Fairness-based Resource Allocation Research Articles

Priority-based subcarrier allocation algorithm for maximal network connectivity in 5G networks

With the widespread adoption of wireless technology, there has been a significant surge in the number of devices seeking wireless connectivity over the past decade. To meet the extensive demand for high-data-rate wireless connectivity, the fifth-generation (5G) cellular network plays a pivotal role. 5G cellular network aims to support a large number of applications with ultra-high data rates by maximizing device connectivity while satisfying quality of service (QoS) requirements. In this paper, we present an innovative priority-based subcarrier allocation (PSA) algorithm to address the challenge of maximizing connectivity in 5G new radio (5G NR) networks. Initially, we formulate the connectivity maximization problem as a subcarrier allocation problem by considering three key parameters: bandwidth requirement, waiting time, and energy level of user devices. The objective of the formulated problem is to optimally allocate subcarriers to multiple users in order to maximize connectivity while maintaining QoS requirements. To address the problem, we propose the PSA algorithm that prioritizes bandwidth, waiting time, and energy parameters using the R-method. To accommodate the network scenarios, we develop three variants of the PSA algorithm—PSA-1, PSA-2, and PSA-3. These variants allocate subcarriers based on the priority-based score of user. We carried out a simulation-based study to illustrate the effectiveness of our proposed algorithm in comparison to traditional methods. The simulation results reveal that our proposed algorithms outperform first come first serve (FCFS) and longest remaining time first (LRTF), and achieves comparable or superior results compared to priority and fairness-based resource allocation with 5G new radio numerology (PFRA-0N) in terms of the number of user allocations, average user allocation ratio, user drop ratios and average connectivity rate. Compared to the shortest job first (SJF) technique, our proposed PSA algorithm performance is slightly inferior in terms of the number of user allocations, average allocation ratio and average connectivity rate; however, it shows superior performance in drop ratios. Further, the proposed algorithms show significant improvements in execution time compared to the optimal exhaustive search solution.

Fairness-Based Resource Allocation for Multiple Weights Opportunistic Beamforming in Internet of Things Networks

Multiple-input–multiple-output (MIMO) is a promising technique in Internet of Things (IoT) Networks, which can effectively multiplex more IoT users and improve the spectrum efficiencies (SEs) of the users. However, when the number of IoT users is large, the complexity becomes enormous. Moreover, perfect channel state information (CSI) is a basic assumption in MIMO systems, which is impractical with a large number of IoT users. In order to reduce the complexity and break the CSI limitation, an opportunistic-beamforming (OBF)-based IoT network is proposed, which can achieve high SE with low complexity and feedback under a large number of IoT users’ conditions. Then, to solve the unfairness problem and further improve SE, a downlink multiple-weight and multiple receive antenna OBF (MW-OBF-MRA) system with the proportional fairness (PF) strategy is proposed, where the transmitter and receiver are jointly designed. Moreover, a joint resource allocation scheme is provided to achieve the maximum SE with PF constraints. Numerical results show that our proposed system can achieve fairness with the minimum SE loss and provide better SE than the other beamforming schemes with the limited feedback information.

Max-Min fairness-based resource allocation in massive MIMO systems

This work deals with power and spectrum allocation approaches for massive MIMO (M-MIMO) systems. An analysis is made to verify the efficiency of the solutions provided by schemes used for the spectral and energy efficiency (SE-EE) trade-off problem in massive antenna-based wireless communication systems. We first introduce the Geometry Based Stochastic Model (GBSM) channel model (One-ring model), describing the behavior of a uniform linear array of antennas (ULA) arrangement, revealing how the channel parameters affect the channel capacity. We also show that under this model, the SE still increases without boundary when the massive number of base-station (BS) antennas $M$ increases, provided that pilot contamination is substantially mitigated or eliminated but when the number of users equipment (UEs) $K$ increases with a fixed number of antennas in the BS, there is a increasing limitation from the combiners in mitigating the inter-user interference, making decoding difficult. The downlink (DL) M-MIMO scenario is analyzed, by introducing the generalized power allocation problem and derived the max-min fairness scheme from it. We propose a procedure to solve the max-min problem and

