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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.


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Proceedings ArticleDOI
06 May 2012
TL;DR: This paper investigates the cancellation of the interfereences among Destination Users (DU's) and the improvement of system rate in the non- regenerative Multiuser Multiple-Input Multiple- Output (MU-MIMO) relay downlink system and proposes a novel design method of transmit weights to successively eliminate the interference.
Abstract: This paper investigates the cancellation of the interfereences among Destination Users (DU's) and the improvement of system rate in the non- regenerative Multiuser Multiple-Input Multiple- Output (MU-MIMO) relay downlink system. A novel design method of transmit weights is proposed to successively eliminate the interference among DU's, each of which is equipped with multiple receive antennas. We firstly study the transmit weights design for the conventional Amplify-and-Forward (AF) relay system where the Relay Station (RS) just retransmits the received signals, then we extend it to the joint design of transmit weights both at the Base Station (BS) and the RS. In the joint design, Singular Value Decomposition (SVD) is used for the first channel link (link between BS and RS), then a user selection algorithm is adopted at the RS to generate the transmit weight of each DU. The channel matrix of system is transformed into a triangular matrix, where the Dirty Paper Coding (DPC) technique is employed to remove the interference among DU's and maintain the achievable rate of system. Simulation results verify the effectiveness of the proposed scheme.
01 Jan 2012
TL;DR: This paper presents an efiort to construct a multiuser (MU) multiple-input multiple- output (MIMO) broadcast channel (BC) model that is able to adequately build in the correlation and mutual coupling, which are important factors afiecting the multi-antenna arrays.
Abstract: This paper presents an efiort to construct a multiuser (MU) multiple-input multiple- output (MIMO) broadcast channel (BC) model. Our method is able to adequately build in the correlation and mutual coupling, which are important factors afiecting the multi-antenna arrays. This helps to decrease the need for complex mathematical analysis and allows an easy simulation of the MU MIMO BC performance. The charac-terizations of MU MIMO systems are done and the impact of varying the number of transmitting and receiving antennas is explored. These insights are useful in physical realization of the systems. 1. INTRODUCTION In recent years, as demands for increasing capacity networks rises, researches in antenna array communication are of much greater interest. Multiple-input-multiple-output (MIMO) systems are hence a natural extension of developments (1) since MIMO systems are able to turn multipath propagation, which is usually a pitfall in wireless transmission, into a beneflt. By doing so, the performance and capacity of the MIMO systems are increased tremendously. The capacity of single-user MIMO Gaussian channels was flrst studied by Foschini and Gans (2), and Telatar (3). The researches on MIMO systems are also extended to the multiuser cases which can be classifled as MIMO multiple access channel (MAC) and MIMO broadcast channels (BC). The sum rate of MIMO MAC system is easy to be obtained due to its convex property (4{7). The sum rate of the MU MIMO BC achieved with Dirty Paper Coding (DPC) (8), however, is not of the convex form. Based on the duality between MIMO MAC and BC systems, sum rate of MU MIMO BC can be obtained (6,7). Also, in (9), Weingarten showed that the capacity region achieved by using the DPC is exactly the same as that of the MU MIMO BC, given that the transmit powers are equal. Here, the method to calculate the (DPC) sum-rate capacity of the BC is the sum power iterative water fllling method proposed in (7). In this paper, our efiort will be focused on modelling the MU MIMO BC system. E-cient and accurate channel modeling is important to help predict the performance of MIMO communication systems. However, due to the large number of parameters that are involved in channel modeling, an analytical approach is usually too di-cult. This leads to the use of computer simulations. The challenge is to employ a method that is able to take all the parameters into account, and is also able to apply to most applications. Various methods are available for the simulation of the capacity of single user MIMO systems (10). Such simulation is lacking in MU MIMO system where extensive researches on its performance are still very much ongoing. In this paper, an e-cient simulation algorithm to simulate the MU MIMO system is presented. The method of modelling a single user MIMO system was proposed by Hui and Wang (11) which involves having scatterers around both the transmitting and receiving antennas to introduce random scattering events. This work is extended in this paper to include the MU MIMO system, where instead of only one single user in the receiving end, two users were implemented. Various MU MIMO systems will be built and characterizations can be done. From there, the impact of varying the number of transmitting antennas and receiving antennas will be examined. In this paper, we consider a two user BC system, where each user j and k has Rj = Rk > 1 re- ceiving antennas and the broadcast transmitter has T ‚ 1transmitting antennas. The transmitting antennas are surrounded by P scatterers where P is flxed at 50 in the far fleld, and this provides the scattering events. On the receiving side, both users are surrounded by Q scatterers which is varied when appreciate to investigate the efiects of scattering on the capacity. Between the two antennas, the paths of the signals are assumed to be completely random and that each propagation paths are associated with a loss factor which follows a Gaussian distributed random number which has a mean of 0 and a variance of 1. The model of the MU BC MIMO system is summarised in Fig. 1. As shown in Fig. 1, the signals follow a propagation path when travelling from the transmitter to the receivers. The loss factor associate with the path will cause the E-fleld that is received in the scatterers at the receiving end, which in turns afiects the current
Proceedings ArticleDOI
TL;DR: In this article, a successive null-space (SNS) precoding scheme was proposed to adjust the inter-user-interference experienced by the receivers in an underloaded or critically loaded downlink multi-user MIMO communication system.
