Proportional Fair Resource Partition for LTE-Advanced Networks with Type I Relay Nodes

Unlike conventional cellular networks where the evolved Node B (eNB) performs centralised scheduling, future relay-enhanced cellular (REC) networks allow relay nodes (RNs) to schedule users independently. This decentralised nature of the REC networks brings about challenges to maintain fairness. In this study, we formulate the generalised proportional fair (GPF) resource allocation problem, where resource partition and routing are included as part of the overall radio resource management aiming to provide fairness across all users served by the eNB and its subordinate RNs. Although the traditional proportional fair scheduling algorithm is executed independently at the eNB and each RN to maintain local fairness, we propose efficient resource partition and routing algorithms to maintain global fairness by optimising the GPF objective for the whole relay-enhanced cell. Through system level simulations, the proposed algorithms are evaluated and compared with both non-relaying and relaying systems with benchmark resource partition and routing algorithms. The simulation results show that the proposed algorithms outperform the existing algorithms in providing a better trade-off between system throughput and fairness performance.

In Long Term Evolution Advanced networks with Type I in‐band half‐duplex decode‐and‐forward relay nodes, proportional fair (PF) resource allocation is aiming at guaranteeing two‐hop match and optimising global proportional fairness. The two‐hop match is defined as equal data rates in the access links and the corresponding backhaul links. The global proportional fairness is between all the user equipments served by the evolved nodes B and the relay nodes. Existing centralised schemes achieve these targets at the cost of enormous channel state information (CSI) exchange. Existing distributed schemes focus on resource partitioning and employ a traditional single‐hop PF scheduling algorithm in access links, with less CSI exchange. The traditional PF scheduling algorithm maximises single‐hop proportional fairness between the data rates in the access links rather than two‐hop proportional fairness between the end‐to‐end data rates in the two hops. In order to reduce CSI exchange and at the same time to maximise the two‐hop proportional fairness, a distributed two‐hop PF resource allocation scheme is proposed. The proposed scheme includes two‐hop PF resource scheduling algorithms and adaptive resource partitioning algorithms, applied in different two‐hop transmission protocols. Simulation results demonstrate the proposed scheme is better than the existing distributed schemes in obtaining better proportional fairness and larger cell‐edge user equipment throughputs. Copyright © 2014 John Wiley & Sons, Ltd.

This paper focuses on how to associate users with serving nodes in order to achieve improved fairness and efficiency for the cellular networks enhanced with multiple relay nodes (RNs). We first formulate the generalized proportional fair (GPF) problem with the aim of providing fairness for all users served by the evolved node B (eNB) and its subordinate RNs. Routing is incorporated into the GPF problem as part of the resource allocation strategy. Assuming traditional proportional fair (PF) scheduling algorithm is executed independently at the eNB and each RN, we propose efficient PF-based routing algorithm aiming at optimizing the GPF objective, which also takes into account the impact of relay link resource allocation. Through system-level simulations, the proposed algorithm is evaluated and compared with both non-relaying and relaying systems with benchmark routing algorithms. Simulation results demonstrate that the proposed algorithm can achieve better system throughput and fairness performance.

In the current 4G era, the dual connectivity technique utilizes radio resources scheduled by two distinct base stations for a single user equipment to enhance the data throughput. Multi- connectivity, as a natural evolution of dual connectivity, is one of the key 5G techniques to improve both the user performance and overall resource utilization, allowing dynamic user traffic steering across multiple connections of one or more radio access technologies (RATs). However, one of the main challenge in multi-connectivity is to efficiently allocate resources across multiple connections under heterogeneous quality of service (QoS) requirements. In this paper, we examine a resource allocation problem under multi- connectivity in an evolved LTE network and propose a utility proportional fairness (UPF) resource allocation that supports QoS in terms of requested rates. We evaluate the proposed policy with the proportional fairness (PF) resource allocation through extensive simulations and characterize performance gain from both the user and network perspectives under different conditions.

Multi-connectivity is considered as a 5G key technique to improve both the user performance and the overall resource utilization. In this paper, we examine a resource allocation problem under multi-connectivity in evolved LTE and propose a utility proportional fair (UPF) resource allocation that preserves users quality-of-service (QoS) considering backhaul capacity limitations. The proposed policy is compared with proportional fair (PF) resource allocation through extensive simulations. Presented results show that multi-connectivity outperforms single-connectivity in terms of network aggregated rate and users QoS satisfaction in different network case studies, i.e., empty and loaded cell scenarios with fixed and variable backhaul capacity.

