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.

Extracting the Most Weighted Throughput in UAV Empowered Wireless Systems With Nonlinear Energy Harvester

Abstract: With the maturity of unmanned aerial vehicle (UAV) technology, this work investigates the integration of UAV into wireless communication systems. Since the UAV is powered by a capacity-limited battery, this work proposes to use the radio energy harvesting technology at the UAV in order to extend the lifetime of UAV empowered base station. To extract the most weighted throughput of UAV empowered wireless systems, the dirty paper coding scheme and information-theoretic uplinkdownlink channel duality are exploited to propose an extracting the most weighted throughput algorithm. Numerical results are used to verify the proposed algorithm.

Linear network coordinated beamforming for cell-boundary users

Abstract: In this paper, low complexity linear network coordinated beamforming (N-CBF) algorithms under a zero inter-user interference constraint are proposed for the multi-cell downlink channel. We consider a downlink scenario with three base stations (BSs) equipped with one transmit antenna each and three mobile users, each of which has more than one receive antenna and is located on the cell-boundary. A single data stream is transmitted to all three cell-boundary users where each user receives his/her own independent data stream (called full broadcast channel), or where two users receive the same data stream while the third user receives a different data stream (called clustered broadcast channel). For the full and clustered broadcast channel scenarios, we propose low complexity linear N-CBF algorithms approaching the sum capacity realized by multi-cell dirty paper coding. Monte Carlo simulations are carried out to verify the proposed algorithms.

Differentiated Rate Sceduling for MIMO Broadcast Channels

Abstract: We consider the problem of differentiated rate scheduling for the fading MIMO Gaussian broadcast channel, in the sense that the rates required by different users must satisfy certain rational rate constraints. When full channel state information (CSI) is available at the transmitter, the problem can be readily solved using dirty paper coding (DPC) and con- vex optimization techniques on the dual multiple-access channel (MAC). However, since in many practical applications full CSI is not feasible, and since the computational complexity may be prohibitive when the number of users is large, we focus on two simple schemes that require very little CSI: time-division opportunistic (TO) beamforming where in different time-slots the transmitter performs opportunistic beamforing only to users requiring the same rate, and weighted opportunistic (WO) beamforing where the random beams are assigned to those users having the largest weighted SINR. In both cases we determine explicit schedules to guarantee the rate constraints and show that, in the limit of a large number of users, the throughput loss compared to the unconstrained sum-rate capacity tends to zero. As a side result, we show that, in this regime, the sum-rate of opportunistic beamforming converges to the optimal sum-rate achieved by DPC, which is a stronger result than the order-optimal results of (10, 13).

On Vector Perturbation Precoding for the MIMO Gaussian Broadcast Channel

Abstract: Precoding schemes in the framework of vector perturbation (VP) for the multiple-input multiple-output (MIMO) Gaussian broadcast channel (GBC) are investigated. The VP scheme, originally a “one-shot” technique, is generalized to encompass processing over multiple time instances. Using lattice-based extended alphabets (“perturbations”), and considering the infinite time-span extension limit, a lower bound on the achievable sum-rate using the generalized VP scheme is analytically obtained. The lower bound is shown to asymptotically achieve the optimum sum-rate in the high signal-to-noise ratio (SNR) regime (both in terms of degrees-of-freedom and power offset), for any number of users and transmit antennas. For the two-user cases, it is shown that the lower bound coincides with the sum-capacity for low SNR. The above lower bound is constructively obtained by means of an efficient practically oriented suboptimal transmit energy minimization algorithm, which exhibits a polynomial complexity in the number of users. The proposed precoding scheme demonstrates that the “shaping gain” is achievable for VP schemes, when employing “good” multidimensional lattices. It is also shown that the suboptimum algorithm has its merits, even when processing over multiple time instances is not employed. For the $2\times 2$ MIMO GBC, the VP scheme is generalized further, and an inner bound for the entire achievable rate region is obtained, by which an interesting correspondence is identified with the ultimate capacity region, as obtained by “dirty paper coding”.

An asymptotic analysis of the MIMO broadcast channel under linear filtering

Abstract: We investigate the MIMO broadcast channel with multi-antenna terminals in the high SNR regime when linear filtering is applied instead of dirty paper coding. Using a recent rate duality where the streams of every single user are not treated as self-interference as in the hitherto existing stream-wise rate dualities for linear filtering, we solve the weighted sum rate maximization problem of the broadcast channel in the dual multiple access channel. Thus, we can exactly quantify the asymptotic rate loss of linear filtering compared to dirty paper coding for any channel realization. We come up with the first rate loss expression that only depends on the channel matrices of all users and not on the precoders of the users as hitherto existing results for multi-antenna terminals do. Having converted the optimum covariance matrices to the broadcast channel by means of the duality, we observe that the optimal covariance matrices in the broadcast channel feature quite complicated but still closed form expressions although the respective transmit covariance matrices in the dual multiple access channel share a very simple structure. We immediately come to the conclusion that block-diagonalization is the asymptotically optimum transmit strategy in the broadcast channel. Out of the set of block-diagonalizing precoders, we present the one which achieves the largest sum rate and thus corresponds to the optimum solution found in the dual multiple access channel. Additionally, we quantify the ergodic rate loss of linear coding compared to dirty paper coding for Gaussian channels with correlations at the mobiles.