Proportional Fairness Research Articles

On the convergence and optimality of the firefly algorithm for opportunistic spectrum access

Meta-heuristic algorithms have been proven to be efficient for engineering optimisation. However, the convergence and accuracy of such algorithms depends on the objective function and also on several choices made during algorithm design. In this paper, we focus on the firefly algorithm for optimal channel allocation in cognitive radio networks. We study the effect of various probability distributions including the Levy alpha stable distribution for randomisation of firefly movement. We also explore various functions for converting firefly positions from the continuous space to the discrete space, as is necessary in the spectrum allocation problem. Simulation results show that in most cases, Levy flight gives better convergence time and results for common optimisation problems such as maximising the overall channel utilisation, maximising the channel allocation for the bottleneck user and maximising proportional fairness. We also note that no single discretisation function gives both good convergence and optimality.

On the convergence and optimality of the firefly algorithm for opportunistic spectrum access

Meta-heuristic algorithms have been proven to be efficient for engineering optimisation. However, the convergence and accuracy of such algorithms depends on the objective function and also on several choices made during algorithm design. In this paper, we focus on the firefly algorithm for optimal channel allocation in cognitive radio networks. We study the effect of various probability distributions including the Lévy alpha stable distribution for randomisation of firefly movement. We also explore various functions for converting firefly positions from the continuous space to the discrete space, as is necessary in the spectrum allocation problem. Simulation results show that in most cases, Lévy flight gives better convergence time and results for common optimisation problems such as maximising the overall channel utilisation, maximising the channel allocation for the bottleneck user and maximising proportional fairness. We also note that no single discretisation function gives both good convergence and optimality.

Parameter aware utility proportional fairness scheduling technique in a communication network

Conventionally, real-time (inelastic) users and non-real-time (elastic) users are scheduled separately. Further, some users may be left without getting any radio resources. In real scenario, such as in high network traffic, less network coverage, resource allocation for all types of users are needed. In the proposed technique, a utility function is defined for each elastic traffic and inelastic traffic type users. Then, it maximises the total utilities by network utility maximisation approach with considering scheduling parameters such as queue length, head of the line delay and experienced channel condition. Rate allocation based on queue awareness improves fairness among elastic traffic users, delay awareness improves QoS performance of inelastic traffic users and channel condition awareness improves throughput of all users. Further, the network utility maximisation approach provides minimum throughput to all users.

Parameter aware utility proportional fairness scheduling technique in a communication network

Conventionally, real-time (inelastic) users and non-real-time (elastic) users are scheduled separately. Further, some users may be left without getting any radio resources. In real scenario, such a...

Semiorthogonal User Scheduling for Millimeter Wave Using Low-Resolution ADCs

Low-resolution Analogue-to-Digital Converters (ADCs) is a promising solution to reduce power consumption for millimeter wave (mmWave) systems in which the number of antennas at base stations is very large. In this paper, we develop downlink user scheduling for mmWave systems using low-resolution ADCs. Although the sum-rate of Maximum Rate algorithm is very impressive in mmWave systems, it is difficult to use in practice, especially for a large number of users due to exhaustive search. Therefore, we proposed a novel scheduling, termed as NOUS, which selects users who are semi-orthogonal to each other in the beamspace. A closed-form expression for the scheduling criteria in the downlink channel over a mmWave system with low-resolution ADCs and Zero-forcing precoder is derived. The numerical results show that the NOUS algorithm almost achieves the performance of Maximum Rate with lower complexity and outperforms the performance of Proportional Fair in terms of the sum rate.

Performance, Fairness, and Tradeoff in UAV Swarm Underlaid mmWave Cellular Networks With Directional Antennas

Unmanned aerial vehicle (UAV) swarm connected to millimeter wave (mmWave) cellular networks is emerging as a new promising solution to provide ubiquitous high-speed and long distance wireless communication services for supporting various applications. To satisfy different quality of service (QoS) requirements in future large-scale applications of such networks, this article investigates the rate performance, fairness and their tradeoff in the networks with directional antennas in terms of sum-rate maximization, fairness index maximization, max-min fair rate and proportional fairness. We first consider a more realistic mmWave 3D directional antenna array model for UAVs and base station (BS), where the antenna gain depends on the radiation angle of the antenna array. Based on this antenna array model, we formulate the performance, fairness and their tradeoff as four constrained optimization problems, and propose corresponding iterative algorithm to solve these problems by jointly optimizing elevation angle, azimuth angle and height of antenna array at BS in the downlink transmission scenario. Furthermore, we also explore them in uplink transmission scenario, where the interference issue among links is carefully considered. Finally, according to the sum rate, minimum rate and fairness index under each optimization problem, numerical results are provided to illustrate the impacts of network parameters on the performance, fairness and their tradeoff, and also to reveal new findings under both downlink and uplink transmission scenarios, respectively.

