Proportional Fair Resource Allocation Research Articles

A bipartite graph based proportional fair scheduling strategy to improve throughput with multiple resource blocks

<p>The fifth-generation wireless communication is expected to provide a huge amount of capacity to cater to the need of an increasing number of mobile consumers, which can be satisfied by device-to-device (D2D) communication. Reusing the cellular user’s resources in an efficient manner helps to increase the spectrum efficiency of the network but it leads to severe interference. The important point in reusing cellular user resources is that D2D communication should not affect the cellular user’s efficiency. After achieving this requirement, the focus is now turned toward the allocation of resources to D2D communication. This resource allocation strategy is to be designed in such a way that it will not affect communication among the cellular user (CU). This scheme improves various performance objectives. This paper aims at designing a proportional fair resource allocation algorithm based on the bipartite graph which maintains the quality of service (QoS) of CUs while providing D2D communication. This algorithm can be merged with any other scheme of resource allocation for improving QoS and adopting changing channels. In this scheme, a D2D pair can be allocated with one or more than one resource blocks. The MATLAB simulations analyze the performance of the proposed scheme.</p>

Optimized Resource Allocation for IoT-D2D Communication Using WSN

The need for a strong system to access radio resources demands a change in operating frequency in wireless networks as a part of Radio Resource Management (RRM). In the fifth-generation (5G) wireless networks, the capacity of the system is expected to be enhanced by Device-to-Device (D2D) communication. The cooperation and Resources Allocation (RA) in the development of Internet of Things (IoT) enabled 5G wireless networks are investigated in this paper. Developing radio RA methods for D2D communication while not affecting any Mobile Users’ (MU) communication is the main challenge of this research. Distinct performance goals such as practising equality in the rates of user data, increasing Network Throughput (NT), and reducing End-to-End Delay (EED) are achieved by RA. The study undertaken on optimising performance for various wireless networks is focused on in this research work. Proposing a polynomial-time Proportional Fair Resource Allocation Method (PFRAM), which considers the MU’s rate requirements, is the prime objective of this paper. Any Resource Allocation Method (RAM) can be used by the proposed method for MU, and the time and differing location channel conditions are the factors to be adapted with. Allotting more than one resource block is allowed by our PFRAM to a D2D pair. The automatic maintenance of battery-less IoT wireless devices’ energy level is done potentially using an Extensible Energy Management System (EEMS). Finally, the device’s Node Transmission Power (NTP) can be managed using an Energy-Saving Algorithm (ESA) designed in this work for Node Uplink Data Transmission (NUDT). The trade-off between the Packet Loss Rate (PLR) and NTP is balanced by the algorithm. The cost of NUDT’s average Energy Consumption (EC) is reduced by locating the optical NTP. In order to free much bandwidth for wireless information, NUDT conserves the harvested energy for minimising Radio Frequency (RF) Energy Transmission (ET). MATLAB simulations are used to assess the proposed EEMS. The IoT device’s NTP is managed using ESA designed for NUDT. The significant minimisation of channel hopping EED and the selection of the premium quality communication channel by the proposed framework are indications of the simulation results. 67.19% is the bandwidth to transmit DPs with the Bandwidth Allocation Algorithm (BAA), which is greater than the cases in its absence.

Proportional Fair Trajectory Design and Resource Allocation for UAV-Assisted SWIPT System

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.

Proportional-Fair Resource Allocation for User-Centric Networks

Considering the computational complexity and fronthaul capacity requirements of network-wide centralized optimization for Cell-free (CF) massive multiple input multiple output (MIMO) networks, we focus on user-centric (UC) networks wherein each user is served by a subset of access points (APs) rather than all the APs. We study a proportional-fair resource allocation (RA) problem, including slot resource allocation and precoding design in UC networks over consecutive time-slots. We formulate the proportional-fair (PF) resource allocation problem as a series of weighted sum-rate maximization problems and develop a two-stage heuristic RA scheme to solve the problem. The scheme adopts a user grouping process to decompose resource allocation problem into intra-group orthogonal resource allocation sub-problems. A modularity-based user grouping algorithm upon a constructed weighted digraph is proposed. Then, relying on the grouping results, a parallel distributed PFRA algorithm consisting of user selection process and a Signal to Leakage plus Noise Ratio (SLNR)-based precoding design is proposed to fulfil intra-group resource allocation. Rather than global channel state information (CSI) collected from the whole network, local CSI obtained cooperatively within each AP subset is utilized in the proposed resource allocation scheme. Numerical results confirm that the proposed RA scheme outperforms in throughput while maintaining comparable fairness among users.

