Data Rate Fairness Research Articles

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%.

Cooperative Power-Domain NOMA Systems: An Overview.

Interference has been a key roadblock against the effectively deployment of applications for end-users in wireless networks including fifth-generation (5G) and beyond fifth-generation (B5G) networks. Protocols and standards for various communication types have been established and utilised by the community in the last few years. However, interference remains a key challenge, preventing end-users from receiving the quality of service (QoS) expected for many 5G applications. The increased need for better data rates and more exposure to multimedia information lead to a non-orthogonal multiple access (NOMA) scheme that aims to enhance spectral efficiency and link additional applications employing successive interference cancellation and superposition coding mechanisms. Recent work suggests that the NOMA scheme performs better when combined with suitable wireless technologies specifically by incorporating antenna diversity including massive multiple-input multiple-output architecture, data rate fairness, energy efficiency, cooperative relaying, beamforming and equalization, network coding, and space-time coding. In this paper, we discuss several cooperative NOMA systems operating under the decode-and-forward and amplify-and-forward protocols. The paper provides an overview of power-domain NOMA-based cooperative communication, and also provides an outlook of future research directions of this area.

Combined-order Algorithm using Promethee Method Approach and Analytic Hierarchy Decision for Chunk Allocation in LTE Uplink Systems

The problem of chunk-based resource allocation for the uplink of Long Term Evolution is investigated. In this paper, a combined order using the promethee method and analytic hierarchy decision for chunk allocation algorithm is proposed. The utility of each order is sorted based on promethee method approach so that the utility of each order could be approximated as the average of all criteria on each order. To decide the best allocation, analytic hierarchy process score is assigned to its order based on their decision criteria weighting factor to find the best allocation. Using a particular weighting factor, the proposed algorithms outperform the previous mean greedy algorithms which use user-order allocation in term of spectral efficiency and data rate fairness without increase the time complexity. It also outperform iterative swapping chunk algorithm in term of data rate fairness.

Resource Allocation for Throughput versus Fairness Trade-Offs under User Data Rate Fairness in NOMA Systems in 5G Networks

In this work, we present a resource allocation scheme for managing trade-offs between total throughput maximisation and system fairness in a non-orthogonal multiple access (NOMA) system for 5G networks. Our proposed approach is designed to improve throughput and fairness as performance metrics of NOMA in 5G networks. We apply integer linear programming for user pairing and adopt particle swarm optimisation as the power allocation scheme for reducing resource allocation complexity. To formulate the multi-objective problem, we use scalarisation of multi-objective optimisation, which exhibits flexibility in assigning different weights to a single objective—in the case of this study, either sum rate or fairness. Moreover, the problem is formulated with a penalty function to prevent optimisation violating the constraints of the optimisation function. Simulation results show that the proposed model outperformed the conventional approach by at least 17% in terms of throughput maximisation and fairness rate.

Joint UAVs' Load Balancing and UEs' Data Rate Fairness Optimization by Diffusion UAV Deployment Algorithm in Multi-UAV Networks.

Unmanned aerial vehicles (UAVs) can be deployed as base stations (BSs) for emergency communications of user equipments (UEs) in 5G/6G networks. In multi-UAV communication networks, UAVs’ load balancing and UEs’ data rate fairness are two challenging problems and can be optimized by UAV deployment strategies. In this work, we found that these two problems are related by the same performance metric, which makes it possible to optimize the two problems simultaneously. To solve this joint optimization problem, we propose a UAV diffusion deployment algorithm based on the virtual force field method. Firstly, according to the unique performance metric, we define two new virtual forces, which are the UAV-UAV force and UE-UAV force defined by FU and FV, respectively. FV is the main contributor to load balancing and UEs’ data rate fairness, and FU contributes to fine tuning the UEs’ data rate fairness performance. Secondly, we propose a diffusion control stratedy to the update UAV-UAV force, which optimizes FV in a distributed manner. In this diffusion strategy, each UAV optimizes the local parameter by exchanging information with neighbor UAVs, which achieve global load balancing in a distributed manner. Thirdly, we adopt the successive convex optimization method to update FU, which is a non-convex problem. The resultant force of FV and FU is used to control the UAVs’ motion. Simulation results show that the proposed algorithm outperforms the baseline algorithm on UAVs’ load balancing and UEs’ data rate fairness.

