Rate Allocation Policy Research Articles

Delay Optimal Transmission Policy in a Wireless Multiaccess Channel

In this correspondence, we consider the problem of delay optimal rate allocation in a (potentially asymmetric) multiaccess channel. The rate feasibility region of such a network is well studied and is shown to be of a polymatroid structure. We consider this problem with unsaturated sources, i.e., jobs arrive at sources at random times and the source has the possibility of being empty. In such a setting, all stable rate allocation policies result in a throughput matched with the average arrival rate. Hence, we are interested in rate allocation policies that minimize expected delay in the system. In this correspondence, we show that a policy of threshold type is optimal in minimizing the average queueing delay. We study the average delay criterion as the limit of an infinite-horizon discounted cost function when the discount factor approaches 1.

On the Distortion SNR Exponent of Some Layered Transmission Schemes

We consider the problem of joint source-channel coding for transmitting K samples of a complex Gaussian source overT bK uses of a block-fading multiple-input multiple-output (MIMO) channel with M transmit and N receive antennas. We consider the case when we are allowed to code over L blocks. The channel gain is assumed to be constant over a block and channel gains for different blocks are assumed to be independent. The performance measure of interest is the rate of decay of the expected mean-squared error with the signal-to-noise ratio (SNR), called the distortion SNR exponent. We first show that using a broadcast strategy similar to that of Gunduz and Erkip, but with a different power and rate allocation policy, the optimal distortion SNR exponent can be achieved for 0 les b les (|N - M| + 1)/ min(M,N) and for b > MNL <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> . This is the first time the optimal exponent is characterized for 1/min(M, N) < b < (|N - M| + 1)/min(M, N). Then, we propose a digital layered transmission scheme that uses both time layering and superposition. The new scheme is at least as good as currently known schemes for the entire range of bandwidth expansion factors b, whereas at least for some M, N, and b, it is strictly better than the currently known schemes.

Combined cross-layer design and HARQ for multiuser systems with outdated channel state information at transmitter (CSIT) in slow fading channels

Cross-layer scheduling and hybrid ARQ (HARQ) are effective means to improve the spectral efficiency of wireless systems. However, most of the existing works handle HARQ and cross-layer scheduling in a decoupled manner based on heuristic approaches. In this paper, we propose an integrated design framework for cross-layer scheduling and HARQ design in multiuser systems with slow fading channel and outdated knowledge of channel state information at the base station (CSIT). We consider both the chase combining and incremental redundancy in HARQ. We define the average system goodput (which measures the average bits successfully delivered to the receiver) as the measure of system performance. Based on information theoretical approach, we derive the asymptotically optimal cross-layer policy (power allocation, rate allocation and user selection policies) to optimize the average system goodput with HARQ. In addition, we derive analytically the closed-form expression of the average system goodput, from which we can obtain useful design insights such as the role of HARQ in the overall cross-layer gain, the tradeoff between the system goodput and the HARQ delay as well as the sensitivity of the average system goodput with respect to the CSIT quality.

Power-Efficient Resource Allocation for Time-Division Multiple Access Over Fading Channels

We investigate resource allocation policies for time-division multiple access (TDMA) over fading channels in the power-limited regime. For frequency-flat block-fading channels and transmitters having full channel state information (CSI), we first minimize power under a weighted sum average rate constraint and show that the optimal rate and time allocation policies can be obtained by a greedy water-filling approach with linear complexity in the number of users. Subsequently, we pursue power minimization under individual average rate constraints and establish that the optimal resource allocation also amounts to a greedy water-filling solution. Our approaches not only provide fundamental power limits when each user can support an infinite-size capacity-achieving codebook (continuous rates), but also yield guidelines for practical designs where users can only support a finite set of adaptive modulation and coding modes (discrete rates).

