Dirty paper coding

About: Dirty paper coding is a research topic. Over the lifetime, 814 publications have been published within this topic receiving 37097 citations.

Information Embedding with Distortion Side Information

Abstract: We use the distortion side information (DSI) frame-work to study the gains in information embedding when the encoder exploits sensitivity of the source samples. Our study for the Gaussian source model extends the dirty paper coding result by Costa to the case of a weighted power constraint with the weights only known to the transmitter. A coding scheme based on fixed codebook variable-partition codes is presented for this problem. We also present another coding scheme that exploits the knowledge of DSI and is robust against intentional attacks. Finally, we study a related problem of Wyner-Ziv coding with reliability side information (RSI) at the decoder. This latter setup illustrates that fixed codebook variable-partition codes could also be fundamental in systems that rely on conventional distortion measures.

A Lower Bound on the Data Rate of Dirty Paper Coding in General Noise and Interference

Abstract: Dirty paper coding (DPC) allows a transmitter to send information to a receiver in the presence of interference that is known (non-causally) to the transmitter. The original version of DPC was derived for the case where the noise and the interference are statistically independent Gaussian random sequences. More recent works extended this approach to the case where the noise and the interference are mutually independent and at least one of them is Gaussian. In this letter we further extend the DPC scheme by relaxing the Gaussian and statistical independence assumptions. We provide lower bounds on the achievable data rates in a DPC setting for the case of possibly dependent noise, interference and input signals. Also, the interference and noise terms are allowed to have arbitrary probability distributions. The bounds are relatively simple, are phrased in terms of second-order statistics, and are tight when the actual noise distribution is close to Gaussian.

Downlink multi-user MIMO scheduling with performance guarantees

Abstract: We consider a long-standing open problem pertaining to scheduling over a wideband multi-user downlink. In this problem a base-station (BS) must assign multiple subbands to its served users such that a weighted sum rate metric is maximized subject to sum power and cardinality constraints. On each subband multiple users can simultaneously be scheduled. Such scheduling is complicated by the fact that the rate achieved by a user on any subband assigned to it depends not only on its own channel condition, but on the set of other users co-scheduled on that subband as well. The latter dependence is via the transmission scheme adopted by the BS in order to simultaneously serve multiple users on the same subband. This problem has received wide attention for over a decade and while numerous heuristics have been designed, there is no known algorithm that offers provable constant-factor worst-case guarantee. In this paper we obtain an important result which demonstrates that when the transmitter employs capacity-optimal dirty paper coding, constant-factor approximation guarantee can be achieved via simple algorithms. Indeed, we show that for a wideband scheduling problem in which a permissible set of user groups is specified as input, a simple deterministic algorithm yields a constant-factor approximation guarantee. Further, for the generalized case where any user group subject to a cardinality constraint is permissible, a greedy algorithm yields a constant-factor guarantee over certain practically relevant regimes.

A lower bound on the data rate of dirty paper coding in general noise and interference

Abstract: Dirty paper coding (DPC) allows a transmitter to send information to a receiver in the presence of interference that is known (non-causally) to the transmitter. The original version of DPC was derived for the case where the noise and the interference are statistically independent Gaussian random sequences. More recent works extended this approach to the case where the noise and the interference are mutually independent and at least one of them is Gaussian. In this letter we further extend the DPC scheme by relaxing the Gaussian and statistical independence assumptions. We provide lower bounds on the achievable data rates in a DPC setting for the case of possibly dependent noise, interference and input signals. Also, the interference and noise terms are allowed to have arbitrary probability distributions. The bounds are relatively simple, are phrased in terms of second-order statistics, and are tight when the actual noise distribution is close to Gaussian.

Precoder design for distributed antenna systems (DAS) with limited channel state information

Abstract: A distributed antenna system (DAS) consists of multiple baseband units (BBUs) connecting to distributed antennas (DAs) via dedicated access links In this study, we investigate a DAS with limited channel state information (CSI) and consider an average rate of users as an objective, where the expectation is taken over the channel uncertainty We propose two distributed precoder designs that are based on a rate lower bound and a rate upper bound, respectively As a benchmark, coordinated precoder and cooperative dirty paper coding (DPC)-based precoder with full CSI are compared with our proposed algorithms Numerical results verifies that the rate performance of our upper-bound based scheme with limited CSI approaches tightly the maximum rates of the full CSI schemes, while that of lower-bound based scheme is relatively worse