Modeling and analysis of self-interference impaired two-user cooperative MIMO-NOMA system

In this work we deal with the performance analysis of a cell-edge user with the precoder and equalizer design in a two-user cooperative multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) system. Since the data rate fairness and outage performance of cell-edge user are crucial issues in MIMO-NOMA systems, we emphasize on how to sort out these issues. To this end, the coefficients of power allocation are updated with the help of beamformers by utilizing the simultaneous wireless information and power transfer (SWIPT) protocol. Particularly, a user at the cell-center plays the role of a relay to help the cell-edge user and the relaying operation is powered by a hybrid time switching/power splitting SWIPT protocol. For this purpose, first we formulate the signal-to-interference-plus noise ratio of downlink MIMO-NOMA system by considering distinct correlation matrices in term of indefinite eigenvalues by utilizing a quadratic formulation in the presence of both self-interference and multi-user interference. Next, a closed-form expression of the outage probability is derived based on the knowledge of receive and transmit correlation matrices at the transmitter side. The analytical expressions are validated through simulations to affirm the accuracy of our analysis. We showcase the extent by which the cell-edge user’s outage performance is improved by using the antenna diversity at near and far end terminals of the MIMO-NOMA system.

Learning Driven Resource Allocation and SIC Ordering in EH Relay Aided NB-IoT Networks

Integrating the energy-harvesting (EH) relay and non-orthogonal multiple access (NOMA) technologies into narrow band internet of things (NB-IoT) networks can efficiently improve the energy and spectrum efficiency of the network and the quality-of-service of edge users. Therefore, we consider an EH relay aided NOMA NB-IoT network in this letter. To reduce the rate variance among NB-IoT devices, we aim to maximize the proportional fairness of data rate across all NB-IoT devices through jointly optimizing the communication resource allocation and successive interference cancellation (SIC) ordering subject to the minimum data rate requirements. Considering the non-convexity of this optimization problem, we propose a deep reinforcement learning based online optimization algorithm to obtain the sub-optimal solution. Simulation results demonstrate that the proposed algorithm can efficiently improve the proportional fairness and the total throughput among NB-IoT devices, in comparison with orthogonal multiple access techniques.

Deep Learning-Based Power Control Scheme for Perfect Fairness in Device-to-Device Communication Systems

The proximity-based device-to-device (D2D) communication allows for internet of things, public safety, and data offloading services. Because of these advantages, D2D communication has been applied to wireless communication networks. In wireless networks using D2D communication, there are challenging problems of the data rate shortage and coverage limitation due to co-channel interference in the proximity communication. To resolve the problems, transmit power control schemes that are based on deep learning have been presented in network-assisted D2D communication systems. The power control schemes have focused on enhancing spectral efficiency and energy efficiency in the presence of interference. However, the data-rate fairness performance may be a key performance metric in D2D communications, because devices in proximity can expect fair quality of service in the system. Hence, in this paper, a transmit power control scheme using a deep-learning algorithm based on convolutional neural network (CNN) is proposed to consider the data-rate fairness performance in network-assisted D2D communication systems, where the wireless channels are modelled by path loss and Nakagami fading. In the proposed scheme, the batch normalization (BN) scheme is introduced in order to further enhance the spectral efficiency of the conventional deep-learning transmit power control scheme. In addition, a loss function for the deep-learning optimization is defined in order to consider both the data-rate fairness and spectral efficiency. Through simulation, we show that the proposed scheme can achieve extremely high fairness performance while improving the spectral efficiency of the conventional schemes. It is also shown that the improvement in the fairness and spectral efficiency is achieved for different Nakagami fading conditions and sizes of area containing the devices.

