MEC-enabled load balancing framework for DFA-IRS aided wearable healthcare networks

The underlying challenges in the wearable electronic market are the limited power and processing capability often resulting in failure to handle complex computations. In order to balance the computational demands with resource constraints, the potential of intelligent reflecting surfaces (IRSs) are exploited in this paper. The paper presents a wearable electronics network in which the wearable nodes communicate with the assistance of double faced active (DFA) IRSs. By simultaneously controlling reflection and transmission links with active amplification, DFA-IRS enables reliable mobile edge computing (MEC)-based task offloading from wearable devices to nearby processing nodes. A resource utilization (RU) algorithm is proposed that associates the devices with DFA-IRSs. The optimal phase shifts of DFA-IRSs are obtained. Further, the impact of transmit power [Formula: see text], number of DFA-IRSs [Formula: see text], number of DFA-IRS elements N, per element amplification [Formula: see text], power budget [Formula: see text] on the average sum rate of the system is evaluated. It is observed that the DFA-IRS aided system offers average sum rate of 8.2bps/Hz with N =120 and [Formula: see text] of 20dBm with optimal phase shifts [Formula: see text] and [Formula: see text] in the reflection and transmission space respectively. Also, there is an improvement of 5.80% in average sum rate over random phase shifts. The comparison with conventional IRS, single faced active (SFA)-IRS and simultaneously transmitting and reflecting (STAR) IRS is also presented. In the end, the use case of proposed network for personalized healthcare is discussed.