Bertsekas recently proposed Asynchronous Policy Iteration (API) as an alternative algorithm of Policy Iteration (PI) for solving the problem of two-player zero-sum Markov games. To quantifying the benefits of API, besides its flexibility for parallel and asynchronous implementation, the focus of this paper is to derive the computational complexity of API. We show that to reach within ϵ error to the optimal value function, the computational complexity of API is at most O (poly (n, m1, m2, ln(1/(1 − γ))), where n is the number of states, m1, m2are the number of actions for player 1 and player 2 respectively, and γ is the discount factor.

Feature selection has been explored in two ways, global feature selection and instance-wise feature selection. Global feature selection picks the same feature selector for the entire dataset, while instance-wise feature selection allows different feature selectors for different data instances. We propose group-wise feature selection, a new setting that sits between global and instance-wise feature selections. In group-wise feature selection, we constrain the number of possible feature selectors to be a finite number K, which allows different feature selectors while regularizing the number of different selectors. This is for flexible trade-offs between expressiveness and model complexity. We propose two techniques to solve the problem: the first applies K-Means Clustering to the instance-wise feature selection algorithm; the second uses the mixture of experts model with Gumbel-Softmax to learn group membership and feature selector simultaneously. We evaluate our techniques and show promising results on both synthetic and real datasets.

Graph node embedding aims at learning a vector representation for all nodes given a graph. It is a central problem in many machine learning tasks (e.g., node classification, recommendation, community detection). The key problem in graph node embedding lies in how to define the dependence to neighbors. Existing approaches specify (either explicitly or implicitly) certain dependencies on neighbors, which may lead to loss of subtle but important structural information within the graph and other dependencies among neighbors. This intrigues us to ask the question: can we design a model to give the adaptive flexibility of dependencies to each node's neighborhood. In this paper, we propose a novel graph node embedding method (named PINE) via a novel notion of partial permutation invariant set function, to capture any possible dependence. Our method 1) can learn an arbitrary form of the representation function from the neighborhood, without losing any potential dependence structures, and 2) is applicable to both homogeneous and heterogeneous graph embedding, the latter of which is challenged by the diversity of node types. Furthermore, we provide theoretical guarantee for the representation capability of our method for general homogeneous and heterogeneous graphs. Empirical evaluation results on benchmark data sets show that our proposed PINE method outperforms the state-of-the-art approaches on producing node vectors for various learning tasks of both homogeneous and heterogeneous graphs.

We consider a wireless-powered ad hoc network with clustered Power Beacons (PBs). The wireless-powered transmitters (WP-Txs) only activate stochastically deployed PBs within the circles centered at their locations. The PBs transmit radio frequency (RF) signals, which are harvested by the WP-Txs to transmit information to their target receivers. The nodes are modeled as following$\beta $-Ginibre Point Processes ($\beta $-GPP), which incorporate repulsion among PBs or active WP-Txs. Lacking analytical mathematical tools to describe the distribution of clustered PBs, we establish lower and upper bounds on the energy outage probability, as well as an approximated expression of it. We then derive the information outage probability, an approximate expression of it by approximating the reduced Palm distribution of$\beta $-GPP as an inhomogeneous Poisson point process (IPPP), and an analytical approximate expression of it by further ignoring the small-scale fading of interfering WP-Tx links. Finally, the transmission capacity is obtained based on the overall outage probability. The simulation results show that the derived theoretical expressions accurately reflect the performance of the wireless-powered ad hoc networks and the performance gap introduced by approximations is negligible. Transmission capacity optimization based on the derived expressions is also possible and demonstrated.

In this paper, we use the perturbed gradient based alternating minimization for solving a class of low-rank matrix factorization problems. Alternating minimization is a simple but popular approach which has been applied to problems in optimization, machine learning, data mining, and signal processing, etc. By leveraging the block structure of the problem, the algorithm updates two blocks of variables in an alternating manner. For the nonconvex optimization problem, it is well-known the alternating minimization algorithm converges to the first-order stationary solution with a global sublinear rate. In this paper, a perturbed alternating proximal point (PA-PP) algorithm is proposed, which 1) minimizes the smooth nonconvex problem by updating two blocks of variables alternatively and 2) adds some random noise occasionally under some conditions to extract the negative curvature of the second-order information of the objective function. We show that the proposed PA-PP is able to converge (with high probability) to the set of second-order stationary solutions (SS2) with a global sublinear rate, and as a consequence quickly finds global optimal solutions for the problems considered.

Convolutional neural networks have achieved great success in speech, image, and video signal processing tasks in recent years. There have been several attempts to justify the convolutional architecture and to generalize the convolution operation for treatment of other data types such as graphs and manifolds. Based on group representation theory and noncommutative harmonic analysis, it has recently been shown that the so-called group equivariance requirement of a feed-forward neural network necessitates the convolutional architectures. In this paper, based on the familiar concepts of linear time-invariant systems, we develop an elementary proof of the same result. The nonlinear activation function, being a necessary components of practical deep neural networks, has been glossed over in previous analyses of the connection between equivariance and convolution. We identify sufficient conditions for the non-linear activation functions to preserve equivariance, and hence the necessity of the group convolution structure. Our analysis method is simple and intuitive, and holds the potential to be applied to more challenging scenarios such as non-transitive domains and multiple simultaneous equivariances.

We study multi-base station (BS) preamble detection schemes for the narrow-band Internet of Things (NB-IoT) random access by using stochastic geometry analysis. Specifically, we compare the preamble detection performance of two baseline detection schemes: Quantize-and-Forward (QnF) and Detect-and-Forward (DnF). QnF requires the feedback of quantized received power levels while DnF requires 1-bit feedback of local detection result. Our results show that DnF scheme outperforms QnF scheme when the backhaul capacity is limited or when the minimum distance between user and BSs is less than a threshold. Our results also show that the use of multiple collaborative BSs can lead to a significant improvement of the preamble detection performance, as well as reduction of the total power of the preamble transmission.

Energy harvesting communications systems are able to provide high quality communications services using green energy sources. This paper presents an autonomous energy harvesting communications system that is able to adapt to any environment, and optimize its behavior with experience to maximize the valuable received data. The considered system is a point-to-point energy harvesting communications system consisting of a source and a destination, and working in an unknown and uncertain environment. The source is an energy harvesting node capable of harvesting solar energy and storing it in a finite capacity battery. Energy can be harvested, stored, and used from continuous ranges of energy values. Channel gains can take any value within a continuous range. Since exact information about future channel gains and harvested energy is unavailable, an architecture based on actor-critic reinforcement learning is proposed to learn a close-to-optimal transmission power allocation policy. The actor uses a stochastic parameterized policy to select actions at states stochastically. The policy is modeled by a normal distribution with a parameterized mean and standard deviation. The actor uses policy gradient to optimize the policy’s parameters. The critic uses a three layer neural network to approximate the action-value function, and to evaluate the optimized policy. Simulation results evaluate the proposed architecture for actor-critic learning, and shows its ability to improve its performance with experience.

