Adaptive channel and power allocation of downlink multi-user MC-CDMA systems

The Multi-Carrier Code Division Multiple Access (MC-CDMA) is becoming a very attractive multiple access technique for high-rate data transmission in the future wireless communication systems. This paper is focused on the joint channel and power allocation in the downlink transmission of multi-user MC-CDMA systems and considers the throughput maximization problem as a mixed integer optimization problem. For simple analysis, the problem is divided into two less complex sub-problems: power allocation and channel allocation, which can be solved by a suboptimal Adaptive Power Allocation (APA) algorithm and an optimal Adaptive Channel Allocation (ACA) algorithm, respectively. By combining APA and ACA algorithms, an adaptive channel and power allocation scheme is proposed. The numerical results show that the proposed APA algorithm is more suitable for MC-CDMA systems than the conventional equal power allocation algorithm, and the proposed channel and power allocation scheme can significantly improve the system throughput performance.     

Optimal timeslot and channel allocation considering fairness for multicell CDMA/TDD systems

The CDMA/TDD system is a highly attractive solution to support the next generation cellular mobile systems which provide unbalanced multimedia services between downlink and uplink. In this paper, we analyze the interference for downlink and uplink timeslots in a multicell CDMA/TDD system. We also mathematically formulate an optimal timeslot and channel allocation problem considering capacity fairness among cells, which is to maximize the system capacity under the given traffic unbalance, and propose an efficient algorithm based on the simulated annealing technique. Extensive experimental results show that the proposed scheme yields a good performance, and fairness among cells improves with a decrease in the system capacity.     

Network flow control under capacity constraints: A case study

In this paper, we demonstrate how tools from nonlinear system theory can play an important role in tackling “hard nonlinearities” and “unknown disturbances” in network flow control problems. Specifically, a nonlinear control law is presented for a communication network buffer management model under physical constraints. Explicit conditions are identified under which the problem of asymptotic regulation of a class of networks against unknown inter-node traffic is solvable, in the presence of control input and state saturation. The conditions include a Lipschitz-type condition and a “PE” condition. Under these conditions, we achieve either asymptotic or practical regulation for a single-node system. We also propose a decentralized, discontinuous control law to achieve (global) asymptotic regulation of large-scale networks. Our main result on controlling large-scale networks is based on an interesting extension of the well-known Young's inequality for the case with saturation nonlinearities. We present computer simulations to illustrate the effectiveness of the proposed flow control schemes.     

Joint cell selection and resource allocation games with backhaul constraints

In this work we study the problem of user association and resource allocation to maximize the proportional fairness of a wireless network with limited backhaul capacity. The optimal solution of this problem requires solving a mixed integer non-linear programming problem which generally cannot be solved in real time. We propose instead to model the problem as a potential game, which decreases dramatically the computational complexity and obtains a user association and resource allocation close to the optimal solution. Additionally, the use of a game-theoretic approach allows an efficient distribution of the computational burden among the computational resources of the network.     

Beam search algorithm for capacity allocation problem in flexible manufacturing systems

This study considers the operation assignment and tool allocation problem in flexible manufacturing systems. A set of operations together with their required tools are selected so as to maximize the total weight. The machines have limited time and tool magazine capacities and the tools are available in limited quantities. We develop a beam search algorithm and obtain near optimal solutions for large size problems very quickly.     

Bounding approaches for operation assignment and capacity allocation problem in flexible manufacturing systems

This study considers an operation assignment and capacity allocation problem that arises in flexible manufacturing systems. Automated machines are assumed to have scarce time and tool magazine capacities and the tools are available in limited quantities. The aim is to select a subset of operations with maximum total weight. The weight of an operation may represent its profit, processing load, relative priority. Several upper bounding procedures have been taken into account. The results of computational tests have revealed that the proposed upper bounding procedures produce satisfactory solutions in reasonable CPU times. We suggest using some of the bounds when the quality of the solutions is more important than the speed of achieving them and some others when the speed is more important than the quality.     

