Jyoti Sekhar Banerjee

An Advance Q Learning (AQL) Approach for Path Planning and Obstacle Avoidance of a Mobile Robot

The goal of this paper is to improve the performance of the well known Q learning algorithm, the robust technique of Machine learning to facilitate path planning in an environment. Until this time the Q learning algorithms like Classical Q learning(CQL)algorithm and Improved Q learning (IQL) algorithm deal with an environment without obstacles, while in a real environment an agent has to face obstacles very frequently. Hence this paper considers an environment with number of obstacles and has coined a new parameter, called ‘immediate penalty’ due to collision with an obstacle. Further the proposed technique has replaced the scalar ‘immediate reward’ function by ‘effective immediate reward’ function which consists of two fuzzy parameters named as, ‘immediate reward’ and ‘immediate penalty’. The fuzzification of these two important parameters not only improves the learning technique, it also strikes a balance between exploration and exploitation, the most challenging problem of Reinforcement Learning. The proposed algorithm stores the Q value for the best possible action at a state; as well it saves significant path planning time by suggesting the best action to adopt at each state to move to the next state. Eventually, the agent becomes more intelligent as it can smartly plan a collision free path avoiding obstacles from distance. The validation of the algorithm is studied through computer simulation in a maze like environment and also on KheperaII platform in real time. An analysis reveals that the Q Table, obtained by the proposed Advanced Q learning (AQL) algorithm, when used for path-planning application of mobile robots outperforms the classical and improved Q-learning. An Advance Q Learning (AQL) Approach for Path Planning and Obstacle Avoidance of a Mobile Robot

Fuzzy Based Relay Selection for Secondary Transmission in Cooperative Cognitive Radio Networks

Cooperative communication plays the vital role in cognitive radio network where intermediate nodes are employed as relays. But it is really tough to select the desired or so called the best relay in a multiple-relay cognitive radio system in order to improve the performance of the secondary network while ensuring the quality-of-service (QoS) of the primary network. In this paper we propose a new fuzzy logic-based decision-making procedure for relay selection unlike to many existing works where Signal-to-Interference-plus-Noise Ratio (SINR) is considered as the only parameter for relay selection. The underlying decision criterion considers SINR with some other important parameter like Relative Link Quality (RLQ) of the relay node from destination & Reliability of the relay node. To find out the best relay using our proposed scheme, we have conducted an extensive simulation study. The simulation results reveal the impact of different parameters on selection of Best relay.

Relay Node Selection Using Analytical Hierarchy Process (AHP) for Secondary Transmission in Multi-user Cooperative Cognitive Radio Systems

In this paper, we have proposed a very new relay selection scheme based on the decision-making technique of analytical hierarchy process (AHP). Unlike many existing works where signal-to-interference-plus-noise ratio (SINR) is considered as the only parameter for relay selection, here the underlying decision criterion considers SINR at secondary destination (SD) as well as reliability and relative link quality (RLQ) of the relay node from destination.

Non-Uniform Quantized Data Fusion Rule Alleviating Control Channel Overhead for Cooperative Spectrum Sensing in Cognitive Radio Networks

In this correspondence, the authors propose a nonuniform quantized data fusion (N-QDF) rule alleviating control channel overhead for energy detection based cooperative spectrum sensing scheme in cognitive radio systems. Though soften hard or quantized data fusion (QDF) technique carries few-bit overhead from each user but it prescribes an improved solution between detection performance and complexity. Again higher-bit QDF provides greater detection probability than lower-bit QDF, due to the loss of more information in lower-bit QDF. In this paper, we derive a non-uniform quantized data fusion (N-QDF) rule that simultaneously enhances the detection probability for a given false alarm probability & higher bit QDF with minimum control channel overhead. We have conducted an extensive simulation study where the performance of variable-bit N-QDF technique is compared with different uniform i.e. 3, 4, 5 bit QDF techniques with respect to different parameters to validate our proposed scheme.

The Extent Analysis Based Fuzzy AHP Approach for Relay Selection in WBAN

A revolutionary technology in the field of healthcare monitoring system to manage illness for maintaining wellness by concentrating on prevention and early detection of disease are popularly known as Wireless Body Area Networks (WBAN) which is highly localized wireless networks along with different sensors placed in the human body or surface mounted on the particular places of the body. Though WBAN is a specially designed sensor network to implement ubiquitous and affordable health care autonomously, anytime and anywhere, it faces numerous challenges like frequent link loss due to postural body movement, size, and complexity of the sensors, channel condition, and power consumption. This letter provides FAHP using the Extent Analysis scheme for relay node selection that prioritizes the vagueness of the decision-makers during the relay node selection procedure.

Non-uniform Quantized Data Fusion Rule for Data Rate Saving and Reducing Control Channel Overhead for Cooperative Spectrum Sensing in Cognitive Radio Networks

AbstractIn this paper a pretty new concept of non-uniform quantized data fusion (N-QDF) rule reducing control channel data overhead has been proposed for energy detection based cooperative spectrum sensing scheme in cognitive radio networks. To strike a balance between efficient detection performance and less complexity, the network has to allow soften hard or quantized data fusion (QDF) technique though this technique incurs few bit overhead on the control channel from each user. Again lower bit QDF causes loss of more information, where as higher bit QDF increases detection probability at the cost of some extra bits per user. Here lies the beauty of NQDF scheme which uses variable number of bits: more number of bits for lower energy region—thus increases detection probability for a given false alarm, and less number of bits for higher energy region—thus data rate gets saved which in turn alleviates control channel overhead. A holistic simulation study has been done in this very paper where the performance of variable bit NQDF scheme is compared with different uniform bit i.e. 2, 3, 4, 5 QDF with respect to different parameters to validate our proposed scheme.