Alternating gradient descent (A-GD) is a simple but popular algorithm in machine learning, which updates two blocks of variables in an alternating manner using gradient descent steps. In this paper, we consider a smooth unconstrained nonconvex optimization problem, and propose a perturbed A-GD (PA-GD) which is able to converge (with high probability) to the second-order stationary points (SOSPs) with a global sublinear rate. Existing analysis on A-GD type algorithm either only guarantees convergence to first-order solutions, or converges to second-order solutions asymptotically (without rates). To the best of our knowledge, this is the first alternating type algorithm that takes $\mathcal{O}(\text{polylog}(d)/\epsilon^2)$ iterations to achieve an ($\epsilon,\sqrt{\epsilon}$)-SOSP with high probability, where polylog$(d)$ denotes the polynomial of the logarithm with respect to problem dimension $d$.

Non‐orthogonal multiple access has recently emerged as a promising multiple access technique for future wireless technology. In this study, the authors investigate superposition coding in a downlink cooperative cellular system based on the orthogonal frequency division multiplexing (OFDM) modulation in the presence of frequency selective multipath fading channels, a setup that has not been studied before. Specifically, the base station communicates with two paired mobile users simultaneously via the OFDM modulation with the help of a half‐duplex relay under either the decode‐and‐forward (DF) or amplify‐and‐forward (AF) scheme, where the power allocation between two mobile users depends on the statistical channel state information (CSI) rather than perfect CSI. They derive the ergodic rate regions under Rayleigh fading channels and present an ad‐hoc approach to determine the power splitting parameters when the target data rates are given. Simulation results based on the outage probabilities show that superposition coding consistently outperforms frequency division, however, the relative advantage of the DF and AF schemes in a superposition‐coded OFDM relay system depends on the system geometry.

We study the performance of uplink transmission in single-cell wireless systems, where all the transmitters have single antennas and the base station has a large number of antennas. We consider both maximum ratio combining and zero-forcing receivers and both small- and large-scale fading channels. We also characterize the achievable total degrees of freedom (DoF) of such a system without assuming channel state information at the receiver. The system DoF turns out to be the same as that of a single-user multiple-input multiple-output system. However, when the number of users is the same as the number of receive antennas, linear receivers are not sufficient for achieving the maximum total DoF. The amount of energy savings that are possible through the increased number of base-station antennas or increased coherence interval are quantified. Furthermore, the training period and training energy allocation under the average and peak power constraints are optimized jointly to maximize the achievable sum spectral efficiency (SE). The improvement on achievable SE provided by the training duration and energy optimization is verified through multiple numerical simulations.

Traditional approaches to secret key establishment based on common randomness have been based on certain restrictive assumptions, such as considering the available common randomness to consist of independent and identically distributed (i.i.d) repetitions of correlated random variables. Unfortunately, the i.i.d assumption does not generally reflect the conditions of real-life scenarios. For this reason, the current paper investigates the key-establishment potential of a more pragmatic model, in which all parties have access to imperfect information about a common source modeled as a Markov chain. Each party's information thus comes in the form of a hidden Markov model and, since the different parties share the same underlying Markov chain, we call the overall model a sibling hidden Markov model (SHMM). This paper studies upper and lower bounds on the secret key capacity for various types of SHMM. The difficulty of the problem emerges from its prohibitive computational cost. To address this obstacle, we represent the joint probability of the observations as the L1norm of a Markov random matrix, and use its convergence to a Lyapunov exponent.

We consider a network of pairs of nodes that perform ad hoc simultaneous communications over frequency-selective channels. We assume that the whole frequency band is divided into a number of subbands, and each transmitter can only use one subband. Assuming that the network is geometrically infinite, we use the transmission capacity (TC) as a measure of the network throughput. We consider the problem of allocating nodes to the subbands so that the total TC is maximized, under the constraint of a fixed total spatial node density. The optimization problem turns out to be nonconvex. We investigate the detailed structure of the functions involved in the optimization and identify a set of properties of the optimal transmitter density over the subbands. An iterative resource allocation scheme with low complexity is derived to obtain the global optimal solution of the TC maximization problem. The solution can be loosely interpreted as a water-filling solution for a nonconvex optimization problem. Based on numerical simulations, it is shown that the optimal solution obtained through the theoretical analysis is consistent with the one obtained through an exhaustive search, which reveals that the outage probability and the total spatial transmitter density are the keys to determining the network TC.

Prolonging the lifetime, and maximizing the throughput are important factors in designing an efficient communications system, especially for energy harvesting-based systems. In this work, the problem of maximizing the throughput of point-to- point energy harvesting communications system, while prolonging its lifetime is investigated. This work considers more real communications system, where this system does not have a priori knowledge about the environment. This system consists of a transmitter and receiver. The transmitter is equipped with an infinite buffer to store data, and energy harvesting capability to harvest renewable energy and store it in a finite battery. The problem of finding an efficient power allocation policy is formulated as a reinforcement learning problem. Two different exploration algorithms are used, which are the convergence- based and the epsilon-greedy algorithms. The first algorithm uses the action-value function convergence error and the exploration time threshold to balance between exploration and exploitation. On the other hand, the second algorithm tries to achieve balancing through the exploration probability (i.e. epsilon). Simulation results show that the convergence-based algorithm outperforms the epsilon-greedy algorithm. Then, the effects of the parameters of each algorithm are investigated.

The reconstruction of gene regulatory networks from gene expression data has been the subject of intense research activity. A variety of models and methods have been developed to address different aspects of this important problem. However, these techniques are narrowly focused on particular biological and experimental platforms, and require experimental data that are typically unavailable and difficult to ascertain. The more recent availability of higher-throughput sequencing platforms, combined with more precise modes of genetic perturbation, presents an opportunity to formulate more robust and comprehensive approaches to gene network inference. Here, we propose a step-wise framework for identifying gene-gene regulatory interactions that expand from a known point of genetic or chemical perturbation using time series gene expression data. This novel approach sequentially identifies non-steady state genes post-perturbation and incorporates them into a growing series of low-complexity optimization problems. The governing ordinary differential equations of this model are rooted in the biophysics of stochastic molecular events that underlie gene regulation, delineating roles for both protein and RNA-mediated gene regulation. We show the successful application of our core algorithms for network inference using simulated and real datasets.

Symmetric nonnegative matrix factorization (SymNMF) plays an important role in applications of many data analytics problems such as community detection, document clustering and image segmentation. In this paper, we consider a stochastic SymNMF problem in which the observation matrix is generated in a random and sequential manner. We propose a stochastic nonconvex splitting method, which not only guarantees convergence to the set of stationary points of the problem (in the mean-square sense), but further achieves a sublinear convergence rate. Numerical results show that for clustering problems over both synthetic and real world datasets, the proposed algorithm converges quickly to the set of stationary points.

Symmetric non-negative matrix factorization (SymNMF) has important applications in data analytics problems such as document clustering, community detection and image segmentation. In this paper, we propose a novel nonconvex variable splitting method for solving SymNMF. Different from the existing works, we prove that the algorithm converges to the set of Karush-Kuhn-Tucker (KKT) points of the nonconvex SymNMF problem with a global sublinear convergence rate. We also show that the algorithm can be efficiently implemented in a distributed manner. Further, we provide sufficient conditions that guarantee the global and local optimality of the obtained solutions. Extensive numerical results performed on both synthetic and real data sets suggest that the proposed algorithm yields high quality of the solutions and converges quickly to the set of local minimum solutions compared with other algorithms.

