Mui Van Nguyen

Cross-Layer Optimization for Congestion and Power Control in OFDM-Based Multi-Hop Cognitive Radio Networks

Efficient and fair power allocation associated with congestion control in orthogonal frequency division multiplexing (OFDM)-based multi-hop cognitive radio networks (CRNs) is a challenging and complicated problem. In this paper, we consider their mutual relationship through a cross-layer optimization design that addresses both aggregate utility maximization and energy consumption minimization. By introducing the unique outage constraint of primary user (PU) protection, the joint congestion control and power control (JCPC) formulation is shown to be a nonlinear non-convex optimization problem. Using dual decomposition approach, we first propose a distributed algorithm that can attain the optimal solution via message passing while maintaining the architectural modularity between the layers. Next, we develop a suboptimal algorithm using a new heuristic method to alleviate the overhead burden of the first solution. Finally, the numerical results confirm that the OFDM-based multi-hop CRNs can optimally exploit the spectrum opportunity if the PU outage probability is kept below the target.

An Energy Efficient Multi-channel MAC Protocol for wireless ad hoc networks

The IEEE 802.11 [1] provides multiple channels for wireless communications at the Physical Layer, but the Medium Access Control (MAC) protocol is only designed for a single channel. If the multiple channels can be exploited by multi-channel MAC protocol, there can be multiple transmissions on different channels. Besides that, the power control algorithm can improve the spatial reuse of wireless channels. In this paper, we combine the multi-channel MAC protocol and the power control algorithm together to exploit multiple channels and improve frequency reuse. The main idea of our proposal is to use IEEE 802.11 Power Saving Scheme (PSM) with different transmission power levels in the ATIM window and the data window. All nodes transmit ATIM/ATIM-ACK/ATIM-RES messages with the maximum power while contending the data channel during the ATIM window, and use the minimum required transmission power in the data window on their negotiated channels. The simulation results show that the proposed E-MMAC improve the performance of the network: aggregate throughput, average delay and energy efficiency.

Cross-Layer Design for Congestion, Contention, and Power Control in CRAHNs under Packet Collision Constraints

In this paper, we investigate the cross-layer design for congestion, contention, and power control in multi-hop cognitive radio ad-hoc networks (CRAHNs). In particular, we develop a unified optimization framework achieving flexible tradeoff between energy efficiency and network utility maximization where we design two novel cross-layer cognitive algorithms comprising efficient powered-controlled MAC protocols for CRAHNs based on the concepts of social welfare and net revenue in economics. The proposed framework can balance interference, collision, and congestion among cognitive users (CUs) including cognitive sources and cognitive links while utilizing stochastic spectrum holes vacated by licensed users (LUs). The former allows both cognitive sources and cognitive links to simultaneously adjust their transmission parameters (i.e., transmit power, persistence probability, and rate) following the law of diminishing returns whereas the latter forces cognitive links to control the persistence probability and transmit power in order to asymptotically balance the offered load regulated by cognitive sources. Our proposed protocols are then validated and their performance is compared with the existing MAC schemes in the literature via numerical studies.

Optimal and sub-optimal resource allocation in multi-hop cognitive radio networks with primary user outage constraint

In this study, the authors propose a cross-layer optimisation framework for cognitive radio networks (CRNs) by jointly taking into account the physical layer and transport layer. In particular, they focus on joint rate and power control for a multi-hop CRN to maximise the network utility without affecting the performance of primary users (PUs). The formulation is shown to be a non-linear non-convex optimisation problem. To solve the problem, the authors first use a successive convex approximation method, which has been proved to converge to the global optimum. Then, the authors propose a novel heuristic method to develop a practical sub-optimal distributed algorithm without explicit message passing. The authors have proved that the proposed distributed algorithm is able to converge to the unique fixed point near the global optimum. Finally, the illustrative results indicate that the proposed sub-optimal algorithm outperforms the high-signal-to-interference-ratio-based algorithm and closely approaches the global optimum.

Cross-layer cognitive MAC design for multi-hop wireless ad-hoc networks with stochastic primary protection

In this paper, we consider the probabilistic channel contention resolution problem for net revenue maximization in multi-hop wireless ad-hoc networks (MHAHNs) under collision-rate-constrained opportunistic spectrum access (OSA) approach. Specifically, we focus on the interference-dependent contention model, in which secondary users (SUs) must coordinate to each other to simultaneously balance between interference and collision, leading a more efficient MAC protocol than the location-dependent one proposed in the literature. By introducing some auxiliary variables and noisy channel estimations, we can then develop a novel heuristic cross-layer cognitive MAC protocol (HCC-MAC) in OSA-based MHAHNs to solve the formulated MAC optimization problem which is shown non-convex and inseparable. More importantly, our proposed protocol can achieve near-optimal throughput in a distributed manner without control overhead. Finally, the numerical results show that HCC-MAC can outperform the existing MAC protocols under OSA paradigm.

Optimal network selection coordination in heterogeneous Cognitive Radio Networks

In this paper we propose a new architecture for applications of Cognitive Radio Network (CRN) system based on network selection issue in which secondary users (SU) is able to connect to heterogeneous Cognitive Radio system and selects a network to perform a single transport control protocol (TCP) connection. We use a cross-layer design approach to consider jointly the spectrum sensing, access decision, physical-layer Adaptive Modulation and Coding scheme, and data-link layer frame size in each Cognitive Radio network to maximize the TCP throughput of SUs. Specifically, we formulate the Cognitive Radio Network as a Markov Decision Process, where the finite-state Markov model is used to characterize the time-varying channel states in each network. Then, we maximize Expected end-to-end TCP throughput in long-term by using Iteration method. This is illustrated by simulation results.

Joint rate adaption, power control, and spectrum allocation for OFDMA-based multi-hop CRNs

The overall performance of multi-hop cognitive radio networks (CRNs) can be improved significantly by deploying the channel diversity of orthogonal licensed channels in spectrum underlay fashion. However, interference due to the sharing of common radio channels among links and congestion due to the contention among those flows that share the same link become an obstacle to meet this challenge. How to control congestion efficiently and allocate power as well as channel optimally in order to obtain a high end-to-end throughput motivates a framework of cross-layer optimization design for OFDMA-based multi-hop CRNs. By taking into account the problem of joint rate adaption, power control, and spectrum allocation (JRPS), we propose a new cross-layer optimization framework. The formulation is shown to be a mix-integer non-linear programming (MINLP) problem, which is NP-hard in general. To solve the problem, we develop a partially distributed solution, which has been proved to converge to the global optimum within reasonable times.