Virtual reality (VR) streaming demands both high data rates and low latency, requiring advanced transmission strategies to enhance system performance in wireless networks. This paper proposes a hierarchical rate-splitting multiple access-based cell-free (HRS-CF) VR transmission strategy, which integrates field of view (FoV)-based user grouping, scalable video coding (SVC)-based message design, and message-centric base station selection. Moreover, to characterize the statistical data rate of VR services under a given delay constraint, we investigate the effective capacity (EC) of the VR video streaming under HRS-CF strategy. Furthermore, we jointly optimize the precoding matrix, rate splitting coefficients, and BS selection using the proximal policy optimization (PPO) algorithm, where a power threshold-based BS selection is introduced to reduce computational complexity. Simulation results show that the proposed HRS-CF strategy outperforms the conventional rate splitting and CF transmission schemes, achieving at least a 16% performance improvement.

Emerging immersive services, such as virtual reality (VR), are featured by multi-modal data streams (audio, video, and haptic). The fusion of distinct service characteristics introduces a significant challenge to wireless communications. This paper considers hybrid VR video and haptic services, where VR video segments and haptic data packets are transmitted in two different time scales. To optimize resource utilization, we employ dynamic time division duplexing (TDD) to address the asymmetry in uplink/downlink (UL/DL) traffic. We formulate a dual time-scale optimization problem that minimizes overall bandwidth while satisfying both the round-trip delay of VR service and the reliability and latency requirements of haptic service. To address this problem, we propose a hierarchical deep reinforcement learning (DRL) framework: 1) deep deterministic policy gradient (DDPG) optimizes total bandwidth at the beginning of each video segment’s transmission; 2) a diffusion-based actor-critic (Diffusion-AC) algorithm determines the UL time resource ratio at each time slot, which is significantly shorter than the transmission duration of a video segment. Simulation results show that the proposed algorithm can reduce the overall bandwidth usage by 20% compared with baseline methods. In addition, it provides greater adaptability across diverse scenarios and converges faster than the baseline methods.

Accurate indoor tracking is a critical component of modern location-based services, fundamentally transforming the way we interact with indoor environments. Traditional state space model (SSM) based tracking often struggles in complex environments due to its reliance on fixed and oversimplified transition and observation functions. In this paper, we propose a novel deep state space model (DSSM) approach for indoor tracking that overcomes these limitations by leveraging trainable neural networks (NNs) in place of fixed transition and observation functions. The proposed DSSM retains the structured representation of SSMs while improving the ability to effectively capture the complex dynamics of both target movements and measurement errors. The proposed model incorporates physics constraints to enable self-supervised learning, eliminating the need for labeled data during training. We evaluate our framework using real-world time of flight (ToF) measurements, demonstrating its superior tracking accuracy compared to conventional methods.

The integrated sensing and communication (ISAC) technology has gained increasing attention in recent years due to its excellent performance of increasing the spectrum and hardware efficiencies. In this paper, we investigate the joint optimization of beam selection and digital beamforming for a millimeter-wave (mmWave) ISAC system to simultaneously improve the performance of communication and sensing. We propose a novel hybrid beamforming scheme based on deep learning by maximizing the sum of communication mutual information (CMI) and sensing mutual information (SMI) to enable multi-user multiple-input multiple-output (MU-MIMO) communication and multiple-input single-output (MISO) radar sensing. Specially, we propose a joint deep reinforcement learning and unfolding network (DRL-UN) to optimize the beam selection and digital beamforming matrices at the base station (BS) in an ISAC system. Simulation results demonstrate that the proposed hybrid beamforming scheme significantly outperforms the existing algorithms in terms of sensing and communication (S&C) sum-rate in a mmWave ISAC system.

Inter-cell interference poses a significant challenge to the performance and reliability of cellular networks due to the complex spatial and temporal relationships between network nodes. Addressing this issue requires accurate prediction and assessment of interference. This paper presents a novel solution leveraging the strengths of a weighted graph convolutional network (WGCN) combined with graph coloring techniques. Specifically, we propose a WGCN-based interference estimation model to accurately derive the real-time inter-cell interference. Then, a graph multi-coloring problem is considered for the interference coordination. To address the color collision between cells and the color (i.e. spectrum resources) requirement of individual cells in the graph coloring problem, we propose a WGCN-assisted graph multi-coloring (WGCN-GMC) algorithm to allocate spectrum resources rationally. Simulation results demonstrate that our approach significantly enhances interference coordination, and achieves an impressive average improvement of 58.2 % compared to the traditional GMC algorithm leading to improved overall network performance.

