Throughput-fairness Trade-off Research Articles

Throughput fairness trade-offs for downlink non-orthogonal multiple access systems in 5G networks

In this work, user pairing and power allocation are proposed as a hybrid scheme to maximise throughput and achieve system fairness in the non-orthogonal multiple access (NOMA) system in 5G networks. The proposed approach is designed to improve the throughput and fairness performance of the downlink NOMA system in 5G networks. User pairing and power allocation schemes are separated to reduce resource allocation complexity. Integer linear programming is applied to perform user pairing, and particle swarm optimisation is used for power allocation. Moreover, the optimisation problem is formulated by converting multi-objective functions into a single function using the scalarisation of multi-objective optimisation problems, and the penalty function is used to prevent optimisation from violating the power, fairness, and data rate constraints. Simulation results show that the proposed model outperforms the conventional numerical approach by at least 9% of throughput maximisation and achieves an acceptable fairness rate.

Achieving arbitrary throughput-fairness trade-offs in the inter cell interference coordination with fixed transmit power problem

We study the problem of inter cell interference coordination (ICIC) with fixed transmit power in OFDMA-based cellular networks, in which each base station (BS) needs to decide as to which subchannel, if any, to allocate to each of its associated mobile stations (MS) for data transmission. In general, there exists a trade-off between the total throughput (sum of throughputs of all the MSs) and fairness under the allocations found by resource allocation schemes. We introduce the concept of \(\tau -\alpha -\)fairness by modifying the concept of \(\alpha -\)fairness, which was earlier proposed in the context of congestion control for packet-switched networks. The concept of \(\tau -\alpha -\)fairness allows us to achieve arbitrary trade-offs between total throughput and fairness by selecting an appropriate value of \(\alpha\) in \([0,\infty )\). We show that for every \(\alpha \in [0,\infty )\) and every \(\tau > 0\), the problem of finding a \(\tau -\alpha -\)fair allocation is NP-Complete. Further, we show that for every \(\alpha \in [0, \infty )\), there exist thresholds such that if the potential interference levels experienced by each MS on every subchannel are above the threshold values, then the problem can be optimally solved in polynomial time by reducing it to the bipartite graph matching problem. Also, we propose a simple, distributed subchannel allocation algorithm for the ICIC problem, which is flexible, requires a small amount of time to operate, and requires information exchange among only neighboring BSs. We investigate via simulations as to how the algorithm parameters should be selected so as to achieve any desired trade-off between the total throughput and fairness. Finally, we compare the performance of the proposed algorithm with those of the simulated annealing based and opportunistic subchannel allocation algorithms in terms of the total throughput, fairness index and computational complexity.

Robust Beamforming and User Clustering for Guaranteed Fairness in Downlink NOMA With Partial Feedback

In this paper, a downlink multiuser non-orthogonal multiple access (NOMA) with full and partial channel state information (CSI) feedback is considered. We investigate beam design and user clustering from the throughput-fairness trade-off perspective. To enhance this trade-off, two proportional fairness (PF) based scheduling algorithms are proposed, each has two stages. The first algorithm is based on integrating the maximum product of effective channel gains and the maximum signal to interference ratio with the PF principle (PF-MPECG-SIR), to select the strong users in the first stage and the weak users in the second stage. This algorithm is designed to maximize the throughput with moderate fairness enhancement. Whereas, in the second algorithm, the MPECG and the maximum correlation are combined within the PF selection criterion (PF-MPECG-CORR) in order to maximize the fairness with a slight degradation in the total throughput. In addition, we present an optimal power allocation that can achieve a high data rate for the overall system without sacrificing the sum-rate of weak users under full and partial CSI. Simulation results show that the proposed PF-MPECG-CORR can significantly improve the fairness up to 50.82% and 44.90% with only 0.42% and 1.13% degradation in the total throughput, for full and partial CSI, respectively. All these performance gains are achieved without increasing the computational complexity.

Optimal Throughput Fairness Tradeoffs for Downlink Non-Orthogonal Multiple Access Over Fading Channels

Recently, non-orthogonal multiple access (NOMA) has attracted considerable interest as one of the 5G-enabling techniques. However, users with better channel conditions in downlink communications intrinsically benefits from NOMA thanks to successive decoding, judicious designs are required to guarantee user fairness. In this paper, a two-user downlink NOMA system over fading channels is considered. For delay-tolerant transmission, the average sum-rate is maximized subject to both average and peak power constraints as well as a minimum average user rate constraint. The optimal resource allocation is obtained using Lagrangian dual decomposition under full channel state information at the transmitter (CSIT), while an effective power allocation policy under partial CSIT is also developed based on analytical results. In parallel, for delay-limited transmission, the sum of delay-limited throughput (DLT) is maximized subject to a maximum allowable user outage constraint under full CSIT, and the analysis for the sum of DLT is also performed under partial CSIT. Furthermore, an optimal orthogonal multiple access (OMA) scheme is also studied as a benchmark to prove the superiority of NOMA over OMA under full CSIT. Finally, the theoretical analysis is verified by simulations via different trade-offs for the average sum-rate (sum-DLT) versus the minimum (maximum) average user rate (outage) requirement.