RESET: A real-time scheduler for energy and temperature aware heterogeneous multi-core systems

Nowadays, multi-core processing systems have to perform complex functionalities on densely packed multi-million gate platforms, making design issues like resource usage efficiency, energy consumption, temperature management of cores, etc. more challenging to handle. Since many of these processing platforms use batteries as their primary source of energy and are prone to an uncontrolled surge in temperatures, researchers have started focusing on these criteria in great detail. In this work, we propose a three-phase hierarchical resource allocation strategy called RESET for scheduling periodic tasks with a bounded number of migrations and context-switches. The first phase divides time into distinct intervals/windows based on deadlines of tasks. The exact proportional fairness needed for the progress of task executions is maintained at all window boundaries. The second phase performs energy-aware task-to-core assignments based on task execution requirements and operating frequency levels of cores. In the last phase, it splits tasks based on an input parameter δ and prepares a temperature-aware schedule for individual cores. Our experimental analysis shows that the presented strategy improves upon the state-of-the-art in terms of resource utilisation and reduces dynamic energy consumption and the average temperature of cores in the system.

Joint Client Association and Random Access Control for MU-MIMO WLANs

In interfering WLANs, clients are usually non-uniformly distributed. Therefore, their throughput can be improved by association control. WLANs adopt DCF (Distributed Coordinating Function) for media access control. The transmit probability of an AP is governed by its CW (Contention Window). Random Access control is to determine the CW size and hence the transmit probability of each AP. Client association control and random access control are coupled. APs associated with more clients require higher channel access opportunities. On the other hand, APs with higher transmit probability should serve more clients. We study the novel problem of maximizing the proportional fairness of client throughput by joint association control and random access control. As the joint problem is non-convex, we first consider that client association is given and propose an optimal policy for random access control. Given the CWs of APs, we propose a factor-2 approximation algorithm, Greedy-Asso to address the client association problem. Inspired by the optimal access control policy and Greedy-Asso, we further propose a distributed algorithm, termed CARA to tackle the joint optimization problem. Extensive simulation and experimental studies show that it outperforms comparison schemes by a wide margin in terms of throughput and fairness.

How Much Can Radio Resource Allocation Help to Improve Secrecy Capacity of V2V Underlay Cellular Networks?

Vehicle-to-vehicle (V2V) underlay cellular communication plays an important role in the fifth-generation (5G) and beyond communications and its security issues attract a lot of attention. Physical layer security, which explores channel characteristics to protect wireless communications, is very attractive due to its low latency. This paper focuses on secrecy capacity maximization in V2V underlay cellular communications, where the secrecy capacity of vehicular user equipments (VUEs) is optimized first, followed by sum proportional fairness function optimization for both VUEs and cellular user equipments (CUEs). The optimization problems can be solved in two sub-problems, e.g., power and subcarrier allocation problems. The power allocation can be done with the first strategy using a one-dimensional searching algorithm, whereas a genetic algorithm (GA) is used to solve the second optimization problem. In addition, as VUEs share uplink resources with CUEs, the co-channel interference exists and the subcarrier allocation sub-problem is a 3D search problem, which is solved by a two-step iterative Hungarian algorithm (IHA). Finally, simulations are performed to validate the performance of the proposed schemes.

Fair transit trip planning in emergency evacuations: A combinatorial approach

This paper introduces the concept of proportional fair trip planning in the context of short-notice transit-based emergency evacuation. Proportional fairness attempts to meet social fairness among evacuees without sacrificing the efficiency of the evacuation process. The proportional fair trip planning concept is compared to the commonly used maximum safety concept that attempts to maximize the summation of safety functions of evacuees. We use a combinatorial approach to model the transit mass emergency evacuation in moving people from dangerous areas to safe shelters. High-density population and medium-density population variations of the problem are studied. For each variation of the problem, we study the computational complexity of the problem. We develop polynomial or pseudo-polynomial algorithms for each problem. Our numerical analysis shows that pure consideration of efficiency may result in highly unfair plans that only consider the portion of the population with the most payoffs (e.g., the population with the highest danger level) while ignoring the rest (potentially the vast majority of the population). While still considering efficiency, proportional fairness is shown to address this issue by also allocating resources to the population that has non-optimal payoffs.