TARTS: A Temperature-Aware Real-Time Deadline-Partitioned Fair Scheduler

Modern multicore systems, which perform complex functionalities on densely packed multi-million gate platforms at high frequencies, are often susceptible to inappropriate surges in core temperatures, if not effectively controlled. Increasing temperature above specified limits not only leads to high cost of cooling but also results in higher dissipation of leakage power together with a reduction in performance and lower system life expectancy. In this work, we propose a two-level low-overhead proportional fair resource allocation strategy called Temperature-Aware Real-Time Deadline-Partitioned Fair Scheduler (TARTS), for periodic real-time tasks to be executed on a thermally bounded multicore device. The proposed algorithm’s first level divides time into distinct slices based on deadlines of tasks, so that exact proportional fairness is maintained at all slice boundaries. The second level accomplishes intra-slice scheduling with the aim of maximising the use of resources while not breaching a specified thermal threshold. Experimental results show that our scheme can deliver a significantly higher resource usage efficiency compared to a baseline greedy approach, TA-MTS. For example, TARTS is able to achieve up to 42% higher task acceptance ratios compared to TA-MTS on a fully loaded quad-core system with 80 tasks and temperature threshold of 90 °C.

An Intelligence-Driven Security-Aware Defense Mechanism for Advanced Persistent Threats

Combined with many different attack forms, advanced persistent threats (APTs) are becoming a major threat to cyber security. Existing security protection works typically either focus on one-shot case, or separate detection from response decisions. Such practices lead to tractable analysis, but miss key inherent APTs persistence and risk heterogeneity. To this end, we propose a Lyapunov-based security-aware defense mechanism backed by threat intelligence, where robust defense strategy-making is based on acquired heterogeneity knowledge. By exploring temporal evolution of risk level, we introduce priority-aware virtual queues, which together with attack queues, enable security-aware response among hosts. Specifically, a long-term time average profit maximization problem is formulated. We first develop risk admission control policy to accommodate hosts’ risk tolerance and response capacity. Under multiple attacker resources, defense control policy is implemented on two-stage decisions, involving proportional fair resource allocation and host-attack assignment. In particular, distributed auction-based assignment algorithm is designed to capture uncertainty in the number of resolved attacks, where high-risk host-attack pairs are prioritized over others. We theoretically prove our mechanism can guarantee bounded queue backlogs, profit optimality, no underflow condition, and robustness to detection errors. Simulations on real-world data set corroborate theoretical analysis and reveal the importance of security awareness.

Security-Aware Proportional Fairness Resource Allocation for Cognitive Heterogeneous Networks

Cognitive radio and heterogeneous wireless networks are important candidate techniques for the fifth generation (5G) communication systems. On the other hand, propagation properties of radio channels in a physical layer are exploited to design efficient secure transmission schemes for 5G wireless networks. In this work, a security-aware joint power and subchannel allocation problem based on the inter-network cooperation is investigated at cognitive heterogeneous networks via imperfect spectral sensing. The security-aware joint power and subchannel allocation is subject to constraints in available subchannels, proportional fairness secrecy transmission rates among secondary mobile terminals (MTs), total interference power threshold, and total power for each secondary MT. In order to determine the impact of imperfect spectral sensing to resource allocation, we first represent the interference power at primary base station in terms of the misdetection probability and false alarm probability. Then, the security-aware power and subchannel allocation problem with inter-network cooperation is formulated as a biconvex optimization problem, and an optimal security-aware power and subchannel allocation algorithm is proposed utilizing the dual decomposition method. Compared to the heuristic power and subchannel allocation algorithms, numerical simulation results show that the proposed algorithms improve the total secrecy throughput and guarantee the proportional fairness among different secondary MTs.

Joint Spectral Efficiency and Energy Efficiency in FFR-Based Wireless Heterogeneous Networks

Heterogeneous cellular network is considered as an important network architecture to improve spectral efficiency and energy efficiency. Extensive research has revealed that spectral efficiency and energy efficiency could be two conflicting performance metrics under most scenarios. Toward the next generation 5G wireless systems, both spectral efficiency and energy efficiency become increasingly important. How to design a wireless system that jointly considers/optimizes these two performance metrics is critical yet challenging. This paper jointly analyzes the area spectral efficiency and area energy efficiency in a wireless heterogeneous network with inter-tier fractional frequency reuse. Proportional fairness resource allocation is used to balance the spectral efficiency and user fairness within each cell. A theoretical framework is formulated to analyze the area spectral efficiency and area energy efficiency in such a system. Fractional frequency reuse and proportional fairness resource allocation define the optimal power reduction factor and fractional bandwidth partition to maximize the joint area spectral efficiency and area energy efficiency. The system performance is evaluated by using the proposed scheme with different system parameter settings including base station densities, cell fractional bandwidth partition, power threshold, and base station power consumptions. The study shows that fractional frequency reuse with proper settings can significantly increase the area spectral efficiency and area energy efficiency in wireless heterogeneous networks.

