Selective Fair Scheduling over Fading Channels

Imposing fairness in resource allocation incurs a loss of system throughput, known as the Price of Fairness ($PoF$). In wireless scheduling, $PoF$ increases when serving users with very poor channel quality because the scheduler wastes resources trying to be fair. This paper proposes a novel resource allocation framework to rigorously address this issue. We introduce selective fairness: being fair only to selected users, and improving $PoF$ by momentarily blocking the rest. We study the associated admission control problem of finding the user selection that minimizes $PoF$ subject to selective fairness, and show that this combinatorial problem can be solved efficiently if the feasibility set satisfies a condition; in our model it suffices that the wireless channels are stochastically dominated. Exploiting selective fairness, we design a stochastic framework where we minimize $PoF$ subject to an SLA, which ensures that an ergodic subscriber is served frequently enough. In this context, we propose an online policy that combines the drift-plus-penalty technique with Gradient-Based Scheduling experts, and we prove it achieves the optimal $PoF$. Simulations show that our intelligent blocking outperforms by 40$\%$ in throughput previous approaches which satisfy the SLA by blocking low-SNR users

Scheduling Policies in Time and Frequency Domains for LTE Downlink Channel: A Performance Comparison

A key feature of the Long-Term Evolution (LTE) system is that the packet scheduler can make use of the channel quality information (CQI), which is periodically reported by user equipment either in an aggregate form for the whole downlink channel or distinguished for each available subchannel. This mechanism allows for wide discretion in resource allocation, thus promoting the flourishing of several scheduling algorithms, with different purposes. It is therefore of great interest to compare the performance of such algorithms under different scenarios. Here, we carry out a thorough performance analysis of different scheduling algorithms for saturated User Datagram Protocol (UDP) and Transmission Control Protocol (TCP) traffic sources, as well as consider both the time- and frequency-domain versions of the schedulers and for both flat and frequency-selective channels. The analysis makes it possible to appreciate the difference among the scheduling algorithms and to assess the performance gain, in terms of cell capacity, users' fairness, and packet service time, obtained by exploiting the richer, but heavier, information carried by subchannel CQI. An important part of this analysis is a throughput guarantee scheduler, which we propose in this paper. The analysis reveals that the proposed scheduler provides a good tradeoff between cell capacity and fairness both for TCP and UDP traffic sources

Random Beamforming with Heterogeneous Users and Selective Feedback: Individual Sum Rate and Individual Scaling Laws

This paper investigates three open problems in random beamforming based communication systems: the scheduling policy with heterogeneous users, the closed form sum rate, and the randomness of multiuser diversity with selective feedback. By employing the cumulative distribution function based scheduling policy, we guarantee fairness among users as well as obtain multiuser diversity gain in the heterogeneous scenario. Under this scheduling framework, the individual sum rate, namely the average rate for a given user multiplied by the number of users, is of interest and analyzed under different feedback schemes. Firstly, under the full feedback scheme, we derive the closed form individual sum rate by employing a decomposition of the probability density function of the selected user's signal-to-interference-plus-noise ratio. This technique is employed to further obtain a closed form rate approximation with selective feedback in the spatial dimension. The analysis is also extended to random beamforming in a wideband OFDMA system with additional selective feedback in the spectral dimension wherein only the best beams for the best-L resource blocks are fed back. We utilize extreme value theory to examine the randomness of multiuser diversity incurred by selective feedback. Finally, by leveraging the tail equivalence method, the multiplicative effect of selective feedback and random observations is observed to establish the individual rate scaling.Comment: Submitted in March 2012. To appear in IEEE Transactions on Wireless Communications. Part of this paper builds upon the following letter: Y. Huang and B. D. Rao, "Closed form sum rate of random beamforming", IEEE Commun. Lett., vol. 16, no. 5, pp. 630-633, May 201

A Framework for Uplink Intercell Interference Modeling with Channel-Based Scheduling

This paper presents a novel framework for modeling the uplink intercell interference (ICI) in a multiuser cellular network. The proposed framework assists in quantifying the impact of various fading channel models and state-of-the-art scheduling schemes on the uplink ICI. Firstly, we derive a semianalytical expression for the distribution of the location of the scheduled user in a given cell considering a wide range of scheduling schemes. Based on this, we derive the distribution and moment generating function (MGF) of the uplink ICI considering a single interfering cell. Consequently, we determine the MGF of the cumulative ICI observed from all interfering cells and derive explicit MGF expressions for three typical fading models. Finally, we utilize the obtained expressions to evaluate important network performance metrics such as the outage probability, ergodic capacity, and average fairness numerically. Monte-Carlo simulation results are provided to demonstrate the efficacy of the derived analytical expressions.Comment: IEEE Transactions on Wireless Communications, 2013. arXiv admin note: substantial text overlap with arXiv:1206.229

Weighted Max-Min Resource Allocation for Frequency Selective Channels

In this paper, we discuss the computation of weighted max-min rate allocation using joint TDM/FDM strategies under a PSD mask constraint. We show that the weighted max-min solution allocates the rates according to a predetermined rate ratio defined by the weights, a fact that is very valuable for telecommunication service providers. Furthermore, we show that the problem can be efficiently solved using linear programming. We also discuss the resource allocation problem in the mixed services scenario where certain users have a required rate, while the others have flexible rate requirements. The solution is relevant to many communication systems that are limited by a power spectral density mask constraint such as WiMax, Wi-Fi and UWB