Power Saving Techniques in 5G Technology for Multiple-Beam Communications

The evolution of mobile technology and computation systems enables User Equipment (UE) to manage tremendous amounts of data transmission. As a result of current 5G technology, several types of wireless traffic in millimeter wave bands can be transmitted at high data rates with ultra-reliable and small latency communications. The 5G networks rely on directional beamforming and mmWave uses to overcome propagation and losses during penetration. To align the best beam pairs and achieve high data rates, beam-search operations are used in 5G. This combined with multibeam reception and high-order modulation techniques deteriorates the battery power of the UE. In the previous 4G radio mobile system, Discontinuous Reception (DRX) techniques were successfully used to save energy. To reduce the energy consumption and latency of multiple-beam 5G radio communications, we will propose in this paper the DRX Beam Measurement technique (DRX-BM). Based on the power-saving factor analysis and the delayed response, we will model DRX-BM into a semi-Markov process to reduce the tracking time. Simulations in MATLAB are used to assess the effectiveness of the proposed model and avoid unnecessary time spent on beam search. Furthermore, the simulation indicates that our proposed technique makes an improvement and saves 14% on energy with a minimum delay

Discontinuous Reception for Multiple-Beam Communication

This is the final version. Available from IEEE via the DOI in this recordDiscontinuous reception (DRX) techniques have successfully been proposed for energy savings in 4G radio access systems, which are deployed on legacy 2GHz spectrum bands with signal features of omni-directional propagation. In upcoming 5G systems, higher frequency spectrum bands will also be utilized. Unfortunately higher frequency bands encounter more significant path loss, thus requiring directional beamforming to aggregate the radiant signal in a certain direction. We, therefore, propose a DRX scheme for multiple beam (DRXB) communication scenarios. The proposed DRXB scheme is designed to avoid unnecessary energy-and-time-consuming beam-training procedures, which enables longer sleep periods and shorter wake-up latency. We provide an analytical model to investigate the receiver-side energy efficiency and transmission latency of the proposed scheme. Through simulations, our approach is shown to have clear performance improvements over the conventional DRX scheme where beam training is conducted in each DRX cycle.Swedish Research CouncilNational Natural Science Foundation of ChinaEuropean Union Horizon 202

Towards efficient support for massive Internet of Things over cellular networks

The usage of Internet of Things (IoT) devices over cellular networks is seeing tremendous growth in recent years, and that growth in only expected to increase in the near future. While existing 4G and 5G cellular networks offer several desirable features for this type of applications, their design has historically focused on accommodating traditional mobile devices (e.g. smartphones). As IoT devices have very different characteristics and use cases, they create a range of problems to current networks which often struggle to accommodate them at scale. Although newer cellular network technologies, such as Narrowband-IoT (NB-IoT), were designed to focus on the IoT characteristics, they were extensively based on 4G and 5G networks to preserve interoperability, and decrease their deployment cost. As such, several inefficiencies of 4G/5G were also carried over to the newer technologies. This thesis focuses on identifying the core issues that hinder the large scale deployment of IoT over cellular networks, and proposes novel protocols to largely alleviate them. We find that the most significant challenges arise mainly in three distinct areas: connection establishment, network resource utilisation and device energy efficiency. Specifically, we make the following contributions. First, we focus on the connection establishment process and argue that the current procedures, when used by IoT devices, result in increased numbers of collisions, network outages and a signalling overhead that is disproportionate to the size of the data transmitted, and the connection duration of IoT devices. Therefore, we propose two mechanisms to alleviate these inefficiencies. Our first mechanism, named ASPIS, focuses on both the number of collisions and the signalling overhead simultaneously, and provides enhancements to increase the number of successful IoT connections, without disrupting existing background traffic. Our second mechanism focuses specifically on the collisions at the connection establishment process, and used a novel approach with Reinforcement Learning, to decrease their number and allow a larger number of IoT devices to access the network with fewer attempts. Second, we propose a new multicasting mechanism to reduce network resource utilisation in NB-IoT networks, by delivering common content (e.g. firmware updates) to multiple similar devices simultaneously. Notably, our mechanism is both more efficient during multicast data transmission, but also frees up resources that would otherwise be perpetually reserved for multicast signalling under the existing scheme. Finally, we focus on energy efficiency and propose novel protocols that are designed for the unique usage characteristics of NB-IoT devices, in order to reduce the device power consumption. Towards this end, we perform a detailed energy consumption analysis, which we use as a basis to develop an energy consumption model for realistic energy consumption assessment. We then take the insights from our analysis, and propose optimisations to significantly reduce the energy consumption of IoT devices, and assess their performance