Proportional Fair Rate Research Articles

Energy-efficient data transmission with proportional rate fairness for NANs of smart grid communication network

In smart grid, the neighborhood area network (NAN) serves as a bridge connecting smart meters and meter data management system (MDMS) and is one of the most important components of the smart grid communication network. In this work, we study the energy-efficient data transmissions from NAN gateways (i.e., routers) to the concentrator that connects the MDMS. Particularly, to assure that each router associated with diverse number of associated smart meters can achieve a required data rate, a set of proportional rate fairness constraints are imposed into the optimization problem. In this case, an optimization problem that balances energy efficiency and fairness is formulated. Due to the fractional form of objective function, the combinatorial constraints on channel allocation variables, and the proportional rate fairness requirements, the formulated energy-efficient data transmission problem is non-convex and extremely computationally complex. In order to solve the problem efficiently, firstly, a subtractive transformation is introduced to handle the objective function, and an iterative algorithm based on Dinkelbach method is proposed; then for the inner loop optimization problem in each iteration, a low-complexity suboptimal algorithm that separates channel allocation and power distribution is developed. Finally, numerical results demonstrate that the proposed algorithm converges in a few steps and achieves a near-optimal energy efficiency performance.

Energy efficient indoor localisation for narrowband internet of things

AbstractThere are an increasing number of Narrow Band IoT devices being manufactured as the technology behind them develops quickly. The high co‐channel interference and signal attenuation seen in edge Narrow Band IoT devices make it challenging to guarantee the service quality of these devices. To maximise the data rate fairness of Narrow Band IoT devices, a multi‐dimensional indoor localisation model is devised, consisting of transmission power, data scheduling, and time slot scheduling, based on a network model that employs non‐orthogonal multiple access via a relay. Based on this network model, the optimisation goal of Narrow Band IoT device data rate ratio fairness is first established by the authors, while taking into account the Narrow Band IoT network: The multi‐dimensional indoor localisation optimisation model of equipment tends to minimize data rate, energy constraints and EH relay energy and data buffer constraints, data scheduling and time slot scheduling. As a result, each Narrow Band IoT device's data rate needs are met while the network's overall performance is optimised. We investigate the model's potential for convex optimisation and offer an algorithm for optimising the distribution of multiple resources using the KKT criterion. The current work primarily considers the NOMA Narrow Band IoT network under a single EH relay. However, the growth of Narrow Band IoT devices also leads to a rise in co‐channel interference, which impacts NOMA's performance enhancement. Through simulation, the proposed approach is successfully shown. These improvements have boosted the network's energy efficiency by 44.1%, data rate proportional fairness by 11.9%, and spectrum efficiency by 55.4%.

Joint subcarrier and power allocation with proportional fairness in multicarrier nonorthogonal multiple access systems

SummaryIn this paper, we investigate joint subcarrier and power allocation in multicarrier nonorthogonal multiple access (MC‐NOMA) systems aims to guarantee rate proportional fairness among all users. To this end, we adopt the sum logarithmic rate function as the objective and formulate a resource optimization problem which is a mixed‐integer nonlinear programming (MINLP) problem. Based on variable relaxation and replacement, we convert this problem into a convex optimization problem. On this basis, the expressions of joint subcarrier and power allocation solutions are obtained by using convex optimization theory and the Karush–Kuhn–Tucker (KKT) condition. However, power and subcarrier allocation solutions are highly coupled, and it is difficult to derive both solutions simultaneously. Fortunately, when the subcarrier allocation strategy is given, the optimal power allocation solution can be derived by solving the equations. Furthermore, we develop an iterative resource allocation algorithm to implement joint subcarrier and power allocation among users. Numerical results reveal that the proposed algorithm is effective in general multiuser and multicarrier scenarios, and a substantial performance improvement can be achieved by the proposed algorithm in contrast with the traditional orthogonal multiple access (OMA) schemes. In specific, the proposed algorithm achieves 63% and 59% gains in terms of utility and fairness performance, respectively.

