Cross‐layer bandwidth and power allocation for a two‐hop link in wireless mesh network

In this paper, a cross‐layer analytical framework is proposed to analyze the throughput and packet delay of a two‐hop wireless link in wireless mesh network (WMN). It considers the adaptive modulation and coding (AMC) process in physical layer and the traffic queuing process in upper layers, taking into account the traffic distribution changes at the output node of each link due to the AMC process therein. Firstly, we model the wireless fading channel and the corresponding AMC process as a finite state Markov chain (FSMC) serving system. Then, a method is proposed to calculate the steady‐state output traffic of each node. Based on this, we derive a modified queuing FSMC model for the relay to gateway link, which consists of a relayed non‐Poisson traffic and an originated Poisson traffic, thus to evaluate the throughput at the mesh gateway. This analytical framework is verified by numerical simulations, and is easy to extend to multi‐hop links. Furthermore, based on the above proposed cross‐layer framework, we consider the problem of optimal power and bandwidth allocation for QoS‐guaranteed services in a two‐hop wireless link, where the total power and bandwidth resources are both sum‐constrained. Secondly, the practical optimal power allocation algorithm and optimal bandwidth allocation algorithm are presented separately. Then, the problem of joint power and bandwidth allocation is analyzed and an iterative algorithm is proposed to solve the problem in a simple way. Finally, numerical simulations are given to evaluate their performances. Copyright © 2008 John Wiley & Sons, Ltd.     

Multi-layer optimization with backpressure and genetic algorithms for multi-hop wireless networks

This paper presents an efficient scheme to optimize multiple layers in multi-hop wireless networks with throughput objectives. Considering channel sensing and power control at the physical layer, a non-convex throughput optimization problem is formulated for resource allocation and a genetic algorithm is designed to allow distributed implementation. To address link and network layers, a localized back-pressure algorithm is designed to make routing, scheduling, and frequency band assignments along with physical-layer considerations. Our multi-layer scheme is extended to cognitive radio networks with different user classes and evaluate our analytical solution via simulations. Hardware-in-the-loop emulation test results obtained with real radio transmissions over emulated channels are presented to verify the performance of our distributed multi-layer optimization solution for multi-hop wireless networks. Finally, a security system is considered, where links have their security levels and data flows require certain security levels on each of its links. This problem is addressed by formulating additional constraints to the optimization problem.     

Simultaneous routing and resource allocation via dual decomposition

In wireless data networks, the optimal routing of data depends on the link capacities which, in turn, are determined by the allocation of communications resources (such as transmit powers and bandwidths) to the links. The optimal performance of the network can only be achieved by simultaneous optimization of routing and resource allocation. In this paper, we formulate the simultaneous routing and resource allocation (SRRA) problem, and exploit problem structure to derive efficient solution methods. We use a capacitated multicommodity flow model to describe the data flows in the network. We assume that the capacity of a wireless link is a concave and increasing function of the communications resources allocated to the link, and the communications resources for groups of links are limited. These assumptions allow us to formulate the SRRA problem as a convex optimization problem over the network flow variables and the communications variables. These two sets of variables are coupled only through the link capacity constraints. We exploit this separable structure by dual decomposition. The resulting solution method attains the optimal coordination of data routing in the network layer and resource allocation in the radio control layer via pricing on the link capacities.     

Joint source coding, routing and power allocation in wireless sensor networks

This paper proposes a cross-layer optimization framework for the wireless sensor networks. In a wireless sensor network, each sensor makes a local observation of the underlying physical phenomenon and sends a quantized version of the observation to a central location via wireless links. As the sensor observations are often partial and correlated, the network performance is a complicated and nonseparable function of individual data rates at each sensor. In addition, due to the shared nature of wireless medium, nearby transmissions often interfere with each other. Thus, the traditional "bit-pipe" model for network link capacity no longer holds. This paper deals with the joint optimization of source quantization, routing, and power control in a wireless sensor network. We follow a separate source and channel coding approach and show that the overall network optimization problem can be naturally decomposed into a source coding subproblem at the application layer and a wireless power control subproblem at the physical layer. The interfaces between the layers are precisely the dual optimization variables. In addition, we introduce a novel source coding model at the application layer, which allows the efficient design of practical source quantization schemes at each sensor. Finally, we propose a dual algorithm for the overall network optimization problem. The dual algorithm, when combined with a column- generation method, allows an efficient solution for the overall network optimization problem.     

