Cognitive Radio Access Networks Research Articles

Cognitive RAN slicing resource allocation based on Stackelberg game

The cognitive network has become a promising method to solve the spectrum resources shortage problem. Especially for the optimization of network slicing resources in the cognitive radio access network (RAN), we are interested in the profit of the mobile virtual network operator (MVNO) and the utility of secondary users (SUs). In cognitive RAN, we aim to find the optimal scheme for the MVNO to efficiently allocate slice resources to SUs. Since the MVNO and SUs are selfish and the game between the MVNO and SUs is difficult to reach equilibrium, we consider modeling this scheme as a Stackelberg game. Leveraging mathematical programming with equilibrium constraints (MPEC) and Karush-Kuhn-Tucker (KKT) conditions, we can obtain a single-level optimization problem, and then prove that the problem is a convex optimization problem. The simulation results show that the proposed method is superior to the noncooperative game. While guaranteeing the Quality of Service (QoS) of primary users (PUs) and SUs, the proposed method can balance the profit of the MVNO and the utility of SUs.

Analysis of Sub-Band Allocation in Multi-Service Cognitive Radio Access Networks

This letter addresses the sub-band allocation problem in dynamic spectrum access (DSA) scheme in a multi-service cognitive radio network (CRN) built over a multi-service primary network (PRN). Both access networks support connection-level and packet-level services, where the Poisson process and the Markov modulated Poisson process are used to describe the connection-level and packet-level services, respectively. By means of a multi-dimensional continuous-time Markov chain model, the DSA scheme is assessed under various traffic load profiles. Numerical results show that the connection-level secondary users's quality of service may be significantly deteriorated by the packet-level primary user service.

Evolutionary algorithms for orthogonal frequency division multiplexing-based dynamic spectrum access systems

This paper proposes two evolutionary algorithms (EAs) to perform dynamic spectrum assignment in distributed OFDM-based cognitive radio access networks. To achieve better radio resource utilization, the central spectrum manager (CSM) jointly considers the type of modulation level which can be used by each radio pair when deciding the number of subcarriers to be assigned. Using the piecewise convex transformations, we reformulate the nonlinear integer programming problem to an integer linear programming so that it is possible to obtain the optimal solution. While the solution to the integer linear programming problem is NP-hard, the proposed EAs provide useful suboptimal solutions especially when the number of radios and subcarriers are large. Our first proposed EA adopts the genetic algorithm. Although the reproduction process generates chromosomes which do not fulfill the constraints, our algorithm integrates the invisible walls technique used in particle swam optimization to retain the diversity while constructing chromosomes for the next generation. The second EA adopts the ant colony optimization approach using a directed multigraph. The vertices are used to represent the subcarriers and each edge corresponds to a possible chosen modulation index of a specific radio. We further obtain the performance of the two EAs through simulations and benchmark them against the optimal solution. Our studies show that ant colony algorithm gives better solutions most of the time, however, its computation time increases much faster compared to generic algorithm when the numbers of users and subcarriers increase.

Spectrum sharing in cognitive radio networks with imperfect sensing: A discrete-time Markov model

An efficient and utmost utilization of currently scarce and underutilized radio spectrum resources has stimulated the introduction of what has been coined Cognitive Radio (CR) access methodologies and implementations. While the long-established approach has been based on licensed (or primary) spectrum access, this new communication paradigm enables an opportunistic secondary access to shared spectrum resources provided mutual interference is kept below acceptable levels. In this paper we address the problem of primary-secondary spectrum sharing in cognitive radio access networks using a framework based on a Discrete Time Markov Chain (DTMC) model. Its applicability and advantages with respect to other approaches is explained and further justified. Spectrum awareness of primary activity by the secondary users is based on spectrum sensing techniques, which are modeled in order to capture sensing errors in the form of false-alarm and missed-detection. Model validation is successfully achieved by means of a system-level simulator which is able to capture the system behavior with high degree of accuracy. Parameter dependencies and potential tradeoffs are identified enabling an enhanced operation for both primary and secondary users. The suitability of the specified model is justified while allowing a wide range of extended implementations and enhanced capabilities to be considered.