The rapid expansion and resource competition among proliferating Low Earth Orbit (LEO) satellite constellations have escalated adversarial jamming threats, critically challenging network reliability. However, most adaptive frequency hopping (AFH) strategies suffer from slow convergence and high switching overhead when confronted with dense and dynamic jamming scenarios. To tackle this problem, we utilize the temporal correlation of jamming from the fixed satellite orbits. Specifically, we derive the conditional outage probabilities between the adjacent time slots to characterize the temporal correlation of jamming. On this basis, the temporal correlation of jamming adaptive frequency-hopping (TCJ-AFH) strategy is proposed by integrating the correlation into the state exploration process of AFH. Simulation results demonstrate that the TCJ-AFH strategy achieves a 39.7% improvement in the average outage probability under jammed time and a 33.9% reduction in the frequency switching count versus the Q-learning baseline to accelerate the convergence process and minimize overhead, revealing its effectiveness in balancing performance and complexity under dense and dynamic jamming scenarios.

Mobile edge caching (MEC) has grown substantially with the rapid development in scale and complexity of data traffic. By exploiting the expansive coverage of autonomous aerial vehicles (AAVs), MEC enables services for massive vehicle users (VUs) simultaneously, which is promising for enhancing network transmission efficiency. Nonetheless, due to challenges arising from the timeliness and freshness of content services caused by AAVs’ limited endurance and airborne capacity, caching strategy considering the real-time of content in large-scale dynamic Internet of Vehicles (IoV) environments remains open. With the above consideration, in this article, the cache refreshing cycle and content placement are jointly optimized in the cache-enabled AAV-assisted vehicular integrated networks (CAVINs) to minimize the content Age of Information (AoI) and energy consumption of the macro AAV. Since the joint optimization problem is variational coupled with nonconvex binary constraints, it is decoupled and solved by a double-iteration method. Specifically, the optimal cache refreshing cycle is derived in semi-closed form with the Karush-Kuhn-Tucker (KKT) conditions. The locally optimal solution of the content placement is obtained through successive convex approximation (SCA). Simulation results corroborate the effectiveness and superiority of the proposed scheme.

Drawing upon a data-driven methodology, deep learning has emerged as an innovative approach for dynamic resource allocation in large-scale cellular networks. This paper proposes an optimization strategy relying on domain adversarial networks to reduce the number of poorly performing base stations (BSs). The approach dynamically allocates radio resources to address real-time mobile traffic needs. We calculate the interference coefficients among BSs and design a performance classifier that evaluates BS performance with respect to provided traffic-resource pairs as either poor or good. Most importantly, we use well-performing BSs as source domain data to reallocate the resources of poorly performing ones through the domain adversarial neural network. Experimental results demonstrate that the proposed domain adversarial resource allocation strategy effectively decreases the number of poorly performing BSs in the cellular network, which in turn outperforms other benchmark algorithms in terms of both the ratio of poor BSs and radio resource consumption.

Target parameter estimation in high-speed scenarios is one of the main challenges in the integrated sensing and communication (ISAC) systems. In an ISAC system, the orthogonal time frequency space (OTFS) signal is able to successfully combat time-frequency-selective channels since the channel exhibits significant delay-Doppler (DD) sparsity characteristic. In this paper, we investigate the problem of parameter estimation of moving targets using OTFS modulation. We firstly derive signal model in the DD domain equivalent channel and recast the problem of parameter estimation into a compressed sensing (CS) problem. In order to improve the estimation performance, we then propose ADMM-Net by deep unfolding the iterations of the Alternating Direction Method of Multipliers (ADMM) algorithm into a deep learning network. Experimental results demonstrate that the proposed ADMM-Net algorithm outperforms the other methods in terms of estimation accuracy and running time for OTFS-based parameter estimation.