A Promising Non-Orthogonal Multiple Access Based Networking Architecture: Motivation, Conception, and Evolution

Since it can offer higher spectral efficiency by granting the served data to share the same spectrum resource synchronously, NOMA is considered as a bright multiple access technique for future wireless networks. Accommodating a large number of users with NOMA mode may easily cause inappropriate power assignment problem and result in performance degradation. As a result, existing NOMA operation unit mainly focuses on the two-user scenario, where a centre user is only paired with an edge user. However, such a user pairing strategy will also introduce an unbalance to their data rate fairness. Hence, it is of great importance to develop an effective structure that facilitates low decoding complexity, high system performance, and reasonable user fairness with NOMA. To this end, this article proposes a novel NOMA operation unit, in which successive interference cancellation can be performed to accommodate an arbitrary number of users by integrating the NOMA technique with the orthogonal resource allocation strategy. The proposed NOMA operation unit can further improve the edge user's achievable rate by incorporating the index modulation technique according to the data service demanded by center users.

Energy Efficient Resource Allocation in EH-Enabled CR Networks for IoT

Cognitive radio (CR) can be leveraged to mitigate the spectrum scarcity problem of Internet of Things (IoT) applications while wireless energy harvesting (WEH) can help reduce recharging/replacing batteries for IoT and CR networks. To this end, we propose to utilize WEH for CR networks in which the CR devices are not only capable of sensing the available radio frequencies in a collaborative manner but also harvesting the wireless energy transferred by an access point. More importantly, we design an optimization framework that captures a fundamental tradeoff between energy efficiency (EE) and spectral efficiency of the network. In particular, we formulate a mixed integer nonlinear programming problem that maximizes EE while taking into consideration of user buffer occupancy, data rate fairness, energy causality constraints, and interference constraints. We further prove that the proposed optimization framework is an NP-hard problem. Thus, we propose a low complexity heuristic algorithm, to solve the resource allocation and energy harvesting optimization problem. The proposed algorithm is shown to be capable of achieving near optimal solution with high accuracy while having polynomial complexity. The efficiency of our proposal is validated through well designed simulations.

Online Distributed User Association for Heterogeneous Radio Access Network.

Future-generation radio access networks (RAN) are projected to fulfill the diverse requirements of user equipment (UE) by adopting a heterogeneous network (HetNet) environment. Necessary integration of different radio access technologies (RAT), such as 2G, 3G, 4G, wireless local area network (WLAN), and visible light communication (VLC) is inevitable. Moreover, UEs equipped with diverse requirements will be capable of accessing some or all the RATs. The complex HetNet environment with diverse requirements of UEs will present many challenges. The HetNet is likely to suffer severely from load imbalance among the base stations (BSs) from inheriting the traditional user association scheme such as max-SINR (signal-to-interference-plus-noise ratio)/max-RSSI (received signal strength indicator), unless some sophisticated schemes are invented. In this paper, a novel scheme is devised for a joint-user association for load balancing, where BSs are densely deployed and UEs typically have a certain degree of mobility. Unlike most of the present works, a dynamic network is considered where the position and channel condition of the UEs are not fixed. We develop two complex and distributed association schemes based on probability and d-choices, while carefully considering both loads of the BSs and SINR experienced by the UEs. Numerical results validate the efficiency of the proposed schemes by showing a received data-rate fairness among UEs and an improvement in the UE’s minimum received data rate.

Novel SWIPT Schemes for 5G Wireless Networks.