Optimization of energy-constrained wireless powered communication networks with heterogeneous nodes

In this paper, we study wireless networks where nodes have two energy sources, namely a battery and radio frequency (RF) energy harvesting circuitry. We formulate two optimization problems with different objective functions, namely maximizing the sum throughput and maximizing the minimum throughput, for enhanced fairness. Furthermore, we show the generality of the proposed system model through characterizing the conditions under which the two formulated optimization problems can be reduced to the corresponding problems of different known wireless networks, namely, conventional wireless networks (battery-powered) and wireless powered communications networks (WPCNs) with only RF energy harvesting nodes. In addition, we introduce WPCNs with two types of nodes, with and without RF energy harvesting capability, in which the nodes without RF energy harvesting are utilized to enhance the sum throughput, even beyond WPCNs with all energy harvesting nodes. We establish the convexity of all formulated problems which opens room for efficient solution using standard techniques. Our numerical results show that the two types of wireless networks, namely WPCNs with only RF energy harvesting nodes and conventional wireless networks, are considered, respectively, as lower and upper bounds on the performance of the generalized problem setting in terms of the maximum sum throughput and the maxmin throughput. Moreover, the results reveal new insights and throughput-fairness trade-offs unique to our new problem setting.

On the Aloha throughput-fairness tradeoff

A well-known inner bound of the stability region of the finite-user slotted Aloha protocol (with fixed contention probabilities) on the collision channel with $n$ users assumes worst case service rates (all user queues non-empty). Using this inner bound as a feasible set of achievable rates, a characterization of the throughput–fairness tradeoff over this set is obtained, where the throughput is defined as the sum of the individual user rates, and two definitions of fairness are considered: the Jain–Chiu–Hawe function and the sum-user $\alpha $ -fair (isoelastic) utility function. This characterization is obtained using both an equality constraint and an inequality constraint on the throughput, and properties of the optimal controls, the optimal rates, and the maximum fairness as a function of the target throughput are established. A key structural property underpinning all theorems is the observation that the vector of contention probabilities that extremizes both fairness objectives has its nonzero components taking at most two distinct values.

Fairness, power allocation, and CSI quantization in block fading multiuser systems

This paper studies the fairness, power allocation, and channel state information (CSI) quantization in multiuser systems utilizing multiple feedback bits per user. For a given set of schedulers, we obtain the system throughput with different power allocation strategies and any combination of different fading distributions. Moreover, the system performance under user different outage probability constraints is investigated. Assuming homogenous users, as a special case, the throughput is determined for fixed and random request networks. Considering nonidentical fading channels between the transmitter and receivers, the throughput is found under the K-significant average feedback bit allocation technique, and two suboptimal fairness schemes are investigated which satisfy different quality-of-service requirements. The results show that using optimal power allocation, the first quantization region (QR) of each user is the only QR for which no power may be allocated. The system outage probability vanishes as the number of users goes to infinity. Users hard outage probability constraints can be satisfied at the cost of one more QR in the channel quantizer. Finally, the proposed fairness schemes are more flexible than the standard proportional fair (PF) scheduling in dealing with the throughput-fairness tradeoff. However, their superiority over PF scheduling depends on the fairness constraint.

Delivering fairness and priority enforcement on asymmetric multicore systems via OS scheduling

Symmetric-ISA (instruction set architecture) asymmetric-performance multicore processors (AMPs) were shown to deliver higher performance per watt and area than symmetric CMPs for applications with diverse architectural requirements. So, it is likely that future multicore processors will combine big power-hungry fast cores and small low-power slow ones. In this paper, we propose a novel thread scheduling algorithm that aims to improve the throughput-fairness trade-off on AMP systems. Our experimental evaluation on real hardware and using scheduler implementations on a general-purpose operating system, reveals that our proposal delivers a better throughput-fairness trade-off than previous schedulers for a wide variety of multi-application workloads including single-threaded and multithreaded applications.

On the scheduling, multiplexing and diversity trade-off in MIMO ad hoc networks: A unified framework

In this paper we study the fundamental scheduling, multiplexing and diversity trade-off in MIMO ad hoc networks. In particular, we propose a unified framework for the scheduling-multiplexing and scheduling-diversity sub-problems that constitutes a major step towards solving the overall problem. The two sub-problems are motivated by a fundamental trade-off between scheduling full multiplexing (diversity) gain non-interfering links and scheduling interfering links using lower multiplexing (diversity) gain in conjunction with interference nulling. First, we cast each sub-problem as a cross-layer optimization problem that jointly decides the scheduling and MIMO stream allocation, subject to signal-to-interference-and-noise-ratio (SINR) constraints. Second, we characterize the problem as non-convex integer programming which is quite challenging to solve. Hence, we shift our focus to characterize the optimal for the simple case of two links. The main result of this paper is that the two fundamentally different sub-problems give rise to structurally similar SINR-based decision rules which constitute the basis for a resource allocation algorithm with linear complexity in the number of links, namely Iterative MIMO Link Scheduling (IMLS), that solves the two sub-problems and achieves significant gains for any number of links. Numerical results exhibit more than twofold/quadratic improvement over scheduling non-interfering links with full multiplexing/diversity gain, for plausible scenarios. IMLS and its variants reveal an important throughput-fairness trade-off which is an interesting topic for future research.

