On the Linear Precoder Design for MIMO Channels with Finite-Alphabet Inputs and Statistical CSI

This paper investigates the linear precoder design that maximizes the average mutual information of multiple-input multiple-output channels with finite-alphabet inputs and statistical channel state information known at the transmitter. This linear precoder design is an important open problem and is extremely difficult to solve: First, average mutual information lacks closed-form expression and involves complicated computations; Second, the optimization problem over precoder is nonconcave. This study explores the solution to this problem and provides the following contributions: 1) A closed-form lower bound of average mutual information is derived. It achieves asymptotic optimality at low and high signal-to-noise ratio regions and, with a constant shift, offers an accurate approximation to the average mutual information; 2) The optimal structure of the precoder is revealed, and a unified two-step iterative algorithm is proposed to solve this problem. Numerical examples show the convergence and the efficacy of the proposed algorithm. Compared to its conventional counterparts, the proposed linear precoding method provides a significant performance gain.Comment: 5 pages, 3 figures, accepted by IEEE Global Communications Conference (GLOBECOM) 2011, Houston, T

Exploring travellers’ risk preferences with regard to travel time reliability on the basis of GPS trip records

Travel time reliability has attracted considerable interest in the field of route choice modelling. Knowing how individuals choose paths with uncertain travel times is fundamental to advancing our understanding of route choice behaviour and thus driving the development of route guidance systems. In general, existing navigation systems provide the shortest path on the basis of distance or travel time, even though many travellers do not intend to choose the shortest path. Several studies have shown that the probability of delay or travel time reliability is an important factor in a traveller’s route choice decision. Learning a traveller’s risk preference with regard to travel time reliability is important for designing a preferable route. Traditionally, route choice data for individual preference analysis are collected by conducting stated preference surveys. However, this approach is difficult to avoid its inherent limitation, namely a lack of honest, accurate, and bias-free reporting. To overcome these problems, the present study proposes a new data collection methodology that facilitates estimation of a traveller’s risk preference on the basis of large-scale GPS trip records. The lower and upper bounds of individual risk preference can be estimated by exhausting a series of reliable paths with different on-time arrival probabilities and using the theory of stochastic dominance. Then, a regression model based on a logistic function is established to explore how socio-demographic and trip characteristics influence the lower and upper bounds. Thus, individual properties, such as age, and pre-trip information, such origindestination (OD) distance, departure time, and day of week, are found to have a significant influence on the degree of risk preference

Linear Precoding for MIMO Multiple Access Channels with Finite Discrete Inputs

In this paper, we study linear precoding for multiple-input multiple-output (MIMO) multiple access channels (MAC) with finite discrete inputs. We derive the constellation-constrained capacity region for the MIMO MAC with an arbitrary number of users and find that the boundary can be achieved by solving the problem of weighted sum rate maximization with constellation and individual power constraints. Due to the non-concavity of the objective function, we obtain a set of necessary conditions for the optimization problem through Karush-Kuhn-Tucker analysis. to find the optimal precoding matrices for all users, we propose an iterative algorithm utilizing alternating optimization strategy. in particular, each iteration of the algorithm involves the gradient descent update with backtracking line search. Numerical results show that when inputs are digital modulated signals and the signal-to-noise ratio is in the medium range, our proposed algorithm offers considerably higher sum rate than non-precoding and the traditional method which maximizes Gaussian-input sum capacity. Furthermore, a low-density parity-check coded system with iterative detection and decoding for MAC is presented to evaluate the bit error rate (BER) performance of precoders. BER results also indicate that the system with the proposed linear precoder achieves significant gains over the non-precoding system and the precoder designed for Gaussian inputs. © 2006 IEEE

Linear Precoding for MIMO Multiple Access Channels with Discrete-constellation Inputs

In this paper, we study linear precoding for multiple-input multiple-output (MIMO) multiple access channels (MAC) with discrete-constellation inputs. We derive the constellation-constrained capacity region for the MIMO MAC with an arbitrary number of users. Due to the non-concavity of the objective function, we obtain the necessary conditions for the weighted sum rate (WSR) maximization problem through Karush-Kuhn-Tucker (KKT) analysis. to find the optimal precoding matrices, we propose an iterative algorithm utilizing alternating optimization strategy and gradient descent update. Numerical results show that when inputs are digital modulated signals and the signal-to-noise ratio (SNR) is in the medium range, our proposed algorithm offers significantly higher sum rate than non-precoding and the traditional method which maximizes Gaussian-input sum capacity. Furthermore, the bit error rate (BER) results of a low-density parity-check (LDPC) coded system also indicate that the system with the proposed linear precoder achieves significant gains over other methods. © 2011 IEEE

A Low-complexity Design of Linear Precoding for MIMO Channels with Finite-alphabet Inputs