Dynamic robust power allocation games under channel uncertainty and time delays

In this paper, we study dynamic robust power allocation strategies under the imperfectness of the channel state information at the transmitters. Considering unknown payoff functions at the transmitters, we propose an heterogeneous Delayed COmbined fully DIstributed Payoff and Strategy Reinforcement Learning (Delayed-CODIPAS-RL) in which each transmitter learns its payoff function as well as its associated optimal strategies in the long-term. We show that equilibrium power allocations can be obtained using the multiplicative weighted imitative CODIPAS-RLs and Bush-Mosteller based CODIPAS-RL. We also show almost sure convergence to the set of global optima for specific scenarios.     

Channel allocation in multi-channel wireless mesh networks

In this article, we survey the latest progress in multi-channel wireless mesh networks, focusing on wireless interference models and channel allocation algorithms with the goal of maximizing the network performance. We present the studies of different interference models and illustrate how they could affect the design of channel assignment. We also summarize channel allocation algorithms with different strategies in both omni-directional and directional antenna networks. We conclude that both static and dynamic channel allocation strategies have advantages and disadvantages, and the design of channel allocation algorithms strongly depends on the interference model and the assumption of network traffic.     

Jointly optimal congestion control, channel allocation and power control in multi-channel wireless multihop networks

In this paper, to increase end-to-end throughput and energy efficiency of the multi-channel wireless multihop networks, a framework of jointly optimize congestion control in the transport layer, channel allocation in the data link layer and power control in the physical layer is proposed. It models the network by a generalized network utility maximization (NUM) problem with elastic link data rate constraints. Through binary linearization and log-transformation, and after relaxing the binary constraints on channel allocation matrix, the NUM problem becomes a convex optimization problem, which can be solved by the gateway centralized through branch and bound algorithm with exponential time complexity. Then, a partially distributed near-optimal jointly congestion control, channel allocation and power control (DCCCAPC) algorithm based on Lagrangian dual decomposition technique is proposed. Performance is assessed through simulations in terms of network utility, energy efficiency and fairness index. Convergence of both centralized and distributed algorithms is proved through theoretic analysis and simulations. As the available network resources increase, the performance gain on network utility increases.     

Three Natural Computation methods for joint channel estimation and symbol detection in multiuser communications

This paper studies three of the most important optimization algorithms belonging to Natural Computation (NC): genetic algorithm (GA), tabu search (TS) and simulated quenching (SQ). A concise overview of these methods, including their fundamentals, drawbacks and comparison, is described in the first half of the paper. Our work is particularized and focused on a specific application: joint channel estimation and symbol detection in a Direct-Sequence/Code-Division Multiple-Access (DS/CDMA) multiuser communications scenario; therefore, its channel model is described and the three methods are explained and particularized for solving this. Important issues such as suboptimal convergence, cycling search or control of the population diversity have deserved special attention. Several numerical simulations analyze the performance of these three methods, showing, as well, comparative results with well-known classical algorithms such as the Minimum Mean Square Error estimator (MMSE), the Matched Filter (MF) or Radial Basis Function (RBF)-based detection schemes. As a consequence, the three proposed methods would allow transmission at higher data rates over channels under more severe fading and interference conditions. Simulations show that our proposals require less computational load in most cases. For instance, the proposed GA saves about 73% of time with respect to the standard GA. Besides, when the number of active users doubles from 10 to 20, the complexity of the proposed GA increases by a factor of 8.33, in contrast to 32 for the optimum maximum likelihood detector. The load of TS and SQ is around 15–25% higher than that of the proposed GA.     