We propose a simple ensemble classification algorithm, which employs a set of N randomly generated linear classifiers, followed by a selection process based on the performance of these classifiers on the whole set of training data. The top n performers are then linearly combined to form the final classifier. We analyze the VC dimension of the resulting hypothesis set from such a construction procedure, and show that it can be controlled by choosing the parameters N and n. The proposed algorithm enjoys low computational complexity, and for the MNIST dataset and several UCI datasets that we tested, the algorithm compares favorably in generalization error rate or running time to competing algorithms including Random Kitchen Sinks and AdaBoost.

Secret key establishment from common randomness has been traditionally investigated under cartain limiting assumptions, of which the most ubiquitous appears to be that the information available to all parties comes in the form of independent and identically distributed (i.i.d.) samples of some correlated random variables. Unfortunately, models employing the i.i.d assumption are often not accurate representations of real scenarios. A more capable model would represent the available information as correlated hidden Markov models (HMMs), based on the same underlying Markov chain. Such a model accurately reflects the scenario where all parties have access to imperfect observations of the same source random process, exhibiting a certain time dependency. In this paper, we derive a computationally-efficient asymptotic converse bound for the secret key capacity of the correlated-HMM scenario. The main obstacle, not only for our model, but also for other non-i.i.d cases, is the computational complexity. We address this by converting the initial bound to a product of Markov random matrices, and using recent results regarding its convergence to a Lyapunov exponent. The methods developed in the paper are easily extensible to derive a secret-key capacity lower bound.

Cognitive radio networks with energy harvesting result in efficient use of both energy and spectrum. By using cooperative relaying, another feature can be achieved, which is the high diversity gain. In this paper, an energy harvesting underlay cognitive radio relaying network is investigated. In this underlay cognitive radio scheme, secondary users are allowed to access the spectrum, respecting a certain primary interference threshold. The secondary nodes employ decode-and-forward relaying in order to maximize the total received data by optimizing their transmit powers. In this context, both the secondary source and relay harvest energy from renewable sources and store it in finite batteries. They are also capable of buffering data in infinite capacity buffers. We derive closed form expressions for transmit power of secondary source and relay that maximize the secondary network throughput. Projected subgradient method is used to find the power allocated to the secondary network. Numerical simulations are conducted to study the performance of the proposed system. Comparisons are made between the proposed system and other conventional scenarios, and it is observed that when the required signal-to interference-plus-noise ratio (SINR) at the primary receiver is high, the proposed harvesting- based scheme and conventional-based scheme perform similarly.

Modern mobile terminals produce massive small data packets. For these short-length packets, it is inefficient to follow the current multiple access schemes to allocate transmission resources due to heavy signaling overhead. We propose a many-access scheme that is well suited for the future communication systems equipped with many receive antennas. The system is modeled as having a block-sparsity pattern with unknown sparsity level (i.e., unknown number of transmitted messages). Block precoding is employed at each single-antenna transmitter to enable the simultaneous transmissions of many users. The number of simultaneously served active users is allowed to be even more than the number of receive antennas. Sparsity-aware recovery is designed at the receiver for joint user detection and symbol demodulation. To better recover the transmitted block-sparse signal vector, interference cancellation based block orthogonal matching pursuit (ICBOMP) algorithm is developed upon the known BOMP algorithm for the recovery.Simulation results demonstrate the effectiveness of the proposed scheme in small packet services, as well as the advantages of ICBOMP in improving signal recovery accuracy and reducing computational cost.

Next-generation sequencing (NGS) has revolutionized the detection of structural variation in genome. Among NGS strategies, read depth is widely used and paramorphism information contained inside is generally ignored. We develop an algorithm that can fully exploit both read depth and paramorphism information. We embed mutation procedure in our system model for estimating prior likelihood of single nucleotide base. Hidden Markov model (HMM) is used to connect single base into segments and belief propagation algorithm is performed for the optimal solution of the HMM model. Simulations show promising results in detecting important types of structural variation. We have applied the algorithm on the maize B73 and MO17 genome data and compared the results with those obtained from array CGH method based micro-array data. Inconsistency between the two sets of data is discussed.

We study the problem of high-dimensional covariance matrix estimation from partial observations. We consider covariance matrices modeled as Kronecker products of matrix factors, and rely on observations with missing values. In the absence of missing data, observation vectors are assumed to be i.i.d multivariate Gaussian. In particular, we propose a new procedure computationally affordable in high dimension to extend an existing permuted rank-penalized least-squares method to the case of missing data. Our approach is applicable to a large variety of missing data mechanisms, whether the process generating missing values is random or not, and does not require imputation techniques. We introduce a novel unbiased estimator and characterize its convergence rate to the true covariance matrix measured by the spectral norm of a permutation operator. We establish a tight outer bound on the square error of our estimate, and elucidate consequences of missing values on the estimation performance. Different schemes are compared by numerical simulations in order to test our proposed estimator.

Modern mobile terminals produce massive small data packets. For these short-length packets, it is inefficient to follow the current multiple access schemes to allocate transmission resources due to heavy signaling overhead. We propose a non-orthogonal many-access scheme that is well suited for the future communication systems equipped with many receive antennas. The system is modeled as having a block-sparsity pattern with unknown sparsity level (i.e., unknown number of transmitted messages). Block precoding is employed at each single-antenna transmitter to enable the simultaneous transmissions of many users. The number of simultaneously served active users is allowed to be even more than the number of receive antennas. Sparsity-aware recovery is designed at the receiver for joint user detection and symbol demodulation. To reduce the effects of channel fading on signal recovery, normalized block orthogonal matching pursuit (BOMP) algorithm is introduced, and based on its approximate performance analysis, we develop interference cancellation-based BOMP (ICBOMP) algorithm. The ICBOMP performs error correction and detection in each iteration of the normalized BOMP. Simulation results demonstrate the effectiveness of the proposed scheme in small packet services, as well as the advantages of ICBOMP in improving signal recovery accuracy and reducing computational cost.

We first consider a single hop wireless X network with K transmitters and J receivers, all with a single antenna. Each transmitter conveys an independent message for each receiver. The channel is assumed to have constant coefficients. We develop an interference alignment scheme for this setup and derive an achievable degrees of freedom (DoF) region. We show that in some cases, the derived region meets a previous outer bound, and hence, is the (exact) DoF region. For our achievability schemes, we divide each message into streams and use real interference alignment on the streams. Several previous results on the DoF region and total DoF for several special cases can be recovered from our result. Next, we consider a network model, such that each transmitter emits an arbitrary number of messages and each receiver can request an arbitrary subset of all emitted messages. We term this network the X network with multicast. We derive an achievability result for the DoF region of such networks, as well as an outer bound. Our achievability scheme is based on a novel extension of real interference alignment that enables us to achieve more points of the DoF region. This approach allows multiple messages from a single user to be aligned with other users' messages. Finally, we discuss some points on the outer bounds that are not achievable with any of the alignment schemes that we present.

