Joint Scheduling and Resource Allocation in the OFDMA Downlink: Utility Maximization Under Imperfect Channel-State Information

Abstract

Abstract-We consider the problem of simultaneous user-scheduling, power-allocation, and rate-selection in an orthogonal frequency division multiple access (OFDMA) downlink, with the goal of maximizing expected sum-utility under a sum-power constraint. In doing so, we consider a family of generic goodput-based utilities that facilitate, e.g., throughput-based pricing, quality-of-service enforcement, and/or the treatment of practical modulationand-coding schemes (MCS). Since perfect knowledge of channel state information (CSI) may be difficult to maintain at the base-station, especially when the number of users and/or subchannels is large, we consider scheduling and resource allocation under imperfect CSI, where the channel state is described by a generic probability distribution. First, we consider the "continuous" case where multiple users and/or code rates can time-share a single OFDMA subchannel and time slot. This yields a nonconvex optimization problem that we convert into a convex optimization problem and solve exactly using a dual optimization approach. Second, we consider the "discrete" case where only a single user and code rate is allowed per OFDMA subchannel per time slot. For the mixed-integer optimization problem that arises, we discuss the connections it has with the continuous case and show that it can solved exactly in some situations. For the other situations, we present a bound on the optimality gap. For both cases, we provide algorithmic implementations of the obtained solution. Finally, we study, numerically, the performance of the proposed algorithms under various degrees of CSI uncertainty, utilities, and OFDMA system configurations. In addition, we demonstrate advantages relative to existing state-of-the-art algorithms.Index Terms-Bisection algorithm, imperfect channel state information (CSI), mixed integer optimization, orthogonal frequency division multiple access (OFDMA), resource allocation, scheduling, utility maximization.

Robust power allocation for massive connections underlaying cognitive radio networks with channel uncertainty

“…The optimal or suboptimal power allocation policies in the underlay mode have been already investigated widely [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. A heuristic algorithm in CR systems under OFDMA has been reported in [4].…”

“…However, the CSI between SC and PU is usually difficult to be estimated accurately in actual situations mainly due to the lack of signaling cooperation between them. Therefore, power allocation with channel uncertainty should be considered in CRN [12][13][14][15][16][17][18][19][20]. A worst-case formulation is utilized in [12] for beamforming design, in which an ellipsoidal channel uncertainty region is assumed.…”

“…Dall' Anese et al [13] considers the probabilistic interference constraints for a CR power control problem, where uncertainty in composite fading channels comprising shadowing and Nakagami fading is accounted for. Power allocation problems for generic OFDMA systems with channel uncertainty have also been investigated extensively [14][15][16]. Ma et al [17] shows the availability of imperfect CSI that includes probabilistic uncertainty and considers its worst effect when performing resource allocation.…”

“…Power allocation along with relay selection schemes is optimized especially considering different channel uncertainty models in [18]. It is worth noting that [12][13][14][15][16][17][18][19] consider the channel uncertainty without the interference between massive SCs. Son et al [20] study the power allocation problem via the water-filling algorithm.…”

Fairness-Adaptive Goodput-Based Resource Allocation in OFDMA Downlink with ARQ

“…In [18], they exploit the use of acknowledgment/negative acknowledgment (ACK/NAK) feedback. In [19], imperfect channel state information (CSI) at the transmitter (CSIT) is assumed, where Aggarwal et al study the continuous (a single resource shared by several users) and discrete cases and show that the continuous case solves the discrete case in some situations, providing a bound for the others. The work in [19] is extended in [20] to the case when ACK/NAK feedback is utilized.…”