Time-varying Radio Environment Research Articles

Load-Balancing Target Channel Selection for Spectrum Handoff in Cognitive Radio Networks

In this study, a new probabilistic approach is proposed to balance the traffic loads of secondary users (SUs) among channels. Furthermore, an adaptive load-balancing model is formulated to meet the time-varying radio environment. We employ the overall system time of SUs to evaluate the latency performance of the proposed approach. Besides, we derive the optimal channel selection probability to minimize the overall system time. Simulation results clearly verify the validity of our approach by comparing it with other alternative channel selection strategies.

Learning algorithms for joint resource block and power allocation in underlay D2D networks

We investigate the problem of resource block (RB) and power allocation jointly and in a distributed manner using game theoretic learning solutions, in an underlay device-to-device network where device pairs communicate directly with each other by reusing the spectrum allocated to the cellular users. We formulate the joint RB and power allocation as multi-agent learning problems with discrete strategy sets; and suggest partially distributed and fully distributed learning algorithms to determine the RB and power level to be used by each device pair. The partially distributed algorithms, viz., Fictitious Play and its variant Fading Memory Joint Strategy Fictitious Play with Inertia, achieve Nash Equilibrium (NE) of the sum-rate maximization game in a static wireless environment. The completely distributed and uncoupled Stochastic Learning Algorithm converges to pure strategy NE of the interference mitigation game in a time-varying radio environment. We provide proofs for the existence of NE and convergence of the learning algorithms to the NE. Performance of the proposed schemes are evaluated in log-normal, Rayleigh and Nakagami fading environments and compared with an existing hybrid scheme and a centralized scheme. The simulation results show that the partially distributed schemes give the same performance as the centralized scheme, and the fully distributed scheme gives similar performance as the hybrid scheme but with much reduced signaling and computation overhead.

MIMO–OFDM Communication System with Opportunistic Diversity-Order Selection and KF-Based Channel Estimation

An opportunistic diversity-order selection algorithm is proposed which aims to keep the bit error-rate (BER) of the 4 × 4 MIMO---OFDM system at a moderate level in time-varying radio environment even if spatial gain needs to be compromised. The proposed algorithm iteratively analyzes the channel fading conditions by looking at the dynamic fading parameter called Combined Un-correlated Paths' Gain at the receiver and selects one of the three diversity orders with sufficient diversity gain to achieve error-free transmission. Kalman filter-based channel state tracking is used in decision directed mode to estimate channel state information at the receiver. The simulation results show that BER performance of the proposed MIMO---OFDM system based on opportunistic diversity-order selection algorithm is maintained at a pre-defined modest level achievable using the same system with diversity-order of two.

Distributed Channel Selection in Time-Varying Radio Environment: Interference Mitigation Game With Uncoupled Stochastic Learning

This paper investigates the problem of distributed channel selection for interference mitigation in a time-varying radio environment without information exchange. Most existing algorithms, which were originally designed for static channels, are costly and inefficient in the presence of time-varying channels. First, we formulate this problem as a noncooperative game, in which the utility of each player is defined as a function of its experienced expected weighted interference. This game is proven to be an exact potential game with the considered network utility (the expected weighted aggregate interference) serving as the potential function. However, most game-theoretic algorithms are not suitable for the considered network, since they are coupled, i.e., the updating procedure is relying on the actions or payoffs of other players. Then, we propose a simple, completely distributed, and uncoupled stochastic learning algorithm, with which the users learn the desirable channel selections from their individual trial-payoff history. It is analytically shown that the proposed algorithm converges to pure strategy Nash equilibrium in time-varying radio environment; moreover, it achieves optimal channel selection profiles and makes the network interference-free for underloaded or equally loaded scenarios, while achieving, on average, near-optimal performance for overloaded scenarios.

Outage capacity optimisation for cognitive radio networks with cooperative communications

Cognitive radio networks with cooperative communications can effectively improve spectrum efficiency and data rate. Under this network scenario, there are three possible transmission modes: direct transmission, multi-hop transmission and cooperative communication. To optimise the system performance, three issues should be carefully considered: whether or not and which relay is needed, which channel is selected and which transmission mode is used. Therefore in this study, the authors solve these three problems jointly to optimise the outage capacity for cognitive radio networks with cooperative communications. Particularly, they emphasise on the imperfect channel sensing situation. Under these setup and the constraints, the authors formulate the problem of relay determination, transmission channel and corresponding transmission mode selection to maximise the outage capacity as a discrete optimisation problem. Then a discrete stochastic optimisation algorithm is proposed to maximise the outage capacity to adaptively determine relay and select the optimal transmission channel and transmission mode. The proposed algorithm has fast convergence rate and low computation complexity. Moreover, the time-varying radio environment scenario is also considered, and they show that the proposed algorithm has good tracking capability for time-varying radio environment. Finally, simulation results are presented to demonstrate the performance of proposed scheme.

Dynamic Spectrum Allocation with Maximum Efficiency and Fairness in Interactive Cognitive Radio Networks

In Interactive Cognitive Radio Networks (ICRNs), secondary usage of spectrum is of competition and cooperation, and radio frequency environment is time-varying. In order to realize real-time secondary spectrum usage, coalition-competition architecture for the ICRNs is introduced in this paper. In the coalition-competition architecture, the ICRNs are divided into many coalitions according to geographical locations, utilization degree of spectrum, frequency range and transmission power level. There exists competition among different coalitions. In the same coalition with competition and cooperation, a model based on cooperative differential games is proposed to solve dynamic spectrum allocation, and cooperative equilibrium solution to the model is given and analyzed in this paper. From an overall perspective, the relationships between available spectrum percentage and the spectrum access rate are studied. How to form a coalition, and the mechanism to allocate total spectrum among secondary users considering Pareto optimality and individual rationality in the coalition are analyzed. The simulation results show the dynamic spectrum allocation model is fair and efficient, and it reflects realistically time-varying radio frequency environment. Cooperative differential games are particularly helpful for the spectrum management in the time-varying radio environment.