We build on the framework of joint scheduling and network coding optimization. The formulation is extended to include rate, energy, and delay in network coding and routing paradigms. We then study energy-rate and delay-rate relationships to see how minimum energy and delay change as functions of multicast rate demand. The main observation is that as the rate demand approaches maximum achievable rate, the solution tends to increasingly use more diverse, longer paths. This translates into non-linearly higher energy and delay for higher input rates. In the case of energy, we are also able to show that network coding provides more benefits (when compared to routing) at higher rates. Another observation is related to the scheduling over maximal independent sets (MISs). We present results on comparing the performance of scheduling over all, exponentially growing MISs and small randomly selected subsets of MISs. Our results point to the effectiveness of the latter in achieving near-optimal rate and energy while reducing the complexity of the problem.

In this paper, we consider a multiuser multiple-input multiple-output (MU-MIMO) communication system between a base station equipped with multiple antennas and multiple mobile users each equipped with a single antenna. The uplink scenario is considered. The uplink channels are acquired by the base station through a training phase. Two linear processing schemes are considered, namely maximum-ratio combining (MRC) and zero-forcing (ZF). We optimize the training period and optimal training energy under the average and peak power constraint so that an achievable sum rate is maximized.

We consider a network of pairs of nodes that perform simultaneous communications over frequency-selective channels. We assume that the whole frequency band is divided into a number of subbands, and each transmitter can only use one subband. Assuming that the network is geometrically infinite, we use the throughput as a measure of network performance. We consider the problem of allocating the nodes to the subbands so that the total throughput is maximized, under the constraint of fixed total spatial node density. The optimization problem turns out to be nonconvex. We investigate the detailed structure of the functions involved in the optimization and identify a set of properties of the optimal transmitters densities over the subbands. An iterative resource allocation algorithm with low complexity is derived. From the simulations, it is shown that the optimal solution obtained through the theoretical analysis is consistent with the one obtained through exhaustive search.

Matrix completion (MC) is a technique of reconstructing a low-rank matrix from a subset of matrix elements. This letter proposes an approach for change imaging from undersampled stepped-frequency-radar data via MC. We demonstrate that MC can be used to reconstruct the unknown samples. Based on the recovered full sample data, we then perform the estimation of the change image using a Bayesian compressive sensing (BCS) approach. Compared with existing compressive sensing (CS)-based techniques, which are sensitive to noise and clutter, the proposed method reduces the false-alarm rate and achieves sparser change imaging, which is due to more available data offered by MC and our explicit consideration of clutter and additive noise in the imaging procedure. The effectiveness of the proposed method is validated with experimental results based on raw radar data.

Due to frequency selectivity, long propagation delay caused by low speed of sound, and fast channel variation, multiple-access protocols that work well in terrestrial networks may not perform as well in underwater environment. We take a physical-layer approach and investigate frequency-selective underwater channels with uncoordinated multiple bursty transmissions. We first derive the statistical distribution of the interference as seen by a typical receiver, considering burstiness of interference transmissions, packet length, and spatial distributions of the interferers. We then derive expressions for the probability density functions of the frequency-dependent signal-to-interference-and-noise ratio (SINR). Expressions for the outage probability of a typical link between two nodes are obtained in the frequency-selective scenarios and the flat-fading special cases. The outage probabilities depend on the bursty transmission probability and the number of interfering transmitters. Previous expressions in the literature on outage probability for flat fading channels and interference-limited systems can be recovered as special cases of our results. Analysis on the throughput of network is provided. Optimization over the number of nodes and bursty transmission probability is performed to maximize the network throughput. Simulation results are presented, which validate the theoretical results and illustrate typical features of the interference in an underwater network.

We study the performance of uplink transmission in a large-scale (massive) MIMO system, where all the transmitters have single antennas and the receiver (base station) has a large number of antennas. Specifically, we first derive the rates that are possible through minimum mean-squared error (MMSE) channel estimation and three possible linear receivers: maximum ratio combining, zero-forcing, and MMSE. Based on the derived rates, we quantify the amount of energy savings that are possible through increased number of base-station antennas or increased coherence interval. We also analyze achievable degrees of freedom of such a system without assuming channel state information at the receiver, which turns out to be the same as that of a point-to-point MIMO channel. Linear receiver is sufficient when the number of users is less than the number of antennas. Otherwise, nonlinear processing is necessary to achieve the full degrees of freedom.

In this paper, the degrees of freedom (DoF) regions of constant coefficient multiple antenna interference channels are investigated. First, we consider a K-user Gaussian interference channel with Mk antennas at transmitter k, 1 ≤ k ≤ K, and Njantennas at receiver j, 1 ≤ j ≤ K, denoted as a (K, [Mk], [Nj]) channel. Relying on a result of simultaneous Diophantine approximation, a real interference alignment scheme with joint receive antenna processing is developed. The scheme is used to obtain an achievable DoF region. The proposed DoF region includes two previously known results as special cases, namely: 1) the total DoF of (K, [N], [N]) interference channel with K users and N antennas at each node is NK/2 and 2) the total DoF of a (K, [M], [N]) channel is at least KMN/(M+N). We next explore constant-coefficient interference networks with K transmitters and J receivers, all having N antennas. Each transmitter emits an independent message and each receiver requests an arbitrary subset of the messages. Employing the novel joint receive antenna processing, the DoF region for this setup is obtained. We finally consider wireless X networks where each node is allowed to have an arbitrary number of antennas. It is shown that the joint receive antenna processing can be used to establish an achievable DoF region, which is larger than what is possible with antenna splitting. As a special case of the derived achievable DoF region for constant coefficient X network, the total DoF of wireless X networks with the same number of antennas at all nodes and with joint antenna processing is tight while the best inner bound based on antenna splitting cannot meet the outer bound. Finally, we obtain a DoF region outer bound based on the technique of transmitter grouping.

We consider a constant K-user Gaussian interference channel with M antennas at each transmitter and N antennas at each receiver, denoted as a (K, M, N) channel. Relying on a result on simultaneous Diophantine approximation, a real interference alignment scheme with joint receive antenna processing is developed. The scheme is used to provide new proofs for two previously known results, namely 1) the total degrees of freedom (DoF) of a (K, N, N) channel is NK/2; and 2) the total DoF of a (K, M, N) channel is at least KMN/(M+N). We also derive the DoF region of the (K, N, N) channel, and an inner bound on the DoF region of the (K, M, N) channel.

Physical-layer network coding (PLNC) allows two remote terminals to exchange information in two time slots via a half-duplex relay. In this paper, we consider PLNC in underwater acoustic (UWA) two-way relay networks, with particular attention on the recovery of the network-coded codeword at the relay. Considering multiple paths in UWA channels with large delay spreads and temporal variations, orthogonal-frequency division-multiplexing (OFDM) is used as an underlying modulation. Distinct from existing works which mainly focus on the flat-fading channel and the non-iterative PLNC decoding, we investigate three iterative receivers at the relay in doubly-selective UWA channels. To estimate the network-coded codeword, the three receivers respectively adopt i) a conventional decoding scheme to recover the codewords from two terminals independently; ii) a decoding scheme to recover the network-coded codeword directly, and iii) a joint decoding scheme to recover a composite codeword formed by the codewords from the two terminals. In the time-varying channel, the intercarrier interference (ICI) in the received signal has been explicitly addressed in three receivers. Extensive simulations and experimental results reveal that the decoding scheme for direct recovery of the network-coded codeword cannot work well in the multipath channel, and that with a lower complexity the performance of the iterative separate-decoding scheme can catch up with that of the joint decoding scheme.