In this paper, we present a few novel simultaneous wireless information and power transfer (SWIPT) schemes that can be effectively used in various 5G wireless network implementations. First, we study the possibility of integrating distributed energy beamforming with the data rate fairness beamforming in a cooperative communication system with multiple cooperative relays and multiple destination users communicating simultaneously. We show that the system exploits significant performance gain using such a joint energy and data rate fairness beamforming scheme. Further, we propose an enhanced version of the SWIPT scheme, the energy-efficient modulation-based non-orthogonal multiple access (M-NOMA) SWIPT scheme, and observe its system efficiency in terms of more harvested energy. Finally, we consider an energy-harvesting SWIPT scheme where the channel response is estimated using the energy-harvesting signal as pilots superimposed on the information signal. For such a scheme, we compute the optimum transmit power ratio between the pilot and information signals under varying SNR conditions and improve the accuracy of the decoding process at the reception.

Power Allocation to Improve Data-Rate Fairness of Non-orthogonal Multiple Access Users

Power Allocation to Improve Data-Rate Fairness of Non-orthogonal Multiple Access Users

Joint 3D Location and Power Optimization for UAV-Enabled Relaying Systems

Communication and networking with unmanned aerial vehicles (UAVs) has attracted increasing attention due to their boundless applications in photography, agriculture, surveillance, and numerous public services. Employing the UAV as a mobile relay is promising to boost the coverage and capacity of the network and shows many advantages over conventional communication networks. In this paper, we investigate joint 3-D location and transmit power optimization of the UAV to accommodate the relaying network with multiple mobile users. The objective is to maximize the sum rate of all the mobile users, subjects to the constraints on line-of-sight connectivity for communication links, information-causality constraint, as well as the strict data rate fairness requirement of all users. However, this challenging problem has non-convex objective function, complicated constraints, and strongly coupled variables. To address this problem, we first prove that the optimal solution to the original sum rate maximization problem can be obtained by equivalently solving a more tractable problem. Then, we develop an efficient algorithm by leveraging the alternating descent framework and successive convex approximation method. Next, we show that the proposed algorithm iteratively improves the objective function and is guaranteed to converge within a finite number of iterations. Furthermore, simulation results demonstrate the effectiveness of the proposed algorithm and reveal the impacts of various parameter configurations on the system performance.

Fairness-Constrained Maximum Sum Rate of Multi-Rate CSMA Networks

This paper presents the sum rate analysis of a saturated $M$ -group multi-rate carrier sense multiple access network, where nodes in different groups have distinct packet transmission rates. An explicit expression of the network sum rate is derived, based on which the maximum sum rate is obtained by optimizing the transmission probabilities of nodes. It is found that to achieve the maximum sum rate, only the group of nodes with the largest transmission rate is allowed to access the channel, which leads to severe unfairness. To ensure certain fairness, two constraints, namely, throughput fairness (TF) and data-rate fairness (DF), are proposed, with which each node acquires a target proportion of the network throughput and the network sum rate, respectively. Explicit expressions of the network maximum sum rate with TF and DF are derived, which show that by including the fairness constraints, the network maximum sum rate becomes inferior to that without fairness constraints as long as there is difference in the transmission rates of nodes. The analysis is further applied to IEEE 802.11 networks, where the optimal initial backoff window sizes of nodes to achieve the network maximum sum rates with both fairness constraints are derived.

Power splitting based SWIPT in network-coded two-way networks with data rate fairness: an information-theoretic perspective

This paper investigates the simultaneous wireless information and power transfer (SWIPT) for network-coded two-way relay network from an information-theoretic perspective, where two sources exchange information via an SWIPT-aware energy harvesting (EH)relay. We present a power splitting (PS)-based two-way relaying (PS-TWR) protocol by employing the PS receiver architecture. To explore the system sum rate limit with data rate fairness, an optimization problem under total power constraint is formulated. Then, some explicit solutions are derived for the problem. Numerical results show that due to the path loss effect on energy transfer, with the same total available power, PS-TWR losses some system performance compared with traditional non-EH two-way relaying, where at relatively low and relatively high signal-to-noise ratio (SNR), the performance loss is relatively small. Another observations is that, in relatively high SNR regime, PS-TWR out-performs time switching-based two-way re-laying (TS-TWR) while in relatively low SNR regime TS-TWR outperforms PS-TWR. It is also shown that with individual available power at the two sources, PS-TWR outperforms TS-TWR in both relatively low and high SNR regimes.