Optimal Resource Allocation in Relay-Assisted Cellular Networks With Partial CSI

Emerging cellular networks are likely to handle users with heterogeneous quality of service requirements attending to the nature of their underlying service application, the quality of their wireless equipment, or even their contract terms. While sharing the same physical resources (power, bandwidth, transmission time), the utility they get from using them may be very different and arbitrage is needed to optimize the global operation of the network. In this respect, resource allocation strategies maximizing network utility under practical constraints are investigated in this paper. In particular, we focus on a cellular network with half-duplex, MIMO terminals and relaying infrastructure in the form of fixed and dedicated relay stations. Whereas orthogonal-frequency-division multiple access is assumed, it is seen as a frequency diversity enabler since path loss is the only channel state information (CSI) known at the transmitters, which is refreshed periodically. With this setup, the performance of a state-of-the art relay-assisted transmission protocol is characterized in terms of the ergodic achievable rates, for which novel concave lower bounds are developed. The use of these bounds allows us to derive two efficient algorithms computing resource allocations in polynomial time, which address the optimization of the uplink and downlink directions jointly. First, a global optimization algorithm providing one Pareto optimal solution maximizing network utility during the validity period of one CSI is studied, which acts as a performance upper bound. Second, a sequential optimization algorithm maximizing network utility frame by frame is considered as a simpler alternative. The performance of both schemes has been compared in practical scenarios, giving special attention to the performance-complexity and throughput-fairness tradeoffs.

Power Allocation and Admission Control in Multiuser Relay Networks via Convex Programming: Centralized and Distributed Schemes

The power allocation problem for multiuser wireless networks is considered under the assumption of amplify-and-forward cooperative diversity. Specifically, optimal centralized and distributed power allocation strategies with and without minimum rate requirements are proposed. We make the following contributions. First, power allocation strategies are developed to maximize either (i) the minimum rate among all users or (ii) the weighted-sum of rates. These two strategies achieve different throughput and fairness tradeoffs which can be chosen by network operators depending on their offering services. Second, the distributed implementation of the weighted-sum of rates maximization-based power allocation is proposed. Third, we consider the case when the requesting users have minimum rate requirements, which may not be all satisfied due to the limited-power relays. Consequently, admission control is needed to select the number of users for further optimal power allocation. As such a joint optimal admission control and power allocation problem is combinatorially hard, a heuristic-based suboptimal algorithm with significantly reduced complexity and remarkably good performance is developed. Numerical results demonstrate the effectiveness of the proposed approaches and reveal interesting throughput-fairness tradeoff in resource allocation.

On Throughput-Fairness Tradeoff in Virtual MIMO Systems with Limited Feedback

We investigate the performance of channel-aware scheduling algorithms designed for the downlink of a wireless communication system. Our study focuses on a two-transmit antenna cellular system, where the base station can only rely on quantized versions of channel state information to carry out scheduling decisions. The motivation is to study the interaction between throughput and fairness of practical spatial multiplexing schemes when implemented using existing physical layer signaling, such as the one that exists in current wideband code division multiple access downlink. Virtual MIMO system selects at each time instant a pair of users that report orthogonal (quantized) channels. Closed-formexpressions for the achievable sum-rate of three different channel-aware scheduling rules are presented using an analytical framework that is derived in this work. Our analysis reveals that simple scheduling procedures allow to reap a large fraction (in the order of 80%) of the sum-rate performance that greedy scheduling provides. This overall throughput performance is obtained without affecting considerably the optimal short-term fairness behavior that the end users would perceive.

Exploring the throughput-fairness tradeoff of deadline scheduling in heterogeneous computing environments

The scalability and computing power of large-scale computational platforms has made them attractive for hosting compute-intensive time-critical applications. Many of these applications are composed of computational tasks that require specific deadlines to be met for successful completion. In this paper, we show that combining redundant scheduling with deadline-based scheduling in these systems leads to a fundamental tradeoff between throughput and fairness. We propose a new scheduling algorithm called Limited Resource Earliest Deadline (LRED) that couples redundant scheduling with deadline-driven scheduling in a flexible way by using a simple tunable parameter to exploit this tradeoff. Our evaluation of LRED shows that LRED provides a powerful mechanism to achieve desired throughput or fairness under high loads and low timeliness environments.

On the Robustness of Scheduling Against Channel Variations

In this paper, we study the robustness of scheduling against channel variations. Specifically, we consider a class of generalized proportional fairness (PF) schedulers that achieve optimal throughput-fairness trade-off and study their robustness in terms of maintaining a certain fairness criterion when the channel statistics change from the additive white Gaussian noise (AWGN) to Rayleigh fading for all users. We conclude that the scheduler is robust only when it is configured to achieve the proportional fairness. For the other scheduler configurations, which are not robust, we show how fairness deviates from the ideal one when the channel statistics change and show how scheduler parameters need to be adjusted to restore the desired fairness. We also show simulation results.