Resource Allocation In Heterogeneous Networks Research Articles

Resource allocation in heterogeneous network with node and edge enhanced graph attention network

Resource allocation in heterogeneous network with node and edge enhanced graph attention network

Cooperative Distributed Resource Allocation in Heterogeneous Networks With D2D Communication

Cooperative Distributed Resource Allocation in Heterogeneous Networks With D2D Communication

Statistical Learning-Based Grant-Free Access for Delay-Sensitive Internet of Things Applications

Mission-critical Internet-of-Things (IoT) applications require communication interfaces that provide ultra-reliability and low latency. Acquiring knowledge regarding the number of active devices and their latency-reliability requirements becomes essential to optimize resource allocation in heterogeneous networks. Due to the inherent heavy computation overheads, the conventional centralized decision-making approaches result in large latency. The distributed computing and device-level prediction of network parameters can play a significant role in designing mission-critical IoT applications operating in dynamic environments. This paper considers the medium access control (MAC) layer of heterogeneous networks employing a framed-ALOHA-based restricted transmission strategy to enhance reliability. We present a statistical learning-based device-level network exploration mechanism in which end-devices use their transmission history to predict different network parameters. The IoT devices share the learned parameters with the base station (BS) to identify different groups presented in the network. The simulation results show that the mean square error (MSE) in predicting different network parameters can be reduced by increasing the history window size. In this regard, the optimal size of the history window under the given accuracy constraints is also determined. We demonstrate that the proposed device-level network load prediction mechanism is more robust as compared to the BS-centered approach.

Satellite Integration into 5G: Deep Reinforcement Learning for Network Selection

This paper proposes a deep-Q-network (DQN) controller for network selection and adaptive resource allocation in heterogeneous networks, developed on the ground of a Markov decision process (MDP) model of the problem. Network selection is an enabling technology for multi-connectivity, one of the core functionalities of 5G. For this reason, the present work considers a realistic network model that takes into account path-loss models and intra-RAT (radio access technology) interference. Numerical simulations validate the proposed approach and show the improvements achieved in terms of connection acceptance, resource allocation, and load balancing. In particular, the DQN algorithm has been tested against classic reinforcement learning one and other baseline approaches.

Energy‐efficient resource allocation in heterogeneous networks under different backhaul capacity constraints

SummaryIn heterogeneous networks (HetNets), sharply increasing data traffic exacerbates the rapid growth in the network energy consumption and at the same time brings tremendous pressure on the backhaul links. Aiming at improving the network energy efficiency (EE) and relieving backhaul data volume traffic pressure, in this paper, we investigate energy efficient resource allocation which restricts small cell data traffic by imposing different kinds of backhaul capacity constraints (BCCs) on small cells, including small cell BCC (SBCC) and macrocell BCC (MBCC). We formulate the joint resource block (RB) and power allocation problem as a fractional programming problem. By using variable relaxation and replacement, we convert this nonconvex optimization problem into a concave–convex fractional programming problem, which can further be converted into a parametric convex problem and solved by convex optimization theory. Based on the Dinkelbach and dual decomposition methods, we propose iterative resource allocation algorithms under different BCCs, where RB and power are jointly allocated among users in each cell. Numerical results reveal that the proposed algorithms have good convergence performance and also evaluates the impact of both SBCC and MBCC on network performance.

GA based energy efficiency resource allocation in heterogeneous networks with MRC

The energy efficiency (EE) resource allocation problem for downlink orthogonal frequency division multiple access (OFDMA) heterogeneous networks (HetNets) of wireless communication is investigated in this article. A coordinated multipoint (CoMP) network consisting of a microcell and multiple picocells interconnected via a backhaul network is considered. The mathematical form of EE maximization is a fractional, mixed-integer, nonlinear programming, and NP-hard to solve. Genetic algorithm (GA) based scheme is developed for solving the formulated maximization problem. For GA based scheme, a normal GA is used for resource blocks (RBs) allocation and for the power distribution process, instead of transforming the fractional objective function into a parametric subtractive form, a multi-objective optimization problem (MOOP) is formed and then non-dominated sorting genetic algorithm II (NSGA-II) is applied to tackle the MOOP. Finally, the simulation process is provided to verify the effectiveness of the proposed method and the results show that the performance of GA based scheme outperforms the benchmark scheme.