Abstract: In this paper, we consider the precoder design for an under-loaded or critically loaded downlink multi-user multiple-input multiple-output (MU-MIMO) communication system. We propose novel precoding and decoding schemes which enhance system performance based on rate splitting at the transmitter and single-stage successive interference cancellation at the receivers. The proposed successive null-space (SNS) precoding scheme utilizes linear combinations of the null-space basis vectors of the successively augmented MIMO channel matrices of the users as precoding vectors to adjust the inter-user-interference experienced by the receivers. We formulate a non-convex weighted sum rate (WSR) optimization problem, and solve it via successive convex approximation to obtain a suboptimal solution for the precoding vectors and the associated power allocation. Our simulation results reveal that the proposed SNS precoders outperform block diagonalization based linear and rate splitting designs, and in many cases, have a relatively small gap to the maximum sum rate achieved by dirty paper coding.
Posted Content
TL;DR: An inner bound is derived for the capacity region in the general discrete memoryless case and a nontrivial outer bound is provided for this case which is better than the trivial outer bound in the case of infinite state variance.
Abstract: We consider a state-dependent multiaccess channel (MAC) with state non-causally known to some encoders. We derive an inner bound for the capacity region in the general discrete memoryless case and specialize to a binary noiseless case. In the case of maximum entropy channel state, we obtain the capacity region for binary noiseless MAC with one informed encoder by deriving a non-trivial outer bound for this case. For a Gaussian state-dependent MAC with one encoder being informed of the channel state, we present an inner bound by applying a slightly generalized dirty paper coding (GDPC) at the informed encoder that allows for partial state cancellation, and a trivial outer bound by providing channel state to the decoder also. The uninformed encoders benefit from the state cancellation in terms of achievable rates, however, appears that GDPC cannot completely eliminate the effect of the channel state on the achievable rate region, in contrast to the case of all encoders being informed. In the case of infinite state variance, we analyze how the uninformed encoder benefits from the informed encoder's actions using the inner bound and also provide a non-trivial outer bound for this case which is better than the trivial outer bound.
Book ChapterDOI
01 Jan 2014
TL;DR: This chapter examines the precoding and user selection algorithms for MIMO multiuser systems with one and multiple receive antennas in flat fading and frequency selective wireless channels and shows different user selection criteria and quantization strategies to reduce feedback load for both single and multicarrier communication systems.
Abstract: In this chapter, we focus on the different feedback strategies for the case of a multiuser wireless communication system with multiple transmitter and receiver antennas. Firstly, we analyze the capacity of the multiuser MIMO system with single receive antenna for uplink and downlink by assuming that the full channel state information (CSI) is available at the transmitter. Secondly, we examine the precoding and user selection algorithms for MIMO multiuser systems with one and multiple receive antennas in flat fading and frequency selective wireless channels. Since optimal precoding using dirty paper coding has a prohibitively high computational complexity due to the associated encoding process, it is a great practical interest to design MIMO multiuser systems with low complexity and a minimum CSI requirement at the transmitter side. One suboptimal approach is to apply linear precoding schemes, such as zero forcing beamforming (ZF-BF) or minimum mean square error criterion. Multiuser MIMO wireless communication with ZF-BF requires a brute-force exhaustive search over all possible user sets and the complexity of an exhaustive search is prohibitive when the number of users is large. In order to decrease the complexity of this search, several suboptimal user scheduling algorithms have been designed. Generally, these algorithms fall into two categories: Capacity-based and Frobenius norm-based algorithm. Lastly, we show the effect of reduced and limited feedback information including user selection at the receiver side and quantization for both single carrier and multicarrier transmissions. We perform user selection at the user side since the users having a poor channel (low norm or/and interference) should not take part in the user selection algorithm, nor feedback their channel information. By using a self-discrimination criterion at the receiver side, it is possible to reduce the feedback load and the complexity of the user selection algorithm at base station. We show different user selection criteria and quantization strategies to reduce feedback load for both single and multicarrier communication systems.
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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20236
202217
202121
202013
201926
201823