We consider a single-hop interference network withKtransmitters andJreceivers, all havingMantennas. Each transmitter emits an independent message and each receiver requests an arbitrary subset of the messages. This generalizes the well-knownK-userM-antenna interference channel, where each message is requested by a unique receiver. For our setup, we derive the degrees of freedom (DoF) region. The achievability scheme generalizes the interference alignment schemes proposed by Cadambe and Jafar. In particular, we achieve general points in the DoF region by using multiple base vectors and aligning all interferers at a given receiver to the interferer with the largest DoF. As a byproduct, we obtain the DoF region for the original interference channel. We also discuss extensions of our approach where the same region can be achieved by considering a reduced set of interference alignment constraints, thus reducing the time-expansion duration needed. The DoF region for the considered system depends only on a subset of receivers whose demands meet certain characteristics. The geometric shape of the DoF region is also discussed.

We study the degrees of freedom (DoF) regions of two-user multiple-input multiple-output Z and full interference channels in this paper. We assume that the receivers always have perfect channel state information. We first derive the DoF region of Z interference channel with channel state information at transmitter (CSIT). For full interference channel without CSIT, the DoF region has been fully characterized recently and it is shown that the previously known outer bound is not achievable. In this paper, we investigate the no-CSIT case further by assuming that one transmitter has the ability of antenna mode switching. We obtain the DoF region as a function of the number of available antenna modes and reveal the incremental gain in DoF that each additional antenna mode can bring. It is shown that, in certain cases, the reconfigurable antennas can increase the DoF. In these cases, the DoF region is maximized when the number of modes is at least equal to the number of receive antennas at the corresponding receiver, in which case the previous outer bound is achieved. In all cases, we propose systematic constructions of the beamforming and nulling matrices for achieving the DoF region. The constructions bear an interesting space-frequency coding interpretation.

We consider a single hop interference network with K transmitters, each with an independent message and J receivers, all having the same number (M) of antennas. Each receiver requests an arbitrary subset of the messages. This generalizes the well-known K user M antenna interference channel, where each message is requested by a unique receiver. For this setup, we derive the exact degrees of freedom (DoF) region. Our achievability scheme generalizes the interference alignment scheme proposed by Cadambe and Jafar '08. In particular, we achieve general points in the DoF region by using multiple base vectors and aligning the interference at each receiver to its largest (in the DoF sense) interferer. As a byproduct of our analysis, we recover the DoF region for the original interference channel.

We present a superposition coding scheme for communication over a network, which combines partial decode and forward with noisy network coding. This hybrid scheme is termed as superposition noisy network coding. The scheme is designed and analyzed for a single relay channel, single source multicast network and multiple source multicast network. The achievable rate region is determined for each case. The special cases of Gaussian single relay channel and two way relay channel are analyzed for superposition noisy network coding. The achievable rate of the proposed scheme is higher than the existing schemes of noisy network coding and compress-forward.

We investigate the Degrees of Freedom (DoF) Region of a cellular network, where the cells can have overlapping areas. Within an overlapping area, the mobile users can access multiple base stations. We consider a case where there are two base stations both equipped with multiple antennas. The mobile stations are all equipped with single antenna and each mobile station can belong to either a single cell or both cells. We completely characterize the DoF region for the uplink channel assuming that global channel state information is available at the transmitters. The achievability scheme is based on interference alignment at the base stations.

Network coding and cooperative communication have received considerable attention from the research community recently in order to mitigate the adverse effects of fading in wireless transmissions and at the same time to achieve high throughput and better spectral efficiency. In this work, we design and analyze deterministic and random network coding schemes for a cooperative communication setup with multiple sources and destinations. We show that our schemes outperform conventional cooperation in terms of the diversity-multiplexing tradeoff (DMT). Specifically, it can offer the maximum diversity order at the expense of a slightly reduced multiplexing rate. We derive the necessary and sufficient conditions to achieve the maximum diversity order. We show that when the parity-check matrix for a systematic maximum distance separable (MDS) code is used as the network coding matrix, the maximum diversity is achieved. We present two ways to generate full-diversity network coding matrices: namely using the Cauchy matrices and the Vandermonde matrices. We also analyze a selection relaying scheme and prove that a multiplicative diversity order is possible with enough number of relay selection rounds.

We study the degrees of freedom (DoF) regions of two-user multiple-input multiple-output (MIMO) Z and full interference channels in this paper. We assume that the receivers always have perfect channel state information. We derive the DoF region of Z interference channel with channel state information at transmitter (CSIT). For full interference channel without CSIT, the DoF region has been obtained in previous work except for a special case M112, N2), where Miand Niare the number of transmit and receive antennas of user i, respectively. We show that for this case the DoF regions of the Z and full interference channels are the same. We establish the achievability based on the assumption of transmitter antenna mode switching. A systematic way of constructing the DoF-achieving nulling and beamforming matrices is presented in this paper.

The resource allocation optimization for multicast service in orthogonal frequency division multiple access (OFDMA) systems is an important research topic. It is known that in traditional multicast schemes the rate in each multicast group is severely limited by the user who has the minimum channel gain (MCG) on each subchannel. To counteract this effect, in this paper, we introduce the multiple description coding (MDC) into the multiple-group multicast service optimization for the downlink OFDMA, and study the weighted sum rate (WSR) maximization methods under various system constraints and assuming discrete-rate modulation. Simulation results show that the proposed method provides a significant performance enhancement than the MCG scheme and an MDC-based near-optimal iterative bit loading (IBL) approach. Besides this, our method converges very fast and enjoys a low complexity that is linear in the numbers of users, subchannels, and modulation bit levels.

Digital receivers for future high-rate high-bandwidth communication systems will require large sampling rate. This is especially true for ultra-wideband (UWB) communications with impulse radio (IR) modulation. Due to high complexity and large power consumption, multibit high-rate analog-to-digital converter (ADC) is difficult to implement. Monobit receiver has been previously proposed to relax the need for high-rate ADC. In this paper, we derive optimal digital processing architecture for receivers based on monobit ADC with a certain over-sampling rate and the corresponding theoretically achievable performance. A practically appealing suboptimal iterative receiver is also proposed. Iterative decision-directed weight estimation, and small sample removal are distinctive features of the proposed detector. Numerical simulations show that compared with full resolution matched filter based receiver, the proposed low complexity monobit receiver incurs only 2 dB signal to noise ratio (SNR) loss in additive white Gaussian noise (AWGN) and 3.5dB SNR loss in standard UWB fading channels.

