Far-end crosstalk severely degrades upstream rates in mixtures of vectored and non-vectored Very high-speed Digital Subscriber Loops (VDSL). As replacement of non-vectored VDSL systems by vectored VDSL systems is expected to be gradual, a crucial problem is the upstream rate optimization of vectored lines while maintaining the rate targets of non-vectored lines. To address this problem, this paper describes an algorithm that selects suitable Upstream Power Back-Off parameters for the vectored and the non-vectored lines. The algorithm has much lower computation requirements compared to Optimal Spectrum Balancing (OSB), yet simulation results show that the achievable vectored rates are very close to the OSB theoretical limits.

The severe interference from neighbor copper lines, commonly known as crosstalk, is a well-known limitation that can reduce the upstream rate of a victim user by 50% or more in dense VDSL (Very high bit-rate Digital Subscriber Lines) deployment. This problem has been partially addressed by the use of UPBO (Upstream Power BackOff). UPBO blindly controls transmit PSD (Power Spectral Densities) based on the pre-defined parameters and channel measurements without considering each user's target data rate. Although UPBO can provide fair protection against crosstalk, it still leaves room for practical improvement. This paper proposes a distributed algorithm that dynamically controls transmit power according to each line's need and capacity. The algorithm effectively improves data rates and line reaches in real VDSL deployment.

The severe interference from neighbor copper lines, commonly known as crosstalk, is a well-known limitation that can cause 50% or larger reduction in the upstream rates of dense VDSL (Very high bit-rate Digital Subscriber Lines) deployments. To handle this problem, UPBO (Upstream Power BackOff) has been studied in literature, and subsequently standardized. UPBO blindly selects transmit PSD (Power Spectral Densities) based on the pre-defined parameters and channel measurement without considering each line's target data rate. Although UPBO can provide significant protection against crosstalk, it still leaves room for practical improvement. This paper proposes a distributed algorithm that dynamically controls transmit power based on each line's need and capability. The algorithm can achieve significant rate and reach gains in real VDSL deployments.

This paper applies multiple-input multiple-output (MIMO) transmission methods to multiwire communication systems. Using channel matrices generated from a binder MIMO channel model, a performance assessment of digital subscriber line (DSL) technology based on MIMO transmission methods finds that symmetric data rates of more than 1 Gbps are achievable over four twisted pairs (category 3) for a 300 m range. Similar results are also obtained for a ldquoquadrdquo cable. To achieve this data rate, this paper proposes that the source and load be excited using common mode.

This paper introduces a multiple-input multiple-output channel model for the characterization of a binder of telephone lines. This model is based on multiconductor transmission line theory, and uses parameters that can be obtained from electromagnetic theory or measured data. The model generates frequency-dependent channel/binder transfer function matrices as a function of cable type, geometric line-spacing and twist-length parameters, and source--load configurations. The model allows the extraction of the magnitude and the phase of individual near end crosstalk, far end crosstalk, split-pair, and phantom transfer functions from the transfer function matrix of the binder. These individual crosstalk transfer functions are often found to be very sensitive to small imperfections in the binder. Examples of category 3 twisted pair American telephone lines and ldquoquadrdquo telephone cables are also presented.

This paper studies the fading multiple-access channel (MAC) with additive Gaussian noise and multiple transmit and receive antennas. It is assumed that the receiver has the perfect channel state information (CSI) while the mobile transmitters have no such knowledge. This paper presents a transmission scheme where the receiver jointly optimizes the signal covariance matrices for all transmitters, based on the channel distribution information (CDI) and the rate demand of each individual user, and then feeds them back to all transmitters. Under this scheme the achievable multi-user power region that is composed of all transmit power-tuples with which a given set of "ergodic" rates is achievable for all transmitters, is characterized by using convex optimization techniques. It is observed from simulation results that the transmit optimization based on the CDI can provide substantial power savings in the fading MAC

