Finite-alphabet Inputs Research Articles

Globally Optimal Precoder Design with Finite-Alphabet Inputs for Cognitive Radio Networks

This paper investigates the linear precoder design for spectrum sharing in multi-antenna cognitive radio networks with finite-alphabet inputs. It formulates the precoding problem by maximizing the constellation-constrained mutual information between the secondary-user transmitter and secondary-user receiver while controlling the interference power to primary-user receivers. This formulation leads to a nonlinear and nonconvex problem, presenting a major barrier to obtain optimal solutions. This work proposes a global optimization algorithm, namely Branch-and-bound Aided Mutual Information Optimization (BAMIO), that solves the precoding problem with arbitrary prescribed tolerance. The BAMIO algorithm is designed based on two key observations: First, the precoding problem for spectrum sharing can be reformulated to a problem minimizing a function with bilinear terms over the intersection of multiple co-centered ellipsoids. Second, these bilinear terms can be relaxed by its convex and concave envelopes. In this way, a sequence of relaxed problems is solved over a shrinking feasible region until the tolerance is achieved. The BAMIO algorithm calculates the optimal precoder and the theoretical limit of the transmission rate for spectrum sharing scenarios. By tuning the prescribed tolerance of the solution, it provides a trade-off between desirable performance and computational complexity. Numerical examples show that the BAMIO algorithm offers near global optimal solution with only several iterations. They also verify that the large performance gain in mutual information achieved by the BAMIO algorithm also represents the large gain in the coded bit-error rate.

Polar Codes for the $m$-User Multiple Access Channel

In this paper, polar codes for the m-user multiple access channel (MAC) with binary inputs are constructed. It is shown that Arikan's polarization technique applied individually to each user transforms independent uses of an m-user binary input MAC into successive uses of extremal MACs. This transformation has a number of desirable properties: 1) the “uniform sum-rate” of the original MAC is preserved, 2) the extremal MACs have uniform rate regions that are not only polymatroids but matroids, and thus, 3) their uniform sum-rate can be reached by each user transmitting either uncoded or fixed bits; in this sense, they are easy to communicate over. A polar code can then be constructed with an encoding and decoding complexity of O(n log n) (where n is the block length), a block error probability of o(exp (- n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/2 - ε</sup> )), and capable of achieving the uniform sum-rate of any binary input MAC with arbitrary many users. Applications of this polar code construction to channels with a finite field input alphabet and to the additive white Gaussian noise channel are also discussed.

Linear Precoding for Finite-Alphabet Signaling Over MIMOME Wiretap Channels

In this paper, we investigate the secrecy rate of finite-alphabet communications over multiple-input-multiple-output-multiple-antenna eavesdropper (MIMOME) channels. Traditional precoding designs based on Gaussian input assumption may lead to substantial secrecy rate loss when the Gaussian input is replaced by practical finite-alphabet input. To address this issue, we investigate linear precoding designs to directly maximize the secrecy rate for MIMOME systems under the constraint of finite-alphabet input. By exploiting the theory of Karush-Kuhn-Tucker (KKT) analysis and matrix calculus, we first present necessary conditions of the optimal precoding design when instantaneous channel-state information (CSI) of the eavesdropper is known at the transmitter. In this light, an iterative algorithm for finding the optimal precoding matrix is developed, utilizing a gradient decent method with backtracking line search. Moreover, we find that the beamforming design in MIMONE systems, which is a secrecy-capacity-achieving approach for Gaussian signaling, no longer provides the maximum secrecy rate for finite-alphabet input data. This case is substantially different from the Gaussian input case. In addition, we derive the closed-form results on the precoding matrix, which maximizes the secrecy rate in the low signal-to-noise ratio (SNR) region, and reveal the optimal precoding structure in the high-SNR region. A novel jamming signal generation method that draws on the CSI of the eavesdropper to additionally increase the secrecy rate is further proposed. The precoding design with only statistical CSI of the eavesdropper available at the transmitter is also considered. Numerical results show that the proposed designs provide significant gains over recent precoding designs through a power control policy and the precoding design with the Gaussian input assumption in various scenarios.

