Adaptive Multi-User Clustering and Power Allocation for NOMA Systems with Imperfect SIC

Non-orthogonal multiple access (NOMA) is recognized as a promising radio access technique for the next generation wireless systems. We consider a practical downlink NOMA system with imperfect successive interference cancellation and derive bounds on the power allocation factors for a given number of users in each cluster. We propose a minimum signal-to-interference-plus-noise ratio difference criterion between two successive NOMA users in a cluster of users to achieve higher rates than an equivalent orthogonal multiple access (OMA) system. We then propose multi-user clustering and power allocation algorithms for downlink NOMA systems. Through extensive simulations, we show that the proposed algorithms achieve higher rates than the state-of-the-art algorithms.

$α$-Fairness User Pairing for Downlink NOMA Systems with Imperfect Successive Interference Cancellation

Non-orthogonal multiple access (NOMA) is considered as one of the predominant multiple access technique for the next-generation cellular networks. We consider a 2-user pair downlink NOMA system with imperfect successive interference cancellation (SIC). We consider bounds on the power allocation factors and then formulate the power allocation as an optimization problem to achieve {$\alpha$-Fairness} among the paired users. We show that {$\alpha$-Fairness} based power allocation factor coincides with lower bound on power allocation factor in case of perfect SIC and $\alpha > 2$. Further, as long as the proposed criterion is satisfied, it converges to the upper bound with increasing imperfection in SIC. Similarly, we show that, for $0<\alpha<1$, the optimal power allocation factor coincides with the derived lower bound on power allocation. Based on these observations, we then propose a low complexity sub-optimal algorithm. Through extensive simulations, we analyse the performance of the proposed algorithm and compare the performance against the state-of-the-art algorithms. We show that even though Near-Far based pairing achieves better fairness than the proposed algorithms, it fails to achieve rates equivalent to its orthogonal multiple access counterparts with increasing imperfections in SIC. Further, we show that the proposed optimal and sub-optimal algorithms achieve significant improvements in terms of fairness as compared to the state-of-the-art algorithms.

An Energy Efficient Health Monitoring Approach with Wireless Body Area Networks

Wireless Body Area Networks (WBANs) comprise a network of sensors subcutaneously implanted or placed near the body surface and facilitate continuous monitoring of health parameters of a patient. Research endeavours involving WBAN are directed towards effective transmission of detected parameters to a Local Processing Unit (LPU, usually a mobile device) and analysis of the parameters at the LPU or a back-end cloud. An important concern in WBAN is the lightweight nature of WBAN nodes and the need to conserve their energy. This is especially true for subcutaneously implanted nodes that cannot be recharged or regularly replaced. Work in energy conservation is mostly aimed at optimising the routing of signals to minimise energy expended. In this paper, a simple yet innovative approach to energy conservation and detection of alarming health status is proposed. Energy conservation is ensured through a two-tier approach wherein the first tier eliminates `uninteresting' health parameter readings at the site of a sensing node and prevents these from being transmitted across the WBAN to the LPU. A reading is categorised as uninteresting if it deviates very slightly from its immediately preceding reading and does not provide new insight on the patient's well being. In addition to this, readings that are faulty and emanate from possible sensor malfunctions are also eliminated. These eliminations are done at the site of the sensor using algorithms that are light enough to effectively function in the extremely resource-constrained environments of the sensor nodes. We notice, through experiments, that this eliminates and thus reduces around 90% of the readings that need to be transmitted to the LPU leading to significant energy savings. Furthermore, the proper functioning of these algorithms in such constrained environments is confirmed and validated over a hardware simulation set up. The second tier of assessment includes a proposed anomaly detection model at the LPU that is capable of identifying anomalies from streaming health parameter readings and indicates an adverse medical condition. In addition to being able to handle streaming data, the model works within the resource-constrained environments of an LPU and eliminates the need of transmitting the data to a back-end cloud, ensuring further energy savings. The anomaly detection capability of the model is validated using data available from the critical care units of hospitals and is shown to be superior to other anomaly detection techniques.