Optimal Spectrum Utilization and Flow Controlling In Heterogeneous Network with Reconfigurable Devices

Fairness provisioning in heterogeneous networks is a prime issue for high-rate data flow, wherein the inter-connectivity property among different communication devices provides higher throughput. In Hetnet, optimal resource utilization is required for efficient resource usage. Proper resource allocation in such a network led to higher data flow performance for real-time applications. In view of optimal resource allocation, a resource utilization approach for a reconfigurable cognitive device with spectrum sensing capability is proposed in this paper.  The allocation of the data flow rate at device level is proposed for optimization of network fairness in a heterogeneous network.  A dynamic approach of rate-inference optimization is proposed to provide fairness in dynamic data traffic conditions. The simulation results validate the improvement in offered quality in comparison to multi-attribute optimization

Integration of decentralized economic models for resource self-management in application layer networks

Resource allocation is one of the challenges for self-management of large scale distributed applications running in a dynamic and heterogeneous environment. Considering Application Layer Networks (ALN) as a general term for such applications including computational Grids, Content Distribution Networks and P2P applications, the characteristics of the ALNs and the environment preclude an efficient resource allocation by a central instance. The approach we propose integrates ideas from decentralized economic models into the architecture of a resource allocation middleware, which allows the scalability towards the participant number and the robustness in very dynamic environments. At the same time, the pursuit of the participants for their individual goals should benefit the global optimization of the application. In this work, we describe the components of this middleware architecture and introduce an ongoing prototype.Peer Reviewe

Dynamic Resource Allocation in Wireless Heterogeneous Networks

Deployment of low power basestations within cellular networks can potentially increase both capacity and coverage. However, such deployments require efficient resource allocation schemes for managing interference from the low power and macro basestations that are located within each other’s transmission range. In this dissertation, we propose novel and efficient dynamic resource allocation algorithms in the frequency, time and space domains. We show that the proposed algorithms perform better than the current state-of-art resource management algorithms. In the first part of the dissertation, we propose an interference management solution in the frequency domain. We introduce a distributed frequency allocation scheme that shares frequencies between macro and low power pico basestations, and guarantees a minimum average throughput to users. The scheme seeks to minimize the total number of frequencies needed to honor the minimum throughput requirements. We evaluate our scheme using detailed simulations and show that it performs on par with the centralized optimum allocation. Moreover, our proposed scheme outperforms a static frequency reuse scheme and the centralized optimal partitioning between the macro and picos. In the second part of the dissertation, we propose a time domain solution to the interference problem. We consider the problem of maximizing the alpha-fairness utility over heterogeneous wireless networks (HetNets) by jointly optimizing user association, wherein each user is associated to any one transmission point (TP) in the network, and activation fractions of all TPs. Activation fraction of a TP is the fraction of the frame duration for which it is active, and together these fractions influence the interference seen in the network. To address this joint optimization problem which we show is NP-hard, we propose an alternating optimization based approach wherein the activation fractions and the user association are optimized in an alternating manner. The subproblem of determining the optimal activation fractions is solved using a provably convergent auxiliary function method. On the other hand, the subproblem of determining the user association is solved via a simple combinatorial algorithm. Meaningful performance guarantees are derived in either case. Simulation results over a practical HetNet topology reveal the superior performance of the proposed algorithms and underscore the significant benefits of the joint optimization. In the final part of the dissertation, we propose a space domain solution to the interference problem. We consider the problem of maximizing system utility by optimizing over the set of user and TP pairs in each subframe, where each user can be served by multiple TPs. To address this optimization problem which is NP-hard, we propose a solution scheme based on difference of submodular function optimization approach. We evaluate our scheme using detailed simulations and show that it performs on par with a much more computationally demanding difference of convex function optimization scheme. Moreover, the proposed scheme performs within a reasonable percentage of the optimal solution. We further demonstrate the advantage of the proposed scheme by studying its performance with variation in different network topology parameters

Fast and Efficient Radio Resource Allocation in Dynamic Ultra-Dense Heterogeneous Networks

Ultra-dense network (UDN) is considered as a promising technology in 5G wireless networks. In an UDN network, dynamic traffic patterns can lead to a high computational complexity and an excessive communications overhead with traditional resource allocation schemes. In this paper, a new resource allocation scheme presenting a low computational overhead and a low subband handoff rate in a dynamic ultra-dense heterogeneous network is presented. The scheme first defines a new interference estimation method that constructs network interference state map, based on which a radio resource allocation scheme is proposed. The resource allocation problem is a MAX-K cut problem and can be solved through a graph- theoretical approach. System level simulations reveal that the proposed scheme decreases the subband handoff rate by 30% with less than 3.2% network throughput degradation

Cross-Layer Optimal Rate Allocation for Heterogeneous Wireless Multicast

Heterogeneous multicast is an efficient communication scheme especially for multimedia applications running over multihop networks. The term heterogeneous refers to the phenomenon when multicast receivers in the same session require service at different rates commensurate with their capabilities. In this paper, we address the problem of resource allocation for a set of heterogeneous multicast sessions over multihop wireless networks. We propose an iterative algorithm that achieves the optimal rates for a set of heterogeneous multicast sessions such that the aggregate utility for all sessions is maximized. We present the formulation of the multicast resource allocation problem as a nonlinear optimization model and highlight the cross-layer framework that can solve this problem in a distributed ad hoc network environment with asynchronous computations. Our simulations show that the algorithm achieves optimal resource utilization, guarantees fairness among multicast sessions, provides flexibility in allocating rates over different parts of the multicast sessions, and adapts to changing conditions such as dynamic channel capacity and node mobility. Our results show that the proposed algorithm not only provides flexibility in allocating resources across multicast sessions, but also increases the aggregate system utility and improves the overall system throughput by almost 30% compared to homogeneous multicast

