Self-organized Resource Allocation Research Articles

Analysis of Dynamic Frequency Reuse Techniques in LTE – A Cellular Network

Inter-cell Interference (ICI) is a significant obstacle which is degrading the efficiency of mobile cellular networks particularly for end users. Frequency reuse is an ICI synchronization technique which helps to mitigate ICI by assigning dissimilar frequency reuse factors on the basis of its position to User equipment (UE). This paper proposes a Self-organized resource allocation (SORA) framework that chooses the reuse factors randomly. The output of this strategy is configured using MATLAB and correlated with numerous reuse factor combinations. Results from simulation show improved efficiency for Cell Edge Users (CEU).

Intelligent-Driven Green Resource Allocation for Industrial Internet of Things in 5G Heterogeneous Networks

The Industrial Internet of Things (IIoT) is one of the important applications under the 5G massive machine type of communication (mMTC) scenario. To ensure the high reliability of IIoT services, it is necessary to apply an efficient resource allocation method under the dynamic and complex environment. In view of the absence of energy-efficient resource management architecture for the entire network, this article proposes an intelligent-driven green resource allocation mechanism for the IIoT under 5G heterogeneous networks. First, an intelligent end-to-end self-organizing resource allocation framework for IIoT service is given. Next, an energy-efficient resource allocation model within the framework is proposed. It is then solved by an intelligent mechanism with the asynchronous advantage actor critic driven deep reinforcement learning algorithm. Through the comparison analysis of different methods and rewards under IIoT scenarios with proper parameters setting, the proposed method can achieve better performance than other traditional deep learning (DL) methods and maintain service quality above accepted levels as well.

Distributed Learning for Low Latency Machine Type Communication in a Massive Internet of Things

The Internet of Things (IoT) will encompass a massive number of machine type devices that must wirelessly transmit, in near real-time, a diverse set of messages sensed from their environment. Designing resource allocation schemes to support such coexistent, heterogeneous communication is hence a key IoT challenge. In particular, there is a need for self-organizing resource allocation solutions that can account for unique IoT features, such as massive scale and stringent resource constraints. In this paper, a novel finite memory multistate sequential learning framework is proposed to enable diverse IoT devices to share limited communication resources, while transmitting both delay-tolerant, periodic messages and urgent, critical messages. The proposed learning framework enables the IoT devices to learn the number of critical messages and to reallocate the communication resources for the periodic messages to be used for the critical messages. Furthermore, the proposed learning framework explicitly accounts for IoT device limitations in terms of memory and computational capabilities. The convergence of the proposed learning framework is proved, and the lowest expected delay that the IoT devices can achieve using this learning framework is derived. Furthermore, the effectiveness of the proposed learning algorithm in IoT networks with different delay targets, network densities, probabilities of detection, and memory sizes is analyzed in terms of the probability of a successful random access (RA) request and percentage of devices that learned correctly. Simulation results show that, for a delay threshold of 1.25 ms, the average achieved delay is 0.71 ms and the delay threshold is satisfied with probability 0.87. Moreover, for a massive network, a delay threshold of 2.5 ms is satisfied with probability 0.92. The results also show that the proposed learning algorithm is very effective in reducing the delay of urgent, critical messages by intelligently reallocating the communication resources allocated to the delay-tolerant, periodic messages.

Appropriate Resource Allocation for Effective Mobile Communication in 4G LTE- Advanced

Objective: Neighboring cells become progressively worse especially for cell edge users due to Inter Cell Interference (ICI) in Orthogonal Frequency Division Multiple Access (OFDMA). Frequency Reuse Factor is to mitigate ICI by proper allocation of resources to users in network layout. Methods/Statistical Analysis: This study presents a self-organized resource allocation scheme for automatically selecting the reuse factor based on number of cell edge users and cell center user. Findings: This study explains about the new scheduling metric based on transmitted energy per bit and this provides the resources to users which are having low ration of transmitted energy per bit to avoid the wastage of resources. Applications/Improvements: Due to consideration of different reuse factor and also scheduling metric, this algorithm provides better performance for edge users in terms of Energy cell efficiency, Signal to Noise Interference Cell Ratio (SINR) and Fairness Index. Keywords: Center Users, Edge Users, 4G LTE Advanced, Round Robin (RR), Maximum Carrier to Interference Ratio (MCI)

