IRS-Aided Physical Layer Network Slicing for URLLC and eMBB

Abstract

5G and beyond 5G (B5G) wireless systems promise to support services with different requirements in the same network, as enhanced mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and massive machine type communication (mMTC). One alternative is to consider the network slicing paradigm, where the wireless network resources are shared (or sliced) among active services with different requirements. In addition, another emerging technology, that is considered as a key enabler for B5G wireless systems, is the intelligent reflecting surfaces (IRS). From the deployment of an IRS, it is possible to improve the received signal quality and consequently increase the overall network capacity. Therefore, in this paper, we investigate the use of IRS to support simultaneous eMBB and URLLC services. We evaluate the achievable rate of an IRS-aided radio access network, where the uplink resources are shared between eMBB and URLLC users either under heterogeneous orthogonal multiple access (H-OMA) or heterogeneous non-orthogonal multiple access (H-NOMA) techniques. Results show that exploiting an IRS can considerably increase the eMBB rate and the URLLC reliability simultaneously, regardless of whether operating under H-OMA or H-NOMA. Moreover, we also provide some insights on the best user pairing strategy, showing that higher rates are achieved by matching many eMBB users near to the IRS with a URLLC user close to the base station.INDEX TERMS Enhanced Mobile Broadband (eMBB), Intelligent Reflecting Surfaces (IRS), Network Slicing, Ultra-Reliable Low-Latency Communications (URLLC).

A Generalized Delay and Backlog Analysis for Multiplexing URLLC and eMBB: Reconfigurable Intelligent Surfaces or Decode-and-Forward?

“…4) Limitations: The above studies [3], [5], [8], [11], [12], [18], [36]- [39] achieved outstanding contributions in the improvement of URLLC and eMBB multiplexing system performance. However, these studies lack a comprehensive analysis of delays and backlog violation behaviors.…”

“…Reconfigurable intelligent surfaces (RIS) and decode-andforward (DF) relay were shown to efficiently improve the system capacity by strengthening the received signal [11], [12]. RIS creates multipath backscatter links by intelligently controlling an array of passive reflecting elements [13], [14].…”

“…In [39], RIS was used to maximize URLLC reliability and minimize eMBB rate loss by jointly optimizing RIS phase shift, frequency, and base station transmission power. RIS-aided radio access network was shown to effectively increase the uplink URLLC reliability and eMBB throughput simultaneously under both the heterogeneous orthogonal multiple access (OMA) and heterogeneous nonorthogonal multiple access (NOMA) frameworks [11]. In [18], a two-phase relay-assisted protocol was developed to support URLLC uplink and minimize transmission power consumption by jointly scheduling relay, transmission power, frequency, and decoding error probability.…”

Resource Allocation and Sharing Methodologies When Reconfigurable Intelligent Surfaces Meet Multiple Base Stations

“…The joint design of transmit power allocation and the RIS reflection coefficients based on the channel estimated through grouping RIS antenna elements has been proposed in [ 4 ]. The deployment of RIS assisting two heterogeneous communication services has been presented in [ 5 ]. The joint design of time allocation, cooperative beamforming, and the reflection coefficients for RIS aided cellular and wireless powered sensor networks have been considered in [ 6 ].…”

“…The resource allocation of RIS-aided dual connectivity has been presented in [ 10 ]. The prospective use cases of 6G includes RIS assisting wireless networks [ 1 ], NOMA [ 3 ], edge computing [ 11 ], etc., as in [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ].…”