Proportional Fairness-Based Energy-Efficient Power Allocation in Downlink MIMO-NOMA Systems with Statistical CSI

In this paper, proportional fairness(PF)-based energy-efficient power allocation is studied for multiple-input multiple-output(MIMO) non-orthogonal multiple access(NOMA) systems. In our schemes, statistical channel state information(CSI) is utilized for perfect CSI is impossible to achieve in practice. PF is used to balance the transmission efficiency and user fairness. Energy efficiency(EE) is formulated under basic data rate requirements and maximum transmitting power constraints. Due to the non-convex nature of EE, a two-step algorithm is proposed to obtain sub-optimal solution with a low complexity. Firstly, power allocation is determined by golden section search for fixed power. Secondly total transmitting power is determined by fractional programming method in the feasible regions. Compared to the performance of MIMO-NOMA without PF constraint, fairness is obtained at expense of decreasing of EE.

Proportional Fairness Based Energy Efficient Routing in Wireless Sensor Network

Wireless Sensor Network (WSN) is an independent device that comprises a discrete collection of Sensor Nodes (SN) to sense environmental positions,device monitoring, and collection of information. Due to limited energy resourcesavailable at SN, the primary issue is to present an energy-efficient framework andconserve the energy while constructing a route path along with each sensor node.However, many energy-efficient techniques focused drastically on energy harvesting and reduced energy consumption but failed to support energy-efficient routingwith minimal energy consumption in WSN. This paper presents an energy-efficientrouting system called Energy-aware Proportional Fairness Multi-user Routing(EPFMR) framework in WSN. EPFMR is deployed in the WSN environment usingthe instance time. The request time sent for the route discovery is the foremost stepdesigned in the EPFMR framework to reduce the energy consumption rate. Theproportional fairness routing in WSN selects the best route path for the packet flowbased on the relationship between the periods of requests between different SN.Route path discovered for packet flow also measure energy on multi-user route pathusing the Greedy Instance Fair Method (GIFM). The GIFM in EPFMR developsnode dependent energy-efficient localized route path, improving the throughput.The energy-aware framework maximizes the throughput rate and performs experimental evaluation on factors such as energy consumption rate during routing,Throughput, RST, node density and average energy per packet in WSN. The RouteSearching Time (RST) is reduced using the Boltzmann Distribution (BD), and as aresult, the energy is minimized on multi-user WSN. Finally, GIFM applies aninstance time difference-based route searching on WSN to attain an optimal energyminimization system. Experimental analysis shows that the EPFMR framework canreduce the RST by 23.47% and improve the throughput by 6.79% compared withthe state-of-the-art works.

The proportional fairness scheduling algorithm on multi-classes

In this paper, we study resource management models and algorithms that satisfy multiple performance objects simultaneously. We realize the proportional fairness principle based QoS model, which defines both delay and loss rate requirements of a class, to include fairness, which is important for the integration of multiple service classes. The resulting Proportional Fairness Scheduling model formalizes the goals of the network performance, user’s QoS requirement and system fairness and exposes the fundamental tradeoffs between these goals. In particular, it is difficult to simultaneously provide these objects. We propose a novel scheduling algorithm called Proportional Fairness Scheduling (PFS) that approximates the model closely and efficiently. We have implemented the PFS scheduling in Linux. By performing simulation and measurement experiments, we evaluate the delay and loss rate proportional fairness of PFS, and determine the computation overhead.

Fairness Aware Rate Adaptation and Proportional Scheduling for IEEE 802.11 WLANs Using FSE

With its rapid development in the wireless markets, IEEE 802.11 WLAN is experiencing a huge popularity. However, due to the limitation of frequency bandwidth of WLANs, it is essential that the available radio resource should be fully utilized to offer different services to multiple users. In order to maximize system throughput while still guaranteeing the fairness among users, a proportional fairness based algorithm is proposed in this work. Since most of the previous resource allocation algorithms were simply based on the channel conditions without taking into account user's demand, in this paper, we introduce the theory of fuzzy synthetic evaluation(FSE) which also allows us to consider user's demand as an important factor. As such, the fairness among users can be improved based on different users' requirements for services. In addition, a channel state information based rate adaptation scheme is also proposed. Through simulation studies, the results clearly validate that our proposed scheme shows advantages on providing user fairness while still improving the system throughput.

