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.

Precoding for Multiple Antenna Gaussian Broadcast Channels With Successive Zero-Forcing

Abstract: In this paper, we consider the multiuser Gaussian broadcast channel with multiple transmit antennas at the base station and multiple receive antennas at each user. Assuming full knowledge of the channel state information at the transmitter and the different receivers, a new transmission scheme that employs partial interference cancellation at the transmitter with dirty-paper encoding and decoding is proposed. The maximal achievable throughput of this system is characterized, and it is shown that given any ordered set of users the proposed scheme is asymptotically optimal in the high signal-to-noise ratio (SNR) regime. In addition, with optimal user ordering, the proposed scheme is shown to be optimal in the low-SNR regime. We also consider a linear transmission scheme which employs only partial interuser interference cancellation at the base station without dirty-paper coding. Given a transmit power constraint at the base station, the sum-rate capacity of this scheme is characterized and a suboptimal precoding algorithm is proposed. In several cases, it is shown that, for all values of the SNR, the achievable throughput of this scheme is strictly larger than a system which employs full interference cancellation at the base station (Spencer et al., 2004). In addition, it is shown that, in some cases, the linear transmission scheme can support simultaneously an increased number of users while achieving a larger system throughput.

Dirty paper coding vs. TDMA for MIMO broadcast channels

Abstract: In this paper we derive an upper bound on the sum-rate gain that dirty-paper coding provides over TDMA for MIMO broadcast channels. We find that the sum-rate capacity (achievable using dirty-paper coding) of the multiple-antenna broadcast channel is at most min(M;K) times the largest single-user capacity (i.e. the TDMA sum-rate) in the system, where M is the number of transmit antennas and K is the number of receivers. This result is independent of the number of receive antennas. We investigate the tightness of this bound in a time-varying channel (assuming perfect channel knowledge at receivers and transmitters) where the channel experiences uncorrelated Rayleigh fading and in some situations we find that the dirty paper gain is upper bounded by the ratio of transmit to receive antennas. We also show that min(M,K) upper bounds the sum rate gain of successive decoding over TDMA for the uplink, where M is the number of receive antennas at the base station and K is the number of transmitters.

Approximative Matrix Inverse Computations for Very-large MIMO and Applications to Linear Pre-coding Systems

Abstract: In very-large multiple-input multiple-output (MIMO) systems, the BS (base station) is equipped with very large number of antennas as compared to previously considered systems. There are various advantages of increasing the number of antennas, and some schemes would require handling large matrices for joint processing (pre-coding) at the base station. The dirty paper coding (DPC) is an optimal pre-coding scheme and has a very high complexity. However with increasing number of BS antennas linear pre-coding performance tends to that of the optimal DPC. Although linear pre-coding is less complex than DPC, there is a need to compute pseudo inverses of large matrices. In this paper we present a low complexity approximation of down-link Zero Forcing linear pre-coding for very-large multi-user MIMO systems. Approximation using a Neumann series expansion is opted for inversion of matrices over traditional exact computations, by making use of special properties of the matrices, thereby reducing the cost of hardware. With this approximation of linear pre-coding, we can significantly reduce the computational complexity for large enough systems, i.e., where we have enough BS antenna elements. For the investigated case of 8 users, we obtain 90% of the full ZF sum rate, with lower computational complexity, when the number of BS antennas per user is about 20 or more. (Less)

On the Loss of Single-Letter Characterization: The Dirty Multiple Access Channel

Abstract: For general memoryless systems, the existing information-theoretic solutions have a ldquosingle-letterrdquo form. This reflects the fact that optimum performance can be approached by a random code (or a random binning scheme), generated using independent and identically distributed copies of some scalar distribution. Is that the form of the solution of any (information-theoretic) problem? In fact, some counter examples are known. The most famous one is the ldquotwo help onerdquo problem: Korner and Marton showed that if we want to decode the modulo-two sum of two correlated binary sources from their independent encodings, then linear coding is better than random coding. In this paper we provide another counter example, the ldquodoubly-dirtyrdquo multiple-access channel (MAC). Like the Korner-Marton problem, this is a multiterminal scenario where side information is distributed among several terminals; each transmitter knows part of the channel interference while the receiver only observes the channel output. We give an explicit solution for the capacity region of the binary doubly-dirty MAC, demonstrate how this region can be approached using a linear coding scheme, and prove that the ldquobest known single-letter regionrdquo is strictly contained in it. We also state a conjecture regarding the capacity loss of single-letter characterization in the Gaussian case.

Beyond Dirty Paper Coding for Multi-Antenna Broadcast Channel With Partial CSIT: A Rate-Splitting Approach

Abstract: Imperfect Channel State Information at the Transmitter (CSIT) is inevitable in modern wireless communication networks, and results in severe multi-user interference in multi-antenna Broadcast Channel (BC). While the capacity of multi-antenna (Gaussian) BC with perfect CSIT is known and achieved by Dirty Paper Coding (DPC), the capacity and the capacity-achieving strategy of multi-antenna BC with imperfect CSIT remain unknown. Conventional approaches therefore rely on applying communication strategies designed for perfect CSIT to the imperfect CSIT setting. In this work, we break this conventional routine and make two major contributions. First, we show that linearly precoded Rate-Splitting (RS), relying on the split of messages into common and private parts and linear precoding at the transmitter, and successive interference cancellation at the receivers, can achieve larger rate region than DPC in multi-antenna BC with partial CSIT. Second, we propose a novel scheme, denoted as Dirty Paper Coded Rate-Splitting (DPCRS), that relies on RS to split the user messages into common and private parts, and DPC to encode the private parts. We show that the rate region of DPCRS in Multiple-Input Single-Output (MISO) BC with partial CSIT is enlarged beyond that of conventional DPC and that of linearly precoded RS. Gaining benefits from the capability of RS to partially decode the interference and partially treat interference as noise, DPCRS is less sensitive to CSIT inaccuracies, networks loads and user deployments compared with DPC and other existing transmission strategies.