On-Device Cognitive Spectrum Allocation for Coexisting URLLC and eMBB Users in 5G Systems

Abstract

Ultra Reliable and Low Latency Communications (URLLC) play a key role in 5G vertical markets, but pose many technical challenges especially when sharing the spectrum with Enhanced Mobile Broadband (eMBB) customers. This study aims to overcome the spectrum inefficiency issue of fully separate (FS) approach and the contention issue of the fully overlap (FO) approach. We present a user-initiated probability elastic resource (UPER) approach by dynamically adjusting the probability of using the shared spectrum for eMBB and URLLC traffic based on the current success and failure status of packet transmission status. The probabilities of successful transmission are derived for UPER, FS, and FO and partially overlap (PO) sharing spectrum approaches. We find that the successful transmission probability of UPER approach is 28% and 46% higher than FS and FO approaches, respectively. We further evaluate the reliability and throughput performance of URLLC and eMBB. When the URLLC packet load is low, the UPER method can almost achieve the best performance of the FS method. When the URLLC packet load is high, we show that UPER can improve the reliability performance up to 54% compared with other methods. Index Terms-Spectrum management, ultra reliable and low latency communications, coexisting systems. I. INTRODUCTIONU LTRA-RELIABLE and low latency communications (URLLC) of the fifth generation (5G) wireless communications aim for providing time-critical machine-to-machine or human-to-machine vertical applications, such as factory automation, vehicular communications, and augmented reality, etc., [1]-[4]. Most URLLC services require 99.999% reliability performance within 1 msec latency in the data plane [5], [6]. Remote monitoring of patient's the vital signals is an important human-to-machine URLLC applications use case. On the other hand, intelligent transportation systems is an important machine-to-human URLLC use case [7], where

Data-Driven Spectrum Partition for Multiplexing URLLC and eMBB

“…However, PO and FO schemes must overcome interference issues because packet collisions deteriorate the performance of high-quality communication services. The FS scheme achieved better communication quality than the overlap-based spectrum partition due to the lack of packet collisions and improve the spectrum efficiency of the multiplexed eMBB and URLLC systems [12]. However, the optimal spectra-sharing strategy for multiplexed URLLC and eMBB is a challenging research issue because it is a multi-objective optimization (MOO) problem subject to complex constraints [13].…”

“…Denote N u and N e as the UE number of the URLLC and that of the eMBB, respectively. Referring to [12], we consider that the grant-free opportunity (GFO) presents the entire time of a single grant-free transmission, in which the URLLC UE receives the acknowledgment (ACK) from the BS until its timer expires. The transmitter retransmits the same ACK data after the first transmission to minimize the negative effect of packet collision.…”

“…The objective of this study is to reduce RB waste under the premise of satisfying URLLC reliability requirements and eMBB throughput. Following [12], we give URLLC traffic a higher priority than eMBB traffic. Therefore, this study maximizes eMBB throughput while satisfying the constraints of the URLLC reliability requirement.…”

Discrete Hughes‐Hartogs optimized dual vector handoff for spectrum management in cognitive radio networks

“…The network distributed nature also forces the user to act in a cooperative manner for increasing the overall network performance. Multiagent reinforcement learning was designed to address the above said issues in a greedy manner, and therefore, improving network lifetime was suggested by Wu et al 5 Yet another spectrum inefficiency model was addressed by utilizing partially overlap sharing mechanisms, which was proposed in Cheng et al 6 A cooperative deep Q‐network with Q‐value transfer (QT‐CDQN) is used to act as a multi‐agent reinforcement learning system in terms of controlling the intersection by the Q network in Ge et al 7 …”

Joint Design of Beamforming and Antenna Selection in Short Blocklength Regime for URLLC in Cognitive Radio Networks

“…By considering both massive machine-type communication (mMTC) and URLLC services for a cognitive unmanned aerial vehicle (UAV)-aided network, the authors in [20] first derived the analytical expressions of the throughput and then developed an efficient algorithm for EE maximization problem. Tackling the stringent requirement of latency and reliability in the URLLC and enhanced mobile broadband (eMBB) coexistence system was studied in [21], where the authors developed a dynamic spectrum allocation scheme to help alleviating the packet collisions of multiplexing URLLC and eMBB packets. However, these works only considered a single antenna at the secondary transmitter while in practice it is often equipped with multiple antennas to guarantee high throughput, which requires an efficient beamforming design.…”