Resource Allocation in UAV-NOMA Communication Systems Based on Proportional Fairness

This paper investigates the power allocation problem in non-orthogonal multiple access(NOMA)integrated unmanned aerial vehicle(UAV)communication systems.In particular,we propose a novel resource allocation scheme to increase the transmission rate of the users that have relatively worse channel state information,while reducing the sum rate loss.To solve this problem efficiently,we decouple the optimization problem into three subproblems.First,we solve the problem of user pairing and subchannel allocation.Second,the optimum power proportional factor is derived to allocate transmit power among different users on the same subchannel.At last,different subchannels are allocated with appropriate power to improve the performance of the subchannels.Simulation results show that the proposed scheme can enjoy a better performance than the benchmark methods since it can achieve a proper trade-off between the system sum rate and the proportional fairness.

QoE-Aware Proportional Fair Scheduling for Multiuser Multiservice Wireless Networks

The effective radio resource allocation al-gorithms, which satisfy diversiform requirements of mobile naltimedia services in wireless cellular net-works, have recently attracted more and more at-tention. This paper proposes a service-aware scheduling algorithm, in which the Mean Opinion Score （MOS） is chosen as the unified metric of the Quality of Experience （QoE）. As the network needs to provide satisfactory services to all the users, the fairness of QoE should be considered. The Propor- tional Fair （PF） principle is adopted to achieve the trade-off between the network perfonmnce and us- er fairness. Then, an integer progranming problem is formed and the QoE-aware PF scheduling princi-ple is derived by solving the relaxed problem. Simu-lation results show that the proposed scheduling principle can perform better in terms of user fair-ness than the previous principle maximizing the sum of MOS. It also outperfoms the max-rain scheduling principle in terms of average MOS.

DISTRIBUTED PROPORTIONAL FAIR FREQUENCY ALLOCATION ACROSS MULTIPLE BASE STATIONS

The problem of distributed proportional fair inter-cell frequency allocation for flat-structured cellular systems is studied in this paper. We firstly propose a framework of the frequency allocation in which the whole frequency allocation process is decomposed into many consecutive stages, then identify that for each stage the key is to find the Maximum Weight Independent Set (MWIS) in a given weighted conflict graph in the distributed manner. A new distributed algorithm for MWIS is described in which each node iteratively exchanges messages with neighbors. With this distributed MWIS algorithm, a new distributed proportional fair frequency allocation scheme is presented. The performance of the proposed algorithm is tested in computer experiments simulating the Long Term Evolution (LTE) cellular systems. Simulation results show the performance of the proposed distributed proportional fair frequency allocation scheme is comparable with the centralized ones.

Downlink CoMP Resource Allocation Based on Limited Backhaul Capacity

Coordinated Multi-Point(CoMP) transmission is put forward in the Long Term Evolution-Advanced(LTE-A) system to improve both average and cell-edge throughput. In this paper, downlink CoMP(DL-CoMP) resource allocation scheme based on limited backhaul capacity is designed to take a tradeoff between system throughput and fairness. Resource allocation of proportional fairness based on querying table is proposed. It updates RB allocation matrix when center cell has completed resource allocation and delivers the matrix to adjacent cells for their own RB allocation. Furthermore, Water-Filling algorithm based on adaptive water level(AWF) is used for power allocation to boost system fairness. In this paper, performance of downlink CoMP based on limited backhaul capacity and single-point transmission is contrasted, and results indicate that CoMP dramatically enhances system throughput and spectral efficiency. Moreover, AWF power allocation scheme obtains higher system fairness than conventional Water-Filling(WF) algorithm, although it gets slightly lower system throughput. Finally, this paper discussed that the system performance is partially affected by the percentage of CoMP resource.

Intelligent Fast Cell Association Scheme Based on Deep Q-Learning in Ultra-Dense Cellular Networks

To support dramatically increased traffic loads,communication networks become ultra-dense.Traditional cell association(CA)schemes are timeconsuming,forcing researchers to seek fast schemes.This paper proposes a deep Q-learning based scheme,whose main idea is to train a deep neural network(DNN)to calculate the Q values of all the state-action pairs and the cell holding the maximum Q value is associated.In the training stage,the intelligent agent continuously generates samples through the trial-anderror method to train the DNN until convergence.In the application stage,state vectors of all the users are inputted to the trained DNN to quickly obtain a satisfied CA result of a scenario with the same BS locations and user distribution.Simulations demonstrate that the proposed scheme provides satisfied CA results in a computational time several orders of magnitudes shorter than traditional schemes.Meanwhile,performance metrics,such as capacity and fairness,can be guaranteed.