Federated learning (FL), as a privacy-enhancing distributed learning paradigm, has recently attracted much attention in wireless systems. By providing communication and computation services, the base station (BS) helps participants collaboratively train a shared model without transmitting raw data. Concurrently, with the advent of integrated sensing and communication (ISAC) and the growing demand for sensing services, it is envisioned that BS will simultaneously serve sensing services, as well as communication and computation services, e.g., FL, in future 6G wireless networks. To this end, we provide a novel integrated sensing, communication and computation (ISCC) system, called Fed-ISCC, where BS conducts sensing and FL in the same time-frequency resource, and the over-the-air computation (AirComp) is adopted to enable fast model aggregation. To mitigate the interference between sensing and FL during uplink transmission, we propose a receive beamforming approach. Subsequently, we analyze the convergence of FL in the Fed-ISCC system, which reveals that the convergence of FL is hindered by device selection error and transmission error caused by sensing interference, channel fading and receiver noise. Based on this analysis, we formulate an optimization problem that considers the optimization of transceiver beamforming vectors and device selection strategy, with the goal of minimizing transmission and device selection errors while ensuring the sensing requirement. To address this problem, we propose a joint optimization algorithm that decouples it into two main problems and then solves them iteratively. Simulation results demonstrate that our proposed algorithm is superior to other comparison schemes and nearly attains the performance of ideal FL.

Given the popularity of flawless telepresence and the resultants explosive growth of wireless video applications, besides handling the traffic surge, satisfying the demanding user requirements for video qualities has become another important goal of network operators. Inspired by this, cooperative edge caching intrinsically amalgamated with scalable video coding is investigated. Explicitly, the concept of a Pareto-optimal semi-distributed multiagent multipolicy deep reinforcement learning (SD-MAMP-DRL) algorithm is conceived for managing the cooperation of heterogeneous network nodes. To elaborate, a multipolicy reinforcement learning algorithm is proposed for finding the Pareto-optimal policies during the training phase, which balances the teletraffic versus the user experience tradeoff. Then the optimal policy/solution can be activated during the execution phase by appropriately selecting the associated weighting coefficient according to the dynamically fluctuating network traffic load. Our experimental results show that the proposed SD-MAMP- acrshort DRL algorithm: 1) achieves better performance than the benchmark algorithms and 2) obtains a near-complete Pareto front in various scenarios and selects the optimal solution by adaptively adjusting the above-mentioned pair of objectives.

Relying on a data-driven methodology, deep learning has emerged as a new approach for dynamic resource allocation in large-scale cellular networks. This paper proposes a knowledge-assisted domain adversarial network to reduce the number of poorly performing base stations (BSs) by dynamically allocating radio resources to meet real-time mobile traffic needs. Firstly, we calculate theoretical inter-cell interference and BS capacity using Voronoi tessellation and stochastic geometry, which are then incorporated into a neural network as key parameters. Secondly, following the practical assessment, a performance classifier evaluates BS performance based on given traffic-resource pairs as either poor or good. Most importantly, we use well-performing BSs as source domain data to reallocate the resources of poorly performing ones through the domain adversarial neural network. Our experimental results demonstrate that the proposed knowledge-assisted domain adversarial resource allocation (KDARA) strategy effectively decreases the number of poorly performing BSs in the cellular network, and in turn, outperforms other benchmark algorithms in terms of both the ratio of poor BSs and radio resource consumption.

This work proposes a covert communication scheme in a cognitive radio network where a secondary transmitter (ST) transmits confidential information to a secondary receiver under the cover of jammers with homogeneous Poisson point process. Specifically, we first analyze the detection performance of the primary transmitter (PT) and Willie under collaboration and non-collaboration modes. We then derive the covert transmission outage probability under ST’s correct and incorrect decisions for whether PT transmits or not and obtain the expression for the effective covert rate (ECR). In order to maximize the ECR, we derive the optimal value of the time allocation ratio, based on which, we also derive the optimal value of ST’s transmit power subject to the covertness constraint and some power constraints. Our examination shows the non-collaboration mode outperforms the collaboration mode in terms of achieving a higher ECR, because the uncertainty of the PT’s transmission in the former one will cause confusion at Willie and lead to an increased detection error rate. In addition, the proposed scheme effectively increases the ECR when compared with the scheme without the jammer.