MIMO Transmission Control in Fading Channels—A Constrained Markov Decision Process Formulation With Monotone Randomized Policies

This paper addresses the optimal power and rate allocation control in multiple-input multiple-output (MIMO) wireless systems over Markovian fading channels. The problem is posed as an infinite horizon average-cost constrained Markov decision process (CMDP) with the goal of minimizing the average transmission power subject to delay constraints. By using a Lagrangian formulation of the CMDP, we use the concepts of stochastic dominance, submodularity, and multimodularity to prove that the optimal randomized policies are monotone. Three important structural results on the nature of the optimal randomized policies are derived. First, we show that the action space can be exponentially reduced by decomposing the rate allocation problem into bit-loading problem across individual antennas and the total rate allocation based on the current buffer occupancy and channel state. Second, we show that the optimal rate allocation policy is a randomized mixture of two pure policies that are monotonically increasing in the buffer occupancy. Finally, we show that the optimal power allocation is piecewise linear in the delay constraint. These three structural results can be exploited to devise efficient online reinforcement learning algorithms for optimal rate allocation.

Precoding and Decoding Paradigms for Distributed Vector Data Compression

In this paper, we consider the problem of lossy coding of correlated vector sources with uncoded side information available at the decoder. In particular, we consider lossy coding of vector source xisinR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sup> which is correlated with vector source yisinR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sup> , known at the decoder. We propose two compression schemes, namely, distributed adaptive compression (DAC) and distributed universal compression (DUC) schemes. The DAC algorithm is inspired by the optimal solution for Gaussian sources and requires computation of the conditional Karhunen-Loegraveve transform (CKLT) of the data at the encoder. The DUC algorithm, however, does not require knowledge of the CKLT at the encoder. The DUC algorithms are based on the approximation of the correlation model between the sources y and x through a linear model y=Hx+n in which H is a matrix and n is a random vector and independent of x. This model can be viewed as a fictitious communication channel with input x and output y. Utilizing channel equalization at the receiver, we convert the original vector source coding problem into a set of manageable scalar source coding problems. Furthermore, inspired by bit loading strategies employed in wireless communication systems, we propose for both compression schemes a rate allocation policy which minimizes the decoding error rate under a total rate constraint. Equalization and bit loading are paired with a quantization scheme for each vector source entry (a slightly simplified version of the so called DISCUS scheme). The merits of our work are as follows: 1) it provides a simple, yet optimized, implementation of Wyner-Ziv quantizers for correlated vector sources, by using the insight gained in the design of communication systems; 2) it provides encoding schemes that, with or without the knowledge of the correlation model at the encoder, enjoy distributed compression gains

POMDP-Based Coding Rate Adaptation for Type-I Hybrid ARQ Systems over Fading Channels with Memory

We address the issue of optimal coding rate scheduling for adaptive type-I hybrid automatic repeat request wireless systems. In this scheme, the coding rate is varied depending on channel, buffer and incoming traffic conditions. In general, we consider the hidden Markov model for both time-varying flat fading channel and bursty correlated incoming traffic. It is shown that the appropriate framework for computing the optimal coding rate allocation policies is partially observable Markov decision process (POMDP). In this framework, the optimal coding rate allocation policy maximizes the reward function, which is a weighted sum of throughput and buffer occupancy with appropriate sign. Since polynomial amount of space is needed to calculate the optimal policy even for a simple POMDP problem, we investigate maximum-likelihood, voting and Q-MDP policy heuristic approaches for the purpose of efficient and real-time solution. Our results show that three heuristics perform close to completely observable system state case if the fading and/or traffic state mixing rate is slow. On the other hand, when the channel fading is fast, Q-MDP heuristic is the most throughput-efficient among considered heuristics. Also, its performance is close to the optimal coding rate allocation policy of fully observable system state case. We also explore the performances of the proposed heuristics in the bursty correlated traffic case and show that maximum-likelihood and voting heuristics consistently outperform the non-adaptive case

Adaptive Joint Source Channel Rate Allocation Policies for Delay Sensitive Applications Over Fading Channels

In this correspondence, we consider a problem of optimal source, channel coding rate pair allocation over a wireless link based on the channel fading state and the queue length of the transmitter. The source is delay sensitive. Therefore, our objective is to minimize the mean delay under given mean power and distortion constraints. We show that by jointly optimizing the source and channel rates, one can gain substantially in performance as compared to only source or channel rate optimization, as is usually considered in the literature. This is shown for single user, single-input-single-output (SISO), multiple-input-multiple-output (MIMO), and multiuser systems. The methods used are from Markov decision theory

Capacity of space-time block codes in MIMO Rayleigh fading channels with adaptive transmission and estimation errors

