Multiple Transmit Power Levels based NOMA for Massive Machine-type Communications

This paper proposes a tractable solution for integrating non-orthogonal multiple access (NOMA) into massive machine-type communications (mMTC) to increase the uplink connectivity. Multiple transmit power levels are provided at the user end to enable open-loop power control, which is absent from the traditional uplink NOMA with the fixed transmit power. The basics of this solution are firstly presented to analytically show the inherent performance gain in terms of the average arrival rate (AAR). Then, a practical framework based on a novel power map is proposed to associate a set of well-designed transmit power levels with each geographical region for handling the no instantaneous channel state information problem. Based on this framework, the semi-grant-free (semi-GF) transmission with two practical protocols is introduced to enhance the connectivity, which has higher AAR than both the conventional grand-based and GF transmissions. When the number of active GF devices in mMTC far exceeds the available resource blocks, the corresponding AAR tends to zero. To solve this problem, user barring techniques are employed into the semi-GF transmission to stable the traffic flow and thus increase the AAR. Lastly, promising research directions are discussed for improving the proposed networks

Fine-grained performance analysis of massive MTC networks with scheduling and data aggregation

Abstract. The Internet of Things (IoT) represents a substantial shift within wireless communication and constitutes a relevant topic of social, economic, and overall technical impact. It refers to resource-constrained devices communicating without or with low human intervention. However, communication among machines imposes several challenges compared to traditional human type communication (HTC). Moreover, as the number of devices increases exponentially, different network management techniques and technologies are needed. Data aggregation is an efficient approach to handle the congestion introduced by a massive number of machine type devices (MTDs). The aggregators not only collect data but also implement scheduling mechanisms to cope with scarce network resources. This thesis provides an overview of the most common IoT applications and the network technologies to support them. We describe the most important challenges in machine type communication (MTC). We use a stochastic geometry (SG) tool known as the meta distribution (MD) of the signal-to-interference ratio (SIR), which is the distribution of the conditional SIR distribution given the wireless nodes’ locations, to provide a fine-grained description of the per-link reliability. Specifically, we analyze the performance of two scheduling methods for data aggregation of MTC: random resource scheduling (RRS) and channel-aware resource scheduling (CRS). The results show the fraction of users in the network that achieves a target reliability, which is an important aspect to consider when designing wireless systems with stringent service requirements. Finally, the impact on the fraction of MTDs that communicate with a target reliability when increasing the aggregators density is investigated

Coverage Analysis of Multi-Stream MIMO HetNets with MRC Receivers

Most of current research on the coverage performance of multi-stream MIMO heterogeneous networks (HetNets) has been focusing on a single data-stream. This does not always provide accurate results as our analysis shows the cross-stream correlation due to interference can greatly affect the coverage performance. This paper analyzes the coverage probability in such systems, and studies the impact of cross-stream correlation. Specifically, we focus on the max-SIR cell association policy, and leverage stochastic geometry to study scenarios whereby a receiver is considered in the coverage, if all of its data-streams are successfully decodeable. Assuming open-loop maximum ratio combining (MRC) at receivers, we consider cases where partial channel state information is available at the receiver. We then obtain an upper-bound on the coverage and formulate crossstream SIR correlation. We further show that approximating such systems based on fully-correlated (non-correlated) datastreams, results in a slight underestimation (substantial overestimation) of the coverage performance. Our results provide insights on the multiplexing regimes where densification improves the coverage performance and spectral efficiency. We also compare MRC with more complex zero-forcing receiver and provide quantitative insights on the design trade-offs. Our analysis is validated via extensive simulations