Mobile edge computing (MEC) and field-of-view (FoV) prediction are two key techniques to enable the wireless virtual reality (VR) service. On this basis, we investigate a practical issue, that is, how to efficiently achieve the user's pre-set quality-of-experience (QoE) requirement on both video quality and delay tolerance. A constrained reward-steering algorithm based on reinforcement learning is proposed in this work to solve this multi-objective optimization problem, which finds the optimal policy approaching the user's targeted QoE. Meanwhile, both an instantaneous service delay constraint and a long-term energy constraint are satisfied by the Lagrangian-based method. Simulation results demonstrate that the proposed algorithm outperforms conventional reinforcement learning relying on weights, i.e. achieving an average reward vector much closer to the user's targeted QoE, and meeting both constraints.

Federated learning (FL) provides a novel framework to collaboratively train a shared model in a distribution fashion by virtue of a central server. However, FL is inappropriate for a serverless scenario and also suffers from some major drawbacks in Industrial Internet of Things (IIoT) networks, such as unresilience to network failures and communication bottleneck effect. In this article, we propose a novel decentralized federated learning (DFL) approach for IIoT devices to achieve model consensus by exchanging model parameters only with their neighbors rather than a central server. We firstly formulate the problem of model consensus in DFL as a fastest mixing Markov chain problem and then optimize the consensus matrix to improve the convergence rate. Meanwhile, a practical medium access control protocol with time slotted channel hopping is taken into account to implement the proposed approach. Furthermore, we also propose an accumulated update compression method to alleviate communication cost. Finally, extensive simulation results demonstrate that the proposed approach improves accuracy and reduces communication cost especially under the nonindependent identically distribution data distribution.

A tremendous amount of visual data are bing collected by the Internet of Video Things (IoVT) systems in which ubiquitous cameras deployed in cities enable new applications in the domains of smart transportation and public security. However, the limited resources in terms of communication, computing, and caching (3C) in the conventional cellular network make it challenging to adopt centralized artificial intelligence (AI) to conduct real-time video-based data analytics. In this work, based on the 5G network architecture with edge servers, a three-phase resource-effective solution is proposed to perform surveillance operations in a large-scale wireless IoVT. The proposed strategy integrates front-end cameras with simple on-chip neural networks performing real-time object-of-interest segmentation, edge servers, and cloud servers with AI functionality carrying out image-based target recognition and video-based target analytics tasks. More importantly, we design the optimal 3C strategy to achieve the best video analytics performance constrained by computing offload ratio, network resource allocation and video-related parameters. Extensive simulations with deep neural networks implemented both at the front-end cameras and in the cloud server have validated the effectiveness of the proposed solution.

In intelligent transportation systems (ITS), incomplete traffic data due to sensor malfunctions and communication faults, seriously restricts the related applications of ITS. Recovering missing data from incomplete traffic data becomes an important issue for ITS. Existing works on traffic data imputation cannot achieve satisfactory accuracy due to inefficiently exploiting the underlying topological structure of the traffic data. In this paper, we model the topology of the road network as a graph and introduce graph Fourier transform (GFT) to process the traffic data. Then we utilize an algebraic framework termed as graph-tensor singular value decompositions (GT-SVD) to extract the hidden spatial information of traffic data. Furthermore, we propose a novel graph spectral regularized tensor completion algorithm based on GT-SVD and construct temporal regularized constraints to improve the recovery accuracy. The extensive experimental results on real traffic datasets demonstrate that the proposed algorithm outperforms the state-of-the-art methods under different missing patterns.

In this paper, we theoretically analyse the uplink (UL) and downlink (DL) performance of massive multiple-input and multiple-output (mMIMO) networks, in term of coverage probability, cell spectral efficiency and network area spectral efficiency, using stochastic geometry. A sophisticated but yet practical system model is considered, taking into account a path loss model differentiating line-of-sight and non-line-of-sight transmissions, an idle mode capability at the base stations and a finite user density. Our analysis pays particular attention to the existence of a finite number of UL pilots for channel estimation and the effect of pilot contamination. We study for the first time the joint impact of the number of UL pilot sequences, the user density, and the base-station density on the pilot contamination issue in a mMIMO network, which in turn characterizes the DL and UL network performance. Moreover, using the proposed framework, we investigate two different scheduling problems—UE and pilot scheduling—, to find the optimal simultaneously scheduled UE density per time-frequency resource as well as the optimal UL pilot number to maximise the spectral efficiency.

