A Framework for Implementation of Wireless Body Area Network over Software Defined Radios

Manna, Tanumay; Misra, Iti Saha; Sanyal, Sugata; Chakraborty, Tamal

doi:10.1109/tensymp.2015.11

Cited by 3 publications

(3 citation statements)

References 7 publications

(8 reference statements)

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“…6,9 As the leased spectrum is fundamentally opportunistic, the cost of leasing the spectrum is expected to be much lower than the cost of purchasing a licensed band. 6,9 As the leased spectrum is fundamentally opportunistic, the cost of leasing the spectrum is expected to be much lower than the cost of purchasing a licensed band.…”

Section: Cognitive Radio Backhaul: a Suitable Choicementioning

confidence: 99%

“…We have developed a complete prototype implementation and validation scenario, as shown in Figure 18, which includes (1) generation of a 10s ECG signal as output of a WBAN using a MATLAB script 60 for the SU, (2) discrete wavelet transform-based compression of the ECG signal into the first 512 discrete wavelet transform coefficients using bior4.4 filter as described in our previous work, 9 (3) development of a CRN using WARP v2 board with one radio daughter card for the CBS and another radio daughter card for the SU, and (4) H2M2 implementation for prediction using ATLYS FPGA 55 board. For the PU system, we have selected the channel number 9-of 2.4-GHz frequency band and have utilized one radio daughter card of a WARP v3 board to emulate the incumbent PU transmitter with exponential ON-OFF traffic.…”

Section: Proof Of Concept: Nrt Wban Transmissionmentioning

confidence: 99%

“…Using CR technology for the last-mile backhaul communication in NRT applications not only addresses the spectrum scarcity problem but also enables low-cost data transfer. 6,9 As the leased spectrum is fundamentally opportunistic, the cost of leasing the spectrum is expected to be much lower than the cost of purchasing a licensed band. 6 This would make IoT services like health care monitoring more affordable to public particularly in developing nations, where the cost of data transfer is still high.…”

Section: Cognitive Radio Backhaul: a Suitable Choicementioning

confidence: 99%

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A prediction and scheduling framework in centralized cognitive radio network for energy efficient non‐real time communication

Manna

Misra

2018

Int J Communication

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Advent of Internet of Things led to an exponential rise in battery-operated sensors transmitting small non-real time (NRT) data regularly. To this end, this work proposes a framework for centralized cognitive radio network (CRN) that facilitates better spectrum utilization and low-cost opportunistic NRT data transfer with high energy efficiency. The novelty of this framework is to incorporate Hidden Markov Model-based prediction within the traditional cognitive radio sensing-transmission cycle. To minimize the prediction time, we design a Hardware-based Hidden Markov Model engine (H2M2) to be used by the cognitive base station (CBS). CBS exploits the H2M2 engine over high primary user (PU) activity channels to minimize the collisions between PUs and NRT secondary users, thereby reducing the SU energy consumption. However, this is at the cost of reduced throughput. Taking this into account, we propose an Intersensing-Prediction Time Optimization algorithm that identifies the predictable PU activity channels and maximizes the throughput within a PU interference threshold. Furthermore, to minimize the total battery consumption of all the SUs within CRN, a Battery Consumption Minimizing Scheduler is designed at the CBS that efficiently allocates the predictable PU channels to the NRT SUs. By exploiting the unutilized high PU activity channels, the proposed Centralized Scheduling, Sensing and Prediction (CSSP) framework improves the spectral efficiency of the CRN. Exhaustive performance studies show that CSSP outperforms traditional nonpredictive sensing techniques in terms of energy efficiency and interference management. Finally, through a proof of concept, we validate the ability of CSSP framework in enabling NRT communication.

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