This paper investigates linear precoding scheme that maximizes mutual information for multiple-input multiple-output (MIMO) channels with finite-alphabet inputs. in contrast with recent studies, optimizing mutual information directly with extensive computational burden, this work proposes a low-complexity and high-performance design. It derives a lower bound that demands low computational effort and approximates, with a constant shift, the mutual information for various settings. based on this bound, the precoding problem is solved efficiently. Numerical examples show the efficacy of this method for constant and fading MIMO channels. Compared to its conventional counterparts, the proposed method reduces the computational complexity without performance loss. © 2012 IEEE

On Interference-aware Precoding for Multi-antenna Channels with Finite-alphabet Inputs

This paper investigates the interference-aware linear precoder design with finite-alphabet inputs. It maximizes the mutual information between the transmitter and intended receiver while controlling the interference power caused to unintended receivers. for this nonconcave problem, this work proposes a global optimization approach, which is based on two key observations: 1) the interference-aware precoding problem can be reformulated to the problem minimizing a function with bilinear terms over the intersection of multiple co-centered ellipsoids; 2) these bilinear terms can be relaxed by their convex and concave envelopes. in this way, the global optimal solution is obtained by solving a sequence of relaxed problems over shrinking feasible regions. the proposed algorithm calculates the optimal precoder and the theoretical limit of the transmission rate with interference constraints. Thus, it offers an important benchmark for performance evaluation of interference constrained networks. © 2012 IEEE

Route search problem considering travel time reliability and CO2 emission in road network

名古屋大学Nagoya University博士(工学)doctoral thesi

Linear Precoding for Finite-alphabet Inputs over MIMO Fading Channels with Statistical CSI

This paper investigates the linear precoder design that maximizes the average mutual information of multiple-input multiple-output fading channels with statistical channel state information known at the transmitter. It formulates the design from the standpoint of finite-alphabet inputs, which leads to a problem that is very important in practice but extremely difficult in theory: First, the average mutual information lacks closed-form expression and involves prohibitive computational burden. Second, the optimization over the precoder is nonconcave and thus easily gets stuck in local maxima. to address these issues, this study first derives lower and upper bounds for the average mutual information, in which the computational complexity is reduced by several orders of magnitude compared to calculating the average mutual information directly. It proves that maximizing the bounds is asymptotically optimal and shows that, with a constant shift, the lower bound actually offers a very accurate approximation to the average mutual information for various fading channels. This paper further proposes utilizing the lower bound as a low-complexity and accurate alternative for developing a two-step algorithm to find a near global optimal precoder. Numerical examples demonstrate the convergence and efficacy of the proposed algorithm. Compared to its conventional counterparts, the proposed linear precoding method provides significant performance gain over existing precoding algorithms. the gain becomes more substantial when the spatial correlation of MIMO channels increases. © 2012 IEEE

On the Power Allocation for Relay Networks with Finite-alphabet Constraints

In this paper, we investigate the optimal power allocation scheme for relay networks with finite-alphabet constraints. It has been shown that the previous work utilizing various design criteria with the Gaussian inputs assumption may lead to significant loss for a practical system with finite constellation set constraint, especially when signal-to-noise ratio (SNR) is in medium-to-high regions, or when the channel coding rate is medium to high. an optimal power allocation scheme is proposed to maximize the mutual information for the relay networks under discrete-constellation input constraint. Numerical examples show that significant gain can be obtained compared to the conventional counterpart for nonfading channels and fading channels. at the same time, we show that the large performance gain on the mutual information will also represent the large gain on the bit error rate (BER), i.e., the benefit of the power allocation scheme predicted by the mutual information can indeed be harvested and can provide considerable performance gain in a practical system. ©2010 IEEE

Opportunistic Cooperation for Multi-antenna Multi-relay Networks

A low-complexity, near-optimal transmit antenna selection algorithm is proposed for multi-relay networks where all nodes are equipped with multiple antennas. We first establish a system model and a unified capacity maximization framework for a two-hop opportunistic relaying scheme where the source node (S) transmits signals to multiple relay nodes (R) in the first time slot, and the selected relay antennas and their corresponding relay nodes receive, decode and forward the messages to the destination (D) in the second time slot. based on the system model, we develop a transmit antenna selection algorithm that maximizes the network capacity assuming that the channel state information is available at the receivers but not available at the transmitters, and total transmit power constraints are imposed on source/relay transmitters. the proposed algorithm first constructs a sorted list of relay antennas with decreasing S-R capacities, then iteratively maximizes the R-D capacity over a candidate antenna set using a low-complexity, near-optimal antenna selection scheme. the candidate set is reduced in the next iteration according to the selected antenna set of the current iteration. the overall network capacity is computed for the selected antenna sets of all iterations, and the set yielding the highest S-R-D capacity is the solution to the maximization problem. We show that this novel iterative algorithm achieves near-optimal solution and has a polynomial-time complexity. We also derive the lower and upper bounds of the achievable network capacity for both average capacity and outage capacity. Numerical examples show the significant performance gains obtained via the proposed scheme compared to its conventional counterparts. © 2010 IEEE