Intelligent reflecting surfaces (IRSs) have been proposed in recent years as a promising technology to enhance the quality of transmissions in high-frequency spectrum. Currently, the research on the performance of large networks with IRSs is still in its infancy. Different from the commonly-used stochastic geometry model for the study of traditional networks, where only transmitters and receivers are modeled as point processes, in an IRS network, the blockages and reflectors also need to be accounted for. In this paper, we study a bipolar network with a line segment object model, and derive the probability that an IRS can successfully reflect a signal from a transmitter to a receiver, as well as the distribution of the distance traveled by the reflected signal. With these analytic results, the signal to interference ratio (SIR) and the achievable rate are obtained in closed-form expressions. From the analysis, we can observe that IRSs have a great potential to enhance the network performance, as they are able to boost the signal power, while preventing the inter-cell interference from rising rapidly. More importantly, we find that even with a limited number of IRSs, the network can still achieve a higher achievable rate than a conventional one without IRSs.

Accurate short-time traffic flow prediction has gained gradually increasing importance for traffic plan and management with the deployment of intelligent transportation systems (ITSs). However, the existing approaches for short-term traffic flow prediction are unable to efficiently capture the complex nonlinearity of traffic flow, which provide unsatisfactory prediction accuracy. In this paper, we propose a deep learning based model which uses hybrid and multiple-layer architectures to automatically extract inherent features of traffic flow data. Firstly, built on the convolutional neural network (CNN) and the long short-term memory (LSTM) network, we develop an attention-based Conv-LSTM module to extract the spatial and short-term temporal features. The attention mechanism is properly designed to distinguish the importance of flow sequences at different times by automatically assigning different weights. Secondly, to further explore long-term temporal features, we propose a bidirectional LSTM (Bi-LSTM) module to extract daily and weekly periodic features so as to capture variance tendency of the traffic flow from both previous and posterior directions. Finally, extensive experimental results are presented to show that the proposed model combining the attention Conv-LSTM and Bi-LSTM achieves better prediction performance compared with other existing approaches.

We discover a new capacity scaling law in ultra-dense networks under practical system assumptions, such as a general multi-piece path loss model, a non-zero base station to user equipment antenna height difference, and a finite user equipment density. The intuition and implication of this new capacity scaling law are completely different from those found in the year 2011. That law indicated that the increase of the interference power caused by a denser network would be exactly compensated by the increase of the signal power due to the reduced distance between transmitters and receivers, and thus, network capacity should grow linearly with network densification. However, we find that both the signal and interference powers become bounded in practical ultra-dense networks, which leads to a constant capacity scaling law. Moreover, our new discovery on the constant capacity scaling law indicates three network optimization problems respectively for base station deployment, user equipment scheduling and base station coordination. These three optimization problems are justified and solved in this paper, shedding new light on the deployment and optimization of ultra-dense networks.

In recent years, with the growing prevalence of personal devices, network latency of devices has drawn much attention due to its significant influence on user experience. Thus network latency estimation is considered to be an important index for network performance evaluation. However, the existing works on network latency estimation are unable to achieve satisfactory estimation accuracy due to the adoption of the conventional matrix or tensor model. In this paper, we construct a novel tensor model based on tensor-SVD for network latency data to make full use of its potential latent factors. Besides, we also propose a graph-laplacian regularization tensor completion algorithm (GLRTC), which mines the underlying spatial information by introducing graph-laplacian regularization constraints to improve the recovery performance. Finally, we conduct extensive simulations on the real-world latency dataset and demonstrate the effectiveness of the proposed algorithm. Comparing with the existing approaches, the proposed algorithm achieves significant improvement in terms of recovery accuracy.

Random frequency diverse array (RFDA) based directional modulation (DM) was proposed as a promising technology in secure communications to achieve a precise transmission of confidential messages, and artificial noise (AN) was considered as an important helper in RFDA-DM. Compared with previous works that only focus on the spot of the desired receiver, in this work, we investigate a secrecy region around the desired receiver, that is, a specific range and angle resolution around the desired receiver. Firstly, the minimum number of antennas and the bandwidth needed to achieve a secrecy region are derived. Moreover, based on the lower bound of the secrecy capacity in RFDA-DM-AN scheme, we investigate the performance impact of AN on the secrecy capacity. From this work, we conclude that: 1) AN is not always beneficial to the secure transmission. Specifically, when the number of antennas is sufficiently large and the transmit power is smaller than a specified value, AN will reduce secrecy capacity due to the consumption of limited transmit power. 2) Increasing bandwidth will enlarge the set for randomly allocating frequencies and thus lead to a higher secrecy capacity. 3) The minimum number of antennas increases as the predefined secrecy transmission rate increases.

