Delay-Based Resource Allocation With Fairness Guarantee and Minimal Loss for eMBB in 5G Heterogeneous Networks

Abstract

Heterogeneity is defined as one of the core merits in the 5th Generation (5G) mobile systems. Such a trait is prominent in modern 5G traffic scenarios, i.e., enhanced Mobile Broadband (eMBB), where coarse Quality of Service (QoS) profiles need to be satisfied over the shared transmission channel. For that, Radio Resource Management (RRM) became a challenging problem that should be figured out based on the traffic scenario. Extensive efforts dwelled in the literature attempt to solve the resource allocation problem in the downlink channel by maximizing the gain for a certain aspect in the QoS profile. This, however, poses a challenge for 5G-related traffic scenarios such that multi-dimension QoS shall be attainable. Therefore, in this paper, a Delay-aware Resource Allocation for Guaranteed Fairness and minimal Loss (DRAGFL) is proposed to enhance the QoS for Real-Time (RT) services in eMBB 5G Heterogeneous Networks. The ultimate objective of DRAGFL is to maintain a steadily low latency for delay-sensitive flows without restraining the service fairness and the data loss in the overload states. DRAGFL enhances the efficiency of the MAC layer scheduler throughout three phases. Firstly, generating delay-derived weight matrix of the resources blocks to radio bearers from eMBB different classes. Then, greedy-based flows prioritization for Resource Blocks (RBs) assignment. Finally, the data rate of the RT and Non RT (NRT) flows is further emphasized through a channel-aware principle that is invoked conditioning to the remaining radio resources and available data to be scheduled in the interval. The performance evaluation using system-level simulations reveals prominent gains of DRAGFL in sustaining a steadily low end-to-end delay in the high congestion states. Moreover, it maintains ideal states of service fairness with low data loss for different traffic loads to indicate a promising MAC scheduler for the developing scenarios of 5G.

An Energy-Aware Tailored Resource Management for Cellular-Based Zero-Touch Deterministic Industrial M2M Networks

“…We evaluated the GBEA's performance and compared it with Round Robin (RR), Proportional Fair (PF), Memetic, and Memetic + MaxSpace (MS) algorithms [29] considering an uplink scenario. Also, GBEA has been compared to DRAGFL [16], DPBRA [17], DSS-JRAA [19], and RGSL [22] approaches.…”

“…This scheme employs a two-staged dynamic priority system that acts as a prioritized contention mechanism, considering intra and inter-frame wait times and transmission-awaiting M2M devices. In [19], a Sleep-Scheduling Scheme is introduced based on Deep Reinforcement Learning, denoted as DSS. Also, a joint resource allocation algorithm is introduced referred to as DSS-JRAA.…”

“…These wearable devices actively monitor patients' well-being and can promptly trigger alerts in the event of a health concern [11]. In 5G networks, such devices must be scheduled to utilize the available network resources for processing with a lower response time [12]. With flexible system protocols that can be modified according to user requirements, 5G networks are expected to accommodate massive critical communication.…”

Directly Matching an MMIC Amplifier Integrated with MIMO Antenna through DNNs for Future Networks

“…, x n ) to find the optimal points in the domain of F with determined initial guess values. This method firstly extracts the derivative of F(x) by using Jacobian matrix as presented in (6).…”

“…Future wireless communication systems, including the fifth and sixth generation (5G and 6G) technologies, are growing day-by-day, resulting in exponentially high traffic data [1][2][3][4][5][6]. To provide a reliable communication system, the overall performances of power amplifiers (PAs) and antenna as active and passive components play important roles [7][8][9][10].…”