Subhasree Bhattacharjee

Channel Allocation for a Single Cell Cognitive Radio Network Using Genetic Algorithm

We have considered a cognitive radio network in which there is a single cell and a Base Station (BS) within that cell. Our objective is to maximize the channel allocation of the active subscribers within that network. Genetic Algorithm (GA) has been used to solve the allocation problem. Power control has not been considered here. Our approach channel allocation using GA (CAGA) yields better result with respect to percentage of customer premise equipment (CPEs) covered than previously reported Dynamic Interference Graph (DIGA) allocation and Minimum Incremental power allocation method (MIPA).

Throughput Maximization Problem in a Cognitive Radio Network

Throughput maximization problem is considered in this paper. Here, throughput analysis is done for fixed allocation of channels. Fixed allocation of channels means number of primary users allocated to a channel is considered to be fixed but the number of users is different for different channels. Primary users allocated to a specific channel compete to access the channel and the cognitive users sense the channel whether the channel is free or not. The transmission probabilities of primary and secondary users are different. In this paper we consider the delay related to channel access of secondary users. We consider number of secondary users is very large. Our objective is to find the optimal number of secondary users to maximize the total throughput of primary and secondary users.

Optimization of probability of false alarm and probability of detection in cognitive radio networks using GA

In this paper, we optimize probability of detection and probability of false alarm in cognitive radio network to minimize probability of error of a particular SU in a centralized cognitive radio network using Genetic algorithm (GA). Our objective is to minimize probability of error and find out optimum values of probability of occupancy detection or probability of detection and probability of false alarm. We use Genetic Algorithm to solve this optimization problem. The result is compared with Differential Evolution algorithm and it is evident from the comparison that DE finds better solution and takes much lesser number of evaluations to find optimum solution.

Performance analysis of CSMA/CA protocol during white space identification in cognitive radio networks

In this paper, we analyze the delay performance of distributed and centralized cooperative sensing approaches to find out which one is suitable for sensing inter packet white space. For simultaneous sharing of channels by primary users (PUs) and secondary users (SUs), the white space has duration of approximately 50 ms. In distributed cooperative sensing, the SUs perform carrier sense multiple access with collision avoidance (CSMA/CA) with distributed coordination function (DCF) for accessing common control channel to share sensing information among each other. In centralized cooperative sensing, SUs access common control channel to send sensing result to a fusion centre (FC) using CSMA/CA point coordination function (PCF) mechanism. Total delay of these two sensing approaches is given as the sum of total channel access delay and queuing delay. We find that for a particular number of SUs, the delay of sensing in centralized environment is within the permissible limit (i.e. within 50 ms). Although distributed cooperative sensing is much more reliable and scalable than centralized cooperative sensing, it has higher sensing delay than centralized sensing approach.

Analysis of Hierarchical Mobile IP Based Fast Mobility Management Schemes

Mobility management in the wireless domain is a challenging issue for the next generation networks. Frequent change of network requires a new IP address to route packets from one host to another. It is done by sending the mobile node (MN) binding update to the Home Agent (HA) when the Care of address (CoA) gets changed. This registration requires large number of location updates, excessive signaling overhead and service delay. This problem is solved by Hierarchical Mobile IP (HMIP) protocol using the concept of hierarchy of Foreign Agent and the Gateway Foreign Agent /Mobility Anchor point to localize the registration information. This article presents an overview of set of HMIP based mobility protocols such as HMIP, PHMIP, RHMIP, DHMIP, FFHMIP, MIFA etc. that will play an important role in IP based mobility management. We analyze the advantages and disadvantages of theses protocols in the context of handoff latency and signaling overhead. Further, we propose a network model by removing the binding update with HA and simulate its performance in OPNET simulator. The simulation results show that our proposed mechanism achieve better performance than other protocols.

Comparative analysis of GA and SA for utility maximization of licensed and unlicensed users in a cognitive radio network

In this paper we consider a cognitive radio network comprising of a number of primary users (PUs) and a number of secondary users (SUs). The spectrum has been divided into channels by means of frequency division multiple access (FDMA). The PUs have license to use the channels and the SUs periodically check the channels for idleness. When the channels are not in use by the PUs, the SUs may bid for them. The PUs auctions off the channels to the purchasers (SUs) who offer most money for them. Thus, PUs earn revenue in place of the spectrum. Here, we intend to compute an allocation of channels/vacant spectrums to the SUs such that the total revenue earned by all PUs is maximized. We also intend to compute another such allocation of channels/vacant spectrum to the SUs such that the payoff earned by the SUs is maximized. Both these optimizations have been performed with the help of real coded genetic algorithm (GA) and simulated annealing (SA) algorithm. We have made comparative analysis of both the algorithms and show that GA provides better near optimal solution compared to SA in terms of computation of a close to optimal solution. But, computational time of GA is more than that of SA. GA suffers from premature convergence, which can be dealt with SA.

Revenue maximization in a CRN using Real Coded Genetic Algorithm

In this paper we consider a cognitive radio network consisting of primary users (PUs) and a set of secondary users (SUs). The spectrum has been divided into channels using frequency division multiple access (FDMA). The PUs are licensed to use the channels. When the channels are idle, i.e., not used by the PUs then PUs lease the vacant spectrum for earning revenue. While the SUs bid for the channels the PUs selects the purchaser offering the highest of all bid values. The main objective is to benefit the seller. Here, using Real Coded Genetic Algorithm (RCGA) we solve the above mentioned single objective function. We also optimize the problem using differential evolution (DE) algorithm and make a comparison of fitness values with RCGA.

Optimization of average packet drop time in CRN using DE algorithm

Abstract In this paper, we analyze the average drop time of packets in a distributed cognitive radio network using differential evolution (DE) algorithm. For sharing sensing information, SUs access common control channel using carrier sense multiple access with collision avoidance protocol. Packet drop time is calculated using markov model analysis of the behavior of a SU. The packet drop time is then optimized using DE algorithm and is compared with GA. Both algorithms work well. It is found that GA gives better results than DE although DE converges earlier than GA.

Performance Analysis of RTS/CTS Protocol in Accessing Control Channel in Distributed Cognitive Radio Networks

In this paper, we analyse the delay performance of RTS/CTS protocol in DCF mode to find out whether it is suitable for sensing inter packet white space. For simultaneous sharing of channels by primary users (PUs) and secondary users (SUs), the white space has duration of approximately 50 ms. In distributed cooperative sensing, the SUs perform RTS/CTS protocol with distributed coordination function (DCF) for accessing common control channel to share sensing information among each other. Total communication delay of this sensing approach is given as the sum of delays of broadcasting RTS, CTS and sensing results by SUs to other SUs. We find that RTS/CTS protocol causes much more delay than basic CSMA/CA in distributed cooperative sensing approach.

Winner Determination Algorithm in Auction Framework of Cognitive Radio Network

In this paper we consider the auction framework of cognitive radio network consisting of a set of primary users and a set of secondary users (SUs). The spectrum has been divided into channels with the help of frequency division multiple access (FDMA). The primary users (PUs) are licensed users who lease the vacant channels to secondary user (SUs) temporarily by auction mechanism. In this paper we try to form an algorithm to determine the secondary user who wins the auction. We also discuss the effect of change of number of users on bandwidth utilization, channel utility and revenue generation by using the algorithm.