Mobile traffic prediction opens a promising avenue to demand-aware large-scale resource allocation with a significant improvement in the spectral efficiency. Various long-term prediction methods have been proposed in the literature. However, when considering the stringent requirement of the real-time and efficient radio resource allocation for future wireless communications, developing short-term prediction methods with high prediction accuracy is more desirable. In this paper, we exploit spatiotemporal correlations among the mobile traffic data and propose a novel machine learning-based short-term prediction method, referred to as spatiotemporal Gaussian Process Kalman filter (ST-GPKL) method, which includes two phases: the model selection and inference. The function of the model selection is to fine-tune the hyperparameters of the designed kernel function, while that of the inference incorporates the Kalman filter to predict the future mobile data traffic. Compared with the conventional methods, the proposed one can significantly improve the prediction accuracy, resulting in much higher efficiency in large-scale resource allocation.

Accurate channel modelling is crucial for network performance analysis, particularly when considering unmanned aerial vehicles (UAVs). Measurement campaigns involving UAVs have shown that the path loss between UAVs and terrestrial base stations (BSs) is highly dependent on the UAV height, rendering previous line-of-sight (LoS) probability models inaccurate. In this paper, the coverage probability, average ergodic rate and area spectral efficiency are theoretically investigated based on a more realistic channel model, with height dependent LoS probabilities. Our results show that the long tail of the LoS probability function in UAV networks has an important performance impact on sparse networks where the BS density is low. Our new findings also show that the coverage of UAV-enabled networks may continuously decrease with the UAV density, since the LoS probability slowly declines with the link range.

Cooperative inter-working of the long-term evolution (LTE) and the wireless fidelity (Wi-Fi) networks have drawn much attention recently, and several strategies have been proposed to enhance their network capacity. In this paper, we propose a new framework to analyze the network performance of several inter-working strategies for the LTE and the Wi-Fi. The proposed framework considers both the LTE and the Wi-Fi systems, both the downlink (DL) and the uplink (UL) transmissions, and the generated interference in both the time and the spatial domains. Based on such a framework, we theoretically analyze for the first time the performance of a Wi-Fi network, taking into account the intra-cell time efficiency and the signal and inter-cell interference with spatial randomness. Moreover, we study the performance of: 1) a coexisting architecture where Wi-Fi coexists with an ideal carrier sense multiple access (CSMA) duplex system, which represents an upper bound performance for the LTE Release 14 licensed assisted access network and 2) a brand-new architecture that allows UL on LTE and DL on Wi-Fi, referred to as the Boost architecture. We derive analytical results for both the DL and the UL network performances in terms of the signal quality distribution and the total area system throughput (AST) in these two architectures, and quantify their performance gain compared with the traditional disjoint LTE Wi-Fi architecture. Simulation results validate our analysis results, and show that, in a typical outdoor scenario, the coexisting architecture and the Boost architecture can, respectively, increase the total AST up to 11% and 25%, compared with the traditional disjoint LTE Wi-Fi.

Evidence indicates that downloading on-demand videos accounts for a dramatic increase in data traffic over cellular networks. Caching popular videos in the storage of small-cell base stations (SBS), namely, small-cell caching, is an efficient technology for reducing the transmission latency while mitigating the redundant transmissions of popular videos over back-haul channels. In this paper, we consider a commercialized small-cell caching system consisting of a network service provider (NSP), several video retailers (VRs), and mobile users (MUs). The NSP leases its SBSs to the VRs for the purpose of making profits, and the VRs, after storing popular videos in the rented SBSs, can provide faster local video transmissions to the MUs, thereby gaining more profits. We conceive this system within the framework of Stackelberg game by treating the SBSs as specific types of resources. We first model the MUs and SBSs as two independent Poisson point processes, and develop, via stochastic geometry theory, the probability of the specific event that an MU obtains the video of its choice directly from the memory of an SBS. Then, based on the probability derived, we formulate a Stackelberg game to jointly maximize the average profit of both the NSP and the VRs. In addition, we investigate the Stackelberg equilibrium by solving a non-convex optimization problem. With the aid of this game theoretic framework, we shed light on the relationship between four important factors: the optimal pricing of leasing an SBS, the SBSs allocation among the VRs, the storage size of the SBSs, and the popularity distribution of the VRs. Monte Carlo simulations show that our stochastic geometry-based analytical results closely match the empirical ones. Numerical results are also provided for quantifying the proposed game-theoretic framework by showing its efficiency on pricing and resource allocation.