Orthogonal space-time block coding (STBC) has recently raised a lot of research interest due to its inherent mathematical feature that enables simple linear decoding at the receiver and provides full diversity over the multiple-input multiple-output (MIMO) fading channel. In this paper, we derive general closed-form expressions for the Shannon capacity achieved by this transmit-diversity scheme over Rayleigh fading channels under adaptive transmission and channel-estimation errors. Adaptive transmission can be performed on a frame-by-frame basis, provided that a channel state information (CSI), consisting of the signal-to-noise ratio (SNR) level as estimated by the receiver, is fed back to the transmitter, thereby allowing for different compromises between the achievable capacity and the corresponding implementation complexity. The closed-form capacity formulas, derived for different power- and rate-allocation policies, are expressed in terms of the number of transmit and receive antennas, the code rate of the STBC mapping, and a single parameter capturing Gaussian channel-estimation errors. Numerical results showing the effects of these parameters on the capacity of STBC subject to the adaptive-transmission policies under consideration are provided.

Variable-Rate Coding for Slowly Fading Gaussian Multiple-Access Channels

We consider a nonergodic multiple-access Gaussian block-fading channel where a fixed number of independent and identically distributed (i.i.d.) fading coefficients affect each codeword. Variable-rate coding with input power constraint enforced on a per-codeword basis is examined. A centralized power and rate allocation policy is determined as a function of the previous and present fading coefficients. The power control policy that optimizes the expected rates is obtained through dynamic programming and the average capacity region and the average capacity region per unit energy are characterized. Moreover, we study the slope of spectral efficiency curve versus E/sub b//N/sub 0/ (dB), and we quantify the penalty incurred by time-division multiple access (TDMA) over superposition coding in the low-power regime.

On generalized max-min rate allocation and distributed convergence algorithm for packet networks

We consider the fundamental problem of bandwidth allocation among flows in a packet-switched network. The classical max-min rate allocation has been widely regarded as a fair rate allocation policy. But, for a flow with a minimum rate requirement and a peak rate constraint, the classical max-min policy no longer suffices to determine rate allocation since it is not capable of supporting either the minimum rate or the peak rate constraint from a flow. We generalize the theory of the classical max-min rate allocation with the support of both the minimum rate and peak rate constraints for each flow. Additionally, to achieve generalized max-min rate allocation in a fully distributed packet network, we present a distributed algorithm that uses a feedback-based flow control mechanism. Our design not only offers a fresh perspective on flow marking technique, but also advances the state-of-the-art flow marking technique favored by other researchers. We provide proof that such a distributed algorithm, through asynchronous iterations, will always converge to the generalized max-min rate allocation under any network configuration and any set of link distances. We use simulation results to demonstrate the fast convergence property of the distributed algorithm.

Generalized max-min rate allocation: Theory and a simple implementation

An important concept in the available bit rate (ABR) service model is the minimum cell rate (MCR) guarantee as well as the peak cell rate (PCR) constraint for each flow. Due to the MCR and PCR requirements, the well-known max-min rate allocation policy no longer suffices to determine the rate allocation for each flow since it does not support either MCR or PCR. In this paper, we present a generalized max-min (GMM) rate allocation policy, which supports both the MCR and PCR requirements for each flow. Furthermore, a simple distributed algorithm using the ABR flow control protocol is developed to achieve the GMM rate allocation in a distributed network environment. The effectiveness of this distributed algorithm is demonstrated by simulation results.

On network bandwidth allocation policies and feedback control algorithms for packet networks

This paper summarizes our experience on the design of network bandwidth allocation policies and distributed rate calculation algorithms for packet-switched networks. In particular, we discuss two rate allocation policies: the generalized max–min (GMM) and the weight-proportional max–min (WPMM) policies, both of which generalize the classical max–min rate allocation policy. For the design of distributed algorithms to achieve these two rate allocation policies, we focus on rate-based distributed flow control where special control packets are employed to achieve the information exchange between a source and the network. We categorize two broad classes of distributed rate calculation algorithms in the literature using live algorithms as illustrations. To give insight, we compare the design tradeoffs between these two classes of algorithms in terms of performance objectives and implementation complexities. Furthermore, we discuss important extensions within each class of algorithms.