Multi-MEC server multi-user resource allocation in heterogeneous network

Aiming at the coexistence of large and small cells in heterogeneous cellular networks, the difference of computing resources between mobile equipment and base stations, and the diversity of service quality demands of users, a resource allocation algorithm based on matching game theory was proposed. We assume that some users have computing needs and can perform MEC offloading or D2D offloading. We define cost performance as the ratio of the data rate per user to its price cost, and resource allocation as the cost performance maximization problem. Considering that the formulation problem is a mixed integer nonlinear problem and it is difficult to obtain the optimal solution, we design an improved Gale-Shapley algorithm based on matching game theory to obtain the feasible solution of the problem. Simulation results show that the proposed multi-MEC server multi-user resource allocation strategy can effectively reduce user service delay and improve system performance in heterogeneous cellular networks.

Evolutionary game theoretical model for stable femtocells’ clusters formation in HetNets

Femtocell deployment is one of the key solutions to achieve the high data rate of the fifth generation mobile communication. Nevertheless, dense femtocell networks face several challenging tasks such as interference control and resource management. In this paper, we address the problem of resource allocation for heterogeneous networks (HetNets), namely dense femtocell networks, by forming stable clusters using an evolutionary game where femtocells learn from the environment and make their decisions considering the achieved payoff. In the literature, clustering has been proposed to organize network topologies by joining nodes (e.g. femtocells) with similar behaviors into logical groups. We focus on cluster stability that is important to obtain good network performance but can be difficult to achieve especially in ultra-dense and heterogeneous networks. In order to guarantee the cluster stability, we use the replicator dynamics that find the evolutionary equilibrium of the evolutionary game. Thus, by guaranteeing cluster stability the network performance is improved and the computational complexity is reduced. In addition, Particle Swarm Optimization (PSO) is used for the resource allocation algorithm that runs locally within each cluster owing to the fact that PSO has been proved to find a satisfying near-optimal solution while having the advantage of speeding up the optimization process. We run simulations for non-dense and dense femtocell networks taking into account two scenarios: fixed public users and public users that keep mobility such as pedestrians or cyclists. Simulation results show that the proposed solution is able to enhance the network throughput, to provide higher subscribers satisfaction, and to reduce the co-tier interference in dense femtocell networks.

Joint user association and resource allocation in HetNets based on user mobility prediction

Virtual small cell (VSC) formed by directional beams is seen as an alternative for the small base station (SBS) within the coverage of macro base station (MBS), to increase system capacity and reduce site cost. However, the flexibility of development for VSC poses challenges to user association and resource allocation in heterogeneous networks. In this paper, we consider the joint problem of user association and resource allocation in VSC aided multi-tier heterogeneous networks. To better analyze the formation of VSC and the impact of user mobility on the system, a user mobility prediction model is firstly constructed based on the Markov model. Then the joint user association and resource allocation problem is formulated to maximize the system capacity. Since the aforementioned problem is a coupling problem, two different solutions, namely, a decoupling solution and a coupling solution, are proposed based on the multi-agent Q-learning (MAQL) method, to find the optimal user association and resource allocation strategy. Moreover, to overcome the state and action space explosion in MAQL and accelerate convergence, the deep Q-network (DQN) is applied. Simulation results reveal that the deployment of VSC can increase the system capacity and spectrum efficiency. The coupling solution achieves better performance than the decoupling solution under a large number of users.

