RAN Resource Slicing Mechanisms for Multiplexing of Multiple Services in 5G Downlink Wireless Networks

Doctoral thesis (2021)

The fifth-generation (5G) of wireless networks majorly supports three categories of services, namely, enhanced mobile broadband (eMBB), ultra-reliable and low latency communications (URLLC), and massive ... [more ▼]

The fifth-generation (5G) of wireless networks majorly supports three categories of services, namely, enhanced mobile broadband (eMBB), ultra-reliable and low latency communications (URLLC), and massive machine-type communications. Every service has its own set of requirements such as higher data rates, lower latency in packet delivery, high reliability, and network energy-efficiency(EE) to support applications including ultra-high definition (UHD) video streaming, virtual reality (VR), autonomous vehicles, vehicular communications, smart farming, and remote-surgery, respectively. The existing one-size-fits-all services network model is not a viable option to support these services with stringent requirements. Therefore, accommodation of these different services on the same physical network while ensuring their distinct QoS requirements is a major challenge. To address this problem, a new concept called network slicing (NS) has emerged as a promising solution for the dynamic allocation of resources to wireless services with different QoS demands. The NS can be formed in both the radio access network (RAN) and core network (CN) parts. In this thesis, we concentrate on RAN resources slicing, and more specifically on challenges that reside in the assignment of limited radio resources to manage the distinct traffic demands occurring from a wide variety of users belonging to heterogeneous services. Specifically, we address the RAN optimization method for joint allocation of time, frequency, and space resources to the eMBB, URLLC, and mMTC users according to their traffic demands (i.e., queue status). Our work in this thesis can be broadly classified into three parts based on the objective function considered in the resource optimization problem: 1) sum-rate maximization, 2) power minimization, 3) EE maximization. In the first part of this thesis, we address an adaptive modulation coding (AMC) based resource allocation problem for dynamic multiplexing of URLLC and eMBB users on the shared resources of the OFDMA-based wireless downlink (DL) network. Specifically, we formulate the resource blocks (RBs) allocation problem as a sum-rate maximization problem subject to the minimum data rate constraint, the latency-related constraint, orthogonality, and reliability constraints. Furthermore, to allocate RBs and transmit power jointly to the users, we also formulate the AMC-based optimization problem to maximize the sum good-put of eMBB users subject to URLLC and eMBB users’ QoS constraints. Importantly, in this problem formulation, we consider a probabilistic constraint to incorporate CSI imperfections and a short-packet communication model for URLLC service. In the second part of this thesis, we formulate the joint RBs and transmit power allocation problem to minimize the transmit power consumption at the BS while guaranteeing the QoS constraints of eMBB, URLLC, and mMTC users and probabilistic constraint to incorporate CSI imperfection, respectively. Importantly, we consider mixed-numerology-based RB grid models to the users according to their queue status/traffic demand for satisfying their stringent requirements. Furthermore, different slicing strategies such as slice-aware (SA) and slice-isolation (SI) resource assignment mechanisms are considered for the efficient co-existence of URLLC, mMTC, and eMBB services on the same RAN infrastructure. The resulting problems in the first and second parts of the thesis are mixed-integer non-linear programming problems (MINLPs) which are intractable to solve. To provide solutions to these problems, we first transform the problems into more tractable using the AMC approximation functions, probabilistic to non-probabilistic conversion functions, Big-M theory, and difference of convex (DC) programming. Later, these transformed problems are solved using the successive convex approximation (SCA) based iterative algorithm. Our simulation results illustrate the performance of our proposed method compared to the baseline methods. Also, the simulation results show the effectiveness of the mixed-numerology-based RB grid model over the fixed numerology grid model and the performance of SA and SI resource slicing strategies in terms of achievable data rates, packet delivery latencies, and queue status, respectively. In the third part of this thesis, different from the first two parts, for the joint assignment of beams, RBs, and transmit power to eMBB and URLLC users, we formulate an EE maximization problem by considering resources scheduling, orthogonality, power-related constraints, and QoS constraints for different services. The resulting mixed-integer non-linear fractional programming problem (MINLFP) is intractable and difficult to solve. To provide a feasible solution, we first transform the formulated problem into a tractable form using fractional programming theory, approximation functions and later utilize the Dinklebach iterative algorithm, DC programming, and SCA to solve it. Finally, we compare the performance of the proposed method against baseline schemes through simulation results. In particular, we show the performance of RAN slicing mechanisms with the mixed and fixed-numerology-based RBs grid models in terms of achievable EE, packet latencies, data rates, total sum-rate, and computational complexity. The proposed algorithm outperforms the baseline schemes in terms of achieving higher data rates for eMBB users. The results also show the trade-off between the total achievable sum rate and EE of the network. The proposed method with mixed numerology grid delivers 20% of higher URLLC packets within 1 ms of latency. Besides, it achieves the lowest computation time than that with the fixed numerology grid. [less ▲]

A RAN Resource Slicing Mechanism for Multiplexing of eMBB and URLLC Services in OFDMA based 5G Wireless Networks

in IEEE Access (2020)

Enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) are the two main expected services in the next generation of wireless networks. Accommodation of these two ... [more ▼]

Enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) are the two main expected services in the next generation of wireless networks. Accommodation of these two services on the same wireless infrastructure leads to a challenging resource allocation problem due to their heterogeneous specifications. To address this problem, slicing has emerged as an architecture that enables a logical network with specific radio access functionality to each of the supported services on the same network infrastructure. The allocation of radio resources to each slice according to their requirements is a fundamental part of the network slicing that is usually executed at the radio access network (RAN). In this work, we formulate the RAN resource allocation problem as a sum-rate maximization problem subject to the orthogonality constraint (i.e., service isolation), latency-related constraint and minimum rate constraint while maintaining the reliability constraint with the incorporation of adaptive modulation and coding (AMC). However, the formulated problem is not mathematically tractable due to the presence of a step-wise function associated with the AMC and a binary assignment variable. Therefore, to solve the proposed optimization problem, first, we relax the mathematical intractability of AMC by using an approximation of the non-linear AMC achievable throughput, and next, the binary constraint is relaxed to a box constraint by using the penalized reformulation of the problem. The result of the above two-step procedure provides a close-to-optimal solution to the original optimization problem. Furthermore, to ease the complexity of the optimization-based scheduling algorithm, a low-complexity heuristic scheduling scheme is proposed for the efficient multiplexing of URLLC and eMBB services. Finally, the effectiveness of the proposed optimization and heuristic schemes is illustrated through extensive numerical simulations. [less ▲]

Margin-based Active Online Learning Techniques for Cooperative Spectrum Sharing in CR Networks

in International Conference on Cognitive Radio Oriented Wireless Networks (CROWNCOM), Poznan, Poland, June 2019 (2019)