Design Optimization of Resource Allocation in OFDMA-Based Cognitive Radio-Enabled Internet of Vehicles (IoVs).

Joint optimal subcarrier and transmit power allocation with QoS guarantee for enhanced packet transmission over Cognitive Radio (CR)-Internet of Vehicles (IoVs) is a challenge. This open issue is considered in this paper. A novel SNBS-based wireless radio resource scheduling scheme in OFDMA CR-IoV network systems is proposed. This novel scheduler is termed the SNBS OFDMA-based overlay CR-Assisted Vehicular NETwork (SNO-CRAVNET) scheduling scheme. It is proposed for efficient joint transmit power and subcarrier allocation for dynamic spectral resource access in cellular OFDMA-based overlay CRAVNs in clusters. The objectives of the optimization model applied in this study include (1) maximization of the overall system throughput of the CR-IoV system, (2) avoiding harmful interference of transmissions of the shared channels’ licensed owners (or primary users (PUs)), (3) guaranteeing the proportional fairness and minimum data-rate requirement of each CR vehicular secondary user (CRV-SU), and (4) ensuring efficient transmit power allocation amongst CRV-SUs. Furthermore, a novel approach which uses Lambert-W function characteristics is introduced. Closed-form analytical solutions were obtained by applying time-sharing variable transformation. Finally, a low-complexity algorithm was developed. This algorithm overcame the iterative processes associated with searching for the optimal solution numerically through iterative programming methods. Theoretical analysis and simulation results demonstrated that, under similar conditions, the proposed solutions outperformed the reference scheduler schemes. In comparison to other scheduling schemes that are fairness-considerate, the SNO-CRAVNET scheme achieved a significantly higher overall average throughput gain. Similarly, the proposed time-sharing SNO-CRAVNET allocation based on the reformulated convex optimization problem is shown to be capable of achieving up to 99.987% for the average of the total theoretical capacity.

A simple proportional fair scheduling for downlink power domain non-orthogonal multiple access systems

Currently, non-orthogonal multiple access technologies have gained high relevance in wireless multi-user broadband communications. Many papers have shown that non-orthogonal multiplexing can significantly increase the capacity of the system. Despite this, it technology is still far from practical implementation due to unresolved issues. One such issue is radio resource scheduling in communication systems using non-orthogonal multiple access which poses a high computational complexity and turns out to be a problem for a base station with a large number of users it serves. In our paper, we propose our own resource scheduling method for systems with non-orthogonal multiplexing using proportional fair strategy. This method allows for simultaneous resource scheduling in two segments – an orthogonal segment (time-frequency domain) and a non-orthogonal segment (power domain). To assess the effectiveness of the proposed method, we use the developed simulation model of resource scheduling for a communication system with a single cell. The effectiveness of the proposed scheduling method is confirmed by the simulation result, and its use allows achieving the proportional fair strategy implementation. In the final part, we use iterative simulation to compare the throughput of systems with orthogonal and non-orthogonal multiplexing using proposed proportional fair scheduler. The result of comparison showed that using the proposed proportional fair scheduling can increase the system capacity by an average of 28%. The results can be applied to create communication systems with different non-orthogonal multiple access implementations.

Hybrid OFDMA Resource Allocation Scheme for Ensuring Required Level of Proportional Fairness

The new-generation wireless communication networks are envisioned to offer higher sum data rates along with the required level of fairness. Previous works tend to suffer from a decayed performance as subcarriers become relatively insufficient in allocation to users. To maximize the sum data rates and ensure the required level of proportional fairness, this paper presents a hybrid OFDMA resource allocation scheme which uses Hungarian algorithm combined with a greedy method for subcarrier allocation and uses bee colony optimization for power allocation. The proposed subcarrier allocation scheme can make full use of advantages of both globally optimal Hungarian algorithm in enhancing sum data rates and locally optimal greedy method in maintaining a reasonable fairness level and can make Hungarian algorithm work in a searching mode for further improvement of sum data rates and fairness. The proposed power allocation scheme can converge to the required level of proportional fairness but with higher sum data rates if the subcarrier allocation does not achieve the required fairness. Simulation results show that the proposed scheme can obtain the required level of proportional fairness but with higher sum data rates even if subcarriers are relatively insufficient in allocation to users. Complexity analysis shows the proposed method has moderate complexity.