Deep Reinforcement Learning-Based Mobility-Aware Robust Proactive Resource Allocation in Heterogeneous Networks

Proactive resource allocation (PRA) is an essential technology boosting intelligent communication, as it can make full use of prediction and significantly improve network performance. However, most promising gains base on perfect prediction which is unrealistic. How to make PRA robust against prediction uncertainty and maximize benefits brought by prediction becomes an important issue. In this paper, we tackle this problem and propose a mobility-aware robust PRA approach (MRPRA) in heterogeneous networks. MRPRA pre-allocates resources in both time and frequency domains among mobile users with users’ trajectories predicted by hidden Markov model. The objective is to minimize service delay under constraints of different levels of quality-of-service (QoS) requirement and mobility intensity. MRPRA is robust against prediction uncertainty by exploiting probabilistic constraint programming to model QoS requirements in a probabilistic sense. To this end, the probabilistic distribution of achievable rate is derived. To flexibly coordinate resource allocation among multiple mobile users over time horizon, a deep reinforcement learning based multi-actor deep deterministic policy gradient algorithm is designed. It learns robust PRA policies by distributed acting and centralized criticizing. Extensive numerical simulations are performed to analyze performances of the proposed approach.

Resource Allocation in Heterogeneous Network With Visible Light Communication and D2D: A Hierarchical Game Approach

Utilizing the illuminating light-emitting diode (LED) for signal transmission, the visible light communication (VLC) offloads the congested traffic in licensed spectrum while providing users with high quality of service. However, in shaded areas, the performance of VLC is affected. In this paper, we apply Device-to-Device (D2D) technology on the VLC network, where some mobile users are able to relay the data transmission to nearby end mobile users. Considering the autonomous behaviors of mobile users as relays (RUs), cellular service provider (CSP) and VLC service provider (VLCSP), we propose a hierarchical game framework and analyze the distributive strategies for each individual. In the game, the data packet size, the price of licensed spectrum and data rates in possible data transmission routes are sequentially determined with equilibrium solutions, and each VLC transmitter (VLCT) determines the optimal data transmission route with stable matching. Simulations depict the high performance when combining VLC and D2D with the proposed strategies.

Dual-Connectivity Enabled Resource Allocation Approach With eICIC for Throughput Maximization in HetNets With Backhaul Constraint

By introducing the concept of dual connectivity (DC), joint user association and resource allocation for heterogeneous networks (HetNets) with backhaul constraint have been investigated. However, co-channel scenario is rarely considered in DC-enabled HetNets due to inter-tier interference. To solve this issue, enhanced intercell interference coordination (eICIC) via almost blank subframe (ABS) is introduced into DC-enabled HetNets with backhaul constraint. The objective is to improve overall system throughout while providing quality-of-service (QoS) guarantees on user’s rate. First, the joint DC-enabled user association and resource allocation problem is formulated as an mixed integer nonlinear programming (MINLP) problem. Then, by linearizing the products of the variables, the original problem is reformulated into a linear problem. Last, a low-complexity heuristic method to achieve the near-optimal solution is proposed. Also, the numerical results show the superiority of our algorithm.

Distributed optimization of enhanced intercell interference coordination and resource allocation in heterogeneous networks

SummaryIn two‐tier heterogeneous networks (HetNets), the cross‐tier interference caused by spectrum sharing between macrocell and small cells poses obstacle to ideal overall network performance. Both enhanced intercell interference coordination (eICIC) at the macrocell base station (MBS) and resource allocation (RA) at small cell base station (SBS) have been considered as efficient approaches to mitigate the cross‐tier interference. In this paper, we propose a distributed optimization framework to jointly optimize the eICIC and RA schemes at different tiers. In specific, an enhanced almost blank subframe (ABS) scheme is performed by the MBS to encourage SBSs to adopt hybrid access policy and admit the offloaded macrocell users (MUs) so as to guarantee their QoS requirements. To model the intricate interaction between two tiers, we formulate the joint optimization problem as a one‐leader multiple‐follower Stackelberg game. We prove the existence of Stackelberg equilibrium (SE) and obtain the optimal strategies for both tiers. Numerical results are presented to show that the proposed framework with hybrid access policy converges to a unique SE and utilities for both tiers is maximized, which creates a win‐win situation.