The Impact of the Superposition Coding Concept on Admission Control Strategy in OFDMA-Based Network

Admission control is a key mechanism to manage the increasing number of the simultaneous demanding services, requiring a desired Quality of Service(QoS) in a spectrum efficient manner. To address this issue,we investigate in this work, the use of the superposition coding technique to increase the system capacity through multiuser diversity exploitation. We propose a novel joint admission control and superposition coding formalism based on different utility functions:Opportunistic(OPSC), Great Fairness(GFSC)and Proportional Fairness(PFSC). Simulation results show the superiority of our proposed approaches over other by providing higher mean of served VoIP users and higher throughput while maintaining an average VoIP packet transmission delay lower than 0.6 ms.

A novel mechanism based on Nash bargaining for primary system game in cognitive radios

According to the utility function and spectrum demand of the cognitive users,a novel mechanism based on Nash bargaining for primary system game was proposed under the wireless environment of Rayleigh fading.On the basis of this mechanism,we proposed a new distributed bargaining algorithm based on Nash product;then the spectrum prices and system utilities were obtained.Theoretical analysis results showed that with a close total utility to the optimal,the Nash bargaining mechanism cannot only improve the fairness between primary systems remarkably,but also reach to the stable equilibrium in finitely repeated games.Finally,simulation results were given to demonstrate the correctness of these conclusions and the efficiency of the algorithm.

Utility-optimization dynamic subcarrier allocation algorithm for SC-FDMA systems

Two utility-optimization dynamic subcarrier allocation(DSA) algorithms are designed for single carrier frequency division multiple access system(SC-FDMA).The two proposed algorithms aim to support diverse transmission capacity requirements in wireless networks,which consider both the channel state information(CSI) and the capacity requirements of each user by setting appropriate utility functions.Simulation results show that with considerable lower computational complexity,the first utility-optimization algorithm can meet the system capacity requirements of each user effectively.However,the rate-sum capacity performance is poor.Furthermore,the second proposed utility-optimization algorithm can contribute a better trade-off between system rate-sum capacity requirement and the capacity requirements of each user by introducing the signal to noise ratio(SNR) information to the utility function based on the first utility-optimization algorithm,which can improve the user requirements processing capability as well as achieve a better sum-rate capacity.

ENERGY-SAVING SCHEDULING FOR LTE MULTICAST SERVICES

In the Long Term Evolution(LTE)downlink multicast scheduling,Base Station(BS)usually allocates transmit power equally among all Resource Blocks(RBs),it may cause the waste of transmit power.To avoid it,this paper put forward a new algorithm for LTE multicast downlink scheduling called the Energy-saving based Inter-group Proportional Fair(EIPF).The basic idea of EIPF is to calculate an appropriate transmitting power for each group according to its data rate respectively,and then follow the inter-group proportional fair principle to allocate RBs among multicast groups.The results of EIPF simulation show that the proposed algorithm not only can reduce the transmit power of BS effectively but also improve the utilization rate of energy.

Downlink Performance of Beamforming for High Mobility Users in 5G Cellular Network

5G cellular infrastructures are supposed to provide higher data rate and lower latency along with the prospects of other various novel applications. But the signal strength seems to fluctuate unexpectedly due to doppler shift resulting in negative impacts on downlink performance parameters over the network for high-speed users. One potential solution to overcome this problem can be the concentration of energy to a particular location using multiple antennas at the base station so that receiving power can be increased for the intended user while suppressing interferences from others. So, this paper has investigated the performance of beamforming with closed loop spatial multiplexing over a specific range of velocity of users. However, the simulation results also demonstrate that by scaling the number of transmitting antennas, beamforming can elevate average throughput, improve quality of service for cell edge users and ensure better spectral efficiency under any existing scheduler with no complexities involved in system designing. Moreover, through the estimations of the channel conditions obtained from the precoding matrix of closed loop spatial multiplexing, the strength of the transmitted signal can be amplified accordingly to improve mean throughput and minimize the bit error rate. Therefore, the proposed scheme of scaling transmitting antennas through CLSM along with beamforming seems to circumvent the repercussions of doppler shift on downlink (DL) performance of high velocity cellular users.

