Distributed cross‐layer optimization for wireless regional area network‐based cognitive radio networks

This paper presents a study of a cross‐layer design through joint optimization of spectrum allocation and power control for cognitive radio networks (CRNs). The spectrum of interest is divided into independent channels licensed to a set of primary users (PUs). The secondary users are activated only if the transmissions do not cause excessive interference to PUs. In particular, this paper studies the downlink channel assignment and power control in a CRN with the coexistence of PUs and secondary users. The objective was to maximize the total throughput of a CRN. A mathematical model is presented and subsequently formulated as a binary integer programming problem, which belongs to the class of non‐deterministic polynomial‐time hard problems. Subsequently, we develop a distributed algorithm to obtain sub‐optimal results with lower computational complexity. The distributed algorithm iteratively improves the network throughput, which consists of several modules including maximum power calculation, excluded channel sets recording, base station throughput estimation, base station sorting, and channel usage implementation. Through investigating the impacts of the different parameters, simulation results demonstrates that the distributed algorithm can achieve a better performance than two other schemes. Copyright © 2011 John Wiley & Sons, Ltd.     

Utility‐optimal cross‐layer resource allocation in distributed wireless cooperative networks

In this paper, we propose a distributed cross‐layer resource allocation algorithm for wireless cooperative networks based on a network utility maximization framework. The algorithm provides solutions to relay selections, flow pass probabilities, transmit rate, and power levels jointly with optimal congestion control and power control through balancing link and physical layers such that the network‐wide utility is optimized. Via dual decomposition and subgradient method, we solve the utility‐optimal resource allocation problem by subproblems in different layers of the protocol stack. Furthermore, by introducing a concept of pseudochannel gain, we model both the primal direct logical link and its corresponding cooperative transmission link as a single virtual direct logical link to simplify our network utility framework. Eventually, the algorithm determines its primal resource allocation levels by employing reverse‐engineering of the pseudochannel gain model. Numerical experiments show that the convergence of the proposed algorithm can be obtained and the performance of the optimized network can be improved significantly. Copyright © 2012 John Wiley & Sons, Ltd.     

A cross‐layer optimization for maximum‐revenue‐based multicast in multichannel multiradio wireless mesh networks

Given a video/audio streaming system installed on a multichannel multiradio wireless mesh network, we are interested in a problem concerning about how to construct a delay‐constrained multicast tree to support concurrent interference‐free transmissions so that the number of serviced mesh clients is maximized. In this paper, we propose a heuristic approach called cross‐layer and load‐oriented (CLLO) algorithm for the problem. On the basis of the cross‐layer design paradigm, our CLLO algorithm can consider application demands, multicast routing, and channel assignment jointly during the formation of a channel‐allocated multicast tree. The experimental results show that the proposed CLLO outperforms the layered approaches in terms of the number of serviced mesh clients and throughputs. This superiority is due to information from higher layers can be used to guide routing selection and channel allocation at the same time. As a result, the CLLO algorithm can explore more solution spaces than the traditional layered approaches. In addition to that, we also propose a channel adjusting procedure to enhance the quality of channel‐allocated multicast trees. According to our simulations, it is proved to be an effective method for improving the performance of the proposed CLLO algorithm. Copyright © 2013 John Wiley & Sons, Ltd.     

Cross‐layer MAC design for location‐aware wireless sensor networks

In this paper, we propose an optimization of MAC protocol design for wireless sensor networks, that accounts for cross‐layering information, in terms of location accuracy for nodes and residual energy levels. In our proposed solution we encode this cross‐layer information within a decreasing backoff function in the MAC. The protocol is optimized by appropriately selecting priority window lengths, and we have shown that accurate cross‐layer information plays a crucial role in achieving an optimal performance at the MAC layer level. The estimation accuracy can be characterized spatially using a location reliability probability distribution function. We show that this distribution function greatly influences the design of the optimal backoff window parameters, and the overall throughput performance of the MAC protocol. Copyright © 2010 John Wiley & Sons, Ltd.     

Partially observed cross‐layer optimization for vehicular communications

Vehicular Ad‐hoc Networks (VANETs) that are characterized by frequently changed topology, worse signal noise ratio, and non‐negligible Doppler effect introduce new non‐trivial challenges to Quality‐of‐Service (QoS) provisioning. The methodology of cross‐layer optimization aims to jointly optimize the working behaviors over different layers to achieve a better network performance, eg, throughput and transmission latency. This paper presents a novel cross‐layer optimization method based on Partially Observed Markov Games (POMG) to improve optimization decision against the inaccurate observed context caused by high‐speed movement, sensor errors, and other unavoidable reasons. POMG extends Markov Decision Process (MDP) and Partially Observed Markov Decision Process (POMDP) to dynamically adjust the concerned actions (eg, transmission range, contention window, and bit rate) according to the observed traffic density and thus can improve optimization performance at several aspects, eg,, throughput, channel utilization, delay, and total number of neighbor nodes. The extensive simulations show that POMG could harvest a high entire gain compared with the traditional fixed policy and the other cross‐layer optimization schemes.     

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.     

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.     

Power efficient scheduling over fading channel for cross‐layer optimization

We consider the minimization of long‐term average power consumption for packet transmission between a mobile station and the base station over Nakagami‐m fading channel. Power consumption is minimized by intelligent transmission scheduling design, with the average queuing delay and joint packet loss across MAC and physical layers being confined below certain levels. The problem is formulated as an infinite horizon constrained Markov decision problem and solved by linear programming (LP) method. The primary intention of this paper is to provide a visible paradigm on using LP method to optimize the performance of mobile wireless communication systems. We elaborate the detailed mathematical solution with consistent simulation experiments and emphasize the effectiveness of adaptive transmission scheduling for cross‐layer QoS provisioning. Copyright © 2010 John Wiley & Sons, Ltd.