Système et gestion des télécommunications

Non-orthogonal multiple access (NOMA) has emerged as a promising technique to satiate the fifth generation (5G) requirements like high spectral efficiency, energy efficiency, increased throughput and optimized sub-channel utilization over the previously deployed orthogonal multiple access (OMA). In this paper, we employ a low-complexity fractional power allocation algorithm to allocate to each user in the Base Stations transmitting area. In this paper, we aim to explore energy efficiency versus spectral efficiency trade off with average signal to noise ratio by employing superposition method to effectively utilize the sub-channel with Successive Interference Cancellation in the downlink case at the receiver end to achieve the expected simulations results. Furthermore, we have also studied the effect on spectral and energy efficiency with increased number of users in the cellular area. Finally, we have presented simulation results to corroborate our proposed results where SE increases when we increase transmission power and Signal to noise ratio. Keywords: Non-Orthogonal Multiple Access • Orthogonal Multiple Access • Successive Interference Cancellation • Sum rate • Energy Efficiency • Spectral Efficiency • Superposition Theorem • Signal to Interference Noise Ratio

A smart grid is the modern form of the power grid that uses communication technology to collect information from the power grid. 5G network slicing is an ideal choice for smart grid services. It divides the network into different isolated networks in which each one is considered as a slice. It allows the power grid to customize each slice according to network requirement to perform a specific task. Simultaneously, it comes with some technical challenges to accommodate the needs of different smart grid requirements. An algorithm is needed to develop forecasting techniques that adjust the allocated slice resources to optimize network utilization. Key functions perform by the novel algorithm is to analyze and predict the traffic of each slice and to control new requests for using particular network slice. As the number of intelligent terminals increases rapidly in SG, scheduling of network resources become much more significant to ensure high priority of urgent and low latency services. Our proposed algorithm will play its part in the green energy grid.

Gigantic rates of data production in the era of Big Data, Internet of Thing (IoT), and Smart Cyber Physical Systems (CPS) pose incessantly escalating demands for massive data processing, storage, and transmission while continuously interacting with the physical world under unpredictable, harsh, and energy-/power-constrained scenarios. Therefore, such systems need to support not only the high-performance capabilities under tight power/energy envelop, but also need to be intelligent/cognitive and robust. This has given rise to a new age of Machine Learning (and, in general Artificial Intelligence) at different levels of the computing stack, ranging from Edge and Fog to the Cloud. In particular, Deep Neural Networks (DNNs) have shown tremendous improvement over the past 6-8 years to achieve a significantly high accuracy for a certain set of tasks, like image classification, object detection, natural language processing, and medical data analytics. However, these DNN require highly complex computations, incurring huge processing, memory, and energy costs. To some extent, Moore’s Law help by packing more transistors in the chip. However, at the same time, every new generation of device technology faces new issues and challenges in terms of energy efficiency, power density, and diverse reliability threats. These technological issues and the escalating challenges posed by the new generation of IoT and CPS systems force to rethink the computing foundations, architectures and the system software for embedded intelligence. Moreover, in the era of growing cyber-security threats, the intelligent features of a smart CPS and IoT system face new type of attacks, requiring novel design principles for enabling Robust Machine Learning. In my research group, we have been extensively investigating the foundations for the next-generation energy-efficient and robust AI computing systems while addressing the above-mentioned challenges across the hardware and software stacks. In this talk, I will present different design challenges for building highly energy-efficient and robust machine learning systems for the Edge, covering both the efficient software and hardware designs. After presenting a quick overview of the design challenges, I will present the research roadmap and results from our Brain-Inspired Computing (BrISC) project, ranging from neural processing with specialized machine learning hardware to efficient neural architecture search algorithms, covering both fundamental and technological challenges, which enable new opportunities for improving the area, power/energy, and performance efficiency of systems by orders of magnitude. Towards the end, I will provide a quick overview of different reliability and security aspects of the machine learning systems deployed in Smart CPS and IoT, specifically at the Edge. This talk will pitch that a cross-layer design flow for machine learning/AI, that jointly leverages efficient optimizations at different software and hardware layers, is a crucial step towards enabling the wide-scale deployment of resource-constrained embedded AI systems like UAVs, autonomous vehicles, Robotics, IoT-Healthcare / Wearables, Industrial-IoT, etc.

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