Robust Energy-Efficient Downlink Resource Allocation in Heterogeneous Networks with Outage Probability Constraint

With the development of the fifth generation communication technology, improving energy efficiency (EE is defined as the ratio of the system throughput over the total power consumption) of wireless communication becomes a hot topic, which has attracted wide attention from industry and academia. Heterogeneous networks (HetNets) have been considered as a new promising technique for expanding network coverage and improving EE. Robust resource allocation is a huge challenge when uncertainty parameters are involved in this issue. The problem is more significant in HetNets since perfect channel state information is not available at femtocell base station’s transmitters. In this paper, we study the downlink resource allocation in HetNets under outage probability constraint, and formulate the EE maximization problem as a nonlinear fractional programming problem. In order to solve the fractional programming problem, firstly, we transform the original problem into an equivalent optimization problem in a parametric subtractive form. Then based on the exponential distribution model under Rayleigh fading environment, the probability constraint is transformed into a deterministic constraint. Finally, we propose a two-loop iteration algorithm to find the optimal solution by using Dinkelbachs method and Lagrangian dual decomposition method. Simulation results demonstrate the convergence and the effectiveness of the proposed algorithm.

Distributed Resource Allocation in SDCN-Based Heterogeneous Networks Utilizing Licensed and Unlicensed Bands

The explosive growth of mobile data traffic and the scarcity of available licensed spectrum make resource allocation in heterogeneous networks a critical issue. A distributed resource allocation algorithm for software defined cellular networks for future 5G networks is proposed. The adoption of integrated femto-WiFi small cells is used to alleviate spectrum shortage, by permitting simultaneous access to both the licensed bands (via cellular interface) and unlicensed bands (via WiFi interface). A weighted utility maximization problem is formulated to optimize resource allocation, utilizing the software defined network controller’s global view. A fully distributed solution based on the weighted utility maximization optimizes resource allocation, keeping the interference from small cells to macrocells below predefined thresholds. The proposed algorithm considers the sDevices, which have both cellular and WiFi interfaces, and the wDevices which have WiFi-only interfaces. Numerical simulations substantiate the superiority of the proposed resource allocation algorithm, which increases significantly the average throughput and average utility of all devices, compared with the traditional and current methods. Throughput gains as large as 41.6% in spectral efficiency for the average of all sDevices and wDevices are achieved by the new designs.

Interference Coordination and Resource Allocation Planning With Predicted Average Channel Gains for HetNets

Future average channel gains are recently reported predictable within a minute-level horizon for a mobile user. Predictive resource allocation for non-realtime service with future average channel gains in a time window of one or more minutes long has been demonstrated effective in improving user experience and network throughput as well as reducing energy consumption. Nonetheless, existing studies for predictive resource allocation never consider inter-cell interference (ICI), which severely limits the user experience and network performance in densely-deployed cellular networks. This paper investigates predictive resource allocation in heterogeneous networks, where some base stations generate strong ICIs to some mobile users requesting non-realtime service. Optimizing the resource allocation in a predictive manner for interference networks is challenging, since how to allocate future resources depends on the future signal to interference-plus-noise ratio, which in turn relies on the assigned resources. To deal with this difficulty, we introduce a predictive interference coordination scheme to divide all BSs and users into groups, where the BS-user pairs in each group can communicate simultaneously in a second-level frame. Then, we optimize a common resource allocation plan for all users in each group. The plan essentially determines which BSs in the group should be muted and the users should be associated with which active BS in each frame of the prediction window. By resorting to graph theory, we obtain the optimal solution and derive a low-complexity algorithm. Simulation results show that the proposed scheme outperforms existing relevant methods in terms of user satisfactory rate in heterogeneous networks with heavy traffic load.

