A User Scheduling Scheme for Reducing Electromagnetic (EM) Emission in the Uplink of Mobile Communication Systems

The ubiquity and convergence of wireless communication services have contributed to an unprecedented popularity of mobile communications. Given that wireless communication systems operate on radiofrequency waves, the electromagnetic (EM) radiation exposure they generate is also unprecedented and, hence, this could have adverse health effects on both humans and animals according to the World Health Organization. In this paper, we propose a user scheduling/power allocation scheme to minimize the EM exposure of users subject to transmitting a target number of bits. Our user scheduling method is based on assigning priority levels to each user and the user with the lowest priority level is scheduled for transmission. Power allocation, on the other hand, is based on the water-filling approach over time by using the past channel gains of a user to compute its water level. Simulation results show that our proposed scheme performs much better than a spectral efficiency based scheme but has a higher EM emission in comparison with a non-practical ideal scheme

Proactive Location-Based Scheduling of Delay-Constrained Traffic Over Fading Channels

In this paper, proactive resource allocation based on user location for point-to-point communication over fading channels is introduced, whereby the source must transmit a packet when the user requests it within a deadline of a single time slot. We introduce a prediction model in which the source predicts the request arrival $T_p$ slots ahead, where $T_p$ denotes the prediction window (PW) size. The source allocates energy to transmit some bits proactively for each time slot of the PW with the objective of reducing the transmission energy over the non-predictive case. The requests are predicted based on the user location utilizing the prior statistics about the user requests at each location. We also assume that the prediction is not perfect. We propose proactive scheduling policies to minimize the expected energy consumption required to transmit the requested packets under two different assumptions on the channel state information at the source. In the first scenario, offline scheduling, we assume the channel states are known a-priori at the source at the beginning of the PW. In the second scenario, online scheduling, it is assumed that the source has causal knowledge of the channel state. Numerical results are presented showing the gains achieved by using proactive scheduling policies compared with classical (reactive) networks. Simulation results also show that increasing the PW size leads to a significant reduction in the consumed transmission energy even with imperfect prediction.Comment: Conference: VTC2016-Fall, At Montreal-Canad

Adaptive Body Area Networks Using Kinematics and Biosignals

The increasing penetration of wearable and implantable devices necessitates energy-efficient and robust ways of connecting them to each other and to the cloud. However, the wireless channel around the human body poses unique challenges such as a high and variable path-loss caused by frequent changes in the relative node positions as well as the surrounding environment. An adaptive wireless body area network (WBAN) scheme is presented that reconfigures the network by learning from body kinematics and biosignals. It has very low overhead since these signals are already captured by the WBAN sensor nodes to support their basic functionality. Periodic channel fluctuations in activities like walking can be exploited by reusing accelerometer data and scheduling packet transmissions at optimal times. Network states can be predicted based on changes in observed biosignals to reconfigure the network parameters in real time. A realistic body channel emulator that evaluates the path-loss for everyday human activities was developed to assess the efficacy of the proposed techniques. Simulation results show up to 41% improvement in packet delivery ratio (PDR) and up to 27% reduction in power consumption by intelligent scheduling at lower transmission power levels. Moreover, experimental results on a custom test-bed demonstrate an average PDR increase of 20% and 18% when using our adaptive EMG- and heart-rate-based transmission power control methods, respectively. The channel emulator and simulation code is made publicly available at https://github.com/a-moin/wban-pathloss.Comment: Accepted for publication in IEEE Journal of Biomedical and Health Informatic