Incentivizing fairness-aware task allocation in mobile crowdsensing with sweep coverage and stability control

Mobile crowdsensing (MCS) harnesses the sensing capabilities of sensors built into a large number of smart devices to collect and analyze sensory data, which can be used by a large number of mobile participants to perform numerous sensing tasks. Sweep coverage and stability control are two key issues that need to be solved in task allocation in MCS, because improper matching of tasks and participants, as well as task overload or idleness will slump social welfare. However, most existing work made an optimistic assumption that all participants unconditionally participate in MCS, without considering the selfishness and rationality of participants in practical scenarios. To tackle these issues, this paper proposes a fairness-aware task allocation policy with sweep coverage and stability control, which consists of an online rating protocol and a stability control scheme. For the first module, we integrate the quality of sensing, rating update, collusion identification, and payment determination to develop an online rating protocol to deal with the “free-riding” and collusion of participants simultaneously. For the second module, despite the unpredictable future information of sensing tasks and participants, we design a stability control scheme that only relies on currently information to make the online control independent strategies for maximizing social welfare and balancing network stability in a proportional fairness, which can maintain system stability and achieve a time average social welfare within O(1∕V) that is arbitrarily close to the optimum for any tunable parameter V>0. Finally, through rigorous theoretical analysis and experimental comparison with two benchmarks, the correctness and efficiency of our proposed policy is jointly demonstrated.

Is Massive MIMO Robust Against Distributed Jammers?

In this paper, we evaluate the uplink spectral efficiency (SE) of a single-cell massive multiple-input-multiple-output (MIMO) system with distributed jammers. We define four different attack scenarios and compare their impact on the massive MIMO system as well as on a conventional single-input-multiple-output (SIMO) system. More specifically, the jammers attack the base station (BS) during both the uplink training phase and data phase. The BS uses either least squares (LS) or linear minimum mean square error (LMMSE) estimators for channel estimation and utilizes either maximum-ratio-combining (MRC) or zero-forcing (ZF) decoding vectors. We show that ZF gives higher SE than MRC but, interestingly, the performance is unaffected by the choice of the estimators. The simulation results show that the performance loss percentage of massive MIMO is less than that of the SIMO system. Moreover, we consider two types of power control algorithms: jamming-aware and jamming-ignorant. In both cases, we consider the max-min and proportional fairness criteria to increase the uplink SE of massive MIMO systems. We notice numerically that max-min fairness is not a good option because if one user is strongly affected by the jamming, it will degrade the other users’ SE as well. On the other hand, proportional fairness improves the sum SE of the system compared with the full power transmission scenario.

Pricing Based Distributed Traffic Allocation for 5G Heterogeneous Networks

Next generation (5G and beyond) mobile networks will highly rely on distributed heterogeneous infrastructure, which is foreseen to create a versatile resource ecosystem. In such network deployments, efficient traffic allocation and management is of prominent priority. The increasing congestion conditions of cellular infrastructures have steered extensive research focus towards Heterogeneous Network (HetNet) access solutions. The deployment of dense multiple radio access technology (RAT) networks combined with the enhanced capabilities of mobile devices to maintain concurrent connections through different RATs, add up to the complexity of the traffic allocation problem. Traffic allocation becomes more challenging when using diverse technologies concurrently to serve multiple traffic classes of different Quality of Service (QoS) demands. Towards a viable solution, we have proposed a pricing-based network selection process for heterogeneous networks, running in a distributed manner, which has proved to lead to equilibrium under the developed policies. In this paper, we extend our pricing algorithm by providing a new approach that introduces personalized pricing based on the load pressure that an allocation decision will impose to a RAT. The central system decisions are confined to dynamically select the UE polling rank policy that is presenting the closest potential performance to that of the optimal centralized solution, used as the benchmark ranking. The optimal policy is based on proportional fairness and is used as a reference centralized policy to drive our distributed solutions. We assess the enhanced pricing scheme proposed in this work, and we employ extensive simulations and testbed experimentation to evaluate its performance in terms of efficient bandwidth allocation, price variation, fairness and induced cost to clients served by the HetNet.

Community solar photovoltaic service strategy for commercial buildings considering profit balancing and fairness

A community solar photovoltaic (PV) system is an effective way to save energy bills in commercial buildings. In such systems, the main problems are how to serve the shared resources considering profit balancing and profit-sharing fairness. In this study, a community solar PV service strategy for commercial buildings is introduced, which does not only pursue profit maximization for the entire community, but also considers two additional issues, namely, the profit balancing between the PV service provider (SP) and participants and the profit-sharing fairness among the participants. A profit-balancing factor is defined as a representation of the nature of the SP, and the profit maximization model, considering fair profit-sharing, is applied. By applying the Lagrangian relaxation and dual ascent method, the optimal solution is achieved in an iterative manner. A case study shows that participants can save 50 and 40% of their total energy bill when the service is operated with the objective of profit maximization and proportional fairness, respectively. The proportional-fairness operation enhances the Jain’s fairness index up to 51% compared with the profit-maximization operation. The effects on the profit and fairness for various service methods are explored to provide guidance in designing community solar PV services.