Energy-Efficient Power Allocation with Rate Proportional Fairness Constraint in Non-Orthogonal Multiple Access Systems

Energy-Efficient Power Allocation with Rate Proportional Fairness Constraint in Non-Orthogonal Multiple Access Systems

Fair Coexistence of Scheduled and Random Access Wireless Networks: Unlicensed LTE/WiFi

We study the fair coexistence of scheduled and random access transmitters sharing the same frequency channel. Interest in coexistence is topical due to the need for emerging unlicensed LTE technologies to coexist fairly with WiFi. However, this interest is not confined to LTE/WiFi as coexistence is likely to become increasingly commonplace in IoT networks and beyond 5G. In this paper, we show that mixing scheduled and random access incurs an inherent throughput/delay cost and the cost of heterogeneity. We derive the joint proportional fair rate allocation, which casts useful light on current LTE/WiFi discussions. We present experimental results on inter-technology detection and consider the impact of imperfect carrier sensing.

On the Energy and Spectral Efficiency Tradeoff in Massive MIMO-Enabled HetNets With Capacity-Constrained Backhaul Links

In this paper, we propose a general framework to study the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in massive multiple-input-multiple-output-enabled heterogenous networks while ensuring proportional rate fairness among users and taking into account the backhaul capacity constraint. We aim at jointly optimizing user association, spectrum allocation, power coordination, and the number of activated antennas, which is formulated as a multi-objective optimization problem maximizing EE and SE simultaneously. With the help of weighted Tchebycheff method, it is then transformed into a single-objective optimization problem, which is a mixed-integer non-convex problem and requires unaffordable computational complexity to find the optimum. Hence, a low-complexity effective algorithm is developed based on primal decomposition, where we solve the power coordination and number of antenna optimization problem and the user association and spectrum allocation problem separately. Both theoretical analysis and numerical results demonstrate that our proposed algorithm can fast converge within several iterations and significantly improve both the EE-SE tradeoff performance and rate fairness among users compared with other algorithms.

Utility Fair Rate Allocation in LTE/802.11 Networks

We consider proportional fair rate allocation in a heterogeneous network with a mix of LTE and 802.11 cells which supports multipath and multihomed operation (simultaneous connection of a user device to multiple LTE BSs and 802.11 APs). We show that the utility fair optimisation problem is non-convex but that a global optimum can be found by solving a sequence of convex optimisations in a distributed fashion. The result is a principled approach to offload from LTE to 802.11 and for exploiting LTE/802.11 path diversity to meet user traffic demands.

Price-Based Power Allocation with Rate Proportional Fairness Constraint in Downlink Non-Orthogonal Multiple Access Systems

Price-Based Power Allocation with Rate Proportional Fairness Constraint in Downlink Non-Orthogonal Multiple Access Systems

Energy and Spectral Efficiency Tradeoff With User Association and Power Coordination in Massive MIMO Enabled HetNets

In this letter, we investigate the tradeoff between energy efficiency (EE) and spectral efficiency (SE) while ensuring proportional rate fairness in massive multiple-input multiple-output enabled heterogenous networks, where user association and power coordination are jointly considered. It is first formulated as a multi-objective optimization problem, and then transformed into a single-objective optimization problem. To solve this mixed-integer non-convex problem, an effective algorithm is developed, where the original problem is separated into lower level power coordination problem and master user association problem. Simulation results verify that our proposed algorithm can significantly improve the performance of EE–SE tradeoff and obtain higher rate fairness compared with other algorithms.

Power Allocation for Max-Sum Rate and Max-Min Rate Proportional Fairness in NOMA

In this letter, we consider proportional fairness scheduling (PFS) for downlink non-orthogonal multiple access (NOMA) with two users. Unlike conventional multiuser diversity systems, the power allocation plays a key role in PFS for NOMA as it can support multiple users simultaneously with positive transmission rates. With different criteria, we find optimal power allocation. Among those, it is shown that the PFS scheme that maximizes the minimum normalized rate can provide not only proportional fairness, but also small variation of transmission rates.