A simple method of proportional fairness-based resource allocation for power-domain NOMA

A simple method of proportional fairness-based resource allocation for power-domain NOMA

Fairness-Based Distributed Resource Allocation in Two-Tier Heterogeneous Networks

Booming demand for mobile intelligent terminal equipment results in the exponential growth of the data flow in the fifth-generation mobile communication network. Small-cell networks have been considered as one of the possible solutions, where interference management and effective resource allocation are outstanding issues due to numerous small base stations. In this paper, the concept of cognitive small-cell networks is introduced, which combines technologies from cognitive radio with small cells. We aim to maximize the total throughput of the cognitive small-cell networks by jointly considering interference management, fairness-based resource allocation, average outage probability, and channel reuse radius. In order to make the optimization problem tractable, we decompose the original problem into three sub-problems. First, we derive the average outage probability function of the network with respect to the spectrum sensing threshold. With a given outage probability threshold, the associated range of the channel reuse radius is obtained. In addition, to maximize the total throughput, a fairness-based distributed resource allocation (FDRA) algorithm is proposed to guarantee the fairness among cognitive small-cell base stations, which defines the satisfaction degree and the traffic requirement indicators to dynamically adjust the resource allocation process. Finally, considering the time-varying traffic load and different geographical environments, an improved FDRA (IFDRA) algorithm is proposed to further improve the throughput of the hot area. The simulation results demonstrate that the proposed FDRA algorithm and IFDRA algorithm could achieve a considerable performance improvement compared with schemes in the literature while providing better fairness among cognitive small-cell base stations.

Proportional Fairness-Based Resource Allocation for LTE-U Coexisting With Wi-Fi

To further boost the performance of LTE to meet the ever-increasing mobile traffic demand in a cost-effective way, applying LTE in unlicensed spectrum, known as LTE-U technology, is considered as a promising complementary solution for achieving the ultra-capacity foreseen in 5G and beyond. In the unlicensed spectrum, LTE-U will share the channel with other unlicensed networks, e.g., Wi-Fi. However, the centralized control architecture of LTE networks is inherently different from the distributed channel access of Wi-Fi network, which poses great challenges to achieve fair coexistence of the two networks. To this end, in this paper, we propose a cross-layer proportional fairness (PF)-based framework to jointly optimize the protocol parameters of the medium access control layer and physical layer of an LTE-U network. Specifically, to achieve throughput-oriented PF between the two heterogeneous networks, the cross-layer optimization framework can be decoupled into a device number weighted time occupation ratio-oriented PF optimization problem and a channel-power allocation-based instantaneous transmission rate-oriented PF optimization problem. Given that LTE-U base stations adopt a listen-before-talk-based channel access scheme, the interactions between the LTE-U and the Wi-Fi networks are modeled by two interactive Markov chains. The effectiveness and the superior performance of the proposed cross-layer PF-based optimization framework are demonstrated and verified by simulations.

Fairness-Based Resource Allocation with Minimum Rate Guarantee in a Multiuser OFDMA System

We propose a resource allocation scheme based on Proportional Fairness (PF) for minimum bit rate guarantee in a multiuser, frequency-selective frame-based OFDMA system. Instead of considering subcarrier-and-bit allocation, we consider a more realistic scheduling which is performed at the beginning of each frame, while allowing time-slots multiplexing among active users. Our scheme aims to guarantee different minimum bit rates to different users, while throughput fairness among users is maintained. We formulate the optimization problem based on a multichannel proportional fairness objective function. Minimum bit rate guarantee is taken as the constraint. The optimization problem is solved by using the Lagrange multiplier method. Then, we use our fast cross-layer approach integrated to our optimization solution to schedule packets from finitely backlogged queues. Simulations with user mobility are conducted. The results show that the proposed scheme can serve users with satisfied minimum bit rate requirement. Furthermore, the simulation results also confirm throughput fairness among users.