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.

Linear Precoding for Relay Networks: A Perspective on Finite-Alphabet Inputs

This paper considers the precoder design for dual-hop amplify-and-forward relay networks and formulates the design from the standpoint of finite-alphabet inputs. In particular, the mutual information is employed as the utility function, which, however, results in a nonlinear and nonconcave problem. This paper exploits the structure of the optimal precoder that maximizes the mutual information and develops a two-step algorithm based on convex optimization and optimization on the Stiefel manifold. By doing so, the proposed algorithm is insensitive to initial point selection and able to achieve a near global optimal precoder solution. Besides, it converges fast and offers high mutual information gain. These advantages are verified by numerical examples, which also show the large performance gain in mutual information also represents the large gain in the coded bit-error rate.

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.

On Secrecy Rate Analysis of MIMO Wiretap Channels Driven by Finite-Alphabet Input

This work investigates the effect of finite-alphabet input constraint on the secrecy rate of a multi-antenna wiretap channel. Most existing works have characterized maximum achievable secrecy rate or secrecy capacity for single and multiple antenna systems based on Gaussian source signals and secrecy code. For practical considerations, we study the effect of finite discrete-constellation on the achievable secrecy rate of multiple-antenna wire-tap channels. Our proposed precoding scheme converts the underlying multi-antenna system into a bank of parallel channels. Based on this precoding strategy, we develop a decentralized power allocation algorithm based on dual decomposition to maximize the achievable secrecy rate. In addition, we analyze the achievable secrecy rate for finite-alphabet inputs in low and high SNR regions. Our results demonstrate substantial difference in secrecy rate between systems given finite-alphabet inputs and systems with Gaussian inputs.

Linear Precoding for MIMO Broadcast Channels With Finite-Alphabet Constraints

We investigate the design of linear transmit precoder for multiple-input multiple-output (MIMO) broadcast channels (BC) with finite alphabet input signals. We first derive an explicit expression for the achievable rate region of the MIMO BC with discrete constellation inputs, which is generally applicable to cases involving arbitrary user number and arbitrary antenna number. We further present a weighted sum rate upper bound of the MIMO BC with identical transmit precoding matrices. The resulting bound exhibits a serious performance loss because of the non-uniquely decodable transmit signals for MIMO BC with finite alphabet inputs in high signal-to-noise ratio (SNR) region. This performance loss motivates the use of a simple precoding to combat the non-unique decodability. Based on a constrained optimization problem formulation, we apply the Karush-Kuhn-Tucker analysis to derive necessary conditions for MIMO BC precoders to maximize the weighted sum-rate. We then propose an iterative gradient descent algorithm with backtracking line search to optimize the linear precoders for each user. Our { simulation} results under the practical transmit symbols of discrete constellations demonstrate significant gains by the proposed algorithm over other precoding schemes including the traditional iterative water-filling (WF) design for the Gaussian input signals. For the low-density parity-check coded systems, our precoder provides considerably coded BER improvement through iterative decoding and detection.

Globally Optimal Linear Precoders for Finite Alphabet Signals Over Complex Vector Gaussian Channels

We study the design optimization of linear precoders for maximizing the mutual information between finite alphabet input and the corresponding output over complex-valued vector channels. This mutual information is a nonlinear and non-concave function of the precoder parameters, posing a major obstacle to precoder design optimization. Our work presents three main contributions: First, we prove that the mutual information is a concave function of a matrix which itself is a quadratic function of the precoder matrix. Second, we propose a parameterized iterative algorithm for finding optimal linear precoders to achieve the global maximum of the mutual information. The proposed iterative algorithm is numerically robust, computationally efficient, and globally convergent. Third, we demonstrate that maximizing the mutual information between a discrete constellation input and the corresponding output of a vector channel not only provides the highest practically achievable rate but also serves as an excellent criterion for minimizing the coded bit error rate. Our numerical examples show that the proposed algorithm achieves mutual information very close to the channel capacity for channel coding rate under 0.75, and also exhibits a large gain over existing linear precoding and/or power allocation algorithms. Moreover, our examples show that certain existing methods are susceptible to being trapped at locally optimal precoders.