Proportional Fair Resource Allocation in Energy Harvesting-Based Wireless Networks

In this paper, we try to solve the imbalance problem of communication data service and energy supply among receivers in energy harvesting based wireless networks, with receivers equipped with modules that could either decode information or harvest energy from radio frequency signals. We formulate a biconvex optimization problem and find the solutions for subchannel and power allocation. Based on the obtained solutions, we propose a proportional fair resource allocation algorithm using an iterative method, and also show the convergence of the proposed algorithm to the partial optimum solution. Simulation results show that the proposed algorithm improves, to a remarkable extent, fairness among the receivers in terms of both the data rate and the harvested energy. At the same time, the proposed algorithm is found to lead to a negligible loss of system throughput compared with other conventional algorithms.

Energy efficient dynamic resource allocation for wireless systems using receiver puncturing technique

Given the massive increase in network infrastructure required to provide the high capacities expected in current wireless systems due to the exponential growth in mobile data consumption, substantial improvement in energy efficiency of the current 4G systems is necessary. High energy efficiency results in low operational cost and sustainable network deployment. In this paper, we propose a new receiver puncturing technique and a novel energy efficient dynamic resource allocation scheme. Improvements in BER performance and 20%~25% transmit-energy expenditure reduction, as compared to conventional proportional fair resource allocation schemes, are shown by both link level and system level simulations of a multi-cell downlink orthogonal frequency-division multiple access (OFDMA) system. Finally, the complexity issues inherent to the proposed technique are also evaluated.

Fair dynamic resource allocation in transit-based evacuation planning

Resource allocation in transit-based emergency evacuation is studied in this paper. The goal is to find a method for allocation of resources to communities in an evacuation process which is (1) fair, (2) reasonably efficient, and (3) able to dynamically adapt to the changes to the emergency situation. Four variations of the resource allocation problem, namely maximum rate, minimum clearance time, maximum social welfare, and proportional fair resource allocation, are modeled and compared. It is shown that the optimal answer to each problem can be found efficiently. Additionally, a distributed and dynamic algorithm based on the Lagrangian dual approach, called PFD2A, is developed to find the proportional fair allocation of resources and update the evacuation process in real time whenever new information becomes available. Numerical results for a sample scenario are presented.

Wireless Powered Communication Networks With Non-Ideal Circuit Power Consumption

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 (BS), 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 (TDMA) 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.

Application-Aware Resource Allocation of Hybrid Traffic in Cellular Networks

The hybrid traffic, a composite of elastic and inelastic data generated by delay-tolerant and real-time applications respectively, forms the bulk of what is carried over modern cellular systems. Since a smooth performance of real-time and delay-tolerant applications is tied to their distinctive needs for resources, networks’ intelligence about representative characteristics of applications’ traffic, networks’ ability to prioritize subscribers, and networks’ sophistication to cope with temporal variations in traffic amounts from the real-time and delay-tolerant applications can help efficiently assign resources accordingly to the preceding issues and consequentially elevate the quality-of-experience. To this end, the manuscript at hand aims at concocting a convex proportional fairness resource allocation method for cognitive cellular systems equipped with the intelligence to consider the traffic type, its temporal variations, and user prioritization. The devised method is formalized as mathematically equivalent centralized and distributed formulations with provided solution algorithms and convergence conditions. It is proved that the centralized approach has a lower transmission overhead, whose some lower bounds are derived for the centralized and distributed methods. Analyses of the sensitivity to changes of user equipments quantity and of application usage are presented under a variation of bidding scenarios in the face of the system dynamics.

Fair Dynamic Resource Allocation in Transit-based Evacuation Planning

Abstract Resource allocation in transit-based emergency evacuation is studied in this paper. The goal is to find a method for allocation of resources to communities in an evacuation process which is (1) fair, (2) reasonably efficient, and (3) able to dynamically adapt to the changes to the emergency situation. Four variations of the resource allocation problem, namely maximum rate, minimum clearance time, maximum social welfare, and proportional fair resource allocation, are modeled and compared. It is shown that the optimal answer to each problem can be found efficiently. Additionally, a distributed and dynamic algorithm based on the Lagrangian dual approach, called PFD2A, is developed to find the proportional fair allocation of resources and update the evacuation process in real time whenever new information becomes available. Numerical results for a sample scenario are presented.

