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Given the context of the challenge of exponentially increasing data traffic on communication networks brought by the 5G era, this paper focuses on how to apply deep reinforcement learning (DRL) techniques to solve the problem of optimizing the energy efficiency of D2D communications in heterogeneous cellular network environments. We propose a joint resource allocation scheme based on multi-intelligent deep reinforcement learning, which enables D2D devices to intelligently switch between license-free and optimal license bands and adjust the transmit power in real time to maximize the energy efficiency improvement. In this work, a multi-intelligent deep reinforcement learning framework is designed to enable D2D users in heterogeneous networks to make collaborative decisions and dynamically adjust their communication strategies according to real-time network status and environmental changes. In this paper, a deep Q-network model with a graph attention network (GAT) as the core structure is constructed; this model can cope with the complexity and diversity of network states and learn and execute optimal resource allocation strategies. In this paper, we propose a targeted loss function design that balances the optimization goal of D2D communication energy efficiency with network stability and long-term gains. Through rigorous simulation experiments, this paper verifies that a DRL-based approach can significantly improve the energy efficiency of D2D communications in heterogeneous cellular networks in real-world scenarios while ensuring the stability of the quality of service (in terms of, e.g., rate, delay, and resource utilization).

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

In this paper, we analyze a hybrid Heterogeneous Cellular Network (HCNet) framework by deploying millimeter Wave (mmWave) small cells with coexisting traditional sub-6GHz macro cells to achieve improved coverage and high data rate. We consider randomly-deployed macro base stations throughout the network whereas mmWave Small Base Stations (SBSs) are deployed in the areas with high User Equipment (UE) density. Such user centric deployment of mmWave SBSs inevitably incurs correlation between UE and SBSs. For a realistic scenario where the UEs are distributed according to Poisson cluster process and directional beamforming with line-of-sight and non-line-of-sight transmissions is adopted for mmWave communication. By using tools from stochastic geometry, we develop an analytical framework to analyze various performance metrics in the downlink hybrid HCNets under biased received power association. For UE clustering we considered Thomas cluster process and derive expressions for the association probability, coverage probability, area spectral efficiency, and energy efficiency. We also provide Monte Carlo simulation results to validate the accuracy of the derived expressions. Furthermore, we analyze the impact of mmWave operating frequency, antenna gain, small cell biasing, and BSs density to get useful engineering insights into the performance of hybrid mmWave HCNets. Our results show that network performance is significantly improved by deploying millimeter wave SBS instead of microwave BS in hot spots.

The extensive utilization of wireless services, coupled with the exponential surge in data traffic, has triggered substantial energy consumption. Thereby, growing attentions have been paid to energy-efficient heterogeneous cellular networks (HCNs). In this paper, we concentrate on the joint resource allocation issue under the framework of the non-orthogonal multiple access (NOMA) assisted HCNs with wireless backhauls. To furthermore improve the total system energy efficiency (EE) while ensuring the quality of service (QoS), we formulate the optimization problem as a hybrid joint resource allocation (HJRA) of integer programming and continuous optimization under the cross-tier interference threshold and minimum data rate constraint. The non-convex nature of the optimization problem arises from the fractional objective function and non-convex constraints, making it intractable to efficiently get an exact solution. In this regard, a quantum-inspired political optimizer (QPO) algorithm is devised to find the optimal solution for the HJRA issue, which realizes synchronous allocation of backhaul bandwidth, sub-channels, and power. Simulation results confirm the superiority of the QPO regarding the total system EE over other strategies. [ABSTRACT FROM AUTHOR]

Device-to-Device (D2D) communication-enabled Heterogeneous Cellular Networks (HCNs) have been a promising technology for satisfying the growing demands of smart mobile devices in fifth-generation mobile networks. The introduction of Millimeter Wave (mm-wave) communications into D2D-enabled HCNs allows higher system capacity and user data rates to be achieved. However, interference among cellular and D2D links remains severe due to spectrum sharing. In this paper, to guarantee user Quality of Service (QoS) requirements and effectively manage the interference among users, we focus on investigating the joint optimization problem of mode selection and channel allocation in D2D-enabled HCNs with mm-wave and cellular bands. The optimization problem is formulated as the maximization of the system sum-rate under QoS constraints of both cellular and D2D users in HCNs. To solve it, a distributed multiagent deep Q-network algorithm is proposed, where the reward function is redefined according to the optimization objective. In addition, to reduce signaling overhead, a partial information sharing strategy that does not observe global information is proposed for D2D agents to select the optimal mode and channel through learning. Simulation results illustrate that the proposed joint optimization algorithm possesses good convergence and achieves better system performance compared with other existing schemes.