Fair coding for inter‐session network coding in wireless mesh networks

Because of the broadcast and overhearing capability of wireless networks, network coding can greatly improve throughput in wireless networks. However, our investigation of existing inter‐session network coding protocols found that the short‐term unfairness that existed in 802.11‐based medium access control (MAC) protocols actually decreases the coding opportunity, which in turn compromises the throughput gain of network coding. To alleviate the negative impact of this unfairness, we propose a coding‐aware cross‐layer heuristic approach to optimize the coordination of network coding and MAC layer protocol, named FairCoding, which can significantly increase coding opportunities for inter‐session network coding through a fair short‐term traffic allocation for different coding flows. Experiment evaluation shows that the proposed mechanism can bring more coding opportunities and improve the total throughput of wireless mesh networks by up to 20%, compared with the coding mechanism, without considering the negative impact of the short‐term unfairness. Copyright © 2015 John Wiley & Sons, Ltd.     

An Efficient Cross-Layer Optimization Algorithm for Data Transmission in Wireless Sensor Networks

In this paper, we propose a cross layer congestion optimization scheme for allocating the resources of wireless sensor networks to achieve maximization of network performance. The congestion control, routing selection, link capacity allocation, and power consumption are all taken account to yield an optimal scheme based on the Lagrangian optimization. The Lagrangian multiplier is adopted to adjust power consumption, congestion rate, routing selection and link capacity allocation, so that the network performance can be satisfied between the trade-off of efficiency and fairness of resource allocation. The proposed algorithm can significantly achieve the maximization of network performance in relieving the network congestion with less power consumption. Excellent simulation results are obtained to demonstrate our innovative idea, and show the efficiency of our proposed algorithm.     

Cross‐Layer Resource Allocation in Multi‐interface Multi‐channel Wireless Multi‐hop Networks

In this paper, an analytical framework is proposed for the optimization of network performance through joint congestion control, channel allocation, rate allocation, power control, scheduling, and routing with the consideration of fairness in multi‐channel wireless multi‐hop networks. More specifically, the framework models the network by a generalized network utility maximization (NUM) problem under an elastic link data rate and power constraints. Using the dual decomposition technique, the NUM problem is decomposed into four subproblems — flow control; next‐hop routing; rate allocation and scheduling; power control; and channel allocation — and finally solved by a low‐complexity distributed method. Simulation results show that the proposed distributed algorithm significantly improves the network throughput and energy efficiency compared with previous algorithms.     

Performance modeling and optimization of multiple‐objective cross‐layer design in multi‐flow ad‐hoc networks

In ad‐hoc wireless networks, to achieve good performance, multiple parameters need to be optimized jointly. However, existing literature lacks a design framework that investigates the synchronic impact of several parameters on overall system performance. Among several design parameters, energy conservation, end‐to‐end delay minimization, and improved throughput are considered most important for efficient operation of these networks. In this paper, we propose a novel scheme for multiple‐objective cross‐layer optimization capable of optimizing all these performance objectives simultaneously for reliable, energy‐efficient, and timely transmission of continuous media information across the network. The three global criteria considered for optimization are incorporated in a single programming problem via linear scalarization. Besides, we employ standard convex optimization method and Lagrangian technique to solve the proposed problem to seek optimality. Extensive simulation results are generated accounting for several topologies with multiple concurrent flows in the network. These results are used to validate the analytical results and demonstrate the efficiency of the proposed optimization model. Efficiency of the model is verified by finding the set of Pareto‐optimal solutions plotted in three‐dimensional objective space. These solution points constituting the Pareto front are used as the best possible balance points among maximum throughput, maximum residual energy, and least network delay. Finally, to emphasize the effectiveness and supremacy of our proposed multiple‐objective cross‐layer design scheme, we compare it with the conventional multiple‐objective genetic algorithm. Simulation results demonstrate that our method provides significant performance gain over the genetic algorithm approach in terms of the above specified three objectives.     

