• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.2A
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• pp.121-127
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• 2010

In this paper, we propose a computationally efficient scheduling algorithm that can arbitrarily control the throughput-fairness tradeoff in a multiuser wireless communication environment. As a new scheduling criterion, we combine linearly two well-known scheduling criteria such as one of achieving the maximum sum throughput and the other of achieving the maximum fairness, so as to control the relative proportion of the throughput and the fairness according to a control factor. For linear combining two different criteria, their optimization directivenesses and the units should be unified first. To meet these requirements, we choose an instantaneous channel capacity as a scheduling criterion for maximizing the sum throughput and the average serving throughput for maximizing the fairness. Through a unified linear combining of two optimization objectives with the control factor, it can provide various throughput-fairness tradeoffs according to the control factors. For further simplification, we exploit a high signal-to-noise ratio (SNR) approximation of the instantaneous channel capacity. Through computer simulations, we evaluate the throughput and fairness performances of the proposed algorithm according to the control factors, assuming an independent Rayleigh fading multiuser channel. We also evaluate the proposed algorithm employing the high SNR approximation. From simulation results, we could see that the proposed algorithm can control arbitrarily the throughput-fairness performance between the performance of the scheduler aiming to the maximum sum throughput and that of the scheduler aiming to the maximum fairness, finally, we see that the high SNR approximation can give a satisfactory performance in this situation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4398-4417
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• 2017

Multi-user Multiple-Input and Multiple-Output (MU-MIMO) has potential for prominently enhancing the capacity of wireless network by simultaneously transmitting to multiple users. User selection is an unavoidable problem which bottlenecks the gain of MU-MIMO to a great extent. Major state-of-the-art works are focusing on improving network throughput by using Channel State Information (CSI), however, the overhead of CSI feedback becomes unacceptable when the number of users is large. Some work does well in balancing tradeoff between complexity and achievable throughput but is lack of consideration of fairness. Current works universally ignore the rational utilizing of time resources, which may lead the improvements of network throughput to a standstill. In this paper, we propose TOUSE, a scalable and fair user selection scheme for MU-MIMO. The core design is dynamic-time-warping-based user selection mechanism for downlink MU-MIMO, which could make full use of concurrent transmitting time. TOUSE also presents a novel data-rate estimation method without any CSI feedback, providing supports for user selections. Simulation result shows that TOUSE significantly outperforms traditional contention-based user selection schemes in both throughput and fairness in an indoor condition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.5820-5834
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• 2017

This paper investigates the channel selection problem with dynamic users and the asymmetric interference relation in distributed opportunistic spectrum access systems. Since users transmitting data are based on their traffic demands, they dynamically compete for the channel occupation. Moreover, the heterogeneous interference range leads to asymmetric interference relation. The dynamic users and asymmetric interference relation bring about new challenges such as dynamic random systems and poor fairness. In this article, we will focus on maximizing the tradeoff between the achievable utility and access cost of each user, formulate the channel selection problem as a directional graphical game and prove it as an exact potential game presenting at least one pure Nash equilibrium point. We show that the best NE point maximizes both the personal and system utility, and employ the stochastic learning approach algorithm for achieving the best NE point. Simulation results show that the algorithm converges, presents near-optimal performance and good fairness, and the directional graphical model improves the systems throughput performance in different asymmetric level systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3012-3028
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• 2017

In this paper, we propose a joint power control strategy for both the uplink and downlink transmission by considering the energy requirements of the user equipments' uplink data transmissions in data and energy integrated communication networks (DEINs). In DEINs, the base station (BS) adopts the power splitting (PS) aided simultaneous wireless information and power transfer (SWIPT) technique in the downlink (DL) transmissions, while the user equipments (UEs) carry out their own uplink (UL) transmissions by exploiting the energy harvested during the BS's DL transmissions. In our DEIN model, there are M UEs served by the BS in order to fulfil both of their DL and UL transmissions. The orthogonal frequency division multiple access (OFDMA) technique is adopted for supporting the simultaneous transmissions of multiple UEs. Furthermore, a transmission frame is divided into N time slots in the medium access control (MAC) layer. The mathematical model is established for maximizing the sum-throughput of the UEs' DL transmissions and for ensuring their fairness during a single transmission frame T, respectively. In order to achieve these goals, in each transmission frame T, we optimally allocate the BS's power for each subcarrier and the PS factor for each UE during a specific time slot. The original optimisation problems are transformed into convex forms, which can be perfectly solved by convex optimisation theories. Our numerical results compare the optimal results by conceiving the objective of maximising the sum-throughput and those by conceiving the objective of maximising the fair-throughput. Furthermore, our numerical results also reveal the inherent tradeoff between the DL and the UL transmissions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.146-170
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• 2017

Joint channel assignment and routing is a well-known problem in multi-radio wireless mesh networks for which optimal configurations is required to optimize the overall throughput and fairness. However, other objectives need to be considered in order to provide a high quality service to network users when it deployed with high traffic dynamic. In this paper, we propose a re-configuration optimization model that optimizes the network throughput in addition to reducing the disruption to the mesh clients' traffic due to the re-configuration process. In this multi-objective optimization model, four objective functions are proposed to be minimized namely maximum link-channel utilization, network average contention, channel re-assignment cost, and re-routing cost. The latter two objectives focus on reducing the re-configuration overhead. This is to reduce the amount of disrupted traffic due to the channel switching and path re-routing resulted from applying the new configuration. In order to adapt to traffic dynamics in the network which might be caused by many factors i.e. users' mobility, a centralized heuristic re-configuration algorithm called State-Aware Joint Routing and Channel Assignment (SA-JRCA) is proposed in this research based on our re-configuration model. The proposed algorithm re-assigns channels to radios and re-configures flows' routes with aim of achieving a tradeoff between maximizing the network throughput and minimizing the re-configuration overhead. The ns-2 simulator is used as simulation tool and various metrics are evaluated. These metrics include channel-link utilization, channel re-assignment cost, re-routing cost, throughput, and delay. Simulation results show the good performance of SA-JRCA in term of packet delivery ratio, aggregated throughput and re-configuration overhead. It also shows higher stability to the traffic variation in comparison with other compared algorithms which suffer from performance degradation when high traffic dynamics is applied.