Distributed Subchannel and Power Allocation for OFDMA-Based Femtocell Networks

The resource allocation of Orthogonal Frequency Division Multiple Access (OFDMA) is one of the core issues in the next generation mobile systems. The improvement in the performance and quality of service (QoS) of communication systems is relying upon the efficient utilization of the available communication resources. The resource allocation of the OFDMA systems is mainly depends on both power and subcarrier allocations of each user for different operation scenarios and channel conditions. This paper proposes and applies Firefly Pack Algorithm (FPA) to find the optimal or near optimal power and subcarrier allocations for OFDMA systems. It takes into consideration the power and subcarrier allocations constrains, channel and noise distributions, distance between users equipments and base station, user priority weight to approximate the most of the variables, constrains, and parameters that encounter in the OFDMA systems. Four important cases for the number of subcarriers and users are addressed, simulated, and analyzed with employing the FPA algorithm under specific operation scenarios to meet the standard specifications. The results demonstrate that FPA is an effective algorithm in finding the optimal or near optimal for both subcarrier and power allocation.

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.

In this paper, we consider an amplify-and-forward wireless relay system where multiple source nodes communicate with their corresponding destination nodes with the help of relay nodes. Conventionally, each relay equally distributes the available resources to its relayed sources. This approach is clearly sub-optimal since each user experiences dissimilar channel conditions, and thus, demands different amount of allocated resources to meet its quality-of-service (QoS) request. Therefore, this paper presents novel power allocation schemes to i) maximize the minimum signal-to-noise ratio among all users; ii) minimize the maximum transmit power over all sources; iii) maximize the network throughput. Moreover, due to limited power, it may be impossible to satisfy the QoS requirement for every user. Consequently, an admission control algorithm should first be carried out to maximize the number of users possibly served. Then, optimal power allocation is performed. Although the joint optimal admission control and power allocation problem is combinatorially hard, we develop an effective heuristic algorithm with significantly reduced complexity. Even though theoretically sub-optimal, it performs remarkably well. The proposed power allocation problems are formulated using geometric programming (GP), a well-studied class of nonlinear and nonconvex optimization. Since a GP problem is readily transformed into an equivalent convex optimization problem, optimal solution can be obtained efficiently. Numerical results demonstrate the effectiveness of our proposed approach.

In this paper, we consider an optimal power allocation problem that maximizes the sum-rate of a single-cell MISO broadcast channel with regularized channel inversion (RCI) beamforming at the base station (BS). Unlike the channel inversion or zero forcing beamforming, the optimal power allocation with RCI precoding at the base station is a nonlinear non-convex optimization problem with many local optima. Here, we investigate this problem in the large system limit, i.e., when the number of users K and antennas at the base station N tend to infinity with their ratio β = K/N being held constant. We assume each user has data symbol with power p k , and slow-varying path-loss a k . In this setting, we first derive the expression for the signal to interference plus noise ratio (SINR). Then, we divide all K users into L groups where all users in each group are assumed to be co-located or to have approximately the same distance from the BS. In other words, the users in each group have the same path-loss which is distance dependent. Moreover, in each group, we assume that the power allocated to each user is the same. Based on this system model, we investigate optimal power allocation schemes that maximize the sum-rate per antenna, firstly under average power constraint, and then under both average and per-user power constraints. We show that both problems are convex and the power allocation mainly follows the well-known water-filling strategy.

Heterogeneous network is a novel network architecture proposed in Long-Term-Evolution~(LTE), which highly increases the capacity and coverage compared with the conventional networks. However, in order to provide the best services, appropriate resource management must be applied. In this paper, we consider the joint optimization problem of user association, subchannel allocation, and power allocation for downlink transmission in Multi-cell Multi-association Orthogonal Frequency Division Multiple Access (OFDMA) heterogeneous networks. To solve the optimization problem, we first divide it into two subproblems: 1) user association and subchannel allocation for fixed power allocation; 2) power allocation for fixed user association and subchannel allocation. Subsequently, we obtain a locally optimal solution for the joint optimization problem by solving these two subproblems alternately. For the first subproblem, we derive the globally optimal solution based on graph theory. For the second subproblem, we obtain a Karush-Kuhn-Tucker (KKT) optimal solution by a low complexity algorithm based on the difference of two convex functions approximation (DCA) method. In addition, the multi-antenna receiver case and the proportional fairness case are also discussed. Simulation results demonstrate that the proposed algorithms can significantly enhance the overall network throughput.

