Department of Mathematics
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Item Economic and reliability analysis of discrete-time G-queue with multi-optional services and Bernoulli feedback(Springer, 2025-07) Kulshrestha, RakheeCellular networks play a crucial role in modern telecommunications, supporting growing numbers of mobile users and various call types ranging from voice calls to multimedia data sessions. Efficient call handling is essential to ensure reliable and timely connections, optimal resource utilization, and a satisfactory Quality of Service (QoS). This study analyzes various call types in cellular networks using a discrete-time queueing model. Specifically, we investigate a discrete-time Geo/Geo/1 G-queue characterized by an unreliable server, k-optional services, and Bernoulli feedback mechanisms. Furthermore, within the framework of this queuing model, various call types are treated as positive customers, while virus attacks are considered negative customers. The arrival of a negative customer interrupts an ongoing service, leading to a server failure. Additionally, we assume that all arriving customers (positive) must undergo the First Essential Service (FES). After completing the FES, the server offers further services, allowing customers to either select one of the k-optional services, rejoin the queue for another FES, or leave the system if they do not wish to utilize additional services. Then, the entire system is modeled as a two-dimensional discrete-time Markov chain, and the matrix-geometric method is utilized to compute the steady-state probability vector, which is then employed to evaluate the numerical results of various performance metrics that depend on the queueing and reliability analysis. Finally, a cost model is established, and the Quasi-Newton method and Particle swarm optimization (PSO) technique are employed to achieve optimal operating conditions with minimal expected cost.Item Analysis of Multiple Queue Model in Cellular Networks with Sub Rating of Channels(King Abdulaziz University Journal, 2012) Kulshrestha, RakheeIn cellular Networks we generally consider a single queue for each cell, some authors proposed a model with a dedicated queue for each transceiver in the cell. We have extended the idea of dedicated queue for each transceiver in the cell with sub-rating channels to improve the Quality of Service (QOS) of the system. In this paper we have compared three models, in model-I we used guard channels to give priority to handoff attempts and a buffer for finite size is provided to give priority to handoff data attempts, further in model-II we have taken sub-rating channel scheme (SCS). In subrating scheme a full rate channel is temporarily divided into two half rate channels in the blocked cell; one half rate channel serve the originating call and another serves handoff call. We proposed a dedicated queue for each transceiver in the cell with sub-rating in model-III. The Fixed Channel Assignment Scheme is considered for all models. The probabilities of handoff failure, blocking probability of new calls, forced termination of handoff calls, probability of noncompleted calls for all models are calculated for varying assumed of values arrival rate of new data calls, arrival rate of new voice calls, buffer size of channels and service rates. We compared and analyzed the numerical results to validate the proposed models.Item Performance Analysis of Fractional Guard Channel Scheme with Buffer for Cellular Mobile Networks(Springer, 2019-07) Kulshrestha, RakheeIn this paper, we have developed an efficient channel assignment scheme using fractional guard channel for utilizing the limited radio recourses efficiently and seamlessly by providing buffer for good-quality signal calls to improve the quality of service. Two models are proposed to prioritize good-quality handoff calls over poor-quality calls. The expressions for blocking probability, call dropped probability of handoff calls, utilization of channels and buffer are obtained for a cellular radio network. Numerical simulation results for both the models illustrate the impact of different parameters on the key performance measures.