Delay-Constrained Video Transmission: Quality-Driven Resource Allocation and Scheduling

Real-time video demands quality-of-service (QoS) guarantees such as delay bounds for end-user satisfaction. Furthermore, the tolerable delay varies depending on the use case such as live streaming or two-way video conferencing. Due to the inherently stochastic nature of wireless fading channels, deterministic delay bounds are difficult to guarantee. Instead, we propose providing statistical delay guarantees using the concept of effective capacity. We consider a multiuser setup whereby different users have (possibly different) delay QoS constraints. We derive the resource allocation policy that maximizes the sum video quality and applies to any quality metric with concave rate-quality mapping. We show that the optimal operating point per user is such that the rate-distortion slope is the inverse of the supported video source rate per unit bandwidth, a key metric we refer to as the source spectral efficiency. We extend the resource allocation policy to capture video quality-driven adaptive user-subcarrier assignment in wideband channels as well as capture the impact of adaptive modulation and coding. We also solve the alternative problem of fairness-based resource allocation whereby the objective is to maximize the minimum video quality across users. Finally, we derive user admission and scheduling policies that enable selecting a maximal user subset such that all selected users can meet their statistical delay requirement. Results show that video users with differentiated QoS requirements can achieve similar video quality with vastly different resource requirements. Thus, QoS-aware scheduling and resource allocation enable supporting significantly more users under the same resource constraints.

Nash Fairness Based Resource Allocation Algorithm for Dirty-Paper Coded Multi-user MIMO-OFDM Systems

Nash Fairness Based Resource Allocation Algorithm for Dirty-Paper Coded Multi-user MIMO-OFDM Systems

Fairness based resource allocation for multiuser MISO-OFDMA systems with beamforming

Resource allocation problem in multiuser multiple input single output-orthogonal frequency division multiple access (MISO-OFDMA) systems with downlink beamforming for frequency selective fading channels is studied. The article aims at maximizing system throughput with the constraints of total power and bit error rate (BER) while supporting fairness among users. The downlink proportional fairness (PF) scheduling problem is reformulated as a maximization of the sum of logarithmic user data rate. From necessary conditions on optimality obtained analytically by Karush-Kuhn-Tucker (KKT) condition, an efficient user selection and resource allocation algorithm is proposed. The computer simulations reveal that the proposed algorithm achieves tradeoff between system throughput and fairness among users.

Performance Improvement of Proportional Fairness-Based Resource Allocation in OFDMA Downlink Systems

We have developed a novel downlink packet scheduling scheme for a multiuser OFDMA system in which a subchannel can be time-multiplexed among multiple users. This scheme which is called Matrixed-based Proportional Fairness can provide a high system throughput while ensuring fairness. The scheme is based on a Proportional Fairness (PF) utility function and can be applied to any of the PF-based schedulers. Our scheduler explores multichannel multiuser diversity by using a two-dimensional matrix combining user selection, subchannel assignment, and time slot allocation. Furthermore, unlike other PF-based schemes, our scheme considers finitely backlogged queues during the time slot allocation. By doing so, it can exploit multichannel multiuser diversity to utilize bandwidth efficiently and with throughput fairness. Additionally, fairness in the time domain is enhanced by limiting the number of allocated time slots. Intensive simulations considering finitely backlogged queues and user mobility prove the scheme's effectiveness.

Fairness based resource allocation for uplink OFDMA systems

This article investigates two fairness criteria with regard to adaptive resource allocation for uplink orthogonal frequency division multiple access (OFDMA) systems. Nash bargaining solution (NBS) fairness and proportional fairness (PF) are two suitable candidates for fairness consideration, and both can provide attractive trade-offs between total throughput and each user's capacity. Utilizing Karush-Kuhn- Tucker (KKT) condition and iterative method, two effective algorithms are designed, to achieve NBS fairness and proportional fairness, respectively. Simulation results show that the proposed resource allocation algorithms achieve good tradeoff between the overall rate and fairness, with little performance loss from the total capacity.