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 Low Complexity Approximation of the MIMO Broadcast Channel Capacity Region

Abstract: Points on the boundary of the MIMO broadcast channel (BC) capacity region are achieved by a combination of dirty paper coding (DPC) and linear precoding. The linear precoding determines the covariance matrices of the transmitted signals. Determining the optimum covariance matrices may lead to an undesirably high computational complexity for systems of high dimension. In this paper, an approach to approximate the MIMO BC capacity region is proposed. The proposed method combines DPC with sub-optimum linear precoding matrices that can be computed with low complexity but provide close to optimum performance. Motivated by multiobjective optimization, an efficient algorithm developed for sum-rate maximization is generalized to computing an achievable rate region. Simulation results show that this rate region, which has low complexity in terms of computing the precoding matrices, well approximates the capacity region of the MIMO BC.

Maximum volume criterion for user selection of multiuser MIMO downlink with multiantenna users and block diagonalization beamforming

Abstract: Compared with capacity-achieving dirty paper coding [1], block diagonalization (BD) algorithm [2] is known as a suboptimal but more practical beamforming scheme for multiuser MIMO downlink systems with multiantenna users. However BD suffers from two major drawbacks: the dimensionality constraint on antennas arrangement and the difficulty in handling spatially correlated users. User selection capable of selecting some most sum-rate-contributive users is a promising measure to alleviating this situation.

Circumventing base station cooperation through Kalman prediction of intercell interference

Abstract: We consider the downlink of a cellular multiple input single output (MISO) system with multiple users per cell. Fast scheduling and spatial signal processing at the base stations result in unpredictable non-stationary intercell interference when the base stations do not cooperate. We show how the per cell sum-rate can be increased when Kalman filters are employed to forecast the interference power for the next transmission. We compare these results for dirty-paper coding and beamforming to systems where the intercell interference powers are perfectly known through feedback channels or cooperating base stations and to systems where outdated information is used.

State-Dependent Gaussian Multiple Access Channels: New Outer Bounds and Capacity Results

Abstract: This paper studies a two-user state-dependent Gaussian multiple-access channel (MAC) with state noncausally known at one encoder. Two scenarios are considered: 1) each user wishes to communicate an independent message to the common receiver; and 2) the two encoders send a common message to the receiver and the non-cognitive encoder (i.e., the encoder that does not know the state) sends an independent individual message (this model is also known as the MAC with degraded message sets). For both scenarios, new outer bounds on the capacity region are derived, which improve uniformly over the best known outer bounds. In the first scenario, the two corner points of the capacity region as well as the sum rate capacity are established, and it is shown that a single-letter solution is adequate to achieve both the corner points and the sum rate capacity. Furthermore, the full capacity region is characterized in situations in which the sum rate capacity is equal to the capacity of the helper problem. The proof exploits the optimal-transportation idea of Polyanskiy and Wu (which was used previously to establish an outer bound on the capacity region of the interference channel) and the worst case Gaussian noise result for the case in which the input and the noise are dependent.