A QoE-based jointly subcarrier and power allocation for multiuser multiservice networks

Quality of experience (QoE) is widely applied to reflect users satisfaction of the network service, which exactly conforms to the user-centric concept in 5G. In this paper, we propose a QoE-based subcarrier and power allocation algorithm for the downlink transmission of a multiuser multiservice system. For the subcarrier allocation algorithm, the rate proportional fairness factor is defined to ensure the fairness between users. Based on different QoE models of three services, i.e., file down (FD), video streaming and voice over internet protocol (VOIP), a multi-objective optimization method is exploited to allocate the power resource by minimizing the total power consumption and maximizing the mean opinion score (MOS) value of users simultaneously. Simulation results indicate that the proposed algorithm has less power consumption and higher QoE performance than the traditional proportional fairness (PF) algorithm. In addition, the proposed algorithm can achieve nearly the same fairness performance as the PF algorithm. Moreover, when the number of subcarriers becomes larger, the power assumption will be less but with little influence on both the QoE and fairness performances.

Energy Efficiency With Proportional Rate Fairness in Multirelay OFDM Networks

This paper investigates the energy efficiency (EE) in multiple relay aided OFDM system, where decode-and-forward (DF) relay beamforming is employed to help the information transmission. In order to explore the EE performance with user fairness for such a system, we formulate an optimization problem to maximize the EE by jointly considering several factors, the transmission mode selection (DF relay beamforming or direct-link transmission), the helping relay set selection, the subcarrier assignment and the power allocation at the source and relays on subcarriers, under nonlinear proportional rate fairness constraints, where both transmit power consumption and linearly rate-dependent circuit power consumption are taken into account. To solve the non-convex optimization problem, we propose a low-complexity scheme to approximate it. Simulation results demonstrate its effectiveness. We also investigate the effects of the circuit power consumption on system performances and observe that with both the constant and the linearly rate-dependent circuit power consumption, system EE grows with the increment of system average channel-to noise ratio (CNR), but the growth rates show different behaviors. For the constant circuit power consumption, system EE increasing rate is an increasing function of the system average CNR, while for the linearly rate-dependent one, system EE increasing rate is a decreasing function of the system average CNR. This observation is very important which indicates that by deducing the circuit dynamic power consumption per unit data rate, system EE can be greatly enhanced. Besides, we also discuss the effects of the number of users and subcarriers on the system EE performance.

Robust Power Control for Cognitive Radio Networks with Proportional Rate Fairness

This paper studies the power control problem in cognitive radio networks where a primary user and multiple secondary users (SUs) coexist. Imperfect channel state information is considered. The objective is to maximize the SUs' sum rate while guaranteeing the proportional rate fairness among SUs. The problem under consideration is non-convex. By doing a transformation, it is equivalently changed to a second-order cone programming problem, which can be efficiently solved by existing standard methods. Simulations have been done to verify the network performance under different channel uncertainty conditions.

Modeling and Performance Evaluation of Novel Scheduling Algorithm for Downlink LTE Cellular Network

Long term evolution is standardized by the 3rd Generation Partnership Project to provide a high data rate up to 100 and 50 Mbps for downlink and uplink transmission respectively and can operate in different bandwidths ranging from 1.4 up to 20 MHz. Downlink LTE system is based on the OFDMA technique and uses several enhanced techniques in order to increase the spectral efficiency and aggregate the cell throughput. The incorporated scheduling mechanisms can significantly contribute to this goal. Scheduling mechanism is the process of allocated the resources (time and frequency) to users transmitting different flows in same time. In this paper, we propose a novel scheduling algorithm for downlink transmission in LTE system. The performances of the newly scheduler are simulated and compare to the performances of three basic scheduler such as Round Robin, Proportional Fair and Max Rate.

Comparison of Resource Scheduling Algorithms for LTE-Based Smart Grid Communications

The long term evolution (LTE) is a promising candidate solution for smart grid communication network due to its high data rate, lower latency, high mobility, and large coverage. In this paper, we discuss three resource scheduling algorithms for the LTE-based smart grids, including proportional fairness, round robin, and maximum rate scheduling schemes. In the simulations, we compare these three resource scheduling algorithms, the results show that the proportional fairness scheduler can achieve the best tradeoff between the average sum rate and the fairness among all the users.