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

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

The Effects of Filtered Channel Quality Feedback on Throughput of Proportional Fair Algorithm

The throughput (total bit per second per hertz) of a modified Proportional Fair Algorithm (PFA) is analyzed and computed. The modified PFA introduces a dynamic threshold to filter the feedbacks of Channel Quality (CQ) based on which the users' priorities are calculated for scheduling. Only those in good CQs (relative to their own average CQ) can be the candidates for scheduling. The results show that the modified PFA with a low CQ threshold can not only reduce some feedback overhead but also maintain the throughput of traditional PFA. Interestingly, for a certain combination of system parameters, the throughput gain over traditional PFA can be achieved by this modified PFA. Two methods of calculating priority, one based on signal to noise ratio, and the other on information rate, are considered. Their performances are generally similar in terms of throughput.

Improved proportional fair dynamic subcarrier allocation with frugality constraint for SC-FDMA system

With the objective of taking full use of channel resource, we proposed two utility based dynamic subcarrier allocation （DSA） algorithms for the single carrier frequency division multiple access （SC-FDMA） system, which are the proportional fair frugality constrained （PF-FC） algorithm and the weighted proportional fair frugality constrained （WPF-FC） algorithm. The two proposed algorithms are designed under the frugality constraint （FC） control consideration so as to avoid service rate waste and improve the spectrum efficiency. Moreover, the queuing buffer model in this paper is established on a finite size structure rather than the traditional infinite queuing manner, which is more consistent with the practical transmission condition. Simulation results indicate that the two proposed algorithms can both achieve significantly better system rate-sum capacity and quality of service （QoS） performance than their primary algorithms, and are more applicable for the heterogeneous traffic.

Joint relay selection and power allocation algorithm in two-way relay networks with proportional fair constraint

In this paper, we consider the joint relay selection and power allocation problem for two-way relay systems with multiple relay nodes. Traditionally, relay selection schemes are primarily focused on selecting one relay node to maximize the transmission sum rate or minimize the outage probability. If so, it is possible to cause certain relay nodes overloaded. In addition, the joint relay selection and power allocation problem is a mixed integer program problem and prohibitive in terms of complexity. Therefore, we propose a novel low complexity joint relay selection and power allocation algorithm with proportional fair scheduling to get the load-balancing among potential relays. Simulation results turn out that, compared with round-robin schemes and max sum rate schemes, the proposed algorithm can achieve the tradeoff between system transmission sum rate and load-balancing.

Resource scheduling in downlink LTE-advanced system with carrier aggregation

In this paper, we focus on the resource scheduling in the downlink of long term evolution advanced （LTE-A） assuming equal power allocation among subcarriers. Considering the backward compatibility, the LTE-A system serves LTE-A and long term evolution （LTE） users together with carrier aggregation （CA） technology. When CA is applied, a well-designed resource scheduling scheme is essential to the LTE-A system. Joint scheduling （JS） and independent scheduling （INS） are two resource scheduling schemes. JS is optimal in performance but with high complexity. Whereas INS is applied, the LTE users will acquire few resources because they can not support CA technology. And the system fairness is disappointing. In order to improve the system fairness without bringing high complexity to the system, an improved proportional fair （PF） scheduling algorithm base on INS is proposed. In this algorithm, we design a weigh factor which is related with the number of the carriers and the percentage of LTE users. Simulation result shows that the proposed algorithm can effectively enhance the throughput of LTE users and improve the system fairness.

Analytical evaluation of hybrid feedback scheme for OFDMA system

This paper first introduces the performance analysis of two classical channel quality indicator （CQI） feedback schemes which are best-n feedback and the threshold based feedback and derives the mathematical expressions of average capacity which is described by Theorem 1 and 2. Then, a reduced feedback scheme is designed for multiple traffics and multi-channel. The novel scheme combines the best-n feedback and the threshold based feedback together to reduce the feedback overhead. The proposed scheme can not only guarantee the quality of service （QoS） requirement of real time （RT） traffic but also reduce feedback overhead at the cost of a marginal increased downlink overhead. Simulation results demonstrated the good performance of the proposed feedback scheme.