The paper provides a proof of the converse for the capacity region of the Gaussian MIMO broadcast channel under total average transmit power constraint. The proof uses several ideas from earlier works on the problem including the recent converse proof by Weingarten, Steinberg and Shamai. First the duality between Gaussian multiple access and broadcast channels is employed to show that every point on the boundary of the dirty paper coding region can be represented as the optimal solution to a convex optimization problem. Using the optimality conditions for this convex problem, a degraded broadcast channel is constructed for each point. It is then shown that the capacity region for this degraded broadcast channel contains the capacity region of the original channel. Moreover, the same point lies on the boundary of the dirty paper coding region for this degraded channel. Finally, the standard entropy power inequality is used to show that this point lies on the boundary of the capacity region of the degraded channel as well and consequently it is on the boundary of the capacity region of the original channel

This paper proposes efficient rate and power allocation algorithms for OFDMA downlink systems where each tone is taken by at most one user. Weighted sum rate maximization (WSRmax) and weighted sum power minimization (WSPmin) problems are considered. Since these resource allocation problems are non-convex, complexity of finding the optimal solutions is prohibitively high. This paper employs the Lagrange dual decomposition method to efficiently solve both optimization problems. Because of their non-convex nature, there is no guarantee for the solution obtained by the dual decomposition method to be optimal. However, it is shown that with practical number of tones, the duality gap is virtually zero and the optimal solutions can be efficiently obtained

In mobile wireless networks, dynamic allocation of resources such as transmit powers, bit-rates, and antenna beams based on the channel state information of mobile users is known to be the general strategy to explore the time-varying nature of the mobile environment. This paper looks at the problem of optimal resource allocation in wireless networks from different information-theoretic points of view and under the assumption that the channel state is completely known at the transmitter and the receiver. In particular, the fading multiple-access channel (MAC) and the fading broadcast channel (BC) with additive Gaussian noise and multiple transmit and receive antennas are focused. The fading MAC is considered first and a complete characterization of its capacity region and power region are provided under various power and rate constraints. The derived results can be considered as nontrivial extensions of the work done by Tse and Hanly from the case of single transmit and receive antenna to the more general scenario with multiple transmit and receive antennas. Efficient numerical algorithms are proposed, which demonstrate the usefulness of the convex optimization techniques in characterizing the capacity and power regions. Analogous results are also obtained for the fading BC thanks to the duality theory between the Gaussian MAC and the Gaussian BC.

Upper and lower bounds on the capacity and minimum energy-per-bit for general additive white Gaussian noise (AWGN) and frequency-division AWGN (FD-AWGN) relay channel models are established. First, the max-flow min-cut bound and the generalized block-Markov coding scheme are used to derive upper and lower bounds on capacity. These bounds are never tight for the general AWGN model and are tight only under certain conditions for the FD-AWGN model. Two coding schemes that do not require the relay to decode any part of the message are then investigated. First, it is shown that the "side-information coding scheme" can outperform the block-Markov coding scheme. It is also shown that the achievable rate of the side-information coding scheme can be improved via time sharing. In the second scheme, the relaying functions are restricted to be linear. The problem is reduced to a "single-letter" nonconvex optimization problem for the FD-AWGN model. The paper also establishes a relationship between the minimum energy-per-bit and capacity of the AWGN relay channel. This relationship together with the lower and upper bounds on capacity are used to establish corresponding lower and upper bounds on the minimum energy-per-bit that do not differ by more than a factor of 1.45 for the FD-AWGN relay channel model and 1.7 for the general AWGN model.

This paper considers two sub-optimal transmission schemes for a family of parallel Gaussian vector broadcast channels. One of the schemes is based on the QR precoding of Ginis et al. (2000). In QR precoding, the maximum achievable throughput depends on the order in which users are encoded. This scheme is a new algorithm for obtaining the best user ordering and channel-input covariance matrix that maximizes the total channel throughput. The proposed algorithm has linear complexity in the number of multi-carrier frequencies. The simplicity of a linear transmitter-and-receiver architecture is attractive for a transmission scheme. The design of a linear transmitter and multiple linear receivers that maximize the total throughput is studied. It is shown that under discrete bit loading assumption for each user, this problem reduces to solving a series of second order cone programming problems.

This paper describes the capacity of frequency-division AWGN relay channels with linear relaying functions. A sequence of nonconvex optimization problems solving are also described in this paper