Global stabilisation via switching output-feedback for power integrator systems with unknown control direction

This paper considers the global output-feedback stabilisation for power integrator systems with unknown control direction. The presence of uncontrollable and unobservable linearisation renders the strategy based on Nussbaum function rather difficult (even impossible) to compensate the unknown control direction. In this paper, to dominate the inherent nonlinearities and unknowns, a powerful switching strategy is proposed by combining an output-feedback scheme and a switching mechanism. First, a special case with known control direction is considered to provide a basic structure and selection rules of design parameters for the expected controller, and then, the essential case with unknown control direction is investigated to build a suitable switching logic to online tune the design parameters. The proposed switching-type output-feedback controller guarantees that all the system signals are globally bounded and ultimately converge to zero. A numerical example is given to illustrate the effectiveness of the proposed methods.     

Evaluation of all one-to-many reliabilities for acyclic multistate-node distributed computing system under cost and capacity constraints

A novel problem is proposed in this study by considering all one-to-many acyclic multistate-node distributed computing system (AMNDCS) reliabilities with limited cost (budget) and memory capacity. The AMNDCS is an extension of the multistate network without satisfying the flow conservation law. A very straightforward and simply programmed exact algorithm is developed for this problem using the universal generating function method (UGFM) and a generalized multiplication operator. The correctness and computational complexity of the proposed algorithm will be analyzed and proven. An illustrative example is presented to demonstrate how this problem is solved using the proposed UGFM.     

Dual time-scale distributed capacity allocation and load redirect algorithms for cloud systems

Resource management remains one of the main issues of cloud computing providers because system resources have to be continuously allocated to handle workload fluctuations while guaranteeing Service Level Agreements (SLA) to the end users. In this paper, we propose novel capacity allocation algorithms able to coordinate multiple distributed resource controllers operating in geographically distributed cloud sites. Capacity allocation solutions are integrated with a load redirection mechanism which, when necessary, distributes incoming requests among different sites. The overall goal is to minimize the costs of allocated resources in terms of virtual machines, while guaranteeing SLA constraints expressed as a threshold on the average response time. We propose a distributed solution which integrates workload prediction and distributed non-linear optimization techniques. Experiments show how the proposed solutions improve other heuristics proposed in literature without penalizing SLAs, and our results are close to the global optimum which can be obtained by an oracle with a perfect knowledge about the future offered load.     

Optimal capacity allocation for Web systems with end-to-end delay guarantees

Providing quality of service guarantees have become a critical issue during the rapid expansion of the e-Commerce area. We consider the problem of finding the optimal capacity allocation in a clustered Web system environment so as to minimize the cost while providing the end-to-end performance guarantees. In particular, we consider constraints on both the average and the tail distribution of the end-to-end response times. We formulate the problem as a nonlinear program to minimize a convex separable function of the capacity assignment vector. We show that under the mean response time guarantees alone, the solution has a nice geometric interpretation. Various methods to solve the problem are presented in detail. For the problem with tail distribution guarantees, we develop an approximation method to solve the problem. We also derive bounds and show that the solution is asymptotically optimal when the service requirement becomes stringent. Numerical results are presented to further demonstrate the robustness of our solutions under data uncertainty.     

Algebraic connectivity metric for spare capacity allocation problem in survivable networks

For studying survivability of telecommunication networks, one should be able to differentiate topologies of networks by means of a robust numerical measure that can characterize the degree of immunity of a given network to possible failures of its elements. An ideal metric should be also sensitive to such topological features as the existence of nodes or links whose failures are catastrophic in that they lead to disintegration of a given network structure. In this paper, we show that the algebraic connectivity, adopted from spectral graph theory, namely the second smallest eigenvalue of the Laplacian matrix of the network topology, is a numerical index that characterizes a network’s survivability better than the average node degree that has been traditionally used for this purpose. This proposition is validated by extensive studies involving solutions of the spare capacity allocation problem for a variety of networks.     

