Mikio Hasegawa

Optimization for Centralized and Decentralized Cognitive Radio Networks

Cognitive radio technology improves radio resource usage by reconfiguring the wireless connection settings according to the optimum decisions, which are made on the basis of the collected context information. This paper focuses on optimization algorithms for decision making to optimize radio resource usage in heterogeneous cognitive wireless networks. For networks with centralized management, we proposed a novel optimization algorithm whose solution is guaranteed to be exactly optimal. In order to avoid an exponential increase of computational complexity in large-scale wireless networks, we model the target optimization problem as a minimum cost-flow problem and find the solution of the problem in polynomial time. For the networks with decentralized management, we propose a distributed algorithm using the distributed energy minimization dynamics of the Hopfield-Tank neural network. Our algorithm minimizes a given objective function without any centralized calculation. We derive the decision-making rule for each terminal to optimize the entire network. We demonstrate the validity of the proposed algorithms by several numerical simulations and the feasibility of the proposed schemes by designing and implementing them on experimental cognitive radio network systems.

Distributed Resource Allocation for D2D Communications Underlay Cellular Networks

In order to enable the benefits of device-to-device (D2D) communications underlay cellular networks, two fundamental problems need to be addressed: 1) how to control the interference to guarantee the quality-of-service (QoS) of cellular user and 2) how to allocate transmit powers among D2D pairs to maximize the data rate. In this letter, we propose a distributed resource allocation to resolve these problems. Specifically, the interference from D2D transmissions to cellular users is coordinated using a pricing scheme, while a D2D pair competes with other pairs to efficiently reuse the available spectrum. We model this competition using a non-cooperative power control game and propose a distributed update rule to reach the Nash equilibrium. Simulation is provided to evaluate the performance of the proposed algorithm.

A Rate Allocation Framework for Multi-Class Services in Software-Defined Networks

Software defined networking (SDN) is a network architecture with a programmable control plane (e.g., controllers) and simple data plane (e.g., forwarders). One of the popular SDN protocols/standards is OpenFlow, for which researchers have recently proposed some quality-of-service (QoS) supports. However, the proposals for rate allocation have some limitations in network scalability and multi-class services’ supports. In the literature, rate allocation formulations are commonly based on the framework of network utility maximization (NUM). Nevertheless, multi-class services are rarely considered in that framework since they make the formulated NUM become nonconvex and prevent its subgradient-based algorithm from converging. In this paper, we propose a scalable QoS rate allocation framework for OpenFlow in which multi-class services are considered. The convergence issue in the algorithm of our NUM-based framework is resolved by an admission control scheme. The network scalability is improved by our decentralized algorithms that can run on multiple parallel controllers. Extensive simulation and emulation results are provided to evaluate the performance of our method.

Joint Downlink and Uplink Interference Management for Device to Device Communication Underlaying Cellular Networks

Interference management is one of the most critical issues in underlaying device-to-device (D2D) communication due to the coexistence of D2D pairs and cellular users that operate under the same spectrum. In this paper, we provide the interference management algorithm to maximize the performance of the D2D communication while satisfying the quality-of-service requirements of the cellular communications in both uplink and downlink phases. The proposed algorithm includes: 1) the admission control and power allocation to ensure that the interference from D2D communication does not affect to the cellular communications and 2) the shared channel assignment to maximize the total throughput of the D2D communication. We prove that our proposed algorithm can achieve at least half of the performance of optimal algorithm. The simulation results validate the feasibility, convergence, and optimality of our algorithm: it cannot only closely approximate the optimal throughput of D2D communication but also outperform existing algorithms.

Implementation and evaluation of a combined optimization scheme for routing and channel assignment in wireless mesh networks

We develop an algorithm to optimize routing and channel assignment for multi-channel wireless mesh networks. We apply our proposed algorithm to a cognitive radio system using TV white space channels, which has been developed in previous researches. To maximize the capacity and throughput of such multi-channel mesh networks, conventional algorithms separately deal with two problems, the channel assignment problem and the routing problem. Because the routing and channel assignment affect each other for maximizing the throughput, the real optimal solutions cannot be obtained by such conventional separated optimization approaches. In this paper, we combine those two problems as one optimization problem, to solve the real optimal state of the channel assignment and the routing. We formulate an objective function of the problem by using a new state variable with constraints. We apply an exact algorithm and a heuristic algorithm to our combined problem and show effectiveness of the proposed scheme by comparing their throughput performance with the conventional scheme using exact algorithms. Furthermore, we implement a wireless mesh network running our proposed algorithm using 2.4GHz wireless LAN. Our experimental results show that the proposed scheme has better performance than the conventional separated optimization even in a real system.

Mathematical analysis on effectiveness of SS code having negative autocorrelation

In the conventional spread spectrum communication systems, the spreading codes generated by linear shift registers have been used. On the other hand, in previous researches on chip-asynchronous CDMA (code division multiple access) systems, effectiveness of the spreading codes generated by the chaotic dynamical systems have been shown by numerical simulations and mathematical analysis. Effective chaotic codes have negative autocorrelation, which takes a negative value at lag 1 with damped oscillations. In this paper, we analyze effectiveness of such chaotic spreading codes having on Direct Sequence / Spread Spectrum (DS/SS) systems. The results of computer simulations show that BER performance becomes best when we use the PN sequences having negative autocorrelation. Moreover, we mathematically analyze the correlation performance and show that the spreading code with a negative autocorrelation becomes best for DS/SS.

Design and control of noise-induced synchronization patterns

We propose a method for controlling synchronization patterns of limit-cycle oscillators by common noisy inputs, i.e., by utilizing noise-induced synchronization. Various synchronization patterns, including fully synchronized and clustered states, can be realized by using linear filters that generate appropriate common noisy signals from given noise. The optimal linear filter can be determined from the linear phase response property of the oscillators and the power spectrum of the given noise. The validity of the proposed method is confirmed by numerical simulations.