Search

The demand for wireless communication in biomedical applications is increasing, thereby necessitating the development of reliable and efficient communication systems that can operate within the human body. This study introduces a novel dual-band two-port multiple-input multiple-output (MIMO) implantable antenna for deep tissue purposes with an overall size of $10.8\times 5.6\times0.254$ mm3 = 15.3 mm3. The proposed MIMO antenna operates in the 915 and 2450 MHz industrial scientific medical bands with a measured bandwidth of 82.5, 148.5 MHz (port 1) and 130, 89 MHz (port 2), in addition peak realized gains of −32.15 and −22.2 dBi, respectively, are observed. High-measured port isolation (over 20 dB and 30 dB at 915 and 2450 MHz bands, respectively) was accomplished by means of meandered resonators. The determined specific absorption rate was within the safe limits. The MIMO performance factors, for instance, the envelope correlation coefficient and diversity gain, were also evaluated and determined to be in the acceptable range. According to our knowledge, this is the first dual-band implantable MIMO antenna developed for biomedical applications. The results from the simulations and measurements indicate that the developed antenna holds great promise for enabling effective communication within the human body for biomedical applications.

The performance of data aggregation in industrial wireless communications can be degraded by environmental interference on Industrial Scientific Medical (ISM) channels. In this paper, cognitive radio (CR) was applied to enable devices to share primary channels with the aim of enhancing the transmission performance of the WirelessHART network. We considered a linear convergecast system, where the packets generated at each device were routed to the gateway (GW) through the aid of neighboring devices. The solar-powered cognitive access points (CAPs) were deployed to improve the network performance by opportunistically allocating the primary channels to the devices for data transmissions. Firstly, we formulate the scheduling problem of long-term throughput maximization as a framework of a Markov decision process with the constraints of the minimum delay, the number of required ISM channels, and the harvested energy at the CAPs. Then, we propose a deep reinforcement learning-based scheduling scheme to optimally assign multiple ISM and primary channels to the field devices in each superframe. The simulation results confirmed the superiority of the proposed scheme compared to existing methods.