Pareto-optimal Nash equilibrium in capacity allocation game for self-managed networks

In this paper we introduce a capacity allocation game which models the problem of maximizing network utility from the perspective of distributed noncooperative agents. Motivated by the idea of self-managed networks, in the developed framework the decision-making entities are associated with individual transmission links, deciding on the way they split capacity among concurrent flows. An efficient decentralized algorithm is given for computing a strongly Pareto-optimal strategies, constituting a pure Nash equilibrium. Subsequently, we discuss the properties of the introduced game related to the Price of Anarchy and Price of Stability. The paper is concluded with an experimental study.     

Resource management policies in GPRS systems

In this paper we consider the problem of resource management in GSM/GPRS cellular networks offering not only mobile telephony services, but also data services for the wireless access to the Internet. In particular, we investigate channel allocation policies that can provide a good tradeoff between the QoS guaranteed to voice and data services end users, considering three different alternatives, and developing analytical techniques for the assessment of their relative merits. The first channel allocation policy, voice priority, gives priority to voice in the access to radio channels; we show that this policy cannot provide acceptable performance to data services, since when all the channels are busy with voice connections, data services perceive long intervals of service interruption. The second channel allocation policy is called R-reservation; it statically reserves a fixed number of channels to data services, thus drastically improving their performance, but subtracting resources from voice users, even when these are not needed for data, thus inducing an unnecessary performance degradation for voice services. The third channel allocation policy is called dynamic reservation; as the name implies, it dynamically allocates channels to data when necessary, using the information about the queue length of GPRS data units within the base station. A threshold on the queue length is used in order to decide when channels must be allocated to data. Numerical results show that the dynamic reservation channel allocation policy can provide effective performance tradeoffs for data and voice services, with the additional advantage of being easily managed through the setting of the threshold value.     

The activity-based aggregate production planning with capacity expansion in manufacturing systems

This paper builds a mixed integer linear programming (MILP) model to mathematically characterize the problem of aggregate production planning (APP) with capacity expansion in a manufacturing system including multiple activity centers. We use the heuristic based on capacity shifting with linear relaxation to solve the model. Two linear relaxations, i.e., a complete linear relaxation (CLR) on all the integer variables and a partial linear relaxation (PLR) on part of the integer variables are investigated and compared in computational experiments. The computational results show that the heuristic based on the capacity shifting with CLR is very fast but yields low-quality solution whereas the capacity shifting with PLR provides high-quality solutions but at the cost of considerable computational time. As a result, we develop a hybrid heuristic combining beam search with capacity shifting, which is capable of producing a high-quality solution within reasonable computational time. The computational experiment on large-scale problems suggests that when solving a practical activity-based APP model with capacity expansion at the industrial level, the capacity shifting with CLR is preferable, and the beam search heuristic could be subsequently utilized as an alternative if the relaxation gap is larger than the acceptable deviation.     

Stabilisation of a class of positive switched nonlinear systems under asynchronous switching

This paper addresses the stabilisation problem for a class of positive switched nonlinear systems under asynchronous switching, which means that the switches between the candidate controllers and the system modes are not synchronous. The continuous and discrete cases are considered respectively. Sufficient conditions are firstly provided for the existence of the asynchronous switching controllers to guarantee the closed-loop system to be positive and exponentially stable, and the corresponding admissible switching signals are presented. As a special case, the stabilisation results for positive switched linear systems under asynchronous switching are provided accordingly. Finally, two numerical examples are given to illustrate the effectiveness of the proposed methods.     

Optimal sensor rules and unified fusion rules for multisensor multi-hypothesis network decision systems with channel errors

In this paper, for general jointly distributed sensor observations, we present optimal sensor rules with channel errors for a given fusion rule. Then, the unified fusion rules problem for multisensor multi-hypothesis network decision systems with channel errors is studied as an extension of our previous results for ideal channels, i.e., people only need to optimize sensor rules under the proposed unified fusion rules to achieve global optimal decision performance. More significantly, the unified fusion rules do not depend on distributions of sensor observations, decision criterion, and the characteristics of fading channels. Finally, several numerical examples support the above analytic results and show some interesting phenomena which can not be seen in ideal channel case.