation scheme for practical asynchronous two-way relay network (TWRN) with fractional delay. Signals from both terminals are separately decoded for ISI reconstruction and elimination. Simulations show that the proposed scheme with even just one iteration can significantly improve the BER performance.

In this paper, we consider the cooperative communication system based on distributed frequency-domain linear convolutive space-frequency codes (FLC-SFC) with multiple carrier frequency offsets (CFOs) when the channels from relay nodes to destination node are flat fading. Through the mathematical derivation, the cooperative system model is simplified. Then, the equivalent banded system model is obtained and the related analysis shows the banded property of the equivalent channel matrix. Furthermore, a banded block minimum mean square error (MMSE) equalization method applying LDLH matrix decomposition is proposed for the banded system model. Besides, a special class of mask matrix is utilized to realize the banded operation. Compared with the traditional MMSE equalization method, the proposed equalization method has a relatively low computational complexity with satisfactory system performance.

In the cooperative relay system, different frames from different relays are not aligned in the time domain, and have different carrier frequency offsets (CFOs). Estimation of these time offsets and multiple CFOs is a crucial and challenging task. In this contribution, a new preamble structure is proposed, and the corresponding timing and frequency synchronization algorithms are also presented for the cooperative relay systems. CAZAC sequence is utilized in the preamble design regarding of its correlation properties. The performance of the proposed algorithms is evaluated by simulations. The simulation results show that the proposed preamble structure and synchronization algorithms have a satisfactory performance in cooperative relay systems.

In this paper, we analyze signal-to-noise ratio (SNR) performance of amplify-and-forward (AF) relay system in the presence of carrier frequency offset (CFO) for fading channels. The expression for the average SNR is derived under one-relay-node scenario, and is further extended to multiple-relay-node scenario. It is shown that the SNR is very sensitive to CFO and the sensitivity of SNR to CFO is mainly determined by the power of the corresponding link channel and gain factor. The analytical results are validated through Monte Carlo simulations for both flat and frequency-selective fading channels.

In this paper, we investigate training aided frequency offset estimation for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems via polynomial rooting over frequency selective fading channels. By designing the training sequences properly, both integer and fractional carrier frequency offsets (CFO) can be estimated from the polynomials corresponding to the cost function. Moreover, we show through analysis that rooting the cost function is equivalent to rooting the first-order derivative of the cost function in the considered MIMO case. Simulation results verify the good performance of our new CFO estimator and the corresponding analytical results.

This paper addresses carrier frequency offset (CFO) estimation and training sequence design for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems over frequency selective fading channels. By exploiting the orthogonality of the training sequences in the frequency domain, integer. CFO (ICFO) is estimated. With the uniformly spaced non-zero pilots in the training sequences and the corresponding geometric mapping, fractional CFO (FCFO) is estimated through the roots of a real polynomial. Furthermore, the condition for the training sequences to guarantee estimation identifiability is developed. Through the analysis of the correlation property of the training sequences, two types of sub-optimal training sequences generated from the Chu sequence are constructed. Simulation results verify the good performance of the CFO estimator assisted by the proposed training sequences.

This paper addresses a low complexity frequency offset estimator for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems over frequency selective fading channels. By exploiting the good correlation property of the training sequences, which are constructed from the Chu sequence, carrier frequency offset (CFO) estimation is obtained with great complexity reduction through factor decomposition for the derivative of the cost function. The CFO estimate's variance and Cramer-Rao bound (CRB) are developed to optimize the parameter of the simplified estimator and also to evaluate the estimation performance. Simulation results verify the good performance of the training-assisted CFO estimator.

In this paper, we present the results of D-InSAR technique and conventional leveling at selected area in Tianjin city, using 8 ENVISAT SAR images between 17 October 2003 and 17 February 2005.The D-InSAR results show that the subsidence rate reached over 5 cm/year (7 cm within 16 months) in highly sinking area, and it is consistent with leveling results. D-InSAR is able to provide more details of urban subsidence, and it is supplement to leveling surveys in those areas with sparse leveling benchmarks.

New frequency domain training sequences are proposed sedfor carrier frequency offset (CFO) estimation in multipleinput multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system over frequency-selective fading channels. By exploiting frequency domain orthogonality of the training sequences, integer CFO (ICFO) can be estimated without matrix inversion operation. With the non-zero pilots in the training sequences uniformly spaced, fractional CFO (FCFO) can be estimated through the roots of a complex polynomial. Moreover, a simplified CFO estimator is also proposed which exploits a geometric mapping to transform the complex polynomial to a real one. Simulation results illustrate the good performances of the CFO estimators assisted by the proposed training sequences.

A ratio-adjustable time-frequency training sequence and the corresponding carrier frequency offset (CFO) estimator are proposed for orthogonal frequency division multiplexing (OFDM) systems over frequency-selective fading channels. The proposed method estimates the coarse and fine CFO through the training sequence in frequency domain and time domain separately. Computation complexity can be greatly decreased by employing predefined lookup table. Simulation results show that the proposed estimator has better performance with almost the same overhead compared with Lei's method in (2004).

This paper addresses the problem of the soft decision equalization. We show the relation between the iterative soft decision interference cancellation (ISDIC) and probabilistic data association (PDA) multiuser detector (PDA-MUD). We prove that ISDIC is equivalent to PDA-MUD, and give the block-wise implementation of them. We also present a soft interference cancellation algorithm for the polynomial expansion linear detector. With its low complexity, simple implementations, and impressive performance offered by iterative soft-decision processing, it is an attractive candidate to deliver efficient reception solutions to practical wireless transmission systems. Simulation comparisons of the new method with ISDIC are presented under the frequency selective channels.