Proportional Fair Resource Allocation Based on Chance-Constrained Programming for Cognitive OFDM Network

Proportional fair resource allocation plays a critical role to balance the spectrum efficiency and fairness for cognitive orthogonal frequency division multiplexing (OFDM) network. However, due to the lack of cooperation between cognitive radio (CR) network and primary network, channel state information between CR base station (CRBS) and primary user (PU) could not be estimated precisely. Therefore, the interference of CRBS---PU couldn't be computed precisely and chance-constrained programming is adopted to formulate the resource allocation problem. In this work, we study the proportional fair resource allocation problem based on chance-constrained programming for cognitive OFDM network. The objective function maximizes the spectral efficiency of cognitive OFDM network over subcarrier and power allocation. The constraint conditions include the interference constraint of PU with the target probability requirement and the proportional fair rate requirement of CR users. In order to solve the above optimization problem, two steps are taken to develop hybrid immune optimization algorithm (HIOA). In the first step, the probabilistic interference constraint condition is transformed as an uncertain function which is computed by a generalized regression neural network (GRNN). In the second step, we combine immune optimization algorithm and GRNN to develop HIOA. Simulation results demonstrate that HIOA yields higher spectral efficiency while the probabilistic interference constraint condition and the proportional fair rate constraint condition could be satisfied very well.

Proportional Fair MU-MIMO in 802.11 WLANs

We consider the proportional fair rate allocation in an 802.11 WLAN that supports multi-user MIMO (MU-MIMO) transmission by one or more stations. We characterise, for the first time, the proportional fair allocation of MU-MIMO spatial streams and station transmission opportunities. While a number of features carry over from the case without MU-MIMO, in general neither flows nor stations need to be allocated equal airtime when MU-MIMO is available.

Two-Way Relaying Cooperative Wireless Networks: Resource Allocation and Performance Analysis

Abstract Relay-based cooperative wireless networks have been widely considered one of the cost-effective solutions to meet the demands in future wireless networks. In order to maximize the overall sum-rate while maintaining proportional fairness among users, we investigate different resource allocation algorithms in two-way relay networks with analog network coding (ANC) protocol and time division broadcast (TDBC) protocol. The algorithms investigated are different from traditional proportional fairness schemes in terms of fairness and computational complexity as we have applied Access Proportional Fairness (APF) and Minimum Rate Proportional Fairness (MRPF) along with load balancing at the relays. A MATLAB simulation has been performed and simulation results show the effectiveness of these algorithms.

Fair resource allocation and DF relay selection for multiuser OFDMA-based cooperative networks

We investigate the joint problem of power and subcarrier allocation with relay selection for a multi-user multi-relay cellular system with selective relaying. We first formulate the optimal problem using Boolean-convex optimization with the objective of improving the average network capacity while providing proportional rate fairness to all users. We show that except for very small network instances the model is computationally very complex to solve. Then, we present a sub-optimal model by decomposing the joint problem into two sub-problems. This model proves to be a simple one, however, the sub-optimal design of the first sub-problem restricts the model from achieving near optimal solutions. Therefore, to achieve near optimal solutions, we propose an iterative two step method where near-optimal solutions can be achieved by iterating between the two steps. Numerical results show that for smaller networks this two step iterative method obtains optimal solutions in much less time. We also show that a selective relaying technique always achieves better performance over the always relaying technique.

Cross‐layer optimization for fair end‐to‐end rate allocation in WMNs with MIMO links

ABSTRACTMultiple‐input multiple‐output (MIMO) technology can bring substantial throughput improvement in wireless mesh networks (WMNs) via a combination of interference suppression and spatial multiplexing. In this paper, we investigate a cross‐layer design problem in MIMO‐based WMNs, which involves end‐to‐end rate allocation, routing, and stream control scheduling. Scheduling is addressed by using a set of transmission configurations (TCs), and a greedy heuristic algorithm is proposed to identify a good subset of all possible TCs. We define three optimization problems to find the maximum throughput, max–min fair and proportional fair rate allocation solutions. We propose a cross‐layer scheme to solve these problems. We also discuss how the proposed cross‐layer scheme can be implemented on the basis of the IEEE 802.16 mesh mode. Simulation results show that the performance of the proposed schemes with the heuristic algorithm is close to the optimal. The computational complexity of obtaining the solution using the proposed heuristic algorithm is substantially less than that of finding the optimal solution. Copyright © 2012 John Wiley & Sons, Ltd.