Heterogeneous cellular networks (HCNs) play a significant role in 5G networks, but have mainly been modeled and analyzed on a two-dimensional (2D) plane. In this paper, we model a three-dimensional (3D) two-tier HCN consisting of macro-cells and small-cells via a stochastic geometry approach, where the base stations (BSs) in each tier are assigned an appropriate antenna downtilt to enhance the downlink signal and suppress the inter-cell interference. Based on the 3D HCN model, we derive the downlink spatially-averaged coverage probability and area spectral efficiency (ASE) as functions of the BS antenna downtilts, BS heights, BS densities and cell-association bias. To facilitate fast numerical evaluation, we propose accurate approximations for the integral parts in the coverage probability expression. We then obtain the optimal BS antenna downtilts for both tiers that maximize the downlink spatially-averaged coverage probability by using partial derivative and the bisection method. Analytical and simulation results show that for given BS heights, densities of small-cell BSs (SBSs) and cell-association biases of SBSs, our optimized BS antenna downtilts of both tiers can significantly enhance the downlink spatially-averaged coverage probability and ASE in comparison with the fixed BS antenna downtilts network. Useful insights into the 3D deployment of HCNs considering BS antenna downtilts are obtained based on the analytical and simulation results.

In order to comprehensively improve the performance of edge users in heterogeneous cellular networks and the fairness of network users, a downlink interference coordination optimization strategy in heterogeneous cellular networks with non-orthogonal multiple access (NOMA) based on the cell range expansion (CRE) and the almost blank subframe (ABS) technology is proposed. Different from the traditional interference coordination strategy, a NOMA user pairing scheme combined with ABS technology and a dynamic NOMA power allocation scheme are designed to maximize the network fairness based on the optimized throughput of the edge users. The simulation results show that the proposed optimization strategy can balance the performance of network users more effectively to improve the throughput of edge users and network fairness than other NOMA user pairing and power allocation algorithms without the complexity being increased. [ABSTRACT FROM AUTHOR]

Caching at network edges has attracted more and more research interests recently for the purpose of alleviating the network traffic pressure especially in backhaul links and improving user experience. We study Heterogeneous Cellular Caching Networks (HCCNs) consisting of macro cells in which $N$ small cell base stations (SBSs) equipped with cache memory operate in conjunction with the macro cell base station (MBS). We provide closed-form expressions of the MBS and SBSs utilization factors and average user-experienced-delay in HCCNs with overlapping coverage regions, considering general traffic models for the request arrivals based on the Independent Reference Model (IRM) and renewal traffic models. Moreover, we propose a novel caching scheme in HCCNs, namely Cooperative Most Popular Caching (CMPC), which outperforms the existing schemes in terms of delay. Subsequently, we present the bandwidth assignment problem aiming to minimize the average user-experienced-delay under stability and cache size constraints in HCCNs with overlapping coverage regions and stochastic request arrivals. Finally, the analytic results are validated through numerical results and real trace-driven experiments. [ABSTRACT FROM AUTHOR]

In heterogeneous cellular networks (HCNs), the energy reduction has a great impact on the operational cost and global carbon footprint, which has attracted more and more attention. To enhance the network energy efficiency (EE), we design two types of association mechanisms integrated with open loop power control (OLPC) for uplink code-division-multiple-access (CDMA) enabled HCNs. Specifically, they are formulated into two optimization problems that maximize the sum of users' EEs and the one of users' EE utilities. It is noteworthy that the one of these two mechanisms guarantees the user fairness by employing a logarithmic function. Considering that these formulated problems are in a fractional mixed-integer form and hard to tackle, we utilize a coalitional game to design two types of association algorithms with guaranteed convergence. To show the effectiveness of designed algorithms, another one is introduced for comparison in the simulation. As for them, we mainly investigate the impacts of user and BS densities on the association performance. [ABSTRACT FROM AUTHOR]