Optimization of Mixed Numerology Profiles for 5G Wireless Communication Scenarios

The management of 5G resources is a demanding task, requiring proper planning of operating numerology indexes and spectrum allocation according to current traffic needs. In addition, any reconfigurations to adapt to the current traffic pattern should be minimized to reduce signaling overhead. In this article, the pre-planning of numerology profiles is proposed to address this problem, and a mathematical optimization model for their planning is developed. The idea is to explore requirements and impairments usually present in a given wireless communication scenario to build numerology profiles and then adopt one of the profiles according to the current users/traffic pattern. The model allows the optimization of mixed numerologies in future 5G systems under any wireless communication scenario, with specific service requirements and impairments, and under any traffic scenario. Results show that, depending on the granularity of the profiles, the proposed optimization model is able to provide satisfaction levels of 60–100%, whereas a non-optimized approach provides 40–65%, while minimizing the total number of numerology indexes in operation.Competitiveness and Internationalization Operational Programme (COMPETE 2020), the Regional Operational Program of the Algarve (2020), and Fundação para a Ciência e Tecnologia; i-Five: Extensão do acesso de espectro dinâmico para rádio 5G, POCI-01-0145-FEDER-030500. This work is also supported by Fundação para a ciência e Tecnologia within CEOT (Center for Electronic, Optoelectronic and Telecommunications) and the UID/MULTI/00631/2020 projectinfo:eu-repo/semantics/publishedVersio

Inter-numerology interference management with adaptive guards: A cross-layer approach

The next-generation communication technologies are evolving towards increased flexibility in various aspects. Although orthogonal frequency division multiplexing (OFDM) remains as the waveform of the upcoming fifth-generation (5G) standard, the new radio provides flexibility in waveform parametrization (a.k.a. numerology) to address diverse requirements. However, managing the peaceful coexistence of mixed numerologies is challenging due to inter-numerology interference (INI). This paper proposes the utilization of adaptive guards in both time and frequency domains as a solution along with a multi-window operation in the physical (PHY) layer. The adaptive windowing operation needs a guard duration to reduce the unwanted emissions, and a guard band is required to handle the INI level on the adjacent band. The guards in both domains are jointly optimized with respect to the subcarrier spacing, use case (i.e., service requirement), and power offset between the numerologies. Also, the multi-window approach provides managing each side of the spectrum independently in case of an asymmetric interference scenario. Since the allowed interference level depends on the numerologies operating in the adjacent bands, the potential of adaptive guards is further increased and exploited with a medium access control (MAC) layer scheduling technique. The proposed INI-based scheduling algorithm decreases the need for guards by allocating the numerologies to the available bands, considering their subcarrier spacing, power level, and SIR requirements. Therefore, INI management is performed with a cross-layer (PHY and MAC) approach in this study. The results show that the precise design that accommodates such flexibility reduces the guards significantly and improves the spectral efficiency of mixed numerology systems

Joint Power and Resource Block Allocation for Mixed-Numerology-Based 5G Downlink Under Imperfect CSI

Fifth-generation (5G) of wireless networks are expected to accommodate different services with contrasting quality of service (QoS) requirements within a common physical infrastructure in an efficient way. In this article, we address the radio access network (RAN) slicing problem and focus on the three 5G primary services, namely, enhanced mobile broadband (eMBB), ultra-reliable and lowlatency communications (URLLC) and massive machine-type communications (mMTC). In particular, we formulate the joint allocation of power and resource blocks to the heterogeneous users in the downlink targeting the transmit power minimization and by considering mixed numerology-based frame structures. Most importantly, the proposed scheme does not only consider the heterogeneous QoS requirements of each service, but also the queue status of each user during the scheduling of resource blocks. In addition, imperfect Channel State Information (CSI) is considered by including an outage probabilistic constraint into the formulation. The resulting non-convex problem is converted to a more tractable problem by exploiting Big-M formulation, probabilistic to non-probabilistic transformation, binary relaxation and successive convex approximation (SCA). The proposed solution is evaluated for different mixed-numerology resource grids within the context of strict slice-isolation and slice-aware radio resource management schemes via extensive numerical simulations

Resource Optimization with Flexible Numerology and Frame Structure for Heterogeneous Services

We explore the potential of optimizing resource allocation with flexible numerology in frequency domain and variable frame structure in time domain, in presence of services with different types of requirements. We analyze the computational complexity and propose a scalable optimization algorithm based on searching in both the primal space and dual space that are complementary to each other. Numerical results show significant advantages of adopting flexibility in both time and frequency domains for capacity enhancement and meeting the requirements of mission critical services.Comment: 4 page

A Survey of Scheduling in 5G URLLC and Outlook for Emerging 6G Systems

Future wireless communication is expected to be a paradigm shift from three basic service requirements of 5th Generation (5G) including enhanced Mobile Broadband (eMBB), Ultra Reliable and Low Latency communication (URLLC) and the massive Machine Type Communication (mMTC). Integration of the three heterogeneous services into a single system is a challenging task. The integration includes several design issues including scheduling network resources with various services. Specially, scheduling the URLLC packets with eMBB and mMTC packets need more attention as it is a promising service of 5G and beyond systems. It needs to meet stringent Quality of Service (QoS) requirements and is used in time-critical applications. Thus through understanding of packet scheduling issues in existing system and potential future challenges is necessary. This paper surveys the potential works that addresses the packet scheduling algorithms for 5G and beyond systems in recent years. It provides state of the art review covering three main perspectives such as decentralised, centralised and joint scheduling techniques. The conventional decentralised algorithms are discussed first followed by the centralised algorithms with specific focus on single and multi-connected network perspective. Joint scheduling algorithms are also discussed in details. In order to provide an in-depth understanding of the key scheduling approaches, the performances of some prominent scheduling algorithms are evaluated and analysed. This paper also provides an insight into the potential challenges and future research directions from the scheduling perspective

Scalable Multiuser Immersive Communications with Multi-numerology and Mini-slot

This paper studies multiuser immersive communications networks in which different user equipment may demand various extended reality (XR) services. In such heterogeneous networks, time-frequency resource allocation needs to be more adaptive since XR services are usually multi-modal and latency-sensitive. To this end, we develop a scalable time-frequency resource allocation method based on multi-numerology and mini-slot. To appropriately determining the discrete parameters of multi-numerology and mini-slot for multiuser immersive communications, the proposed method first presents a novel flexible time-frequency resource block configuration, then it leverages the deep reinforcement learning to maximize the total quality-of-experience (QoE) under different users' QoE constraints. The results confirm the efficiency and scalability of the proposed time-frequency resource allocation method