In this paper, we study two schemes for the fair resource allocation in wireless powered communication networks (WPCNs): a non-orthogonal multiple access (NOMA) scheme, and a proportional fair (PF) scheduling scheme. The considered WPCN consists of a base station (BS) that broadcast radio frequency (RF) energy over the downlink, and N energy harvesting users (EHUs). If NOMA is employed, all EHUs concurrently transmit information over the uplink with successive interference cancellation employed at the BS. If PF scheduling is employed, a single EHU is selected for uplink transmission in each frame. For both schemes, we arrive at optimal allocations for the BS transmit power and the time sharing between uplink and downlink transmissions that maximize the uplink sum-rate, while maintaining high level of system fairness. For the PF scheme, we also derive the optimal scheduling policy. Compared to the state-of-the art schemes based upon time division multiple access (TDMA), both schemes significantly improve the system fairness at the expense of minor (or nonexistent) rate degradation. Key words: energy harvesting; wireless powered communication networks; non-orthogonal multiple access; successive interference cancelation; proportional fair scheduling REFERENCES: [1] P. Grover, A. Sahai: Shannon meets Tesla: wireless information and power transfer, Proc. IEEE ISIT 2010, pp. 2363–2367, Austin, USA, June 2010. [2] D. Gunduz, K. Stamatiou, N. Michelusi, M. Zorzi: Designing intelligent energy harvesting communication systems, IEEE Commun. Magazine, 52, 1, 210–216 (Jan.2014). [3] C. K. Ho, R. Zhang: Optimal energy allocation for wireless communications with energy harvesting constraints, IEEE Trans. Signal Proccessing, 60, 9, 4808–4818 (May 2012). [4] H. Ju, R. Zhang: Throughput maximization in wireless powered communication networks, IEEE Trans. Wireless Commun., 13, 1, 418–428 (Jan. 2014). [5] X. Kang, C. Ho Keong, S. Sun: Optimal time allocation for dynamic-TDMA-based wireless powered communication networks, Proc. IEEE Globecom 2014, Austin, USA, Dec. 2014. [6] H. Ju, R. Zhang: Optimal resource allocation in full-duplex wireless-powered communication network, IEEE Trans. on Commun., 62, 10, 3528–3540 (Oct. 2014). [7] T. Takeda, K. Higuchi: Enhanced user fairness using non-orthogonal access with SIC in cellular uplink, VTC 2011, San Francisco, USA, pp. 1–5, 2011. [8] Z. Ding, Z. Yang, P. Fan, H. V. Poor: On the performance of non-orthogonal multiple access in 5G systems with randomly deployed users, IEEE Signal Process. Lett., 21, 12, 1501–1505 (2014). [9] S. Timotheou, I. Krikidis: Fairness for non-orthogonal multiple access in 5G systems, IEEE Signal Process. Lett., 22, 10, 1462–1465 (2015). [10] H. Chingoska, Z. Hadzi-Velkov, I. Nikoloska, N. Zlatanov: Resource Allocation in Wireless Powered Communication Networks with Non-Orthogonal Multiple Access, IEEE Wireless Communications Letters, 5 (6), 684–687 (2016). [11] P. Viswanath, D. N. Tse, R. Laroia: Opportunistic beamforming using dumb antennas, IEEE Trans. Information Theory, 46, 6, 1277–1294 (June 2002). [12] N. Tekbiyik, T. Girici, E. Uysal-Biyikoglu, K. Leblebicioglu: Proportional fair resource allocation on an energy harvesting downlink, IEEE Trans. Wireless Communications, 12, 4, 1699–1711 (April 2013). [13] H. Chingoska, I. Nikoloska, Z. Hadzi-Velkov, N. Zlatanov: Proportional fair scheduling in wireless powered communication networks, 23rd International Conference on Telecommunications (ICT), May 2013. [14] Z. Hadzi-Velkov, I. Nikoloska, H. Chingoska, N. Zlatanov, Proportional fair scheduling in wireless networks with RF energy harvesting and processing cost, IEEE Comm. Letters, 20, 10, 2107–2110 (2016). [15] T.-D. Nguyen, Y. Han: A Proportional Fairness Algorithm with QoS Provision in Downlink OFDMA Systems, IEEE Comm. Letters, 10, 11 (Nov. 2006). [16] Z. Hadzi-Velkov, I. Nikoloska, G. K. Karagiannidis, T. Q. Duong: Wireless networks with energy harvesting and power transfer: joint power and time allocation, IEEE Signal Process. Letters, 23, 1, 50–54 (Jan. 2016). [17] R. Jain, D. Chiu, W. Hawe: A Quantitative measure of fairness and discrimination for resource allocation in shared computer systems, Tech. Rep. TR-301, DEC, September 1984. [18] W. Yu, R. Lui: Dual methods for nonconvex spectrum optimization of multicarrier systems, IEEE Trans. Commun., 54, 7, 1310–1322 (Jul. 2006). [19] L. Liu, R. Zhang, K.-C. Chua: Wireless information transfer with opportunistic energy harvesting, IEEE Trans. Wireless. Commun., 12, 1, 288–300 (Jan. 2013).

