Dynamic resource assignment using network flows in wireless data networks

We propose a framework for network-assisted radio resource management in wireless data networks. This type of radio resource management techniques offer implementation and capacity benefits compared to conventional, interference-measurement based, dynamic channel assignment (DCA) algorithms. The basic idea is to use interbase signaling to shift most of the burden of the resource allocation from the air interface to the backbone infrastructure. By exchanging channel assignment as well as other relevant information in real time through the backbone network, each base can calculate the impact of a resource assignment on the system. As a result, rapid interference measurements, which are typically needed to implement DCA schemes, are replaced by a limited amount of path loss measurements and the computation of interference conditions by the base stations. This significantly reduces the measurement and over-the-air signaling requirements, and can also provide an opportunity for a better optimization of the system performance. We focus on two specific algorithms: network-assisted least-interference-based dynamic packet assignment (NA-LI-DPA) and network-assisted dynamic packet assignment with throughput optimization (NA-DPA). NA-LI-DPA closely resembles a least-interference-based dynamic channel assignment algorithm, and NA-DPA attempts to further improve the overall system throughput. The algorithms, as defined, are appropriate for a best-effort data service, where the primary goal is to provide a higher throughput. However, it will be clear from the discussion that it is also feasible to alter the algorithms to optimize performance metrics other than throughput, e.g., to ensure a certain quality of service. We show through simulation that, for a system like enhanced general packet radio service (EGPRS) system, NA-DPA can provide a throughput that is 50% higher than random packet assignment, and 25% higher than that obtained by conventional DCA algorithms.

Mobility of the wireless user creates uncertainty in demand and non-optimum use of radio resources. Current wireless networks are periodically re-configured manually to improve network performance. Dynamic channel assignment (DCA) methods can be used to reconfigure radio channels automatically. But many of these DCA techniques use "parochial" allocation schemes which consider only the received interference levels and do not consider the impact of the new interference generated by a particular channel assignment on other cells. These DCA algorithms typically allocate channels on an instantaneous need basis so there is little computation time to consider the impact of network wide interference. This paper will propose a new DCA approach which uses time intervals to estimate traffic trends. These traffic trends will be used to estimate radio requirements for each period. All investigated DCA algorithms will consider network wide interference impact and select radio configurations which minimize overall interference. This paper will examine the performance of two different DCA approaches, the gradient descent algorithm (GDA) and cell level radio tuning algorithm (CLRTA) methods. The different DCA algorithms will also be compared to static allocation algorithms. The GDA is shown to have superior performance to that of the CLRTA by approximately 2-4 dB depending on the number of available radio channels. The DCA approach is approximately 3-5 dB better than conventional fixed channel allocation (FCA) scheme.

In unstructured environments, dynamic resource assignment is required for effective cooperation of robot teams. In some scenarios, robots are in charge of executing multiple missions simultaneously. This creates risks of deadlock due to the presence of shared resources among various missions. The main contribution of this paper is the development of a novel approach that combines the one-step look-ahead deadlock avoidance policy with dynamic resource assignment. The dynamic resource assignment is achieved using greedy resource assignment for multi-mission robot teams in the framework of a matrix-based discrete event controller. Simulation results are presented in MATLAB® to discuss in detail the proposed control strategy. The paper also discusses the toolkit developed in LabVIEW® which is used to implement this control framework using a suitable example.

An insight into dynamic channel assignment in cellular mobile communications systems

Heterogeneous networks constitute a promising solution to the emerging challenges of 5G networks. According to the specific network architecture, a macro-cell base station (MBS) shares the same spectral resources with a number of small cell base stations (SBSs), resulting in increased co-channel interference (CCI). The efficient management of CCI has been studied extensively in the literature and various dynamic channel assignment (DCA) schemes have been proposed. However, the majority of these schemes consider a uniform approach for the users without taking into account the different quality requirements of each application. In this work, we propose an algorithm for enabling dynamic channel assignment in the 5G era that receives information about the interference and QoS levels and dynamically assigns the best channel. This algorithm is compared to state-of-the-art channel assignment algorithm. Results show an increase of performance, e.g., in terms of throughput and air interface latency. Finally, potential challenges and way forward are also discussed.