On the Secrecy Rate of Multi-Antenna Wiretap Channel under Finite-Alphabet Input

This work investigates the effect of limiting source signals to finite-alphabet on the secrecy rate of a multi-antenna wiretap system. Existing works have characterized maximum achievable secrecy rate or secrecy capacity for single- and multi-antenna systems based on Gaussian source signals and secrecy code. Despite the impracticality of Gaussian source, its compact closed-form expression of mutual information motivated broad use of Gaussian input assumption. For practical consideration, we study the effect of finite discrete-constellation on instantaneous and ergodic secrecy rate of multiple-antenna wire-tap channels. Our results demonstrate substantial difference between systems involving finite-alphabet inputs and systems with Gaussian inputs. Results from Gaussian inputs serve as upper-bound of secrecy rate for practical systems with finite alphabet inputs.

A Message-Passing Approach to Distributed Resource Allocation in Uplink DFT-Spread-OFDMA Systems

In this paper, we consider the problem of resource allocation in the DFT-Spread-OFDMA (DFT-S-OFDMA) uplink. We show that the resource allocation problem can be formulated as a set packing problem, which in general is NP-hard. We propose polynomial-time message-passing based algorithms, one of which is guaranteed to yield a solution that is within a constant fraction of the optimal solution and is also asymptotically optimal in the limit as the number of subcarriers in the system goes to infinity. The message-passing based algorithm is also extended to solve the resource allocation problem over a multi-cell uplink in a distributed fashion. Our algorithms account for finite input alphabets and non-ideal practical outer codes. Extensive simulations are performed to assess the performance of the proposed algorithms and it is shown that they yield near-optimal solutions at a low complexity and with a low memory requirement.

On the stability of MPC with a Finite Input Alphabet

AbstractThis paper studies stability of Model Predictive Control for systems with a finite input alphabet. Since this kind of systems may present a steady-state error under closed-loop control, the forms is on stability in the sense of ultimate boundedness of solutions. To derive sufficient conditions for stability, two different approaches are presented. The first one approximates the finite input alphabet via saturation-control allowing us to analyze the problem from a robust control perspective. In the second approach, a direct analysis of the problem is carried out. The results thus obtained are shown to be less conservative regarding ultimate bounded set than those obtained via the robust control approach.

Lattice precoding and pre-distorted constellation in degraded broadcast channel with finite input alphabets

In this letter, we propose a novel transmission scheme based on lattice precoding and pre-distorted constellation for the two-user AWGN degraded broadcast channel. In the proposed scheme, the lattice precoding is used for the weaker user to cancel the interference from the stronger user. The predistorted constellation is also proposed for the stronger user to overcome the nonlinear distortion due to the lattice precoding. Numerical results show that the achievable rate of the proposed transmission scheme is beyond the achievable rate region of the superposition coding and successive interference cancelation with finite input alphabets.

An Auction Approach to Resource Allocation in Uplink OFDMA Systems

We propose resource allocation algorithms based on the auction method for uplink OFDMA cellular networks. We consider cellular systems that employ the traditional static frequency reuse as well as the next-generation systems that aim to achieve a universal frequency reuse via base-station coordination. Our algorithms are designed for finite input alphabets and also account for nonideal practical outer codes, and they can be implemented in a distributed manner, when applied for multicell resource allocations. We also address power and bandwidth constraints that are motivated by practical concerns. The proposed algorithms exhibit very low complexity and are well suited for parallel implementations. Simulation results demonstrate that they offer near-optimal performance.

Constrained Capacities for Faster-Than-Nyquist Signaling

This paper deals with capacity computations of faster-than-Nyquist (FTN) signaling. It shows that the capacity of FTN is higher than the orthogonal pulse linear modulation capacity for all pulse shapes except the sinc. FTN signals can in fact achieve the ultimate capacity for the signal power spectral density (PSD). The paper lower- and upper-bounds the FTN capacity under the constraint of finite input alphabet. It is often higher than the capacity for comparable orthogonal pulse systems; sometimes it is superior to all forms of orthogonal signaling with the same PSD.

