Topic
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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08 Jul 2018TL;DR: This work applies a simple large systems analysis to determine the asymptotic performance of RO-ZF designs, determine the optimal ZF orders, and compare to optimal and ZF linear and Dirty Paper Coding (DPC) designs.
Abstract: Optimal linear transmitter beamformers in multi-antenna multi-user systems are of the Minimum Mean Squared Error (MMSE) type (dual uplink MMSE receivers). MMSE designs make an optimal compromise between noise enhancement and interference suppression and reduce to matched filters at low SNR and zero-forcing at high SNR. We consider a realistic scenario of user channels of varying attenuation and constrain the beamformers to either zero-force or ignore each interference term. This leads to a reduced-order zero-forcing (RO-ZF) design in which the number of interference sources being zero-forced increases with SNR. We apply a simple large systems analysis (applicable to Massive MIMO) to determine the asymptotic performance of RO-ZF designs, determine the optimal ZF orders, and compare to optimal and ZF linear and Dirty Paper Coding (DPC) designs. RO-ZF designs lead to variable reductions of computational complexity and channel state information (CSI) requirements (esp. in future multi-cell extensions), both important considerations in Massive MIMO systems.
5 citations
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01 Dec 2014TL;DR: In Gaussian broadcast channels, taking into account the receivers' power constraints, it is shown that multi-user transmission schemes, previously proven to be optimal for maximizing spectral efficiency, are not always optimal.
Abstract: Communication can consume a significant fraction of the energy for many simple sensor devices for which battery life is an important consideration in deployment. Battery power is consumed not only by transmit power amplifier but also in the radio frequency circuits and digital processors during transmission and reception. When communication requirements are bursty, many devices incorporate a ‘sleep’ state where the circuit power consumption is also reduced by turning circuits off. Delaying transmission can allow devices to sleep more and conserve energy. We consider the optimal tradeoff between receiver energy consumption and average throughput, and derive insights on multi-user downlink communication. We reformulate the problem with generalized power constraints on the transmitter's and the receiver's power consumption: depending on their states, either transmit/receive or sleep, they consume different amounts of power. We show how these changes of power constraints affect average spectral efficiency. In Gaussian broadcast channels, taking into account the receivers' power constraints, we show that multi-user transmission schemes, previously proven to be optimal for maximizing spectral efficiency, such as superposition coding and dirty paper coding (DPC) are not always optimal. We characterize the condition, under which these schemes remain optimal, in terms of receivers' power constraints. These models are suited for machine-to-machine (M2M) communications and wireless sensor networks where 1) transmitters and/or receivers are battery-powered devices, 2) their locations are static once deployed, and 3) their data characteristic is not delay-sensitive.
5 citations
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22 Apr 2007
TL;DR: Simulation result shows that the proposed multi-level zero-forcing method can achieve a higher sum-capacity than that of (F. Boccardi et al., 2006) under PAPC.
Abstract: Multiuser downlink beamforming methods have been studied recently. As known, the MIMO broadcast channel capacity can be achieved by dirty paper coding (DPC). However, the high complexity of DPC is hard to implement. Linear zero-forcing beamforming strategy can achieve the same asymptotic sum-rate as that of DPC, when the number of users goes to infinity. For the linear zero-forcing system, (D. Bartolome et al., 2004) gives several power allocation methods among users for different objectives. The above power allocation has an assumption of sum power constraint (SPC), however, recently some researcher study a more practical power constraint name per-antenna power constraint (PAPC). The optimum power allocation aims for maximize the sum-rate under the PAPC is given in (F. Boccardi et al., 2006). In this paper, we find that if the transmit antenna number M is larger than the user number K, a multi-level zero-forcing method can be adopted, and a multi-layer structure is proposed for realizing the method. A sub-optimal power allocation algorithm is also proposed to find the power allocation matrix. Simulation result shows that the proposed multi-level zero-forcing method can achieve a higher sum-capacity than that of (F. Boccardi et al., 2006) under PAPC.
5 citations
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TL;DR: In this paper, a new linear precoding technique called soft interference nulling (SIN) is proposed, which performs at least as well as zero-forcing (ZF) beamforming.
Abstract: In a cooperative multiple-antenna downlink cellular network, maximization of a concave function of user rates is considered. A new linear precoding technique called soft interference nulling (SIN) is proposed, which performs at least as well as zero-forcing (ZF) beamforming. All base stations share channel state information, but each user's message is only routed to those that participate in the user's coordination cluster. SIN precoding is particularly useful when clusters of limited sizes overlap in the network, in which case traditional techniques such as dirty paper coding or ZF do not directly apply. The SIN precoder is computed by solving a sequence of convex optimization problems. SIN under partial network coordination can outperform ZF under full network coordination at moderate SNRs. Under overlapping coordination clusters, SIN precoding achieves considerably higher throughput compared to myopic ZF, especially when the clusters are large.
5 citations
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TL;DR: The corresponding numerical examples show that the proposed combined coding scheme outperforms the existing schemes in the sense of achievable rate region and the effectiveness of the optimal power allocation between the two cognitive nodes is shown.
Abstract: In this study, the authors consider a state-dependent two user interference channel. The two users sharing the spectrum are assumed to be cognitive and each user has a non-causal access to the signal from the other user. For this channel model, an achievable rate region is established for both discrete memoryless model and Gaussian channel. In particular, the achievable rate region is obtained by combining Han–Kobayashi rate splitting coding scheme, superposition coding, Gelfand–Pinsker coding scheme and zero-forcing dirty paper coding. Furthermore, the sum rate maximisation and the associated power allocation problem are studied, numerically and theoretically. The corresponding numerical examples show that the proposed combined coding scheme outperforms the existing schemes in the sense of achievable rate region. Moreover, the effectiveness of the optimal power allocation between the two cognitive nodes is also shown.
5 citations