PSO-based Resource Allocation in Software-Defined Heterogeneous Cellular Networks

How many small cell (SC) access points (APs) are required to guarantee a chosen quality of service in a heterogeneous network? In this chapter, we answer this question considering two different network models. The first is the downlink of a finite-area SC network where the locations of APs within the chosen area are uniformly distributed. A key step in obtaining the closed-form expressions is to generalize the well-accepted moment matching approximation for the linear combination of lognormal random variables. For the second model, we focus on a two-layer downlink heterogeneous network with frequency reuse-1 hexagonal macro cells (MCs), and SC APs that are placed at locations that do not meet a chosen quality of service from macro base stations (BSs). An important property of this model is that the SC AP locations are coupled with the MC coverage. Here, simple bounds for the average total interference within an MC makes the formulation possible for the percentage of MC area in outage, as well as the required average number of SCs (per MC) to overcome outage, assuming isolated SCs. Introduction Heterogeneous cellular networks (HCNs) are being considered as an efficient way to improve system capacity as well as effectively enhance network coverage [1, 2]. Comprising multiple layers of access points (APs), HCNs encompass a conventional macro cellular network (first layer) overlaid with a diverse set of small cells (SCs) (higher layers). Cell deployment is an important problem in heterogeneous networks, both in terms of the number and positioning of the SCs. Traditional network models are either impractically simple (such as the Wyner model [3]) or excessively complex (e.g., the general case of random user locations in a hexagonal lattice network [4]) to accurately model SC networks. A useful mathematical model that accounts for the randomness in SC locations and irregularity in the cells uses spatial point processes, such as Poisson point process (PPP), to model the location of SCs in the network [5–10]. The independent placement of SCs from the MC layer, has the advantage of analytical tractability and leads to many useful SINR and/or rate expressions. However, even assuming that wireless providers would deploy SCs to support mobile broadband services, the dominant assumption remains that SCs are deployed randomly and independent of the MC layer [11].

In this paper, we propose a joint power control and resource allocation approach for co-channel deployed high dense small-cells in macro-cell networks, denoted as Two-tier Optimized Resource and Power Allocation approach (TORPA). The ultimate goal of our contribution is to mitigate the co-tier and cross-tier interference faced by indoor and outdoor users in downlink communications and efficiently satisfy their resource requirements. With this in mind, we formulate the resulting optimization problem as a Mixed-Integer Linear Program (MILP) that aims to minimize the total transmit power and achieve an efficient two-tier physical resource allocation. Then, we propose a semi-centralized scheme to solve it. Extensive simulations show that TORPA can efficiently allocate power and distribute spectrum resources to both Small-cell Users (SUs) and Macro-Users (MUs) compared to the distributed resource allocation scheme and a prominent state of the art gametheoretic approach. In particular, TORPA improves the SU’s throughput by up to 9 times and 22% in comparison to these both schemes.

In heterogeneous cellular networks (HCNs), the growing demand of small cell data traffic puts great pressure on the capacity limited backhaul links. Resource allocation is an efficient approach to mitigate the severe interference and improve the network performance in HCNs. However, differentiated backhaul capacity constraints introduce the inherent nonconvexity to the resource optimization problem, which hinders the development of the optimal solutions. In this article, we investigate the joint resource block (RB) and power allocation problem under two different backhaul capacity limitations. First, we consider the small cell backhaul limitation scenario where the backhaul capacity constraint is imposed on each small cell. Then, we extend the resource optimization to the macrocell base station backhaul limitation scenario which considers the overall backhaul capacity constraint on the whole small cell network. Besides, the quality of service (QoS) requirements of users and the cross‐tier interference mitigation are also considered. This resource allocation problem is formulated as a nonconvex mixed‐integer nonlinear programming (MINLP) problem. By variable substitution and constraints relaxation, we convert the original optimization problem into a convex one and obtain the optimal solutions. Accordingly, joint RB and power allocation algorithms are proposed. Numerical results verify the effectiveness of the proposed algorithms and evaluate the impact of different types of backhaul capacity limitations on system performance.

Next generation high data rate applications such as High-Definition (HD) video transmission place a heavy load on wireless network resources. To achieve the strict quality of service requirements of HD video in a multi-user network requires efficient resource allocation. Rate-distortion theory provides an appropriate basis for this. The Nash Bargaining Solution (NBS) is proven to generate an optimal solution to a rate-distortion based convex resource allocation problem. Particle Swarm Optimization (PSO) is a meta-heuristic optimizer capable of generating near-optimal solutions. PSO, however, is not constrained to convex optimization problems. The motivation of this work is to demonstrate a PSO implementation capable of achieving an optimal solution to the resource allocation problem. The capability of the PSO approach to reach an optimal solution suggests its potential for resource allocation in a network of heterogeneous applications where convex optimization methods do not apply.