RAPTAP: a socio-inspired approach to resource allocation and interference management in dense small cells

Femto Cells offer higher data rates to users within closed spaces. Dense deployment of small cells is a characteristic of pre-5G/LTE-Advanced Pro (LTE-A Pro) networks and is a precursor to the future 5G cellular networks. Combined with a Frequency Reuse Factor (FRF) of one, the dense small cell systems result in high co-tier interference which is undesirable. High interference Optimized scheduling decisions and interference management techniques are required to guarantee certain minimum data rates to the users at the cell-edge and increase the overall throughput of the system. This work presents a centralized scheduling approach that mitigates the detrimental impact of interference, thereby maximizing the overall throughput of the system. In doing so, we make use of concepts from Social theory and incorporate these ideas in the solution design. The centralized approach uses optimized scheduling algorithm (RAPTA) which takes feedback from the indoor users of all Femtos as the input and formulates a Mixed Integer Non-Linear Programming (MINLP) problem. Thereafter, the MINLP problem is relaxed and its solution carries out the Resource Block allocation and ensures optimal power transmission for every allocated resource block in all Femtos. We implement the proposed OPT algorithm on top of Proportional Fair (PF) in the Vienna Simulator. Since the RAPTA is NP-Hard and takes a considerably long time to solve the MINLP problem, we derive from it a polynomial time Heuristic algorithm (RAPTAP) which performs Resource Block allocation and sub-optimal Power transmission quite close to the RAPTA algorithm in terms of performance. RAPTAP is a two-stage approach each of which is inspired by ideas from two different strands of Sociological theory. We demonstrate through experiments that the proposed RAPTA + PF achieves 60.67% improvement in the services offered to the mobile users when compared to the classic PF algorithm with Soft Fractional Frequency Reuse (SFFR) interference management technique in the built environment. The Socio-inspired RAPTAP performs almost as well as the RAPTA + PF algorithm, with only a marginal 4% drop in the overall system throughput as compared to the latter. Further, we evaluate the RAPTAP heuristic in scenarios involving mobile users and demonstrate 14.52% improvement when compared to classic PF algorithm with Fractional Frequency Reuse-Full Isolation (FFR-FI) interference management. Finally, we compare the proposed Socio-inspired RAPTAP + PF with two state-of-the-art algorithms, viz., a Genetic Algorithm approach to resource allocation (NSGA-UDN) and a recent work on LTE-Unlicensed (LTE-U) + PF. Proposed Socio-inspired solution outperforms the LTE-U + PF and NSGA-UDN by 28.26% and 31.41%, respectively, in terms of throughput.

Victim Aware AP-PF CoMP Clustering for Resource Allocation in Ultra-Dense Heterogeneous Small-Cell Networks

Heterogeneous networks with dense deployment of femto cells has provided the promising solution to enhance the system throughputs for the next generation wireless communication. When the large number of heterogeneous networks are overlapped, then traditional intercell interface technique failed to mitigate the interference in between the cells. So, to mitigate the interference, it requires advanced approach for improving the cell edge throughputs and spectral efficiency. For this, the paper presents a frame work to allocate the efficient resource among the users in dense networks. We proposed affinity propagation unsupervised learning to form the cluster with center and then regularized the cluster for effectively allocated the resource. Users on the cluster edge has suffering the inter cluster interface, a victim aware and coordination multipoint mechanism is further proposed to allocated the required resources for these victimized users. We analyzed the performance of our proposed framework with proportional fair based criteria. The total throughputs, edge throughput and spectral efficiency of the system are significantly enhanced in our simulation results through this proposed framework.

Fairness Optimization of Fixed-Rate Wireless Networks With RF Energy Harvesting Transmitters

The letter proposes two simple and yet effective resource allocation schemes for wireless powered communication networks based on static time division multiple access (TDMA). The energy harvesting users (EHUs) do not have channel state information (CSI) available and transmit at fixed power and fixed rate to the base station (BS). In order to facilitate fairness, the network employs either the proportional fairness criterion or the max-min criterion for the EHUs’ rates by optimizing the key system parameters: the BS transmit power, the durations of energy harvesting and information transmission phases, and the EHU’s rates. Unlike the existing schemes that adapt the system parameters in each channel fading state, our proposed schemes are much simpler for practical implementation.