Distributed resource allocation with fairness for cognitive radios in wireless mobile ad hoc networks

Both spectrum sensing and power allocation have crucial effects on the performance of wireless cognitive ad hoc networks. In order to obtain the optimal available subcarrier sets and transmission powers, we propose in this paper a distributed resource allocation framework for cognitive ad hoc networks using the orthogonal frequency division multiple access (OFDMA) modulation. This framework integrates together the constraints of quality of service (QoS), maximum powers, and minimum rates. The fairness of resource allocation is guaranteed by introducing into the link capacity expression the probability that a subcarrier is occupied. An incremental subgradient approach is applied to solve the optimization problems that maximize the weighted sum capacities of all links without or with fairness constraints. Distributed subcarrier selection and power allocation algorithms are presented explicitly. Simulations confirm that the approach converges to the optimal solution faster than the ordinary subgradient method and demonstrate the effects of the key parameters on the system performance. It has been observed that the algorithms proposed in our paper outperform the existing ones in terms of the throughput and number of secondary links admitted and the fairness of resource allocation.     

Traffic‐aware downlink power allocation in multiuser OFDM system

Multiuser orthogonal frequency division multiplexing (OFDM) is a promising technology to achieve high uplink/downlink (DL) capacities in the next generation broadband wireless networks such as WiMAX (Worldwide Interoperability for Microwave Access). In this paper, we investigate the DL adaptive power allocation (APA) in multiuser OFDM system from the perspective of cross‐layer design. Specifically, we formulate APA as an optimization problem with the traffic profile of each user asit a priori knowledge. To solve the optimization problem, we develop a fairness‐constrained optimal prioritized effective throughput (PET) strategy and the corresponding iterative algorithms, aiming at balancing the prioritized effective throughput and the linear or logarithmic user satisfaction‐based fairness. Simulation results show that our proposed APA optimization approach can achieve satisfying performance. Copyright © 2011 John Wiley & Sons, Ltd.     

Cross‐layer optimization for full‐duplex cognitive radio network with cooperative spectrum sensing

In this paper, the cross‐layer optimal design of multihop ad hoc network employing full‐duplex cognitive radios (CRs) is investigated. Firstly, the analytical expressions of cooperative spectrum sensing performance for multihop CR networks over composite fading channels are derived. Then, the opportunistic throughput and transmit power of CRs are presented based on the derivation of false alarm and missed detection probability. Finally, a multiobjective optimization model is proposed to maximize the opportunistic throughputs and minimize the transmitting power. Simulation results indicate that Pareto optimal solution of sensing duration, decision threshold, and transmit power can be achieved by cross‐layer multiobjective optimization, it can balance the conflicts between different objective functions and reap the acceptable outcomes for multihop CR network.     

Cross-layer resource allocation over wireless relay networks for quality of service provisioning

The authors propose a physical-datalink cross-layer resource allocation scheme over wireless relay networks for quality-of-service (QoS) guarantees. By integrating information theory with the concept of effective capacity, the proposed scheme aims at maximizing the relay network throughput subject to a given delay QoS constraint. This delay constraint is characterized by the so-called QoS exponent thetas, which is the only requested information exchanged between the physical layer and the datalink layer in our cross-layer design based scheme. Over both amplify-and-forwards (AF) and decode-and-forward (DF) relay networks; the authors develop the associated dynamic resource allocation algorithms for wireless multimedia communications. Over DF relay network, the authors also study a fixed power allocation scheme to provide QoS guarantees. The simulations and numerical results verify that our proposed cross-layer resource allocation can efficiently support diverse QoS requirements over wireless relay networks. Both AF and DF relays show significant superiorities over direct transmissions when the delay QoS constraints are stringent. On the other hand, the results demonstrate the importance of deploying the dynamic resource allocation for stringent delay QoS guarantees.     