Near-capacity performance of turbo codes is generally achieved with a large number of decoding iterations. Various iteration stopping rules introduced in the literature often induce performance loss. This paper proposes a novel partial decoding iteration scheme using a bit-level convergence test. We first establish decoding optimality of windowed partial iteration for non-converged bits given that convergence has been achieved on window boundaries. We next present two criteria for testing bit convergence based on cross-entropy, and propose a windowed partial iteration algorithm. The overall complexity and memory requirements of the new algorithm are evaluated and compared with known algorithms. Simulations reveal that the proposed scheme suffers essentially no performance loss compared to full iterations, while reducing the decoding complexity. We also briefly discuss possible extensions of the proposed scheme to general iterative receivers.

Feedback delay can severely affect the performance of transmit beamforming (TB) and the analytical quantification of the performance degradation has attracted much research interest recently. In this letter, we study the effect of delayed and limited-rate codebook index feedback on the error rate performance of TB systems over Rayleigh fading channels. We derive closed-form expressions for the moment generating function (MGF) and the probability density function (PDF) of the receiver output signal-to-noise ratio (SNR) including the effects of outdated and finite-rate feedback and further provide accurate analytical error rate expressions, which are verified by simulation results. The coding gain gap between the full-rate and limited-rate feedback and the coding gain advantage of multiple transmit antennas to the single antenna are analyzed, for limited and delayed feedback. These results are simple and concise and provide new analytical insight into the achievable diversity and combining gains and the loss caused by feedback delay for different system parameters and modulation formats.

Consider finite-rate channel-direction feedback in a system with multiple transmit but single receive antennas. We investigate how the transmitter should be optimized for symbol error rate with finite-rate feedback, and how the symbol error rate and outage probability improve as a function of the number of feedback bits. It is found that when the number of feedback directions is equal to or larger than the number of transmit antennas, transmit beamforming is optimal. Otherwise, the antennas should be divided into two groups, where antenna selection is used in the first group to choose the strongest channel, and equal power allocation is used in the second group. At high signal to noise ratio (SNR), the optimal power allocation between these two antenna groups is proportional to the number of antennas in each group. Based on high SNR analysis, we quantify the power gain of each feedback bit. It is shown that the incremental gain increases initially and diminishes when the number of feedback bits surpasses the logarithm (base 2) of the number of transmit antennas.

Multiple-input multiple-output system capacity can be improved when there is channel state information at the transmitter (CSIT). This paper proposes a transmission optimization strategy that aims to maximize the mutual information using statistical CSIT. A second order approximation method is used to optimize the transmit covariance matrix. The proposed scheme is compared to the first order approximation, equal power allocation, and waterfilling schemes.

We study the problem of stream number selection in an ad hoc multiple-input multiple-output (MIMO) network, where there are a number of co-existent MIMO transmitter-receiver pairs. We assume that there is no channel state information (CSI) at the transmitters, and the receivers use single-user detection. It is shown that the link capacities at high signal-to-noise ratio (SNR) is mainly limited by the number of degrees of freedom available at the receiver side. In general the number of streams per transmitter should not be more than Nr/L when the interference level is high, where Nris the number of receive antennas and L is the number of simultaneous links. When the interference level is low, the number of streams should be set equal to the number of transmit antennas.

Receiver design for impulse radio (IR) based ultra-wideband (UWB) communication is a challenge, because full-resolution digital receiver is difficult to implement under today's technology due to high sampling rate required. Some trade-offs can be made to the digital receiver, such as limiting amplitude resolution to only 1 bit, which results in a previously considered so-termed mono-bit receiver. In this paper, we consider the design of finite-resolution digital UWB receiver. We derive the optimal post-quantization processing, and analyze the achievable bit-error rate (BER) performance using an approximation of the log-likelihood ratio. We evaluate the effect of quantization threshold. The optimal threshold for two-bit quantization is obtained. Our work discloses the incremental gain that each sampling bit could bring and the results provide guidelines for designing IR UWB digital receivers.receivers.

Performing end-to-end energy management for the data aggregation application poses certain unique challenges particularly when the computational demands on the individual nodes are significant. In this paper, we address the problem of minimizing the total energy consumption of data aggregation with an end-to-end latency constraint while taking into account both the computational and communication workloads in the network. We consider a model where individual nodes support both dynamic voltage scaling (DVS) and dynamic modulation scaling (DMS) power management techniques and explore the energy-time tradeoffs these techniques offer. Specifically, we make the following contributions in this paper. First, we present an analytical problem formulation for the ideal case where each node can scale its frequency and modulation continuously. Second, we prove that the problem is NP-hard for practical scenarios where such continuity cannot be supported. We then present a mixed integer linear programming (MILP) formulation to obtain the optimal solution for the practical problem. Further, we present polynomial time heuristic algorithms which employ the energy-gain metric. We evaluated the performance of the proposed algorithms for a variety of scenarios and our results show that the energy savings obtained by the proposed algorithms are comparable to that of MILP.

Receiver design for impulse radio based ultrawideband (UWB) communication is a challenge. High sampling rate high resolution digital receiver is usually difficult to implement. Some tradeoffs can be made on the digital receiver, such as limiting amplitude resolution to only one bit, which results in a previously considered monobit receiver. In this paper, we consider the design of finite-resolution digital UWB receivers. We derive the optimal post-quantization processing, and analyze the achievable bit-error rate performance using an approximation of the log-likelihood ratio. Optimal thresholds for 4- and 3-level quantization are obtained. Training-based receiver template estimation is presented. Our work discloses the incremental gain that additional quantization levels offer and the results provide useful guidelines for designing impulse radio UWB digital receivers.

In this paper, we address the problem of scheduling a set of periodic real-time messages in a wireless network with the objective of minimizing the total energy consumption while meeting deadline and reliability constraints. We formally prove that this problem is NP-hard and solve it in two stages. First, we consider a simple model that assumes that the wireless channel is completely reliable and the network is fully provisioned. Using the technique of employing multiple hop-by-hop transmissions instead of a single direct hop transmission as the basis, we prove that the strategy of choosing the hop distances such that they are equidistant is optimal in terms of energy consumption under the deadline constraint. Based on the intuition provided by the optimal strategy, we present heuristic scheduling algorithms for a more realistic wireless channel model and network condition. Our simulation results show that the proposed scheduling algorithms provide significant energy savings over the baseline algorithms

We present a new maximum ratio combining (MRC) technique that performs the maximum ratio combining at the radio frequency (RF) level, thereby requiring only one RF chain and one matched filter. We show that it can achieve the same performance as the conventional MRC technique that performs the combining at the baseband (post-detection), which requires multiple RF chains and multiple matched filters. The proposed approach can be extended to multiple input and multiple output (MIMO) system and significantly reduce the hardware complexity.

Based on an analysis on the recursive computation of the iterative maximum a posteriori (MAP) algorithm for turbo decoding, this paper considers a modified MAP scheme with reduced block lengths for symbols with unreliable detection after some initial iterations. Applying symbol selection based on cross-entropy measurement for parallel concatenated convolutional codes, we develop partial, windowed iterations for selected symbols. By omitting computations for symbols with reliable detection results, this approach significantly reduces complexity but well maintains the performance by complete iterations.

