Non cooperative Cournot economic game model for heterogeneous coexistence in TVWS

In this paper, we formulated heterogeneous coexistence strategy in TVWS (TV White Space) between Primary Users (PUs), Cognitive Secondary Networks (CSNs) and TVWS coexistence standards using Cournot economic game model. Cournot economic game model is based on quantity and it is appropriate because, there are fewer TV transmitters in rural areas and hence, lots of TVWS spectrum channels per footprint. The robustness of the proposed model is that, it takes into account the unique SINR requirements of both PU and CSNs. While simultaneously, maintaining the key functionalities of heterogeneous coexistence framework. Contrary to the norm that, as PU continues to sell their licensed spectrum, revenue is maximized. Rather, it was discovered by way of simulations, such assumption never hold. However, Cournot point is simulated in which PU can maximize both revenue and QoS. In spectrum auction, an arbitrator is required to act as a spectrum broker, we propose the use of existing TVWS standards for this purpose. Thus, enabling coexistence between PUs and TVWS networks. View les

Spectrum Efficient Cognitive Radio Sensor Network for IoT with Low Energy Consumption

Cognitive Radio Sensor Networks (CRSNs) have emerged as a promising solution for efficient utilization of the limited frequency spectrum. One of the key challenges in CRSNs is achieving spectrum efficiency by avoiding interference and maximizing the use of the available spectrum. Particle Swarm Optimization (PSO) techniques have been widely used to optimize the spectrum allocation and improve the spectrum efficiency of CRSNs. In this paper the study provides an overview of the research on spectrum efficiency in CRSNs using PSO techniques and also discussed the key parameters that affect the spectrum efficiency, such as the swarm size, sensor's threshold and maximum number of iterations and highlights the importance of identifying the optimal combination of these parameters. This paper also emphasizes the need for further research and development in this area to improve the efficiency and effectiveness of PSO-based optimization techniques for CRSNs and to adapt them to various real-world scenarios. Achieving spectrum efficiency in CRSNs is critical for enabling effective wireless communication systems and improving the overall utilization of the available frequency spectrum

A Quality of Service Based Model for Supporting Mobile Secondary Users in Cognitive Radio Technology

Current wireless networks are characterized by a static spectrum allocation policy, where governmental agencies assign wireless spectrum to license holders on a long-term basis for large geographical regions. The operators claim that the spectrum bands for mobile operation are highly occupied. Even then, a significant amount of licensed spectrum remains underutilized. Cognitive radio senses the radio environment with a twofold objective: identify those subbands of the radio spectrum that are underutilized by the primary (i.e., legacy) users and providing the means for making those bands available for employment by secondary (i.e., unlicensed) users. For unlicensed communication, the Quality of Service parameters need to be considered. Quality of Service comprises of channel availability, accessibility, and maintainability. Assessment of vacant channels of licensed band in a geographical region is termed as availability. An analysis of the collected data lead to arrive at the conclusion that more than one-eighth part of resources of each band are nearly permanently vacant, which is enough to design in-band common control signaling methods for cognitive radio. Measurement result plot of vacant channels in cities with known population will help to assess availability of vacant channels for any city and hence, measurement complexity can be avoided. The strategy to occupy the vacant channels without disturbing the primary user operation is referred as accessibility (or selection). Accessibility of a channel is dependent on blocking probability (or Quality of Service) measured in duration of minutes instead of hours. Instantaneous blocking probability has been calculated based on current minute occupancy for all available channels as reference. A comprehensive prediction model is employed in the proposed work to compute the instantaneous blocking probability both on immediate minute occupancy basis and its preceding 60 min basis from time of request by SU. Validation through actual data establishes that channelized blocking probability estimation model has lower error value compared to estimation through prediction models of other researchers. It was also observed that hourly basis prediction model has constant blocking probability value during clock hour, whereas minutewise Grade of Service (GoS) prediction model addresses the local peak demand and hence leads to a stringent GoS estimation. On secondary user request for vacant channel, the cognitive radio network needs to evaluate the expected holding time of the particular Secondary User and to ensure channel maintainability (or allocation), and it shall predict that the allotted channel shall be able to provide interruption-free service for holding time duration. Minutewise channel occupancy traffic is bumpy in nature; hence, the present work predicts call arrival rate using Holt Winter’s method. Also, at the instant of SU channel request, the channel allocation processor inputs all PU channel status minutewise, calculates actual mean residual lifetime (MRL) in minutes for each vacant channel and selects the channel with highest predicted free time. A simulation program runs on data collected from mobile switch of cellular network, which creates pseudo-live environment for channel allocation. The present work has compared the mean residual lifetime (MRL) method with the other researchers using probabilistic method of channel allocation and MRL method has been established as more accurate. The selection and allocation process with defined blocking probability model has been verified retrieving big data from data warehouse