Abstract-The use of wireless implant technology requires correct delivery of the vital physiological signs of the patient along with the energy management in power-constrained devices. Toward these goals, we present an augmentation protocol for the physical layer of the Medical Implant Communications Service (MICS) with focus on the energy efficiency of deployed devices over the MICS frequency band. The present protocol uses the rateless code with the Frequency Shift Keying (FSK) modulation scheme to overcome the reliability and power cost concerns in tiny implantable sensors due to the considerable attenuation of propagated signals across the human body. In addition, the protocol allows a fast start-up time for the transceiver circuitry. The main advantage of using rateless codes is to provide an inherent adaptive duty-cycling for power management, due to the flexibility of the rateless code rate. Analytical results demonstrate that an 80% energy saving is achievable with the proposed protocol when compared to the IEEE 802.15.4 physical layer standard with the same structure used for wireless sensor networks. Numerical results show that the optimized rateless coded FSK is more energy efficient than that of the uncoded FSK scheme for deep tissue (e.g., digestive endoscopy) applications, where the optimization is performed over modulation and coding parameters.

Due to the unique characteristics of sensor devices, finding the energy-efficient modulation with a lowcomplexity implementation (refereed to as green modulation) poses significant challenges in the physical layer design of Wireless Sensor Networks (WSNs). Toward this goal, we present an in-depth analysis on the energy efficiency of various modulation schemes using realistic models in the IEEE 802.15.4 standard to find the optimum distance-based scheme in a WSN over Rayleigh and Rician fading channels with path-loss. We describe a proactive system model according to a flexible duty-cycling mechanism utilized in practical sensor apparatus. The present analysis includes the effect of the channel bandwidth and the active mode duration on the energy consumption of popular modulation designs. Path-loss exponent and DC-DC converter efficiency are also taken into consideration.In considering the energy efficiency and complexity, it is demonstrated that among various sinusoidal carrier-based modulations, the optimized Non-Coherent M-ary Frequency Shift Keying (NC-MFSK) is the most energy-efficient scheme in sparse WSNs for each value of the path-loss exponent, where the optimization is performed over the modulation parameters. In addition, we show that the On-Off Keying (OOK) displays a significant energy saving as compared to the optimized NC-MFSK in dense WSNs with small values of path-loss exponent.

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A non-regenerative dual-hop wireless system based on a distributed space-time-coding strategy is considered. It is assumed that each relay retransmits an appropriately scaled space-time coded version of received signals. The main goal of this paper is to investigate a power allocation strategy in relay stations using analytical and simulation arguments to satisfy the quality of service requirements. In the high signal-to-noise ratio regime for the relay-destination link, it is shown that the optimum power allocation strategy in each relay which minimizes the outage probability is to remain silent, if its channel gain with the source is less than a prespecified threshold level. The Monte-Carlo simulations show that the near-optimal power allocation scheme in each relay in order to minimize the outage probability or the frame-error rate is the threshold-based on-off power scheme. Also, the numerical results demonstrate a dramatic improvement in the system performance by using this scheme compared to the case that the relay stations forward their received signals with full power. Finally, a hybrid amplify-and-forward/detect-and-forward scheme is numerically evaluated.

Emails: {abouei, kostas and pas}@comm.utoronto.ca. AbstractDue to unique characteristics of sensor nodes, choosing energy-efficient modulation scheme with low-complexity implementation (refereed to as green modulation) is a critical factor in the physical layer of Wireless Sensor Networks (WSNs). This paper presents (to the best of our knowledge) the first in-depth analysis of energy efficiency of various modulation schemes using realistic models in IEEE 802.15.4 standard and present state-of-the art technology, to find the best scheme in a proactive WSN over Rayleigh and Rician flat-fading channel models with path-loss. For this purpose, we describe the system model according to a pre-determined time-based process in practical sensor nodes. The present analysis also includes the effect of bandwidth and active mode duration on energy efficiency of popular modulation designs in the pass-band and Ultra-WideBand (UWB) categories. Experimental results show that among various pass-band and UWB modulation schemes, Non-Coherent M-ary Frequency Shift Keying (NC-MFSK) with small order of M and On-Off Keying (OOK) have significant energy saving compared to other schemes for short range scenarios, and could be considered as realistic candidates in WSNs. In addition, NC-MFSK and OOK have the advantage of less complexity and cost in implementation than the other schemes.

A groundbreaking design of radio access networks (RANs) is needed to fulfill 5G traffic requirements. To this aim, a cost-effective and flexible strategy consists of complementing terrestrial RANs with unmanned aerial vehicles (UAVs). However, several problems must be solved in order to effectively deploy such UAV-based RANs (U-RANs). Indeed, due to the high complexity and heterogeneity of these networks, model-based design approaches, often relying on restrictive assumptions and constraints, exhibit severe limitation in real-world scenarios. Moreover, design of a set of appropriate protocols for such U-RANs is a highly sophisticated task. In this context, machine learning (ML) emerges as a useful tool to obtain practical and effective solutions. In this paper, we discuss why, how, and which types of ML methods are useful for designing U-RANs, by focusing in particular on supervised and reinforcement learning strategies.

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