In emerging multi-radio multi-channel wireless mesh networks (WMNs), how to allocate network resources to provide individual users with their fair rate share is a central but very complex issue due to their inherent multi-channel diversity and multi-hop connectivity. In this paper, we attempt to apply and extend a software-defined networking (SDN) approach to user-level fair resource allocation problems where both proportional fairness and max-min fairness are examined. We first mathematically formulate fair user resource allocation problems in multi-channel WMNs by seeking the maximization of objective functions under the network utility maximization framework. We then design both SDN controller-side and user device-side algorithms to solve the formulated problem in a centralized holistic manner. We apply the algorithms to control per-user link-layer rates according to a fairness criterion while we resort to traditional transport-layer congestion control for the regulation of individual flows for each user. For performance evaluation, we conduct a system-level simulation study to verify the convergence property of the proposed user fairness resource allocation solution and highlight the benefits of the SDN approach.

With the widespread application of wireless networks and the requirements of different user equipments (UEs), energy has become a scarcer resource as well as spectrum. In this paper, considering the actual scenarios of imperfect channel state information (CSI), we study a resource allocation scheme in the downlink orthogonal frequency division multiple access (OFDMA) systems. To balance between energy efficiency (EE) and proportional fairness (PF), the problem is formulated as maximizing average achievable EE with the constraints of PF of users and QoS assurance. To solve the optimal problem, we divide it into two layers. The sub-problem P1 of inner layer is solved to maximize PF, with the parameter of total transmit power which is updated by the sub-problem P2 of outer layer. In outer layer, with the allocation scheme from P1, a gradient-based adaptation resource allocation algorithm is proposed to achieve the maximum EE with total transmit power updated in every gradient iteration. Moreover, the impacts of the imperfect CSI on EE and PF are analysed. Simulation results are presented to show the superior performance of the proposed algorithms and verify the analytical findings.

In this study, we investigate the proportional fair trajectory design and resource allocation for an unmanned-aerial-vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) system, where multiple ground nodes (GNs) receive information and harvest energy from the signal transmitted by the UAV using a power-splitting (PS) policy. With this system, we aim to maximize the sum of the logarithmic average spectral efficiency (SE) of the GNs while guaranteeing the average harvested energy requirement to improve the average SE and user fairness simultaneously. To deal with the nonconvexity of the optimization problem, we adopt the quadratic transform and first-order Taylor expansion, proposing an iterative algorithm to find the optimal trajectory and transmit the power of the UAV and the PS ratio of the GNs. Through simulations, we confirm that the proposed scheme achieves a higher average SE compared with the conventional baseline schemes and ensures a level of user fairness similar to that of the state-of-the-art baseline scheme.

We study efficiency of proportional fairness (PF) and dominant resource fairness (DRF) in the aspect of resource usage and total throughput when there are two different type resources are available. With a unified convex optimization describing both fair resource allocation schemes, we characterize bottleneck resources (resources in full utilization) of PF and DRF and prove that PF always more efficiently utilizes the available resources with less waste than DRF does. We provide conditions when PF has higher throughput than DRF and give examples showing that PF may have smaller throughput than DRF even though PF has higher resource usage than DRF.

In the MIMO-OFDM multiple access channel (MIMO-OFDM-MAC) uplink scenario, the base station decides the uplink parameters for multiple users based on channel state information (CSI) from each user in the system. The performance of MIMO-OFDM-MAC systems can be significantly improved by using an adaptive transmission and resource allocation schemes which consider the correlation effect of line of sight (LOS) and non line of sight (NLOS) channel conditions for different users in the system. A lot of papers have been published on resource allocation schemes for MIMO-OFDM systems. However, most of these resource allocation schemes have been considered for MIMO-OFDMA systems, where users are separated in the frequency domain and each user uses the same uplink and downlink channels in the same channel conditions. On the other hand, in the mulituser MIMO-OFDM systems, more than one user can be assigned the same frequency and channel conditions for the MIMO-OFDM broadcast channel (downlink) and MIMO-OFDM-MAC channel (uplink) are not the same. Therefore, the same resource allocation schemes for the conventional MIMO-OFDM systems can not be applied to multiuser MIMO-OFDM systems with different uplink and downlink channel conditions. Until now, most of the resource allocation schemes have been considered only for downlink MIMO-OFDM broadcast (MIMO-OFDM-BC) channel and very few papers tackle the fairness among users. Moreover, no paper considers a scheme to realize proportional data rate fairness among users in the MIMO-OFDM-MAC condition. In this paper, we propose a proportional data rate fairness resource allocation scheme with adaptive bit loading for MIMO-ODFM-MAC systems by considering the correlation effects of LOS and NLOS channel conditions in both spatial and frequency domains. Computer simulation results show that the proposed scheme can give larger system capacity while maintaining the proportional data rate fairness requirements among users in the system under the constraint of total transmit power and predetermined target BER.

