Optimal Resource Allocation Using Deep Learning-Based Adaptive Compression For Mhealth Applications

In the last few years the number of patients with chronic diseases that require constant monitoring increases rapidly; which motivates the researchers to develop scalable remote health applications. Nevertheless, transmitting big real-time data through a dynamic network limited by the bandwidth, end-to-end delay and transmission energy; will be an obstacle against having an efficient transmission of the data. The problem can be resolved by applying data reduction techniques on the vital signs at the transmitter side and reconstructing the data at the receiver side (i.e. the m-Health center). However, a new problem will be introduced which is the ability to receive the vital signs at the server side with an acceptable distortion rate (i.e. deformation of vital signs because of inefficient data reduction). In this thesis, we integrate efficient data reduction with wireless networking to deliver an adaptive compression with an acceptable distortion, while reacting to the wireless network dynamics such as channel fading and user mobility. A Deep Learning (DL) approach was used to implement an adaptive compression technique to compress and reconstruct the vital signs in general and specifically the Electroencephalogram Signal (EEG) with the minimum distortion. Then, a resource allocation framework was introduced to minimize the transmission energy along with the distortion of the reconstructed signa

Energy efficient and distributed resource allocation for wireless powered OFDMA multi-cell networks

In this paper, we investigate the energy efficient resource allocation problem for the wireless powered OFDMA multi-cell networks. In the considered system, the users who have data to transmit in the uplink can only be empowered by the wireless power obtained from multiple base stations (BSs) with a large scale of multiple antennas in the downlink. A time division protocol is considered to divide the time of wireless power transfer (WPT) in the downlink and wireless information transfer (WIT) in the uplink into separate time slot. With the objective to improve the energy efficiency (EE) of the system, we propose the antenna selection, time allocation, subcarrier and power allocation schemes. Due to the non-convexity of the formulated optimization problem, we first apply the nonlinear programming scheme to convert it to a convex optimization problem and then address it through an efficient alternating direction method of multipliers based distributed resource allocation algorithm. Extensive simulations are conducted to show the effectiveness of proposed schemes.peerReviewe