Abstract not found

We characterize the sum capacity of the vector Gaussian broadcast channel by showing that the existing inner bound of Marton and the existing upper bound of Sato are tight for this channel. We exploit an intimate four-way connection between the vector broadcast channel, the corresponding point-to-point channel (where the receivers can cooperate), the multiple access channel (where the role of transmitters and receivers are reversed), and the corresponding point-to-point channel (where the transmitters can cooperate). 1

In this paper, the effect of spatial diversity on the throughput and reliability of wireless networks is examined. Spatial diversity is realized through multiple independently fading transmit/receive antenna paths in single-user communication and through independently fading links in multiuser communication. Adopting spatial diversity as a central theme, we start by studying its information-theoretic foundations, then we illustrate its benefits across the physical (signal transmission/coding and receiver signal processing) and networking (resource allocation, routing, and applications) layers. Throughout the paper, we discuss engineering intuition and tradeoffs, emphasizing the strong interactions between the various network functionalities.

We characterize the sum capacity of the multiple antenna Gaussian broadcast channel by showing that the existing inner bound of Marton and the existing upper bound of Sato are tight for this channel. We exploit an intimate four-way connection between the multiple antenna broadcast channel, the corresponding point-to-point channel (where the receivers can cooperate), the multiple access channel (where the role of transmitters and receivers are reversed), and the corresponding point-to-point channel (where the transmitters can cooperate).

Abstract—In recent years, considerable attention has been paid to the joint optimal transceiver design for multi-input multi-output (MIMO) communication systems. In this paper, we propose a joint transceiver design that combines the geometric mean decomposition (GMD) with either the conventional zero-forcing VBLAST decoder or the more recent zero-forcing dirty paper precoder (ZFDP). Our scheme decomposes a MIMO channel into multiple identical parallel subchannels, which can make it rather convenient to design modulation/demodulation and coding/decoding schemes. Moreover, we prove that our scheme is asymptotically optimal for (moderately) high SNR in terms of both channel throughput and bit error rate (BER) performance. This desirable property is not shared by any other conventional schemes. We also consider the subchannel selection issues when some of the subchannels are too poor to be useful. Our scheme can also be combined with orthogonal frequency division multiplexing (OFDM) for intersymbol interference (ISI) suppression. The effectiveness of our approaches has been validated by both theoretical analyses and numerical simulations. Index Terms—Channel capacity, dirty paper precoding, intersymbol interference suppression, joint transceiver design, matrix

Abstract—Assuming the availability of the channel state information at the transmitter (CSIT) and receiver (CSIR), we consider the joint optimal transceiver design for multi-input multi-output (MIMO) communication systems. Using the geometric mean decomposition (GMD), we propose a transceiver design that can decompose, in a strictly capacity lossless manner, a MIMO channel into multiple subchannels with identical capacities. This uniform channel decomposition (UCD) scheme has two implementation forms. One is the combination of a linear precoder and a minimum mean-squared-error VBLAST (MMSE-VBLAST) detector, which is referred to as UCD-VBLAST, and the other includes a dirty paper (DP) precoder and a linear equalizer followed by a DP decoder, which we refer to as UCD-DP. The UCD scheme can provide much convenience for the modulation/demodulation and coding/decoding procedures due to obviating the need for bit allocation. We also show that UCD can achieve the maximal diversity gain. The simulation results show that the UCD scheme exhibits excellent performance, even without the use of any error correcting codes. Index Terms—Channel capacity, DBLAST, dirty paper precoder, diversity gain, geometric mean decomposition, joint transceiver