In this paper, an energy efficient resource allocation algorithm for massive multiple-input-multiple-output (MIMO) systems powered by wireless power transfer (WPT) with proportional fairness is investigated. In the system, multiple sensor nodes (SNs) harvest energy from the power beacon (PB) by WPT, and then the SNs communicate with the base station (BS) by exploiting the harvested energy. A proportional fair energy efficient resource allocation algorithm that combines time and power allocation is proposed to maximize the energy efficiency (EE). Compared with the minimum EE maximization algorithm and the total user's energy efficiency maximization algorithm, simulation results verify the proposed algorithm can obtain a good tradeoff between the total EE of the system and minimum EE of the users.

Dynamic particle swarm optimization (PSO) problems are generally characterized by the exhaustively examined issues of the changing location of optima, the changing fitness of optima, and measurement noise/errors. However, the challenging issue of continuously changing problem dimensionality has not been similarly examined. Given that in anytime dynamic resource allocation it is necessary to maintain a high quality solution, we argue that, rather than restarting the PSO algorithm, a more appropriate approach is to design an algorithm that robustly handles changing problem dimensionality. Specifically, we propose an indirect particle encoding scheme specifically designed for a dynamic multi-dimensional PSO algorithm for proportional fair constrained resource allocation. This PSO algorithm is implemented for the proportional fair allocation of power and users to channels within a simulation of an Orthogonal Frequency-Division Multiple Access (OFDMA) wireless network with mobile users switching cells as they traverse the simulation environment. The proposed PSO algorithm is evaluated using simulations, which demonstrate the ability of the proposed indirect encoding scheme to maximize the overall proportional fair optimization goal, without unfairly penalizing the individual components of the solution related to newly introduced problem dimensions.

In this paper, we propose a hybrid maximum-rate (MR) and proportional-fairness (PF) resource allocation scheme in the downlink transmissions of Long Term Evolution (LTE) networks. When the users are low-density or have small channel diversities, the proposed scheme performs closely like a PF manner because the PF policy can provide a superior fairness performance with a modest reduction in throughput as the user average channel qualities are fairly uniform. With the growth of user densities or user channel diversities, the proposed scheme behaves like a MR fashion so as to maximize the system throughput superior to providing user fairness. The simulation results demonstrate that our scheme can balance the tradeoff between system throughput and user fairness and thus optimize the overall LTE transmission performance.

By executing offloaded tasks from mobile users, edge computing augments mobile devices with computing/communications resources from edge nodes (ENs), enabling new services/applications (e.g., real-time gaming, virtual/augmented reality). However, despite being more resourceful than mobile devices, allocating ENs' computing/communications resources to given favorable sets of users may block other devices from their service. This is often the case for most existing task offloading and resource allocation approaches that only aim to maximize the network social welfare (e.g., minimizing the total energy consumption) but not consider the computing/battery status of each mobile device. This work develops a proportional fair task offloading and resource allocation framework for a multi-layer cooperative edge computing network to serve all user equipment (UEs) while considering both their service requirements and individual energy/battery levels. The resulting optimization involves both binary (offloading decisions) and real variables (resource allocations), making it NP-hard. To tackle it, we leverage the fact that the relaxed problem is convex and propose a distributed algorithm, namely the dynamic branchand-bound Benders decomposition (DBBD). DBBD decomposes the original problem into a master problem (MP) for the offloading decision and subproblems (SPs) for resource allocation. The SPs can either find their closed-form solutions or be solved in parallel at ENs, thus help reduce the complexity. The numerical results show that the DBBD returns the optimal solution of the problem maximizing the fairness between UEs. The DBBD has higher fairness indexes, i.e., Jain's index and min-max ratio, in comparing with the existing ones that minimize the total consumed energy.