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.

On Gaussian MIMO compound wiretap channels

Abstract: We study the two-user one-eavesdropper discrete memoryless compound wiretap channel, where the transmitter sends a common confidential message to both users, which needs to be kept perfectly secret from the eavesdropper. We provide a new achievable secrecy rate which is shown to be potentially better than the best known lower bound for the secrecy capacity of this compound wiretap channel. We next consider the two-user one-eavesdropper Gaussian multiple-input multiple-output (MIMO) compound wiretap channel. We obtain an achievable secrecy rate for the Gaussian MIMO compound wiretap channel by using dirty-paper coding (DPC) in the achievable scheme we provided for the discrete memoryless case. We show that the corresponding achievable secrecy rate achieves at least half of the secrecy capacity of the two-user one-eavesdropper Gaussian MIMO wiretap channel. We also obtain the secrecy capacity of the two-user one-eavesdropper Gaussian MIMO compound wiretap channel when the eavesdropper is degraded with respect to one of the two users.

Sum Rate Maximization of MIMO Broadcast Channels with Coordination of Base Stations

Abstract: We consider cooperative downlink transmission in multiuser, multi-cell and multiple-antenna cellular networks. It has been shown that multi-base coordinated transmission has significant spectral efficiency gains over that without coordination. The capacity limits can be achieved using a non-linear preceding technique known as dirty paper coding, which is still infeasible to implement in practice. This motivates investigation of a simpler linear preceding technique based on generalized zero-forcing known as block diagonalization (BD). In this paper, an enhanced form of BD is proposed for multiple-input multiple-output (MIMO) multi-base coordinated network. It involves optimizing the precoding over the entire null space of other users' transmissions. The performance limits of the multiple-antenna downlink with multi-base coordination are studied using duality of MIMO broadcast channels (BC) and MIMO multiple-access channels (MAC) under per-antenna power constraint, which has been established recently.

On the Capacity of Gaussian Weak Interference Channels with Degraded Message sets

Abstract: This paper is motivated by a sensor network on a correlated field where nearby sensors share information, and can thus assist rather than interfere with one another. We consider a special class of two-user Gaussian interference channels (IFCs) where one of the two transmitters knows both the messages to be conveyed to the two receivers. Both achievability and converse arguments are provided for a channel with Gaussian inputs and Gaussian noise when the interference is weaker than the direct link (a so called weak IFC). In general, this region serves as an outer bound on the capacity of weak IFCs with no shared knowledge between transmitters.

Capacity Region of MISO Broadcast Channel for Simultaneous Wireless Information and Power Transfer

Abstract: This paper studies a multiple-input–single-output (MISO) broadcast channel (BC) featuring simultaneous wireless information and power transfer, where a multiantenna access point (AP) delivers both information and energy via radio signals to multiple single-antenna receivers simultaneously, and each receiver implements either information decoding (ID) or energy harvesting (EH). In particular, pseudorandom sequences that are a priori known and therefore can be cancelled at each ID receiver are used as the energy signals, and the information-theoretically optimal dirty paper coding is employed for the information transmission. We characterize the capacity region for ID receivers by solving a sequence of weighted sum-rate (WSR) maximization (WSRMax) problems subject to a maximum sum-power constraint for the AP, and a set of minimum harvested power constraints for individual EH receivers. The problem corresponds to a new form of WSRMax problem in MISO-BC with combined maximum and minimum linear transmit covariance constraints (MaxLTCCs and MinLTCCs), which differs from the celebrated capacity region characterization problem for MISO-BC under a set of MaxLTCCs only and is challenging to solve. By extending the general BC–multiple-access-channel duality, which is only applicable to WSRMax problems with MaxLTCCs, and applying the ellipsoid method, we propose an efficient iterative algorithm to solve this problem globally optimally. Furthermore, we also propose two suboptimal algorithms with lower complexity by assuming that the information and energy signals are designed separately. Finally, numerical results are provided to validate our proposed algorithms.

A computationally efficient genetic algorithm for MIMO broadcast scheduling

Abstract: Graphical abstractDisplay Omitted HighlightsMultiple-input multiple-output (MIMO) is suitable technique to ensure high speed data transmission as well as low delay communication networksIn MIMO, dirty paper coding (DPC) is an efficient scheme to support multiple users with optimum sum rate capacity of the system However, DPC is a complex scheme where the user encoding sequence is important to transmit data to multiple usersAn optimal exhaustive search as used in DPC is prohibited due to the extremely large size of the search space in this optimization problemEvolutionary algorithm (genetic algorithm) can be used as an alternative for this optimization problem to reduce the complexity of the search (scheduling problem) The performance of the genetic algorithm with elitism and adaptive mutation is demonstrated to be near optimal as obtained with an exhaustive search It has been demonstrated in this paper that GA achieves about 98-99% of system sum rate as obtained with DPC with significant reduction in time and computational complexityThe proposed BGA is able to provide the optimum solution well within the packet duration of modern wireless packet data communications In conventional single-input single-output (SISO) systems, the capacity is limited as base station can provide service to only one user at any instant However, multiuser (MU) multiple-input multiple-output (MIMO) systems deliver optimum system capacity by providing service to multiple users (as many as transmit antennas) simultaneously according to dirty paper coding (DPC) scheme However, DPC is an exhaustive search algorithm (ESA) where the user encoding sequence is important to transmit data to multiple users Exhaustive search becomes imperative as the search space grows with number of users and number of transmit antennas in the MU MIMO system This can be treated as an optimization problem of maximizing the achievable system sum-rate In this paper, it has been demonstrated that combined user and antenna scheduling (CUAS) with binary genetic algorithm (BGA) adopting elitism and adaptive mutation (AM) achieves about 97-99% of system sum-rate obtained by ESA (DPC) with significantly reduced computational and time complexity It has been shown that BGA is able to find the globally optimum solution for MU MIMO systems well within the time interval of modern wireless packet data communications However, it is interesting to observe that BGA is able to find a solution to CUAS close to the optimum value quite rapidly In this paper, it is also shown that BGA with elitism and AM achieves higher throughput than limited feedback scheduling schemes as well