Echo State Networks for Self-Organizing Resource Allocation in LTE-U With Uplink–Downlink Decoupling

Uplink-downlink decoupling in which users can be associated to different base stations in the uplink and downlink of heterogeneous small cell networks (SCNs) has attracted significant attention recently. However, most existing works focus on simple association mechanisms in LTE SCNs that operate only in the licensed band. In contrast, in this paper, the problem of resource allocation with uplink-downlink decoupling is studied for an SCN that incorporates LTE in the unlicensed band. Here, the users can access both licensed and unlicensed bands while being associated to different base stations. This problem is formulated as a noncooperative game that incorporates user association, spectrum allocation, and load balancing. To solve this problem, a distributed algorithm based on the machine learning framework of echo state networks (ESNs) is proposed. This proposed algorithm allows the small base stations to autonomously choose their optimal resource allocation strategies given only limited information on the network's and users' states. It is shown that the proposed algorithm converges to a stationary mixed-strategy distribution, which constitutes a mixed strategy Nash equilibrium for their studied game. Simulation results show that the proposed approach yields significant gain, in terms of the sum-rate of the 50th percentile of users, that reaches up to 167% compared with a Q-learning algorithm. The results also show that the ESN significantly provides a considerable reduction of information exchange for the wireless network.

Self-Organized Resource Allocation for Femtocell Network to Mitigate Downlink Interference

In this paper, we consider the femto users and their mutual interference as graph elements, nodes and weighted edges, respectively. The total bandwidth is divided into a number of resource blocks (RBs) and these are assigned to the femto user equipment (FUEs) using a graph coloring algorithm. In addition, resources blocks are assigned to the femto users to avoid inter-cell interference. The proposed scheme is compared with the traditional scheduling schemes in terms of throughput and fairness and performance improvement is achieved by exploiting the graph coloring scheme.

Simulated annealing variants for self-organized resource allocation in small cell networks

This paper discusses the application of simulated annealing (SA) based meta-heuristics to self-organized orthogonal resource allocation problems in small cell networks (SCN)s, for static and dynamic topologies. We consider the graph coloring formulation of the orthogonal resource allocation problem, where a planar graph is used to model interference relations in a SCN comprising of randomly deployed mutually interfering cells. The aim is to color the underlying conflict graph in a distributed way, for which different variants of SA such as SA with focusing heuristic (i.e., limiting the local moves only to the cells that are in conflict), and fixed temperature, are investigated. For static topologies, distributed algorithms are used, in which no dedicated message-passing is required between the cells, except for the symmetrization of conflict graph. To enable distributed SA in dynamic topologies, a distributed temperature control protocol based on message-passing is considered. Different aspects relevant to self-organizing cellular networks are analyzed using simulations. These include the number of cells with resource conflicts, number of resource reconfigurations required by the cells to resolve the conflicts, requirements on dedicated message-passing between the cells, and sensitivity to the temperature parameter that guides the stochastic search process. Simulation results indicate that the considered algorithms are inherently suitable for SCNs, thereby enabling efficient resource allocation in a self-organized way. Furthermore, the underlying concepts and the key conclusions are general, and relevant to other problems that can be solved by distributed graph coloring.

A self‐organized resource allocation scheme for heterogeneous macro‐femto networks

AbstractThis paper investigates the radio resource management (RRM) issues in a heterogeneous macro‐femto network. The objective of femto deployment is to improve coverage, capacity, and experienced quality of service of indoor users. The location and density of user‐deployed femtos is not known a‐priori. This makes interference management crucial. In particular, with co‐channel allocation (to improve resource utilization efficiency), RRM becomes involved because of both cross‐layer and co‐layer interference. In this paper, we review the resource allocation strategies available in the literature for heterogeneous macro‐femto network. Then, we propose a self‐organized resource allocation (SO‐RA) scheme for an orthogonal frequency division multiple access based macro‐femto network to mitigate co‐layer interference in the downlink transmission. We compare its performance with the existing schemes like Reuse‐1, adaptive frequency reuse (AFR), and AFR with power control (one of our proposed modification to AFR approach) in terms of 10 percentile user throughput and fairness to femto users. The performance of AFR with power control scheme matches closely with Reuse‐1, while the SO‐RA scheme achieves improved throughput and fairness performance. SO‐RA scheme ensures minimum throughput guarantee to all femto users and exhibits better performance than the existing state‐of‐the‐art resource allocation schemes.Copyright © 2014 John Wiley & Sons, Ltd.