The optimal tradeoff between diversity gain and multiplexing gain for multiple-inputmultiple-output (MIMO) channels has been studied recently under the independent and identically distributed (i.i.d.) Rayleigh-fading assumption. In this correspondence, this result is extended and the optimal tradeoff performance is derived for generalized fading channel conditions, including different fading types, nonidentical fading distributions, spatial correlation, and nonzero channel means. Our results include many known models as special cases and shed light on the effects of different channel parameters on the optimal tradeoff performance

Abstract We present iterative channel estimation and decoding schemes for multi‐input multi‐output (MIMO) Rayleigh block fading channels in spatially correlated noise. An expectation‐maximization (EM) algorithm is utilized to find the maximum likelihood (ML) estimates of the channel and spatial noise covariance matrices, and to compute soft information of coded symbols which is sent to an error‐control decoder. The extrinsic information produced by the decoder is then used to refine channel estimation. Several iterations are performed between the above channel estimation and decoding steps. We derive modified Cramer–Rao Bound (MCRB) for the unknown channel and noise parameters, and show that the proposed EM‐based channel estimation scheme achieves the MCRB at medium and high SNRs. For a bit error rate of 10−6and long frame length, there is negligible performance difference between the proposed scheme and the ideal coherent detector that utilizes the true channel and noise covariance matrices. Copyright © 2006 John Wiley & Sons, Ltd.

In this letter, we study the asymptotic performance of hybrid-selection/maximal-ratio combining (HS/MRC) and postdetection HS/equal-gain combining (HS/EGC) over generalized fading channels for large average signal-to-noise ratios (ASNRs). By evaluating the asymptotic moment generating function of the HS/MRC output SNR at high ASNR, we derive the diversity and coding gains for HS/MRC for a large class of modulation formats and versatile fading conditions, including different types of fading channels and nonidentical SNR statistics across diversity branches. Our analytical results reveal that the diversity gains of HS/MRC and HS/EGC are equivalent to that of MRC, and the difference in the coding gains for different modulation formats is manifested in terms of a modulation factor defined in this letter. Some new analytical results about effects of the number of combined branches for HS/MRC and noncoherent combining loss of HS/EGC are also provided.

Ultra wide-band (UWB) communication holds great potential for a significantly improved data rate in future wireless systems. Accurate channel estimation and synchronization are critical for successful operation of a UWB system. We propose in this letter a completely blind channel estimation algorithm for UWB systems that employ pulse-position modulation, assuming perfect synchronization at the receiver. The algorithm exploits the first-order cyclostationarity in the received signal and performs certain circular deconvolution. The complexity is extremely low: Only some "overlap-add" operations and fast Fourier transform operations are needed. The algorithm is capable of simultaneously estimating multiple (say, more than 60) channel taps, and there is no ambiguity in either the amplitude or the phase of the estimated channel. It is shown that using estimated channel from 500 information symbols, the performance can approach that with a known channel within 3 dB in signal-to-noise ratio for a bit error rate of 0.001.

Ultra wideband (UWB) communication holds great potential for significantly improved data rate in future wireless systems. Accurate channel estimation and synchronization are critical for successful operation of a UWB system. We propose in this paper a completely blind channel estimation and synchronization algorithm for UWB systems that employ pulse-position modulation. The algorithm exploits the first-order cyclostationarity in the received signal and performs certain circular deconvolution. The complexity is extremely low - only some "overlap-add" operations and FFT operations are needed. The algorithm is capable of simultaneously estimating multiple (say, more than 60) channel taps and there is no ambiguity in either the amplitude or the phase of the estimated channel. It is shown that using the estimated channel from 500 information symbols, the performance can approach that with the known channel within 2 dB in signal to noise ratio for bit error rate less than 0.01.

We compare two block transmission systems over frequency-selective fading channels: orthogonal frequency-division multiplexing (OFDM) versus single-carrier modulated blocks with zero padding (ZP). We first compare their peak-to-average power ratio (PAR) and the corresponding power amplifier backoff for phase-shift keying or quadrature amplitude modulation. Then, we study the effects of carrier frequency offset on their performance and throughput. We further compare the performance and complexity of uncoded and coded transmissions over random dispersive channels, including Rayleigh fading channels, as well as practical HIPERLAN/2 indoor and outdoor channels. We establish that unlike OFDM, uncoded block transmissions with ZP enjoy maximum diversity and coding gains within the class of linearly precoded block transmissions. Analysis and computer simulations confirm the considerable edge of ZP-only in terms of PAR, robustness to carrier frequency offset, and uncoded performance, at the price of slightly increased complexity. In the coded case, ZP is preferable when the code rate is high (e.g., 3/4), while coded OFDM is to be preferred in terms of both performance and complexity when the code rate is low (e.g., 1/2) and the error-correcting capability is enhanced. As ZP block transmissions can approximate serial single-carrier systems as well, the scope of the present comparison is broader.

We derive analytical expressions for the probability density function (pdf) of the random mutual information between transmitted and received vector signals of a random space-time independent and identically distributed (i.i.d.) multiple-input multiple-output (MIMO) channel, assuming that the transmitted signals from the multiple antennas are Gaussian i.i.d.. We show that this pdf can be well approximated by a Gaussian distribution, and such a Gaussian approximation is based on expressions for the given pdfs mean and variance that we derive. We prove that at high signal-to-noise ratio (SNR), every factor of 2 increase in SNR leads to an increase in outage rate in the amount of min(M,N) bits, where M and N denote the number of transmit and receive antennas, respectively. A simple expression for the moment generating function (MGF) of the mutual information pdf is also provided, based on which we establish normality of the pdf, when both M and N are large, and the SNR is large.

We combine error-control coding with linear precoding (LP) for flat-fading channels, as well as for wireless orthogonal frequency-division multiplexing transmissions through frequency-selective fading channels. The performance is analyzed and compared with the corresponding error-control-coded system without precoding. By wedding LP with conventional error-control coding, the diversity order becomes equal to the error-control code's minimum Hamming distance times the precoder size. We also derive a low-complexity turbo-decoding algorithm for joint coded-precoded transmissions. We analyze the decoding complexity and compare it with an error-control-coded system without LP. Extensive simulations with convolutional and turbo codes for HiperLan/2 channels support the analysis and demonstrate superior performance of the proposed system.

In this paper, we study the asymptotic performance of generalized selection combining (GSC) over different fading channels for large average signal-to-noise ratio (ASNR). Employing a moment generating function (MGF) method and the polynomial approximation of the fading channel probability density function (pdf), we derive general asymptotic MGF expressions for the GSC output SNR over generalized independent fading channels. The diversity and coding gains for GSC over different fading channels are derived. Our analytical results reveal that the diversity gain of GSC is equivalent to that of maximum ratio combining (MRC), for different modulations and generalized fading channels. We also show that the difference in the coding gains for different modulations is manifested in terms of a modulation factor defined in this paper, and thus the performance gaps between different modulations can be analytically predicted. Asymptotic performance gap between GSC and MRC is also studied in terms of the coding gain. Numerical examples are presented to illustrate the application of the new results.