Capacity and Random-Coding Exponents for Channel Coding With Side Information

Capacity formulas and random-coding exponents are derived for a generalized family of Gel'fand-Pinsker coding problems. These exponents yield asymptotic upper bounds on the achievable log probability of error. In our model, information is to be reliably transmitted through a noisy channel with finite input and output alphabets and random state sequence, and the channel is selected by a hypothetical adversary. Partial information about the state sequence is available to the encoder, adversary, and decoder. The design of the transmitter is subject to a cost constraint. Two families of channels are considered: 1) compound discrete memoryless channels (CDMC), and 2) channels with arbitrary memory, subject to an additive cost constraint, or more generally, to a hard constraint on the conditional type of the channel output given the input. Both problems are closely connected. The random-coding exponent is achieved using a stacked binning scheme and a maximum penalized mutual information decoder, which may be thought of as an empirical generalized maximum a posteriori decoder. For channels with arbitrary memory, the random-coding exponents are larger than their CDMC counterparts. Applications of this study include watermarking, data hiding, communication in presence of partially known interferers, and problems such as broadcast channels, all of which involve the fundamental idea of binning

Approximation to the symmetric capacity of Rayleigh fading channels with multi-level signals

A simple approximation for the symmetric capacity of Rayleigh fading channels with finite input alphabet and ideal channel state information is proposed in this letter. This approximation is quite tight over all SNR ranges and can be considered as a good alternative for estimating the symmetric channel capacity for both AWGN and Rayleigh fading channels.

BLIND IDENTIFIABILITY ANALYSIS IN A MIMO LTI SYSTEM WITH INPUTS FROM A FINITE-ALPHABET SET

A blind separation problem in a multiple-input-multiple-output (MIMO) linear time-invariant (LTI) system with finite-alphabet inputs is considered. A discrete-time matrix equation model is used to describe the input-output relation of the system in order to make full use of the advantages of modern digital signal processing techniques. At first, ambiguity problem is investigated. Then, based on the results of the investigation, a new identifiability condition is proposed for the case of an alphabet set which is widely used in digital communication. The proposed condition is compared to an existing condition in terms of the probability of satisfying each condition for an arbitrary input matrix. A probability bound such that an arbitrary input matrix satisfies the identifiability condition is derived. Monte-Carlo simulation is performed to demonstrate the fact that the identifiability conditions found so far are still very loose.

Performance Analysis of Adaptive Volterra Filters in the Finite-Alphabet Input Case

This paper deals with the analysis of adaptive Volterra filters, driven by the LMS algorithm, in the finite-alphabet inputs case. A tailored approach for the input context is presented and used to analyze the behavior of this nonlinear adaptive filter. Complete and rigorous mean square analysis is provided without any constraining independence assumption. Exact transient and steady-state performances expressed in terms of critical step size, rate of transient decrease, optimal step size, excess mean square error in stationary mode, and tracking nonstationarities are deduced.

Capacities of time-varying multiple-access channels with side information

We determine the capacity regions for a class of time-varying multiple-access channels (TVMACs), when the underlying channel state evolves in time according to a probability law which is known to the transmitters and the receiver. Additionally, the transmitters and the receiver have access to varying degrees of channel state information (CSI) concerning the condition of the channel. Discrete-time channels with finite input, output, and state alphabets are considered first. The special case of a TVMAC, with the channel state process being a time-invariant, indecomposable, aperiodic Markov chain, shows a surprising anomaly in that imperfect transmitter CSI can cause the capacity under some distributions for the initial state to be strictly larger than that under a stationary distribution for the initial state. We also study a time-varying multiple-access fading channel with additive Gaussian noise, when various amounts of CSI are provided to the transmitters and perfect CSI is available to the receiver, and the fades are assumed to be stationary and ergodic. Implications for transmitter power control are discussed.