In order to mitigate inter-cell interference and cope with network, traffic, and channel fluctuations in space and time, Orthogonal Frequency Division Multiple Access (OFDMA) based cellular networks should be equipped with dynamic frequency and power allocation algorithms. Although dynamic frequency and power allocation has been well studied separately, very few results have been reported on dynamic joint frequency and power allocation. In this paper, we propose a dynamic frequency and power allocation algorithm for the DownLink (DL) of OFDMA-based cellular networks, where frequency resources are allocated to cells by a central broker to minimize long-term cell-edge inter-cell interference. Moreover, each cell independently assigns its available frequency resources and power to users in a way that its total DL transmission power is minimized while meeting the throughput demands of its users. Simulation results show that the proposed algorithm provides significant improvements in network outage performance, capacity, and throughput as compared with existing schemes.

In this paper, a joint subcarrier and power allocation algorithm is proposed to improve the physical layer security in cooperative orthogonal frequency division multiple access (OFDMA) networks, where several source-destination pairs and one untrusted relay are involved. The relay is friendly and intends to help these pairs to enhance their communications. However, it may be overheard by a malicious eavesdropper at the same time. To optimize the joint subcarrier and power allocation with low complexity, we divide the optimization problem into two simpler subproblems. Firstly, the subcarriers are assigned to the source-destination pairs by employing the dual approach under the assumption that the relay power is equally allocated to all the subcarriers. Then, the relay power is allocated to the subcarriers based on the alternative ascending clock auction mechanism. In addition, we prove that the two subproblems can converge after a finite number of iterations. We also find that the proposed auction is cheat-proof and, thus, can avoid the cheating behaviors in the auction process. Numerical results demonstrate that our proposed algorithm can effectively improve the system sum secrecy rate, and the convergence performance is also desirable.

In this study, the authors propose a novel dynamic channel assignment scheme called graph colouring based fractional frequency reuse (GC-FFR) to improve system performance for the downlink of enterprise femtocell networks based on orthogonal frequency division multiple access with frequency division duplex. In order to mitigate co-tier interference from neighbour femtocell access points, the proposed GC-FFR scheme uses a heuristic method that consists of two steps: multiple femtocell user equipment (FUE) clustering step and dynamic subchannel assignment step. Simulation results show that the proposed GC-FFR scheme outperforms other schemes in terms of the mean FUE capacity and unsatisfied FUE probability.

This paper investigates the optimal resource allocation of a downlink non-orthogonal multiple access (NOMA) system consisting of one base station and multiple users. Unlike existing short-term NOMA designs that focused on the resource allocation for only the current transmission timeslot, we aim to maximize a long-term network utility by jointly optimizing the data rate control at the network layer and the power allocation among multiple users at the physical layer, subject to practical constraints on both the short-term and long-term power consumptions. To solve this problem, we leverage the recently-developed Lyapunov optimization framework to convert the original long-term optimization problem into a series of online rate control and power allocation problems in each timeslot. The power allocation problem, however, is shown to be non-convex in nature and thus cannot be solved with a standard method. However, we explore two structures of the optimal solution and develop a dynamic programming based power allocation algorithm, which can derive a globally optimal solution, with a polynomial computational complexity. Extensive simulation results are provided to evaluate the performance of the proposed joint rate control and power allocation framework for NOMA systems, which demonstrate that the proposed NOMA design can significantly outperform multiple benchmark schemes, including orthogonal multiple access (OMA) schemes with optimal power allocation and NOMA schemes with non-optimal power allocation, in terms of average throughput and data delay.

This letter considers the optimum transmission power allocation strategy for maximizing the aggregate communication rate of a desired tier in a multi-tier network for a multi-carrier scenario. Users of the tier of interest form a multiple access channel with their base station. They share the same resources with other tiers, and cause inter-tier interference. Optimal power control for users of the tier of interest is found by maximizing their sum data rate while limiting the power transmitted per user and interference caused per tier. The power allocation is found to be a power and interference regulated water-filling solution which is a combination of conventional power-filling with interference-filling. The superiority of our proposed algorithm over orthogonal frequency division multiple access and equal power allocation schemes is demonstrated.