SummaryWe investigate the problem of joint downlink wireless backhaul bandwidth (WBB) and power allocation in heterogeneous cellular networks (HCNs). A WBB partitioning scheme is considered, which allocates the whole bandwidth between the macrocell and small cells for data transmission and backhauling. We formulate an optimization problem to maximize the weighted sum logarithmic utility function by jointly optimizing WBB portion and fronthaul power allocation of each base station with consideration of the backhaul capacity limitation on each small cell. In order to solve this joint optimization problem, we propose a hierarchical two‐level approach and decompose the original problem into two independent subproblems: the WBB allocation at the macrocell base station (MBS) and the power allocation at both the MBS and small cell base stations (SBSs). Accordingly, the optimal WBB portion and power allocation solutions are obtained, respectively. Furthermore, we develop a distributed algorithm to implement the joint WBB and power allocation. Numerical results verify the effectiveness of the proposed approach and analyze the impact of the weighted coefficient and backhaul capacity limitation on the network performance. In addition, significant performance gains can be achieved by the proposed approach over the benchmark.

This paper focuses on the multi‐pair amplify‐and‐forward massive multiple‐input multiple‐output (MIMO) relay enabled two‐tier heterogeneous cellular networks in terms of aggregate spectral efficiency (SE), where the macro cells are overlaid with dense small cells. Specially, the maximum ratio combining/maximal ratio transmission and zero‐forcing processing schemes are employed at massive MIMO relay, respectively. The macro base stations located in the center of the cells are equipped with massive MIMO antenna array, while the randomly distributed macro users (MUs), small cell base stations, and small cell users are equipped with single antenna. The positions of MUs, small cell users and small cell base stations are modelled as independent Poisson point processes. For such two‐tier heterogeneous cellular networks, to obtain the aggregate SE in target cell, we first formulate the end‐to‐end signal‐to‐interference noise ratios for a communication pair as well as the expression of power amplification factor at target macro base stations so that the total spectrum efficiency per cell is formulated. Then, by employing stochastic geometry, the closed‐form expressions of the power amplification factor and the expectation of the reciprocal of the equivalent end‐to‐end signal‐to‐interference noise ratios are achieved. With these results, we derive the lower bounds of SEs for maximum ratio combining/maximal ratio transmission and zero‐forcing schemes. The derivations display a clear perspective about the impact of network parameters on the system‐level performance, which includes macro cell radius, small cell radius, macro cell guard radius, the mean number of MUs per cell and so on. To achieve optimal (or suboptimal) system performance, we find that the radius of small cell should be less than the guard radius. For the guard radius, there exists an upper bound, only under which the increase of the guard radius would contribute to the improvement of SE. At the same time, our results show that with given network parameters, especially the guard radius and the small cell radius, there exists a unique greatest lower bound in terms of macro cell radius. When the macro cell radius is less than the greatest lower bound, the aggregate SE is increasing with the macro cell radius. On the contrary, the achievable aggregate SE saturates to the corresponding constant, approximately. In addition, the greatest lower bound of the macro cell radius is the function of the guard radius and the small cell radius. Copyright © 2016 John Wiley & Sons, Ltd.

This paper investigates the problem of joint base station selection and resource allocation in an orthogonal frequency division multiple access (OFDMA) heterogeneous cellular network. The original throughput maximization problem is NP-hard and we propose solving it by using game theoretic stochastic learning approaches. To this end, we first transform the original problem into a tractable form, which has a weighted utility function. Then we prove that an exact potential game applies and it exists the best Nash equilibria which is a near optimal solution of the original problem when an efficient solution method of the weights is employed. To obtain the optimal solution, we redesign the utility function by leveraging a state space to formulate the original problem into an ordinal state based potential game, which is proved that it exists a recurrent state equilibrium point that maximizes system throughput. Furthermore, we propose two different variants of Max-logit learning algorithm based on these two games respectively: one is a simultaneous learning algorithm with less information exchange, which achieves the best Nash equilibria point of the exact potential game and the other is an efficient learning algorithm for the ordinal state based potential game, which can converge to the global optimization solution. Finally, numerical results are given to validate that theoretical findings.

The heterogeneous cellular network (HCN) is a promising technology to handle the rising number of devices due to their universal presence. This rising popularity HCN based Machine-to-Machine (M2M) communications is opening new opportunities and also bringing forth new system design issues. However, the main challenge of M2M communication is the possibility of huge traffic and significant difference in the nature of M2M traffic than the current commercial traffic for which current cellular network is designed and optimized. In this article, we investigate the uplink resource allocation problem of M2M devices (MDs) in the multiple Femto base station's coverage. We first model the uplink power and sub-carrier allocation in femtocells independently; Based on the cooperative game resource allocation among MDs is analyzed through non-transferable utility game to enhance the data rate performance with minimum utilization of power. Simulation results show that the resource allocation model based on cooperative game is able to provide a fair distribution of data rate compared with non-cooperative and greedy type of MDs.

