Rohit Aggarwal

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

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 modulation-and-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.

Rate adaptation via link-layer feedback for goodput maximization over a time-varying channel

We consider adapting the transmission rate to maximize the goodput, i.e., the amount of data transmitted without error, over a continuous Markov flat-fading wireless channel. In particular, we consider schemes in which transmitter channel state is inferred from degraded causal error-rate feedback, such as packet-level ACK/NAKs in an automatic repeat request (ARQ) system. In such schemes, the choice of transmission rate affects not only the subsequent goodput but also the subsequent feedback, implying that the optimal rate schedule is given by a partially observable Markov decision process (POMDP). Because solution of the POMDP is computationally impractical, we consider simple suboptimal greedy rate assignment and show that the optimal scheme would itself be greedy if the error-rate feedback was non-degraded. Furthermore, we show that greedy rate assignment using non-degraded feedback yields a total goodput that upper bounds that of optimal rate assignment using degraded feedback. We then detail the implementation of the greedy scheme and propose a reduced-complexity greedy scheme that adapts the transmission rate only once per block of packets. We also investigate the performance of the schemes numerically, and show that the proposed greedy scheme achieves steady-state goodputs that are reasonably close to the upper bound on goodput calculated using non-degraded feedback. A similar improvement is obtained in steady-state goodput, drop rate, and average buffer occupancy in the presence of data buffers. We also investigate an upper bound on the performance of optimal rate assignment for a discrete approximation of the channel and show that such quantization leads to a significant loss in achievable goodput.

On the Design of Large Scale Wireless Systems

In this paper, we consider the downlink of large OFDMA-based networks and study their performance bounds as a function of the number of - transmitters B, users K, and resource-blocks N. Here, a resource block is a collection of subcarriers such that all such collections, that are disjoint have associated independently fading channels. In particular, we analyze the expected achievable sum-rate as a function of above variables and derive novel upper and lower bounds for a general spatial geometry of transmitters, a truncated path-gain model, and a variety of fading models. We establish the associated scaling laws for dense and extended networks, and propose design guidelines for the regulators to guarantee various QoS constraints and, at the same time, maximize revenue for the service providers. Thereafter, we develop a distributed resource allocation scheme that achieves the same sum-rate scaling as that of the proposed upper bound for a wide range of K, B, N. Based on it, we compare low-powered peer-to-peer networks to high-powered single-transmitter networks and give an additional design principle. Finally, we also show how our results can be extended to the scenario where each of the B transmitters have M (>;1) co-located antennas.

Joint Scheduling and Resource Allocation in OFDMA Downlink Systems Via ACK/NAK Feedback

In this paper, we consider the problem of joint scheduling and resource allocation in the orthogonal-frequency-division multiple-access (OFDMA) downlink, with the goal of maximizing an expected long-term goodput-based utility subject to an instantaneous sum-power constraint, and where the feedback to the base station consists only of acknowledgements/negative acknowledgements (ACK/NAKs) from recently scheduled users. We first establish that the optimal solution is a partially observable Markov decision process (POMDP), which is impractical to implement. In response, we propose a greedy approach to joint scheduling and resource allocation that maintains a posterior channel distribution for every user, and has only polynomial complexity. For frequency-selective channels with Markov time-variation, we then outline a recursive method to update the channel posteriors, based on the ACK/NAK feedback, that is made computationally efficient through the use of particle filtering. To gauge the performance of our greedy approach relative to that of the optimal POMDP, we derive a POMDP performance upper-bound. Numerical experiments show that, for slowly fading channels, the performance of our greedy scheme is relatively close to the upper bound, and much better than fixed-power random user scheduling (FP-RUS), despite its relatively low complexity.

Performance bounds and associated design principles for multi-cellular wireless OFDMA systems

In this paper, we consider the downlink of large-scale multi-cellular OFDMA-based networks and study performance bounds of the system as a function of the number of users K, the number of base-stations B, and the number of resource-blocks N. Here, a resource block is a collection of subcarriers such that all such collections, that are disjoint have associated independently fading channels. We derive novel upper and lower bounds on the sum-utility for a general spatial geometry of base stations, a truncated path loss model, and a variety of fading models (Rayleigh, Nakagami-m, Weibull, and LogNormal). We also establish the associated scaling laws and show that, in the special case of fixed number of resource blocks, a grid-based network of base stations, and Rayleigh-fading channels, the sum information capacity of the system scales as Θ(B log log K/B) for extended networks, and as O(B log log K) and Ω(log log K) for dense networks. Interpreting these results, we develop some design principles for the service providers along with some guidelines for the regulators in order to achieve provisioning of various QoS guarantees for the end users and, at the same time, maximize revenue for the service providers.

Optimal resource allocation in OFDMA downlink systems with imperfect CSI

In this paper, we address the problem of joint scheduling and resource allocation in the downlink of an orthogonal frequency division multiple access (OFDMA)-based wireless network when the per-user SNR is known only in distribution. In particular, we consider sum-utility maximization over user schedules, powers, and code rates, subject to an instantaneous sum-power constraint. We consider both a “continuous” scenario where, during a time-slot, each OFDMA subchannel can be time-shared among multiple users and/or code rates, and a “discrete” scenario where no time-sharing is allowed. For the non-convex optimization problem arising in the continuous case, we propose an efficient exact solution. For the mixed-integer optimization problem arising in the discrete case, we propose a polynomial-complexity approximate solution and derive a bound on its optimality gap. We also provide a numerical study of goodput maximization for the SNR distribution that results from the use of pilot-aided MMSE channel estimation.