Algorithms for Self-Organized Resource Allocation in Wireless Networks

We consider the problem of allocating orthogonal resources in a self-organized manner on conflict graphs that describe discretized interference couplings of wireless networks. The target is to find a global optimum, i.e., a conflict-free allocation. We consider both random planar and nonplanar graphs that result from a path-loss model in a wireless network. Two algorithms for the self-organized coloring of arbitrary graphs are devised, and their performances are compared with each other and a rule-based reasoning algorithm that is known from the literature. The semigreedy distributed local search (SDLS) algorithm, which is a particularly simple algorithm that is proposed here, is shown to outperform other algorithms in several cases. In a cellular system setting, we consider a negotiated worst coupling procedure to symmetrize the interference coupling between cells, targeting an improvement of the channel quality of cell-edge users. We compare this approach with an interference coupling based on the path loss that was experienced between base stations and see significant gains in cell-edge performance. In addition, we compare the channel quality that was experienced by users in a cellular network that employs SDLS with the channel quality in a network that employs an interference-reducing network algorithm which finds a local optimum in terms of real-valued interference couplings. In some cases, attempting global optimization based on a conflict graph interpretation outperforms local real-valued optimization.

Self-Organized Resource Allocation Using Inter-Cell Interference Coordination(ICIC) in Relay-Asisted Cellular Networks

In a multi-cell scenario, the inter-cell interference (ICI) is detrimental in achieving the intended system performance, in particular for the edge users. There is paucity of work available in literature on ICI coordination (ICIC) for relay-assisted cellular networks (RACN). In this paper, we do a survey on the ICIC schemes in cellular networks and RACN. We then propose a self-organized resource allocation plan for RACN to improve the edge user’s performance by ICIC. We compare the performance of reuse-1, reuse-3, soft frequency reuse (SFR) scheme, proposed plan with and without relays. The performance metrics for comparison are edge user’s spectral efficiency, their signal-to-interference-and-noise ratio (SINR) and system’s area spectral efficiency. We show by the simulation results that our proposed plan performs better than the existing resource allocation schemes in static allocation scenario. Next, we propose to make our resource allocation plan dynamic and self-organized. The distinct features of our proposed plan are: One, it achieves a trade-off between the system’s area spectral efficiency and the edge user’s spectral efficiency performance. Secondly, it introduces a novel concept of interfering neighbor set to achieve ICIC by local interaction between the entities.

A SELF-ORGANIZED RESOURCE ALLOCATION USING INTER-CELL INTERFERENCE COORDINATION (ICIC) IN RELAY-ASSISTED CELLULAR NETWORKS

In a multi-cell scenario, the inter-cell interference (ICI) is detrimental in achieving the intended system performance, in particular for the edge users. There is paucity of work available in literature on ICI coordination (ICIC) for relay-assisted cellular networks (RACN). In this paper, we do a survey on the ICIC schemes in cellular networks and RACN. We then propose a self-organized resource allocation plan for RACN to improve the edge user's performance by ICIC. We compare the performance of reuse-1, reuse-3, soft frequency reuse (SFR) scheme, proposed plan with and without relays. The performance metrics for comparison are edge user's spectral efficiency, their signal-to-interference-and-noise ratio (SINR) and system's area spectral efficiency. We show by the simulation results that our proposed plan performs better than the existing resource allocation schemes in static allocation scenario. Next, we propose to make our resource allocation plan dynamic and self-organized. The distinct features of our proposed plan are: One, it achieves a trade-off between the system's area spectral efficiency and the edge user's spectral efficiency performance. Secondly, it introduces a novel concept of interfering neighbor set to achieve ICIC by local interaction between the entities.

A simulation of an economic, self-organising resource allocation approach for application layer networks

Application Layer Networks like Utility Grid Networks or Cloud Computing Systems, often depicted as large scale self-organising architectures using a shared infrastructure, will need innovative management approaches for controlling and matching services demand and supply. This article shows a self-organising resource allocation approach based on principles of the Catallaxy, an economic concept of F.A. von Hayek. The implementation uses a coevolutionary learning algorithm for adaptation of the agent strategy to the dynamic Application Layer Network environment. The simulation study performs a sensitivity analysis of a large scale Application Layer Network scenario with 2000 agents. A set of representative agents is selected and their evolutionary behaviour analysed.