A Cross-Layer Optimization Framework for Multihop Multicast in Wireless Mesh Networks

The optimal and distributed provisioning of high throughput in mesh networks is known as a fundamental but hard problem. The situation is exacerbated in a wireless setting due to the interference among local wireless transmissions. In this paper, we propose a cross-layer optimization framework for throughput maximization in wireless mesh networks, in which the data routing problem and the wireless medium contention problem are jointly optimized for multihop multicast. We show that the throughput maximization problem can be decomposed into two subproblems: a data routing subproblem at the network layer, and a power control subproblem at the physical layer with a set of Lagrangian dual variables coordinating interlayer coupling. Various effective solutions are discussed for each subproblem. We emphasize the network coding technique for multicast routing and a game theoretic method for interference management, for which efficient and distributed solutions are derived and illustrated. Finally, we show that the proposed framework can be extended to take into account physical-layer wireless multicast in mesh networks     

Optimal Resource Allocation for Data Service in CDMA Reverse Link

The optimal resource allocation policy is studied for non-real-time users in CDMA reverse link. The resource allocation policy of interest includes channel coding, spreading gain control and power allocation under the conventional receiver operation. The constraints in the optimization include peak transmit power of the mobile station, total received power at the base station and QoS in the form of minimum SINR for each user. The coding and spreading gain control can be separated from the power allocation strategy. Our results show that the optimal power allocation policy depends on the objective function: a greedy policy is optimal to maximize the sum of throughput from each user, whereas a fair policy is optimal to maximize the product of throughput from each user. A unified approach is taken to derive the optimal policies, and it can also be applied to other power allocation problems in CDMA reverse link. Numerical results of the channel capacity are presented for both objectives along with the effect of QoS constraints.     

Coding‐aware routing and scheduling in WiMAX‐based mesh networks: a cross‐layer design approach

In this paper, we propose a cross‐layer design framework for the joint problem of coding‐aware routing and scheduling in WiMAX‐based mesh networks with unicast sessions. The model attempts to maximize the system throughput by exploiting opportunistic coding opportunities through appropriate routing and by achieving efficient spectrum reuse through appropriate link scheduling. We assume centralized scheduling at the base station and focus on minimizing the total schedule length to satisfy a certain traffic demand. Minimizing the schedule length is equivalent to maximizing the system throughput. We present a linear programming optimization model for the joint problem, which relies on the enumeration of all possible schedules. Given its complexity, we decompose the problem using a column generation approach. Our numerical results show that significant gains may be achieved when network coding is incorporated into the design. We compare the performance with that of a joint coding‐oblivious model with and without transmission power control. Copyright © 2011 John Wiley & Sons, Ltd.     

Link scheduling with power control for throughput enhancement in multihop wireless networks

Joint scheduling and power control schemes have previously been proposed to reduce power dissipation in wireless ad hoc networks. However, instead of power consumption, throughput is a more important performance concern for some emerging multihop wireless networks, such as wireless mesh networks. This paper examines joint link scheduling and power control with the objective of throughput improvement. The MAximum THroughput link Scheduling with Power Control (MATH-SPC) problem is first formulated and then a mixed integer linear programming (MILP) formulation is presented to provide optimal solutions. However, simply maximizing the throughput may lead to a severe bias on bandwidth allocation among links. To achieve a good tradeoff between throughput and fairness, a new parameter called the demand satisfaction factor (DSF) to characterize the fairness of bandwidth allocation and formulate the MAximum Throughput fAir link Scheduling with Power Control (MATA-SPC) problem is defined. An MILP formulation and an effective polynomial-time heuristic algorithm, namely, the serial linear programming rounding (SLPR) heuristic, to solve the MATA-SPC problem are also presented. Numerical results show that bandwidth can be fairly allocated among all links/flows by solving the MILP formulation or by using the heuristic algorithm at the cost of a minor reduction of network throughput. In addition, extensions to end-to-end throughput and fairness and multiradio wireless multihop networks are discussed.     

