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.

A Broadcast Scheme for MIMO Systems with Channel State Information at the Transmitter

Abstract: We propose a new broadcast strategy for a multiple-input multiple-output (MIMO) system with N transmit antennas at the transmitter and M≤N single antenna receivers. The proposed method, based on dirty-paper coding (DPC), spatially separates the M users but does not suffer from the power loss of classical spatial division multiple access (SDMA). For the special case of M=N=2 and when the two single antenna receivers are assumed to be co-located, the proposed scheme produces a 2 transmit, 2 receiver antenna MIMO transmission system that doubles the symbol rate of MIMO space-time block code (STBC) systems from one to two symbol per transmission time. It is proved theoretically and experimentally that the proposed scheme provides the same performance level as that of MIMO STBC systems (i. e., the Alamouti scheme) for the first symbol, and the same performance as the Bell labs layered space-time (BLAST) system for the second symbol. When compared to the BLAST system, the proposed scheme has the same symbol rate, but achieves significantly better performance, since it provides 2 level diversity per symbol on the first symbol while the BLAST system does not provide any diversity.

Block Diagonalization for Frequency-Selective Multiuser-MIMO Downlinks

Abstract: The capacity of frequency-flat multiuser MIMO downlinks is achieved with nonlinear dirty paper coding (DPC). Linear processing techniques, such as block diagonalization (BD), offer relatively low computational cost with respect to DPC and can also achieve capacity in certain cases. However, BD is not easily translated to frequency-selective channels since the channel matrix has a space-time structure (unlike the space-only information in frequency-flat channels), which induces an additional inter-symbol interference (ISI) component in the received signal. In this paper, we demonstrate that BD is feasible in frequency- selective channels when the transmitted block length is sufficiently large and the number of transmit antennas is greater than or equal to the number of users. We also present three novel techniques based on BD for frequency-selective MU- MIMO systems: time-reversal-based BD (TRBD), equalized BD (EBD), and joint processing BD (JPBD). These techniques use perfect channel state information at the transmitter (and also at the receiver, in the case of JPBD) to mitigate or eliminate both ISI and inter-user interference. We demonstrate that TRBD and EBD yield zero multiplexing gain (high SNR regime) but perform well under low SNR, while JPBD has a multiplexing gain approximately equal to the number of users for a sufficiently large transmit block length. Extensive numerical simulations show that the achievable rate performance of the proposed techniques improves that of time-reversal beamforming, a common technique for frequency- selective MU-MIMO downlinks.

Flat fading channel estimation based on Dirty Paper Coding

Abstract: Channel estimation is a transversal problem in signal processing (for example, it is used in digital communications, image restoration, digital forensics, acoustics, etc.). Among channel estimation algorithms, pilot-based estimation techniques stand out as being among the most frequently used. These techniques devote part of the total available power, which is usually limited, to send pilot signals that are used later to estimate the channel. The frequent need to send pilot signals in order to be able to track the channel variations, which lowers the information rate, becomes as one of their major drawbacks. Recently, the idea of concurrently sending a known training sequence with the information-bearing signal (also known as host) by means of arithmetically adding both sequences was proposed. These techniques are usually referred as superimposed training techniques. By implementing this idea, there is no drop in the information rate; however, part of the power available to send the information must be used by the added superimposed sequence thus causing a power loss in the information-bearing sequence. In addition, the original signal interferes with the pilot sequence of the superimposed training techniques, causing a decrease in the estimate performance, which is measured in terms of mean square error between the estimation and the actual channel gain. To tackle this issue, some solutions have been provided that use part of the power to partial cancel the host-interference. In this thesis, we have found a connection between superimposed training and digital watermarking. Indeed, this partially cancellation of the host of pilot sequences, known as PDD was independently proposed in digital watermarking, where is called ISS. We propose to obtain full cancellation of host-interference for estimation by applying the DPC paradigm that successfully was used in digital watermarking with several implementations (e.g., SCS, DC-DM, etc.). Specifically in this thesis, first we focus on the study of the flat fading channel estimation based on dirty paper coding for the case of real valued signals. Due to its interesting asymptotic properties, we design our estimation technique using MLE. In order to do that, the pdf of the random variables modeling the involved signals is required; in general, those pdfs are hard to handle mathematically and, as a consequence, so is the MLE cost function. Therefore, we have proposed a set of approximations of the pdf whose accuracy is validated in the cases for which they have been designed. In addition, a modification of the technique whenever the variances of the original signal and the channel noise are unknown is presented. In addition, this thesis proposes how to make full use of the Spread-Transform (an established concept of digital watermarking) to estimate the channel gain. In addition, a theoretical study is introduced following an estimation theory perspective, which indicates that asymptotically our scheme is not only not harmed by the host but it helps for estimation, and an information theory perspective, whose results determine that the induced structure of the transmitted signal helps the estimation of the gain of the channel. Both analyses show an improvement on the estimation performance of our technique with respect to Spread-Spectrum and PDD. The computational and time requirements needed to implement MLE, even using our pdf approximations, are not affordable in many applications. To tackle this, we introduce a set of MLE-based practical algorithms for estimation, designed with computational and temporal constraints. These algorithms take advantage of the statistical and deterministic properties of the problem. Several performance tests, measuring the accuracy of our algorithm, indicate that it outperforms other existing techniques whenever the structure of the sent signal becomes patent, and requires much shorter computational time than other existing DPC-based estimation techniques. With the aim of gaining insight into the wide range of practical uses of our algorithms, this thesis presents a set of applications of the proposed technique. For example, we use our algorithms to make dirty paper coding watermarking robust to gain attacks. By using both synthetic signals and real images, the obtained results validate the efficacy of our techniques in dealing with such attacks. We also show, in a flat fading channel communications scenario, how to equalize the gain estimated with our algorithms. The results show that our techniques improve the performance with respect to equalizing techniques based either on the second moment estimation or on superimposed training. Finally, we also propose how to adapt our estimation algorithm to the case of complex signals and complex gains, whose performance indicates that host also helps in the estimation.

Dirty Paper Coded Rate-Splitting for Non-Orthogonal Unicast and Multicast Transmission with Partial CSIT

Abstract: A Non-Orthogonal Unicast and Multicast (NOUM) transmission system allows a multicast stream intended to all receivers to be jointly transmitted with unicast streams in the same time-frequency resource blocks. While the capacity of the two-user multi-antenna NOUM with perfect Channel State Information at the Transmitter (CSIT) is known and achieved by Dirty Paper Coding (DPC)-assisted NOUM with Superposition Coding (SC), the capacity and the capacity-achieving strategy of the multi-antenna NOUM with partial CSIT remain unknown. In this work, we focus on the partial CSIT setting and make two major contributions. First, we show that linearly precoded Rate-Splitting (RS), relying on splitting unicast messages into common and private parts, encoding the common parts together with the multicast message and linearly precoding at the transmitter, can achieve larger rate regions than DPC-assisted NOUM with partial CSIT. Second, we study Dirty Paper Coded Rate-Splitting (DPCRS), that marries RS and DPC. We show that the rate region of DPCRS-assisted NOUM is enlarged beyond that of conventional DPC-assisted NOUM and that of linearly precoded RS-assisted NOUM with partial CSIT.