Data Rate Fairness Cooperative Beamforming Techniques for Cognitive Radio Systems in the Presence of Asynchronous Interferences

In this paper, we develop a data rate fairness optimal cooperative beamforming technique for a cognitive radio system with multiple secondary receivers (SRs) and multiple primary receivers (PRs) in the presence of asynchronous interferences. In particular, the data rate fairness beamforming design is formulated as an optimization problem to maximize the minimum data rate of the SRs subject to transmission power constraints at the secondary cooperative transmitters (SCTs) and asynchronous interference power constraints at the PRs. The optimal beamforming design is a quadratically constrained quadratic program max-min optimization problem, which is non-convex and non-linear. Therefore, we reformulate the optimal beamforming design as a quasi-convex problem using the semidefinite program (SDP) relaxation, which can be solved using the SDP solvers and bisection method. The complexity of the optimal beamforming scheme stands against its practical implementation. So, we study important properties of the optimization problem. By exploiting these properties, we also propose low complexity suboptimal beamforming techniques. Furthermore, we extend the beamforming techniques to incorporate the uncertainties in channel and propagation delay estimation between SCTs and PRs.

Cluster-Based Resource Allocation for Spectrum-Sharing Femtocell Networks

Femtocells in two-tier femto-macro networks can enhance indoor coverage and improve overall network performance. Macro networks may share spectrum with overlaid femtocells so as to improve spectral efficiency. However, the deployment of femtocells also brings co-tier and cross-tier interferences, which will significantly degrade system performance. In order to solve this problem efficiently, we propose a distributed scheme to manage wireless resources in this heterogeneous networks. The feasible solution can be obtained by dividing the problem into two sub-problems. First, we propose a femtocells clustering scheme, which uses a mathematical modeling idea based on LINGO, an optimization software that can solve the joint clustering problem for the femtocell access points (FAPs). The proposed branch-and-bound algorithm and the simplex algorithm are used jointly to find the optimal solution by LINGO. The optimality of the proposed clustering algorithm is verified both theoretically and through simulations where the comparison with other algorithms is made. Second, a novel algorithm is proposed to allocate sub-channels to the femtocell users (FUEs). Compared with other related schemes, the proposed channel-allocation algorithm can reduce the interference more effectively and achieve higher data-rate fairness among FUEs. Specifically, according to the situation that the FUEs move in the room, the FUE mobility model is proposed to predict the change tendency of path loss values of the FUEs, which can guarantee the mobile service quality and improve system capacity effectively. Finally, the power of the FAPs is adjusted dynamically through setting the interference threshold to further improve the performance of the system.

Resource Allocation Optimization for Users with Different Levels of Service in Multicarrier Systems

We optimize the throughput of a single cell multiuser orthogonal frequency division multiplexing system with proportional data rate fairness among the users. The concept is to support mobile users with different levels of service. The optimization problem is a mixed integer nonlinear programming problem, which is computationally very expensive. We propose a novel and efficient near-optimal solution adopting a two-phase optimization approach that separates the subcarrier and power allocation. In the first phase, we relax the strict proportional data rate requirements and employ an iterative subcarrier allocation approach that coarsely satisfies desired data rate proportionality constraints. In the second phase, we reallocate the power among the users in an iterative way to further enhance the adherence to the desired proportions by exploiting the normalized proportionality deviation measure. The simulation results show that the proposed solution exhibits very strong adherence to the desired proportional data rate fairness while achieving higher system throughput compared to the other existing solutions.

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.