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.

Effects of noisy side information at the decoder in dirty-paper and dirty-tape coding

Abstract: This paper examines the problems of dirty-paper and dirty-tape coding with some additional side information (SI) at the decoder. In particular we focus on the situation in which the decoder SI is a noisy version of the SI at the encoder. We show that noisy SI at the decoder is advantageous for many channels in terms of capacity and, in general, the optimal coding strategy needs to take it into account. In particular, the capacities of the binary dirty-paper and dirty-tape channels are computed when the decoder SI is modeled as the output of a binary symmetric channel with the encoder SI as input. Moreover, the advantage offered by the decoder SI on the minimum achievable Eb /NO in the dirty-tape scenario with Gaussian interference is demonstrated

Performance Analysis of Linear and Nonlinear Precoding in MIMO Systems

Abstract: A MIMO system which is used to achieve high data rate and capacity in transmission medium is one of the significant emerging technologies today. The interferences occurring between the different antenna elements in MIMO OFDM can be mitigated to improve the performance of the system. The precoding technique, where the data is coded and transmitted to reduce the bit error rate can be used for this. There are linear precoding and nonlinear precoding methods are there.Linear precoding schemes have low complexity and can achieve a reasonable capacity. The nonlinear precoding can access more capacity with much receiver complexity. In this paper, the BER performance of different precoding schemes like Channel Inversion, Block Diagonalization, DPC and TH precoding are analysed to get a better performance for the MIMO systems. Simulation results shows that the nonlinear precoding technique Dirty Paper Coding (DPC) achieves better performance than all other precoding methods.

An Empirical Study of the Achievable Rates of Several Indoor Network-MIMO Techniques

Abstract: This paper presents an empirical study of the achievable data rates of network multiple-input multiple-output (MIMO) techniques including zero-forcing (ZF), zero-forcing dirty paper coding (ZF-DPC) and dirty paper coding (DPC) using actual 4-by-4 indoor wireless channel measurements at 3.5 GHz. Their performances are contrasted with those of conventional techniques, in which either the base stations are not coordinated (NC), or their interference is avoided using frequency division (FD) multiplexing. The measurements were taken in aisle-to-office and large unobstructed hall scenarios. The study of these results reveals that, at high signal-to-noise ratios (SNRs), DPC and ZF-DPC can yield more than a three-fold increase in attainable data rates when compared to NC and FD. The gains obtained using ZF are smaller, but still significant. At low SNRs the system is noise-(rather than interference-) limited, and only DPC exhibits gains. The evaluations in this paper also show that collaborative systems such as DPC can benefit from interference-prone environments to yield increased transmission capacity. With regard to the propagation channel, the classical log-normal plus Rayleigh/Ricean fading model, with parameters fitted to the scenario type, was found to be good at predicting the statistics of the achievable data rates of all the strategies considered.

On the Designs and Challenges of Practical Binary Dirty Paper Coding

Abstract: We propose a practical scheme for binary dirty-paper channels. By exploiting the concept of random binning instead of superposition coding, the complexity of the system is greatly reduced. For comparison, the existing approaches require one of the native codes to be of non-uniform a priori distribution, which is generally achieved by combining a symbol mapper and high-order-alphabet low-density parity-check (LDPC) codes. Using high-order alphabets increases significantly the complexity and the resulting method is not flexible for designing systems of practical channel parameters. In contrast, we propose to implement the random binning concept using only binary LDPC and binary convolutional codes. In this work, some design challenges of this random binning approach are identified and addressed. Our systems are optimized by the joint use of density evolution (DE) and the extrinsic information transfer (EXIT) analysis. Simulation results using practical Quasi-Cyclic LDPC codes show that our system achieves similar performance to the state-of-the-art, high-order-alphabet LDPC-based systems while demonstrating significant advantages in terms of complexity and flexibility of system design.

Dirty paper coding and decoding method based on joint lattice forming technology in cognitive network

Abstract: The invention provides a dirty paper coding and decoding method based on a joint lattice forming technology in a cognitive network, and the method is applicable to the occasion that a main network has only one main user and a cognitive wireless network has multiple secondary users. The method includes the specific steps that to guarantee that a signal to noise ratio of a main user receiving end is not changed, secondary user sending ends forward a sending signal of the main user while the secondary user sending ends send the corresponding signals through multiple transmitting antennas of the secondary user sending ends; grouping is conducted on signals transmitted to all secondary user receiving ends through the secondary user sending ends, channel coding and joint lattice forming coding are conduced respectively, and then a coding bit sequence is mapped into a symbol sequence and zero-forcing dirty paper coding is conducted so as to eliminate interference of other users; signals sent out by the main user are overlapped and then transmitted to multiple secondary user receiving ends through a channel by means of multiple transmitting antennas of a main user sending end so as to improve secondary user receiving performance. A traditional channel convolutional decoding method, namely signal restoring, is adopted for the secondary user receiving ends respectively. The method is simple in operation steps, lower in calculation complexity and high in practicability.