Utility-Based Resource Allocation under Multi-Connectivity in Evolved LTE

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.

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.

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.

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.

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.

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).

Line of Sight (LoS) blockages are a common occurrence in densely deployed cellular networks, as is the case with 5G. This leads to a significant deterioration in the signal quality on the user side. Modeling LoS blockages is crucial for simulations to obtain reliable results, but also challenging since LoS might appear and disappear occasionally because how often an LoS happens depends on the environment and the user speed. To capture LoS blockages in a realistic manner for a particular scenario in a given environment, we propose to model blockages geometrically by considering all static and mobile objects in the environment such as buildings, cars, busses and humans, including self-blockages from the user. This enables a better evaluation of the metrics of interest, such as handover rate. In dense network deployments, users make frequent handovers, which deteriorates their experience and reduces the network capacity. Also, operators should strive to provide fairness in resource allocation to all users as well as to guarantee a minimum Quality of Service (QoS). Thus, handover decisions should be considered jointly with resource allocation. To that end, in this paper, we formulate an optimization problem that provides proportional fair resource allocation, while simultaneously reducing the handover rate, and providing a minimum data rate for all users at all times. It is an integer non-linear program, which is NP-hard. We relax it to a linear problem, which allows us to find a near-optimal user-to-BS assignment and resource proportion for every user quickly. We compare the result from our optimal and relaxed approaches with other two benchmarks showing that it outperforms them considerably in terms of fairness, handover rate reduction and users' rate satisfaction. Moreover, our relaxed approach performs within above 90% of the optimum and reduces the handover rate up to 40%.

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.

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 3GPP LTE-Advanced networks deployed with type I relay nodes (RNs), resource partition is required to support in-band relaying. This paper focuses on how to partition system resources in order to attain improved fairness and efficiency. We first formulate the generalized proportional fair (GPF) resource allocation problem to provide fairness for all users served by the evolved node B (eNB) and its subordinate RNs. Assuming traditional proportional fair scheduling is executed independently at the eNB and each RN to achieve local fairness, we propose the proportional fair resource partition algorithm to tackle the GPF problem and ensure global fairness. Through system level simulations, the proposed algorithm is evaluated and compared with both non-relaying and relaying systems with the fixed resource partition approach. Simulation results demonstrate that the proposed algorithm can achieve a good trade-off between system throughput and fairness performance.

We investigate an outage optimal adaptive resource allocation scheme for the upstream of two-hop OFDMA based decode-and-forward cooperative relay systems. The objective of this work is to design resource allocation strategy, which addresses the needs of the users minimizing their outage probability. This scheme utilizes the subchannel-pairing and proportional fairness in two-hop multiuser multirelay network to achieve the user required percentage throughput ( i.e., if the user's application can tolerate 5% outage then guaranteeing the 100% availability is actually a wastage of scarce radio resources). Using outage optimal resource allocation scheme, we achieve the complimentary fairness along with the required data rate on each node. Simulation results show that the proposed scheme achieve better throughput-fairness trade-off compared to proportional fair scheduling (PFS) and MaxMin resource allocation schemes.

Assuming non-ideal circuit power consumption at the energy harvesting (EH) nodes, we propose two practical protocols that optimize the performance of the harvest-then-transmit wireless powered communication networks (WPCNs) under two different objectives: (1) proportional fair (PF) resource allocation, and (2) sum rate maximization. These objectives lead to optimal allocations for the transmit power by the base station, which broadcasts RF radiation over the downlink, and optimal durations of the EH phase and the uplink information transmission phases within the dynamic time-division multiple access frame. Compared to the max-sum-rate protocol, the PF protocol attains a higher level of system fairness at the expense of the sum rate degradation. The PF protocol is advantageous over the max-sum-rate protocol in terms of system fairness regardless of the circuit power consumption, whereas the uplink sum rates of both protocols converge when this power consumption increases.

Massive MIMO and small cell are both recognized as the key technologies for the future 5G wireless systems. In this paper, we investigate the problem of user association in a heterogeneous network (HetNet) with massive MIMO and small cells, where the macro base station (BS) is equipped with a massive MIMO and the picocell BS's are equipped with regular MIMOs. We first develop centralized user association algorithms with proven optimality, considering various objectives such as rate maximization, proportional fairness, and joint user association and resource allocation. We then model the massive MIMO HetNet as a repeated game, which leads to distributed user association algorithms with proven convergence to the Nash Equilibrium (NE). We demonstrate the efficacy of these optimal schemes by comparison with several greedy algorithms through simulations.

As a key technology in 4G-LTE, heterogeneous networks can effectively extend the coverage and capacity of wireless networks by deploying multiple micro-nodes on top of the conventional macro base stations (BS). The deployed micro-nodes differ in transmission power and processing capabilities, leading to new challenges in interference management, mobile association, and radio resource management (RRM). In this paper, we consider RRM for heterogeneous networks with relays (RN) where the RNs have full RRM capabilities and can be viewed as micro BSs. A radio resource allocation framework is proposed with the objective to ensure proportional fairness among the UEs. An asymptotically optimal solution is derived by applying the gradient-based scheduling scheme and the Karush-Kuhn-Tucker (KKT) conditions for optimality. To implement RRM in networks with RNs, the resource consumption in the backhaul links, which depends on the demand of the UEs associated with the RN, should be counted at both the BS and the RN. The derived resource allocation scheme gives insight on the optimal radio resource allocation for heterogeneous networks with RNs.

In order to improve the total system throughput by analyzing the resource allocation problems in backhaul links and access links of base stations in ultra-dense network (UDN), a proportional fair resource allocation (PFRA) algorithm was designed for the wireless hybrid hierarchical backhaul network in this paper. First, the base stations were stratified and the frame structure of each base station in different layeies was re-designed. Then, the objective function was constructed based on the proportional fair utility function to optimize the system throughput. Finally, an iterative algorithm was derived based on Lagrange Multiplier algorithm to obtain the optimal solution of spectrum resource allocation in hybrid hierarchical backhaul network. The performance of the PFRA algorithm has been simulated and the results show that the PFRA algorithm can improve network throughput effectively at the cost of losing a certain network coverage rate. In the future, PFRA needs to be optimized to reach the best balance between network throughput and network coverage rate.