Large System Analysis of Resource Allocation in Heterogeneous Networks With Wireless Backhaul

Small-cell networks and massive multiple-input multiple-output (MIMO) systems are regarded as important candidate techniques for 5G communication systems. This paper considers a heterogeneous network composed of a macrocell tier overlaid with an extremely dense tier of small-cells. In the network, the macrocell base station (BS), which applies massive MIMO, does not only serve macro user equipment units but also provides wireless backhaul for small-cell access points (APs). The wireless backhaul shares the same spectrum resource with radio access networks without creating extra spectrum resources. However, due to the densification of small-cells, the inter- and intra-tier interferences become severe. To mitigate the interferences, we use the regularized zero-forcing precoding combined with a projection technique is used at the BS in downlink (DL) to avoid interference to the APs in uplink (UL). Meanwhile, the joint linear minimum mean square error detection is applied in UL to mitigate the inter-tier interference. We derive deterministic expressions for ergodic UL and DL sum rates (SRs) by leveraging the large-dimensional random matrix theory. The expressions only depend on statistical channel information and can be used to optimize the bandwidth division between radio access links and wireless backhaul, as well as the time allocation between DL and UL operation intervals. Numerical results show that the deterministic SR equivalents are accurate and that the proposed resource allocation method can significantly improve system performance.

Quick resource allocation in heterogeneous networks

In this paper, an innovative technique is given to reduce the implementation time taken for doing the seamless communication in heterogeneous networks. When a new user arrives into the locality of one of the heterogeneous networks, resource allocation is to be done fastly by that heterogeneous network so as not to interrupt the data transfer to the newly arrived user. To do this, heterogeneous network allots optimal powers to S channels for maximizing the capacity of the newly arrived user while confirming to the interference bounds of the other users of the heterogeneous network and at the same time preserving the power (to save the energy) in heterogeneous network. To resolve this power allocation problem, the proposed algorithm finds out the tight lower bound and tight upper bound to the number of positive powers ( $$=N$$ ) first and then finds out the indices of positive power allotted channels thereby discerning N itself (so as to calculate N powers exclusively). This is the unprecedented method as existing algorithms are iterative algorithms that deal with all of the assigned S channels. Hence, the proposed algorithm reduces the worst-case computational complexity of the erstwhile algorithms by the factor of $$O(S^2)$$ .

Optimal Joint User Association and Multi-Pattern Resource Allocation in Heterogeneous Networks

This paper studies the joint user association and resource allocation in heterogeneous networks (HetNets) from a novel perspective, motivated by and generalizing the idea of fractional frequency reuse. By treating the multi-cell multi-user resource allocation as resource partitioning among multiple reuse patterns, we propose a unified framework to analyze and compare a wide range of user association and resource allocation strategies for HetNets, and provide an optimal benchmark for network performance. The enabling mechanisms are a novel formulation to consider all possible interference patterns or any pre-defined subset of patterns, and efficient sparsity-pursuit algorithms to find the solution. A notable feature of this formulation is that the patterns remain fixed during the resource optimization process. This creates a favorable opportunity for convex formulations while still considering interference coupling. More important, in view of the fact that multi-cell resource allocation is very computational demanding, our framework provides a systematic way to trade off performance for the reduction of computational complexity by restricting the candidate patterns to a small number of feature patterns. Relying on the sparsity-pursuit capability of the proposed algorithms, we develop a practical guideline to identify the feature patterns. Numerical results show that the identified feature patterns can significantly improve the existing strategies, and jointly optimizing the user association and resource allocation indeed brings considerable gain.

An enhanced spectrum resource allocation algorithm in Hetnets

Efficient spectrum resource allocation in wireless heterogeneous networks is important for improving the system throughput and guaranteeing the user's Quality-of-Service (QoS). In this paper, we propose an enhanced algorithm for spectrum resource allocation in heterogeneous networks. First, the bandwidth of each user is determined by the user's rate demand and the channel state. Second, graph theory is enhanced and used to improve the spectrum efficiency. Third, spectrum resource is dynamically split between macrocell and femtocells with the changes of users' conditions. Our simulation results show that the proposed algorithm improves the system throughput significantly and also guarantees the fairness for the users.