Advanced traffic management based on the dynamic resource assignment allows a broadband satellite system such as euroskyway (ESW) to dynamically assign the resources of connections. The mechanisms of the dynamic assignment exploit variations of burstiness exhibited by real time and non‐real time variable bit rate traffic sources to perform an optimized resource redistribution. The efficiency of the dynamic bandwidth allocation capability (DBAC) depends on the accuracy of the traffic source description; inaccurate assessment of the arrival process will cause an overhead and a degraded utilisation of system resources.In this paper a flexible traffic burstiness predictor for dynamic bandwidth resource allocation based on neural network is presented. The approach is able to perform an online estimation of expected resource requests implementing traffic resource assignment by using a sub‐symbolic adaptive representation of the traffic source. The achieved results prove that the flexible approach is more effective than the ones based on fixed schemes designed using analytical traffic source description when applied on the satellite terminal ESW system component. Copyright © 2003 John Wiley & Sons, Ltd.

A major challenge in the operation of wireless communications systems is the efficient use of radio resources. One important component of radio resource management is power control, which has been studied extensively in the context of voice communications. With the increasing demand for wireless data services, it is necessary to establish power control algorithms for information sources other than voice. We present a power control solution for wireless data in the analytical setting of a game theoretic framework. In this context, the quality of service (QoS) a wireless terminal receives is referred to as the utility and distributed power control is a noncooperative power control game where users maximize their utility. The outcome of the game results in a Nash (1951) equilibrium that is inefficient. We introduce pricing of transmit powers in order to obtain Pareto improvement of the noncooperative power control game, i.e., to obtain improvements in user utilities relative to the case with no pricing. Specifically, we consider a pricing function that is a linear function of the transmit power. The simplicity of the pricing function allows a distributed implementation where the price can be broadcast by the base station to all the terminals. We see that pricing is especially helpful in a heavily loaded system.

The recent growth in the maritime industry stemming to increase in global commerce as well as searching for hydrocarbons at offshore locations, the high data rate and cost affordable maritime communication is acquired. A mesh network is envisaged for maritime communications because of its expanded coverage, self-healing, and high-capacity. With the development of multi-radio technology, the frequency interference can be decreased sharply with proper channel assignment. The static channel assignment optimization is often applied to decrease the wireless frequency interference which is known as an NP-hard problem. In this paper, we focus on the static channel assignment issue and propose a heuristic algorithm to solve the optimization problem. The problem is addressed by assigning channels to communication links to minimize the interference from overall network. A modified particle swarm optimization (PSO) algorithm is proposed to optimize the problem, and a new merging solution is adopted to reassign channels for nodes, which violate the radio constraints. Multi-radio simulation is performed in NS-3 to validate the effectiveness of the proposed channel assignment algorithm. The results show that the algorithm is able to find an optimized assignment with fewer iterations than the previous work and improve network performance.

Personal communications and next generation wireless networks pose challenging problems in performance modeling and analysis due to type and pattern of source mobility, radio propagation and fading effects, interference environment, reduced cell sizes, call delivery, dynamic resource assignment and heterogeneity of traffic. In this paper, we discuss issues in mobility and teletraffic characterization of mobile sources. We note that the system architecture plays an important role in determining the overall system performance.