Comparison between Static and Dynamic Modeling Approaches for Heterogeneous Cellular Networks

In order to accommodate growing traffic demands, next generation cellular networks must become highly heterogeneous to achieve capacity gains. Heterogeneous cellular networks composed of macro base stations and low-power base stations of different types are able to improve spectral efficiency per unit area, and to eliminate coverage holes. In such networks, intelligent user association and resource allocation schemes are needed to achieve gains in performance. We focus on heterogeneous cellular networks that consist of macro and pico BSs, and study the interplay between user association and resource allocation using two modeling approaches, namely a static modeling approach and a dynamic modeling approach. Our first study focuses on modeling heterogeneous cellular networks with a static approach. We propose a unified static framework to study the interplay of user association and resource allocation under a well-defined set of assumptions. This framework allows us to compare the performance of three resource allocation strategies: partially Shared deployment, orthogonal deployment, and co-channel deployment when the user association is optimized. We have formulated joint optimization problems that are non-linear integer programs which are NP-hard. We have, therefore, developed techniques to obtain upper bounds on the system's performance. We also propose a simple association rule that performs much better than all existing user association rules. We have used these upper bounds as benchmarks to provide many engineering insights, and to quantify how well different combinations of user association rules and resource allocation schemes perform. Our second study focuses on modeling heterogeneous cellular networks with a dynamic modeling approach. We propose a unified framework to study the interplay of user association, resource allocation, user arrival, and delay. We select three different performance metrics: the highest possible arrival rate, the network average delay, and the delay-constrained maximum throughput, and formulate three different optimal user association problems to optimize our performance metrics. The proposed problems are non-linear integer programs which are hard to solve efficiently. We have developed numerical techniques to compute either the exact solutions or tight lower bounds to these problems. We have used these lower bounds and the exact solutions as benchmarks to provide many engineering insights, and to quantify how well different user association rules and resource allocation schemes perform. Finally, using our numerical results, we compare the static and dynamic modeling approaches to study the robustness of our results. Our numerical results show that engineering insights on the resource allocation schemes drawn out the static study are valid in a dynamic context, and vice versa. However, the engineering insights on user association rules drawn out of the static study are not always consistent with the insights drawn out of the dynamic study.4 month

Task allocation in group of nodes in the IoT: A consensus approach

The realization of the Internet of Things (IoT) paradigm relies on the implementation of systems of cooperative intelligent objects with key interoperability capabilities. In order for objects to dynamically cooperate to IoT applications' execution, they need to make their resources available in a flexible way. However, available resources such as electrical energy, memory, processing, and object capability to perform a given task, are often limited. Therefore, resource allocation that ensures the fulfilment of network requirements is a critical challenge. In this paper, we propose a distributed optimization protocol based on consensus algorithm, to solve the problem of resource allocation and management in IoT heterogeneous networks. The proposed protocol is robust against links or nodes failures, so it's adaptive in dynamic scenarios where the network topology changes in runtime. We consider an IoT scenario where nodes involved in the same IoT task need to adjust their task frequency and buffer occupancy. We demonstrate that, using the proposed protocol, the network converges to a solution where resources are homogeneously allocated among nodes. Performance evaluation of experiments in simulation mode and in real scenarios show that the algorithm converges with a percentage error of about±5% with respect to the optimal allocation obtainable with a centralized approach

Traffic-Driven Spectrum Allocation in Heterogeneous Networks

Next generation cellular networks will be heterogeneous with dense deployment of small cells in order to deliver high data rate per unit area. Traffic variations are more pronounced in a small cell, which in turn lead to more dynamic interference to other cells. It is crucial to adapt radio resource management to traffic conditions in such a heterogeneous network (HetNet). This paper studies the optimization of spectrum allocation in HetNets on a relatively slow timescale based on average traffic and channel conditions (typically over seconds or minutes). Specifically, in a cluster with $n$ base transceiver stations (BTSs), the optimal partition of the spectrum into $2^n$ segments is determined, corresponding to all possible spectrum reuse patterns in the downlink. Each BTS's traffic is modeled using a queue with Poisson arrivals, the service rate of which is a linear function of the combined bandwidth of all assigned spectrum segments. With the system average packet sojourn time as the objective, a convex optimization problem is first formulated, where it is shown that the optimal allocation divides the spectrum into at most $n$ segments. A second, refined model is then proposed to address queue interactions due to interference, where the corresponding optimal allocation problem admits an efficient suboptimal solution. Both allocation schemes attain the entire throughput region of a given network. Simulation results show the two schemes perform similarly in the heavy-traffic regime, in which case they significantly outperform both the orthogonal allocation and the full-frequency-reuse allocation. The refined allocation shows the best performance under all traffic conditions.Comment: 13 pages, 11 figures, accepted for publication by JSAC-HC