Multicast Systems With Fair Scheduling in Non-identically Distributed Fading Channels

Abstract

Abstract-Multicasting is emerging as an efficient method to deliver the same data to a group of users thereby saving network resources. The fairness between different multicast groups is an important quality of service (QoS) indication, but it has not been given significant attention. In this paper, we propose a normalized signal to noise ratio-based fair scheduling for multiple multicast groups in multicast systems. The system fairness and capacity are then analyzed and compared for both fair scheduling and greedy scheduling over independent but non-identically distributed (i.n.d.) fading channels. Closed-form expressions in terms of the system spectral efficiency, outage probability, system fairness and average bit error rate are derived in an uncoded/coded M-ary quadrature amplitude modulationbased adaptive transmission multicast system over i.n.d. Rayleigh fading channels. Numerical results show that compared with greedy scheduling, fair scheduling achieves considerably high fairness at the cost of slight system capacity loss regardless of the number of multicast groups. Our focus is on the physical layer without rate loss, but we also briefly discuss applications of the proposed scheduling in a cross-layer design subject to the loss rate QoS constraint.

Maximum Rate Scheduling With Adaptive Modulation in Mixed Impulsive Noise and Additive White Gaussian Noise Environments

“…). (7) In order to compensate the gap between the true SER given by ( 5) and the approximate SER by (7) especially in high SNR regime, the SNR ratio α defined as SNR of (5) over SNR of ( 7) is introduced to reduce the gap. Note that α is close to one when A < 0.2, because the infinite m terms are well approximated by two terms (m ≤ 1) due to sporadic appearances of the noise [35].…”

“…. For BPSK, by putting d 2 and P to 1 (d 2 = P = 1), (8) is reduced to (7). Note tht P equals to 1, 2, 3 and 7/2 for BPSK, 4-QAM, 16-QAM and 64-QAM, respectively, and d 2 is 1, 2, 10 and 42 for BPSK, 4-QAM, 16-QAM and 64-QAM, respectively.…”

“…where P s (T ) is the exact SER equation, which is defined in (5) for BPSK, Ps (T ) is the approximate SER in (7) for BPSK, and P −1 s (SER T ) and P −1 s (SER T ) are the inverse functions of those SER equations, respectively. The inverse functions P −1 s (SER T ) and P −1 s (SER T ) are used to compute SNR ratio α.…”

“…modulation and its approximations. In [6], [7], performance of multicast system with opportunistic scheduling in the presence of independent and identically distributed (i.i.d.) and nonidentically distributed fading channels are analyzed.…”

Dynamic Resource Scheduling Optimization With Network Coding for Multi-User Services in the Internet of Vehicles

“…Liu et al [15] present an analytical model for the performance analysis of dynamic proportional fair scheduling, and design a low-complexity algorithm for joint power allocation and user set selection. Li et al [16] also propose normalized Signal-to-Noise Ratio (SNR) based fair scheduling for a multicast system. To achieve throughput optimality with flow-level scheduling, Chen et al [17] propose a scheduling algorithm for fairly scheduling TCP flows in wireless networks with time varying channel conditions.Velkov et al [18] realize optimal proportional fair scheduling when each frame consists of either a downlink energy harvesting phase or an uplink information transmission phase.…”