Abstract. Given a complex matrix H, we consider the decomposition H = QRP ∗ , where R is upper triangular and Q and P have orthonormal columns. Special instances of this decomposition include (a) the singular value decomposition (SVD) where R is a diagonal matrix containing the singular values on the diagonal, (b) the Schur decomposition where R is an upper triangular matrix with the eigenvalues of H on the diagonal, (c) the geometric mean decomposition (GMD) [The Geometric Mean Decomposition, Y. Jiang, W. W. Hager, and J. Li, December 7, 2003] where the diagonal of R is the geometric mean of the positive singular values. We show that any diagonal for R can be achieved that satisfies Weyl’s multiplicative majorization conditions: k� k� |ri | ≤ σi, 1 ≤ k < K, i=1 i=1 K� K� |ri | = σi, where K is the rank of H, σi is the i-th largest singular value of H, and ri is the i-th largest (in magnitude) diagonal element of R. We call the decomposition H = QRP ∗ , where the diagonal of R satisfies Weyl’s conditions, the generalized triangular decomposition (GTD). The existence of the GTD is established using a result of Horn [On the eigenvalues of a matrix with prescribed singular values, Proc. Amer. Math. Soc., 5 (1954), pp. 4–7]. In addition, we present a direct (nonrecursive) algorithm that starts with the SVD and applies a series of permutations and Givens rotations to obtain the GTD. The GMD has application to signal processing and the design of multiple-input multiple-output (MIMO) systems; the lossless filters Q and P minimize the maximum error rate of the network. The GTD is more flexible than the GMD since the diagonal elements of R need not be identical. With this additional freedom, the performance of a communication channel can be optimized, while taking into account differences in priority or differences in quality of service requirements for subchannels. Another application of the GTD is to inverse eigenvalue problems where the goal is to construct matrices with prescribed eigenvalues and singular values. Key words. Generalized triangular decomposition, geometric mean decomposition, matrix factorization, unitary factorization, singular value decomposition, Schur decomposition, MIMO systems, inverse eigenvalue problems

This paper focuses on Multiple-Input MultipleOutput (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems, where the MIMO channel order is larger than the length of the Cyclic Prefix (CP). By swapping the filtering operations of the MIMO channel and the Fast Fourier Transform (FFT), it is shown that each tone of a MIMO OFDM system can be viewed as a MIMO Single-Carrier (SC) system. As a result, the existing equalization approach for MIMO SC systems can be applied to each tone of a MIMO OFDM system. This so-called pertone equalization (PTEQ) approach for MIMO OFDM systems is an attractive alternative for the recently developed time-domain equalization (TEQ) approach for MIMO OFDM systems. The main difference between the PTEQ and TEQ approach is that a per-tone equalizer equalizes a single tone on the symbol level (low rate), whereas a time-domain equalizer equalizes all tones together on the sample level (high rate). Next to some other advantages, this means that a per-tone equalizer can much more easily be designed in practice than a time-domain equalizer. This is illustrated in the second part, where we adapt an existing semi-blind equalization algorithm for a Generalized Space-Time Block Coded (GSTBC) MIMO SC system to a semi-blind per-tone equalization algorithm for a GSTBC MIMO OFDM system.

Multiple input, multiple output (MIMO) wireless communications links have been shown to have the potential for significant increases in capacity, provided they are deployed in an environment with rich multipath scattering. To realize these gains, a number of space-time coding strategies have recently been proposed. Most of these algorithms assume that, via training data, the channel is known at least on one end of the link. However, if the channel is time-varying or even just quasi-stationary, the training overhead can offset much of the throughput gain. In this paper, a space-time coding scheme is presented that allows for simultaneous blind or semi-blind channel estimation and decoding of the symbols transmitted by multiple users. The method relies on the use of "diagonal " space-time codes in which the same symbol is successively transmitted from each antenna in turn. This structure leads to a simple subspace-based algorithm that produces closed-form estimates of both the channel and the transmitted symbols. The algorithm is shown to be applicable to cases involving fewer receive than transmit antennas, rank-deficient channels, flat or frequency selective fading, and multiple users. 1.

Abstract—This paper investigates the advantages of adaptive waveform amplitude design for estimating parameters of an unknown channel/medium under average energy constraints. We present a statistical framework for sequential design (e.g., design of waveforms in adaptive sensing) of experiments that improves parameter estimation (e.g., unknown channel parameters) performance in terms of reduction in mean-squared error (MSE). We treat an time step design problem for a linear Gaussian model where the shape of the input design vectors (one per time step) remains constant and their amplitudes are chosen as a function of past measurements to minimize MSE. For aP, we derive the optimal energy allocation at the second step as a function of the first measurement. Our adaptive two-step strategy yields an MSE improvement of at least 1.65 dB relative to the optimal nonadaptive strategy, but is not implementable since it requires knowledge of the noise amplitude. We then present an implementable design for the two-step strategy which asymptotically achieves optimal performance. Motivated by the optimal two-step strategy, we propose a suboptimal adaptive-step energy allocation strategy that can achieve an MSE improvement of more than 5 dB for aSH. We demonstrate our general approach in the context of MIMO channel estimation and inverse scattering problems. Index Terms—Channel estimation, energy management, inverse scattering, maximum likelihood, parameter estimation, sequential design. I.