Heterogeneous cellular networks (HCNs) with embedded small cells are considered, where multiple mobile users wish to download network content of different popularity. By caching data into the small-cell base stations, we will design distributed caching optimization algorithms via belief propagation (BP) for minimizing the downloading latency. First, we derive the delay-minimization objective function and formulate an optimization problem. Then, we develop a framework for modeling the underlying HCN topology with the aid of a factor graph. Furthermore, a distributed BP algorithm is proposed based on the network's factor graph. Next, we prove that a fixed point of convergence exists for our distributed BP algorithm. In order to reduce the complexity of the BP, we propose a heuristic BP algorithm. Furthermore, we evaluate the average downloading performance of our HCN for different numbers and locations of the base stations and mobile users, with the aid of stochastic geometry theory. By modeling the nodes distributions using a Poisson point process, we develop the expressions of the average factor graph degree distribution, as well as an upper bound of the outage probability for random caching schemes. We also improve the performance of random caching. Our simulations show that 1) the proposed distributed BP algorithm has a near-optimal delay performance, approaching that of the high-complexity exhaustive search method; 2) the modified BP offers a good delay performance at low communication complexity; 3) both the average degree distribution and the outage upper bound analysis relying on stochastic geometry match well with our Monte-Carlo simulations; and 4) the optimization based on the upper bound provides both a better outage and a better delay performance than the benchmarks.

In heterogeneous networks (HetNets), the load between macro-cell base stations (MBSs) and small-cell BSs (SBSs) is imbalanced due to transmit power disparities and ad-hoc deployment of SBSs. This significantly impacts the system performance and user experience. Associating more users to the SBSs is an effective way to solve this problem. In this paper, we formulate the user-BS association problem as a distributed optimization problem with proportional fairness as the objective. Specifically, we propose a novel distribute algorithm based on the belief propagation (BP) method to solve the user-BS association problem via iteratively message passing between the users and BSs. Also, we develop an approximation calculation in the BP method to reduce the computational complexity and transmission overhead of message passing. Simulation results show that the proposed algorithm well approaches the optimal system performance (by exhausting search) with low complexity and fast convergence.

Inter-cell interference coordination (ICIC) and resource allocation problems are fundamental challenges for the design of wireless networks. In this paper, we propose a distributed network inter-cell control scheme, and introduce a Belief Propagation (BP) framework to solve the optimization problem. The goal is to maximize the sum rate of those Base Stations (BS). This new approach assumes that the inter-cell interference is a set of stochastic variables. Based on a set of prior distributions, it calculates the posterior distributions of the scheduling variables. The solution allocates PRBs to Mobile Stations (MS) in the cells, including optimization of the transmit powers in each subcarrier. Numerical results demonstrate that this algorithm achieves a good result in typically a couple of iterations.

In this paper, we introduce the concept of average per-user rate to the multiuser Multiple-Input, Multiple-Output (MIMO) system with the frequency domain packet scheduler (FDPS) at base stations, which provides an estimate of the rate that the system could provide for each admitted user. The proposed admission control is designed by comparing the user's quality of service (QoS) requirements with the transmission rate that the system can offer. The analytical model is based on the generalized 3GPP LTE downlink transmission for which two Spatial Division Multiplexing (SDM) multiuser MIMO schemes are investigated, namely, Single User (SU) and Multi-user (MU) MIMO schemes. The main contribution of this paper is the derivation of the achievable rate for each user in the SDM MIMO systems based on a mathematical model of the Signal to Interference plus Noise Ratio (SINR) distribution with the frequency domain packet scheduler. The achievable rate provides insights into the system's performance from a different perspective.