In this paper, we study the energy-efficient user access control (UAC) based\non resource allocation (RA) in heterogeneous cellular networks (HCNs) with the\nrequired downlink data rate under non-ideal power amplifiers (PAs) and circuit\npower. It is proved that the energy consumption minimization is achieved when\nthe typical user accesses only one base station (BS), while the other BSs\nremain in idle mode on the transmission resource allocated to this user. For\nthis purpose, we reformulate the original non-convex optimization problem into\na series of convex optimization problems where, in each case, the transmit\npower and duration of the accessed BS are determined. Then, the BS with the\nminimal energy consumption is selected for transmission. Considering the\napproximate situation, it is showed that the optimal transmit duration of the\naccessed BS can be estimated in closed form. The benefits of our proposed UAC\nand RA schemes are validated using numerical simulations, which also\ncharacterize the effect that non-ideal PAs have on the total energy consumption\nof different transmission schemes.\n

Problem statement. Due to their mobility, flexibility, ease of deployment, and low cost, Autonomous Aerial Vehicle (AAV) play an important role in future wireless networks. However, their pratical implementation faces challenges, including energy constraints, dynamic channel variations, interference management, and the need for efficient resource allocation to ensure seamless connectivity for ground users. Traditional optimization methods often fail to adapt to these complexities in real-time, limiting the effectiveness of AAV-assisted wireless networks. The aim of the work provides a comprehensive review of resource allocation in AAV-assisted wireless networks, focusing on power and bandwidth optimization strategies, as well as the key challenges in ensuring efficient and reliable communication. Methods: used in this study include a systematic review of existing literature, analyzing optimization approaches such as Game Theory, Artificial Intelligence for efficient resource allocation in AAV-assisted wireless networks. Novelty: this study analyzes resource allocation challenges in AAV-assisted network, focusing on the interdependence of power and bandwidth allocation. It explores optimization techniques like Game Theory, and Artificial Intelligence. Results. The analysis in this paper demonstrates that Game Theory and Artificial Intelligence based approaches significantly improve resource allocation efficiency. Additionally, the study identifies key challenges, including heterogeneous density network, security concerns, and complex channel modeling, providing insights for future research. Practical / Theoretical Relevance: This study advences the theoretical understanding of resource allocation in AAV-assisted wireless networks by integrating optimization strategies from Game Theory and Artificial Intelligence. Pratically, it provides insight into enhancing network efficiency, adaptability and security, making AAV-based communication more viable for real-world applications, such as disaster recovery, remote areas coverage, and IoT data collection.

D2D (Device-to-Device) communication is one of the promising technologies in the LTE (Long Term Evolution)-Advanced communication network. From system perspective, this technology can increase frequency efficiency of the whole macro cell. From user perspective, however, a D2D pair which shares the same frequency resource of CUE (Cellular User Equipment) can cause interference with the CUE in the downlink. Thus, reducing interference for the cellular users and allocating frequency resource for the D2D pairs are important in D2D communication. In this paper, two resource allocation schemes which are based on PSO (Particle Swarm Optimization) and hybrid PSO-GA (Genetic Algorithm) are proposed to maximize the system throughput by allowing up to two D2D pairs to share the same frequency resource with one CUE. Moreover, a scheme has been considered to mitigate the interference caused to CUEs. The simulation results show that service of CUEs and system performance can be effectively guaranteed by using the proposed schemes.

With the explosive development of wireless communication technologies and popularization of smart phones, mobile data traffic grows rapidly. The 5G heterogeneous networks constituted by macro cells and femtocells are widely researched as a promising approach to expand the network capability. However, for the cases of moving user equipments (UEs), this flexible and complicated networking method may lead to frequent handover and unsatisfied resource assignment. In this paper, we propose a mobility prediction scheme to optimize the resource allocation in heterogeneous networks based on order-2 Hidden Markov Model (HMM). With the prediction result of the possible next location of UE, the target cell may prepare necessary resource according to the service requirements. The user's historical movement trajectory is clustered into several groups to represent its main activity states. The user's service request preference in these locations are analyzed accordingly. Then, we build the mobility prediction model with order-2 HMM. Corresponding required resources are also analyzed and allocated in advance. The simulation results show that the proposed scheme has a good performance in prediction accuracy, drop rate and resource utility for the heterogeneous networks.

Semi-cooperative game theoretic framework for resource allocation in cognitive cellular networks

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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> 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.

We introduce a sleep mechanism for energy efficiency in heterogeneous cellular networks, where macro base stations and small base stations coexist in the same frequency band. The strategy is to put high-power macro base stations into sleep mode and offload the users to low-power small base stations or neighboring macro base stations. To do so, we first formulate a joint optimization problem to minimize the total energy consumption while maintaining the QoS of users. Then we decouple it into two subproblems: user association and resource allocation, and macro base station sleep mechanism. We propose a modified many-to-one matching algorithm to solve the first one and a voting-based dynamic sleep mechanism for the second one. Simulation results show that our scheme has both lower energy consumption and lower user blocking ratio compared with existing sleep mechanisms.