In the planning of cellular networks, link budget calculation is the first step to get the maximum allowable cell radius that guarantees a target signal to interference plus noise ratio (SINR) with a certain level of outage probability at the cell border. The interference margin as one of key inputs for link budget is used to compensate for co-channel interference. In this paper, a mathematical analysis for interference margin as a function of system load is proposed in orthogonal frequency division multiple access (OFDMA) cellular networks. The analysis is derived from OFDMA features based on a general expression of exponential effective SINR mapping (EESM). The proposed approach can be easily applied to the evaluation of interference from neighboring cells, and help to improve the accuracy of the link budget calculation in OFDMA systems. All the theoretical analysis given in this paper have been proved by Monte Carlo simulations.

While helping better utilize the system's resources, full-duplex technologies have the ability to double the efficiency of wireless networks. However, these networks would suffer from added types of interferences, namely self-interference and intra-cell co-channel interference. As such, efficiently addressing scheduling and power allocation in full-duplex networks is vital. In this article, we formulate a queue-aware, fair scheduling and power allocation problem for full duplex orthogonal frequency division multiple access networks. Due to its intractability, we decompose this problem into two: a scheduling problem and a power allocation problem. We compare our proposal to the state-of-the-art, and show that it improves fairness among the user equipment at no cost in the system's performance. Simulation results show a significant increase in throughput values for full-duplex users in comparison with their half-duplex counterparts, and that power allocation saves on expenditure while improving user performance.

We study a one-hop device-to-device (D2D)-assisted wireless caching network, where popular files are randomly and independently cached in the memory of end users. Each user may obtain the requested files from its own memory without any transmission, or from a helper through a one-hop D2D transmission, or from the base station. We formulate a joint D2D link scheduling and power allocation problem to maximize the system throughput. However, the problem is non-convex, and obtaining an optimal solution is computationally hard. Alternatively, we decompose the problem into a D2D link-scheduling problem and an optimal power allocation problem. To solve the two subproblems, we first develop a D2D link-scheduling algorithm to select the largest number of D2D links satisfying both the signal to interference plus noise ratio and the transmit power constraints. Then, we develop an optimal power allocation algorithm to maximize the minimum transmission rate of the scheduled D2D links. Numerical results indicate that both the number of the scheduled D2D links and the system throughput can be improved simultaneously with the Zipf-distribution caching scheme, the proposed D2D link-scheduling algorithm, and the proposed optimal power allocation algorithm compared with the state of the arts.

The explosive demands for mobile broadband service bring a major challenge to 5G wireless networks. Device-to-device communication, adopting side links for user-direct communication, is regarded as a main technical source for offloading large volume of mobile traffic from cellular base station. This article investigates the joint power and subcarrier allocation scheme for device-to-device communication in 5G time division duplex systems. In time division duplex system, instead of utilizing an exclusive portion of the precious cellular spectrum, device-to-device pairs reuse the subcarriers occupied by cellular users, thus producing harmful interference to cellular users in both uplink and downlink communication, and strongly limiting the spectrum efficiency of the system. To this end, we focus on the maximization of device-to-device throughput while guaranteeing both uplink and downlink channel quality of service of cellular users as well as device-to-device pairs. The problem is formulated as a mixed integer non-linear programming (MINLP) problem. To make it tractable, we separate the original MINLP problem into two sub problems: power allocation and sub-carrier reusing. The former is to develop optimal power allocation for each device-to-device pair and each cellular user, with the constraints of maximum power and quality of service. It is solved by geometric programming technique in convex optimization method. The latter is derived as a one-to-many matching problem for scheduling multiple subcarriers occupied by cellulars to device-to-device pairs. It is solved by Hungarian method. Simulation results show that the proposed scheme significantly improves system capacity of the device-to-device underlay network, with quality of service of both device-to-device users and cellular users guaranteed.

Optimizing resource allocation for enhanced urban connectivity in LEO-UAV-RIS networks