Channel bonding is one promising approach to cope with rising WLAN data demand, given scarce spectrum resources. An access point (AP) can aggregate multiple contiguous channels to satisfy demand. We discuss how to optimally utilize available frequency bands under uncertainty in AP demands using two stochastic optimization frameworks: a static scheme which minimizes the total occupied bandwidth while satisfying the demand of each AP with probability at least β, and an adaptive scheme that allows adaptability of the bandwidth allocation in response to the AP demand variations. Given its complexity, we propose a novel framework to solve the adaptive stochastic optimization problem efficiently. The proposed framework exploits the special structure of the problem through decomposition into two subproblems. A particle swarm optimization (PSO)-based algorithm is tailored to the first-stage problem in order to obtain good solutions. The second-stage problem is further decomposed into several subproblems that can be solved independently in parallel. Our numerical results (i) demonstrate the advantages of stochastic compared to deterministic allocation, (ii) illustrate that the proposed framework reaches the optimal solution for the two-stage problem in few iterations, and (iii) explain the bandwidth-user satisfaction trade-off provided by the adaptive allocation approach.

With the convergence of mobile and fixed communication networks, mobile users are increasingly demanding applications offering the same quality of service (QoS) as provided by wired access. High-speed time division duplex access mode is typical of high-speed mobile communication systems in order to simultaneously handle real-time, interactive, and best effort traffic classes. This paper proposes a medium access control (MAC) protocol that makes efficient use of radio resources for access. The proposed protocol, named dynamic-time division duplexing (D-TDD), is based on a multiple-access TDD air interface capable of supporting uplink and downlink traffic asymmetries. Validation of the D-TDD good performance was achieved by comparison with an ideal scheme and a different MAC protocol recently proposed in the literature on this subject.

In this paper, we propose a distributed algorithm for channel assignment in multi-radio, multi-channel mesh networks. Channel assignment in multi-radio mesh networks is complicated because, in addition to affecting the interference between spatially closely located links, binding interfaces to channels affects the topology of the network. If the channel assignment is done without taking the topology into consideration, distance between any two nodes may increase arbitrarily. We propose a novel distributed channel assignment algorithm that reduces interference and at the same time provides bounds on the distance between any two nodes in the topology resulting from channel assignment. Our algorithm is based on the concept of spanners. We also develop and implement a modification of AODV, which uses the cumulative metric WCETT [4] as the routing metric. We use this routing algorithm to analyze the performance of our channel assignment algorithm. Through extensive simulations in NS-2, we show that the proposed channel assignment algorithm provides nearly 30% improvement in throughput over a simple, minimal-connectivity preserving channel assignment algorithm.

It has been said that dynamic guard channel (DGC) assignment scheme based on traffic intensities averages QoS and performs better than the prioritized guard channel assignment scheme with queue (QPGC) during traffic congestion. This work has extended the investigation to DGC with queue (QDGC) and carried out its comparison with other call traffic channel assignment schemes. Mathematical analysis of the models was done using Markov chain and simulation was carried out in MATLAB. When traffic arrival rates were symmetric, the QPGC had the lower call blocking probability than the QDGC but became the same when the system was congested. However, the performance of the two queuing schemes was the same when handover call traffic was more than new calls traffic.

NOVN: A named-object based virtual network architecture to support advanced mobile edge computing services

Our research aims to improve energy efficiency in data centres by combining cloud computing infrastructure with machine learning techniques. We propose that dynamic resource assignment, combined with intelligent optimisation of cooling systems, can reduce power waste and operational costs. Real-time sensor data from various data centre components such as servers, cooling systems, and power distribution units is collected and fed into machine learning models for analysis. In this way, we can create power arrangements that are tailored to the resources and needs of various applications. The experimental results show that energy consumption has been reduced by an average of 30% compared to traditional methods. Furthermore, our machine learning models are quite accurate in predicting cooling system performance. For example, an Artificial Neural Network (ANN) has an accuracy rate of 98.78%. This result demonstrates the efficacy of our approach in promoting energy efficiency and operational performance in data centres: it not only provides a scalable, cost-effective solution to industry energy efficiency challenges, but it also improves day-to-day data centre operations by reducing electrical consumption. Our approach, which is based on dynamic allocation of computational resources and real-time data analysis for optimising cooling systems, not only saves energy but also improves operational efficiency. With energy in mind, we must all work towards a more sustainable and green approach to data centre management. Future research can look into other potential optimisations, as well as issues with scalability and application in real-world data centre environments.