Power‐efficient resource allocation with QoS guarantees for TDMA fading channels

This paper proposes two power‐efficient resource allocation policies with statistical delay Quality of Service (QoS) guarantees for uplink time‐division multiple access (TDMA) communication links. Specifically, the first policy aims at maximizing the system throughput while fulfilling the delay QoS and average power constraints, and the second policy is devised as an effort to minimize the total average power subject to individual delay QoS constraints. Convex optimization problems associated with the resource allocation policies are formulated based on a cross‐layer framework, where the queue at the data link layer is served by the resource allocation policy. By employing the Lagrangian duality theory and the dual decomposition theory, two subgradient iteration algorithms are developed to obtain the globally optimal solutions. The aforementioned resource allocation policies have been shown to be deterministic functions of delay QoS requirements and channel fading states. Moreover, numerical results are provided to demonstrate the performance of the proposed resource allocation policies. Copyright © 2010 John Wiley & Sons, Ltd.     

Throughput-Optimal Configuration of Fixed Wireless Networks

In this paper, we address the following two questions concerning the capacity and configuration of fixed wireless networks: (i) given a set of wireless nodes with arbitrary but fixed locations, and a set of data flows, what is the max–min achievable throughput? and (ii) how should the network be configured to achieve the optimum? We consider these questions from a networking standpoint assuming point-to-point links, and employ a rigorous physical layer model to model conflict relationships between them. Since we seek capacity results, we assume that the network is operated using an appropriate schedule of conflict-free link activations. We develop and investigate a novel optimization framework to determine the optimal throughput and configuration, i.e., flow routes, link activation schedules and physical layer parameters. Determining the optimal throughput is a computationally hard problem, in general. However, using a smart enumerative technique we obtain numerical results for several different scenarios of interest. We obtain several important insights into the structure of the optimal routes, schedules and physical layer parameters. Besides determining the achievable throughput, we believe that our optimization-based framework can also be used as a tool, for configuring scheduled wireless networks, such as those based on IEEE 802.16.      

Cross-Layer Multi-Packet Reception Based Medium Access Control and Resource Allocation for Space-Time Coded MIMO/OFDM

In this paper, a cross-layer design framework for multi-input multi-output (MIMO)/orthogonal frequency division multiplexing (OFDM) based wireless local area networks (WLANs) is proposed. In contrast to conventional systems where the medium access control (MAC) and physical (PHY) layers are separately optimized, our proposed methodology jointly designs a multi-packet reception (MPR) based protocol with adaptive resource allocation. Specifically, a realistic collision model is employed by taking into consideration the PHY layer parameters such as channel information, space-time coded beamforming and multiuser detection, as well as sub-carrier, bit, and power allocation. The allocation problem is formulated, so as to maximize the system throughput, subject to the constraints from both the MAC and PHY layers. These constraints depend on the results of access contention, data packets? length, users? spatial correlation and the quality of channel feedback information. An iterative algorithm is then provided to obtain the optimal solution. Simulation results will show that our proposed approach achieves significant improvement in system performance such as average throughput and packet delay, compared with conventional schemes where cross-layer design and optimization is not used.     

Cross‐layer optimization for CDMA/TDD wireless mesh networks

This paper proposes a new cross‐layer optimization algorithm for wireless mesh networks (WMNs). CDMA/TDD (code division multiple access/time division duplex) is utilized and a couple of TDD timeslot scheduling schemes are proposed for the mesh network backbone. Cross‐layer optimization involves simultaneous consideration of the signal to interference‐plus‐noise ratio (SINR) at the physical layer, traffic load estimation and allocation at medium access control (MAC) layer, and routing decision at the network layer. Adaptive antennas are utilized by the wireless mesh routers to take advantage of directional beamforming. The optimization formulation is subject to routing constraints and can be solved by general nonlinear optimization techniques. Comparisons are made with respect to the classic shortest‐path routing algorithm in the network layer. The results reveal that the average end‐to‐end successful packet rate (SPR) can be significantly improved by the cross‐layer approach. The corresponding optimized routing decisions are able to reduce the traffic congestion. Copyright © 2010 John Wiley & Sons, Ltd.