Link-Layer Rate of Multiple Access Technologies with Short-Packet Communications for uRLLC

Mission-critical applications such as autonomous vehicles, tactile Internet, and factory automation require seamless connectivity with stringent requirements of latency and reliability. These futuristic applications are supported with the service class of ultra reliable and low-latency communications (uRLLC). In this thesis, the performance of core enablers of the uRLLC, non-orthogonal multiple access (NOMA), and NOMA-random access (NOMA-RA) in conjunction with the short-packet communications regime is investigated. More specifically, the achievable effective capacity (EC) of two-user and multi-user NOMA and conditional throughput of the NOMA-RA with short-packet communications are derived. A closed-form expressions for the EC of two-user NOMA network in finite blocklength regime (short-packet communication) is derived, while considering transmissions over Rayleigh fading channels and adopting a practical path-loss model. While considering the multi-user NOMA network, the total EC of two-user NOMA subsets is derived, which shows that the NOMA set with users having distinct channel conditions achieve maximum aggregate EC. The comparison of link-layer rate of NOMA and orthogonal multiple access (OMA) shows that OMA with short-packet communications outperformed the NOMA at low SNR (20dB). However, at high SNR region (from 20dB to 40dB), the two-user NOMA performs much better than OMA. To further investigate the impact of the channel conditions on the link-layer rate of NOMA and OMA, the simulation results with generalized fading model, i.e., Nakagami-m are also presented. The NOMA-RA with short-packet communications is also regarded as the core enabler of uRLLC. How the NOMA-RA with short-packet communications access the link-layer resources is investigated in detail. The conditional throughput of NOMA-RA is derived and compared with the conventional multiple access scheme. It is clear that NOMA-RA with optimal access probability region (from 0.05 to 0.1) shows maximum performance. Finally, the thesis is concluded with future work, and impact of this research on the industrial practice are also highlighted

Cross-layer design for mission-critical IoT in mobile edge computing systems

In this paper, we establish a cross-layer framework for optimizing user association, packet offloading rates, and bandwidth allocation for mission-critical Internet-of-Things (MC-IoT) services with short packets in mobile edge computing (MEC) systems, where enhanced mobile broadband (eMBB) services with long packets are considered as background services. To reduce communication delay, the fifth generation new radio is adopted in radio access networks. To avoid long queueing delay for short packets from MC-IoT, processor-sharing (PS) servers are deployed at MEC systems, where the service rate of the server is equally allocated to all the packets in the buffer. We derive the distribution of latency experienced by short packets in closed form, and minimize the overall packet loss probability subject to the end-to-end delay requirement. To solve the nonconvex optimization problem, we propose an algorithm that converges to a near optimal solution when the throughput of eMBB services is much higher than MC-IoT services, and extend it into more general scenarios. Furthermore, we derive the optimal solutions in two asymptotic cases: communication or computing is the bottleneck of reliability. The simulation and numerical results validate our analysis and show that the PS server outperforms first-come-first-serve servers

Radio Resource Management for New Application Scenarios in 5G: Optimization and Deep Learning

The fifth-generation (5G) New Radio (NR) systems are expected to support a wide range of emerging applications with diverse Quality-of-Service (QoS) requirements. New application scenarios in 5G NR include enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low-latency communications (URLLC). New wireless architectures, such as full-dimension (FD) massive multiple-input multiple-output (MIMO) and mobile edge computing (MEC) system, and new coding scheme, such as short block-length channel coding, are envisioned as enablers of QoS requirements for 5G NR applications. Resource management in these new wireless architectures is crucial in guaranteeing the QoS requirements of 5G NR systems. The traditional optimization problems, such as subcarriers and user association, are usually non-convex or Non-deterministic Polynomial-time (NP)-hard. It is time-consuming and computing-expensive to find the optimal solution, especially in a large-scale network. To solve these problems, one approach is to design a low-complexity algorithm with near optimal performance. In some cases, the low complexity algorithms are hard to obtain, deep learning can be used as an accurate approximator that maps environment parameters, such as the channel state information and traffic state, to the optimal solutions. In this thesis, we design low-complexity optimization algorithms, and deep learning frameworks in different architectures of 5G NR to resolve optimization problems subject to QoS requirements. First, we propose a low-complexity algorithm for a joint cooperative beamforming and user association problem for eMBB in 5G NR to maximize the network capacity. Next, we propose a deep learning (DL) framework to optimize user association, resource allocation, and offloading probabilities for delay-tolerant services and URLLC in 5G NR. Finally, we address the issue of time-varying traffic and network conditions on resource management in 5G NR