Joint relay selection and resource allocation in cooperative device-to-device communications

Device-to-device (D2D) communications allow proximate cellular user equipments (UEs) to communicate with each other directly under the control of base station (BS). In this paper, considering the selection relaying (SR) rule which allows a subset of potential relays to forward the source’s data to the corresponding destination, we first establish a multi-relay system model where a D2D UE can act as not only a source but also a potential relay for another D2D link, and then analyze the cooperation behaviors among selfish UEs. Next a simple strategy is proposed to deal with the relay selection problem, and then the fair and efficient resource sharing problem among cooperating D2D UEs is formulated as a bargaining game. Since the game function is non-convex, we turn to an iterative method by introducing an auxiliary function to get the proportional fair resource allocation results. The system efficiency and fairness are proved by numeral simulation results.

Proportional-Fair Resource Allocation for Coordinated Multi-Point Transmission in LTE-Advanced

Coordinated multi-point (CoMP) transmission and reception are introduced as a promising technology in the 3GPP LTE-advanced standard, to manage interference, improve the overall system performance, and enhance system reliability. In this paper, a resource allocation problem is studied for downlink CoMP coordinated beamforming systems, where each base station (BS) serves its own mobile stations. Multiple-input-multiple-output (MIMO) transmit precoding and resource allocation are linked to the underlying proportional-fair scheduling to ensure a good trade-off between cell-average and cell-edge user spectral-efficiency. Due to the coupled interference among mobile stations, the resulting proportional-fair resource allocation optimization problem becomes nonconvex. To solve for optimal operating point for MIMO CoMP network, a parallel successive convex approximation-based algorithm is introduced. The introduced scheme enables all BSs to update their optimization variables in parallel by solving a sequence of strongly convex subproblems. Closed-form expressions of the locally optimal solution in both the high and low signal-to-noise regimes are characterized. The performance of the introduced scheme is also investigated through simulations. Numerical results show the efficiency of the introduced algorithm.

WIRELESS INFORMATION AND ENERGY TRANSFER: TRADEOFF FOR FAIR RESOURCE ALLOCATION

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).

Genetic Algorithm Aided Proportional Fair Resource Allocation in Multicast OFDM Systems

The next-generation wireless communication networks are envisioned to offer many multimedia services such as audio/video clips, mobile TV, web browsing, video conference, etc., with diverse quality of service (QoS) requirements. Multicasting/broadcasting has been recognized as an emerging and enabling technique for such multimedia transmissions over wireless networks. By employing multicast transmission, a base station can transmit the same data content to several groups of users requiring flexible QoS. In this paper, we investigate subchannel and power allocation problems in an OFDM-based wireless multicast system. With the goal of maximizing the total system capacity, a low complexity, novel genetic algorithm aided efficient subchannel allocation scheme taking into account the constraints of total available bandwidth, proportional data rate fairness and total transmit power budget at the base station is proposed. The subchannel allocation is then followed by optimal power allocation. Unlike previous work where either no fairness or fairness based on number of subchannels allocated to the different groups was considered, we incorporate a set of system parameters in the problem formulation such that the ratio of the group data rates strictly follow the set of system parameters after resource allocation. Simulation results show that the proposed method obtains higher sum capacities while maintaining proportional data rate fairness among different multicast groups, without being awfully complex.

User Association in Massive MIMO HetNets

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.

A QoE-Aware Proportional Fair Resource Allocation for Multi-Cell OFDMA Networks

In this letter, we focus on a quality of experience (QoE) aware resource allocation that considers users' time varying channel status and heterogeneous service requirements in multi-cell OFDMA networks. We propose a QoE-based proportional fair (PF) scheduling considering network-wide users' QoE maximization as well as fairness among users. Our utility function is based on the mean opinion score (MOS) model reflecting the application-specific characteristics. We introduce a Bezier curve based continuously differentiable MOS model instead of using the conventional bounded logarithmic MOS model. Then, we propose a utility function, which is proven to be concave, for QoE provision to achieve the global optimality with opportunistic gradient scheduling. Our proposed technique is shown to be easily incorporated with inter-cell interference coordination (ICIC). Simulation results show that the proposed QoE-aware PF scheme significantly improves the network-wide users' average QoE as well as the edge users' performance, e.g., the 5th percentile QoE.