We quantify the performance of wireless transmissions over random fading channels at high signal-to-noise ratio (SNR). The performance criteria we consider are average probability of:error and outage probability. We show that as functions of the average SNR, they can both be characterized by two parameters: the diversity and coding gains. They both exhibit identical diversity orders, but their coding gains in decibels differ by a constant. The diversity and coding gains are found to depend on the behavior of-the random SNR's probability density function only at the origin, or equivalently, on the decaying order of the corresponding moment generating function (i.e., how fast the moment generating function goes to zero as its argument goes to infinity). Diversity and coding gains for diversity combining systems are expressed in terms of the diversity branches' individual diversity and coding gains, where the branches can come from any diversity technique such as space, time, frequency, or, multipath. The proposed analysis offers a simple and unifying approach to evaluating the performance of uncoded and (possibly space-time) coded transmissions over fading channels, and the method applies to almost all digital modulation schemes, including M-ary phaseshift keying, quadrature amplitude modulation, and frequency-shift keying with coherent or noncoherent detection.

Orthogonal frequency-division multiplexing (OFDM) converts a time-dispersive channel into parallel subchannels, and thus facilitates equalization and (de)coding. But when the channel has nulls close to or on the fast Fourier transform (FFT) grid, uncoded OFDM faces serious symbol recovery problems. As an alternative to various error-control coding techniques that have been proposed to ameliorate the problem, we perform complex-field coding (CFC) before the symbols are multiplexed. We quantify the maximum achievable diversity order for independent and identically distributed (i.i.d.) or correlated Rayleigh-fading channels, and also provide design rules for achieving the maximum diversity order. The maximum coding gain is given, and the encoder enabling the maximum coding gain is also found. Simulated performance comparisons of CFC-OFDM with existing block and convolutionally coded OFDM alternatives favor CFC-OFDM for the code rates used in a HiperLAN2 experiment.

We present a unified approach to designing space-time (ST) block codes using linear constellation precoding (LCP). Our designs are based either on parameterizations of unitary matrices, or on algebraic number-theoretic constructions. With an arbitrary number of N/sub t/ transmit- and N/sub r/ receive-antennas, ST-LCP achieves rate 1 symbol/s/Hz and enjoys diversity gain as high as N/sub t/N/sub r/ over (possibly correlated) quasi-static and fast fading channels. As figures of merit, we use diversity and coding gains, as well as mutual information of the underlying multiple-input-multiple-output system. We show that over quadrature-amplitude modulation and pulse-amplitude modulation, our LCP achieves the upper bound on the coding gain of all linear precoders for certain values of N/sub t/ and comes close to this upper bound for other values of N/sub t/, in both correlated and independent fading channels. Compared with existing ST block codes adhering to an orthogonal design (ST-OD), ST-LCP offers not only better performance, but also higher mutual information for N/sub t/>2. For decoding ST-LCP, we adopt the near-optimum sphere-decoding algorithm, as well as reduced-complexity suboptimum alternatives. Although ST-OD codes afford simpler decoding, the tradeoff between performance and rate versus complexity favors the ST-LCP codes when N/sub t/, N/sub r/, or the spectral efficiency of the system increase. Simulations corroborate our theoretical findings.

We quantify the average performance of digital transmissions over fading channels at high signal-to-noise ratio (SNR). The performance criteria considered here are probability of error and outage probability. We show that as functions of the average SNR, they can both be characterized by two parameters: the diversity and coding gains. They have the same diversity order, but their coding gains in dB differ by a constant. The diversity and coding gains are found to be related to the behavior of the probability density function (PDF) only at the origin, or equivalently, to the decaying order of the characteristic function. Diversity and coding gains for diversity combining systems are found in terms of branch average SNR's for arbitrarily distributed independent (in some cases, correlated) branches, which can allow one to analyze, e.g., coded transmissions through independent or correlated fading channels.

This paper addresses the design of a novel scheme that combines error control coding (EC) and unitary precoding (UP) in order to obtain high diversity gains in OFDM systems with low complexity. The overall diversity of the proposed system is shown to be the product of the individual diversities achievable by the error control coding and by the unitary precoding, while the complexity is just a linear multiple of the sum of their individual complexities. In a practical HiperLan/2 setup, the proposed system achieves a gain of 4 dB, (3.3 dB), at a bit error rate (BER) of 10/sup -3/, relative to conventional OFDM systems that only deploy convolutional (turbo) coding.

We consider the class of linear precoded (LP) orthogonal frequency division multiplexing (OFDM) systems. We first show that single-carrier zero-padded transmissions, termed ZP-only, can be viewed as a special case of LP-OFDM. By resorting to the pair-wise error probability analysis, we establish the optimality of ZP-only among the LP-OFDM class in terms of its performance in random frequency-selective channels. It is shown that ZP-only enjoys maximum diversity and coding gains. We also consider various decoding options for ZP-only, and compare them in terms of performance and complexity.

Zero padding (ZP) of multicarrier transmissions has been proposed as an appealing alternative to the traditional cyclic prefix (CP) orthogonal frequency-division multiplexing (OFDM) to ensure symbol recovery regardless of the channel zero locations. In this paper, both systems are studied to delineate their relative merits in wireless systems where channel knowledge is not available at the transmitter. Two novel equalizers are developed for ZP-OFDM to tradeoff performance with implementation complexity. Both CP-OFDM and ZP-OFDM are then compared in terms of transmitter nonlinearities and required power backoff. Next, both systems are tested in terms of channel estimation and tracking capabilities. Simulations tailored to the realistic context of the standard for wireless local area network HIPERLAN/2 illustrate the pertinent tradeoffs.

We apply algebraic number theoretic tools to designing linear space-time constellation-rotating (ST-CR) block codes. With an arbitrary number of M transmit- and N receive-antennas, our ST-CR designs achieve a rate of 1 symbol/second and enjoy maximum diversity gains MN over quasi-static fading channels. When M is an Euler number, /spl phi/(P) for P/spl ne/0 (mod 4), or, when M=2/sup m/ for some positive integer m, the designed ST-CR precoders also maximize coding gains over QAM constellations. When M takes other integer values, we construct a method to design precoders with large coding gains that can be computed explicitly. Simulations corroborate our theoretical findings.

We present a unified approach to constructing linear space-time (ST) block codes based on unitary constellation-rotating (ST-CR) precoders. We show that with an arbitrary number of M-transmit and N-receive antennas, ST-CR precoders achieve 1 symbol/sec rate and enjoy maximum diversity gain MN over both quasi-static and fast fading channels. We also compare real with complex rotations to delineate the tradeoff between performance and complexity. Based on a simplified decoder, we study diversity and coding gains as well as information-theoretic aspects of the proposed ST-CR scheme. Compared with ST orthogonally designed (ST-OD) codes, ST-CR precoding provides larger coding gain and maximum mutual information. Though ST-OD codes afford simpler decoding, the tradeoff between performance and rate versus complexity favors the ST-CR codes when M, N or the spectral efficiency of the system increase.

Potential increase in capacity along with the need to provide multimedia services and cope with multiuser interference (MUI) and intersymbol interference (ISI) arising due to wireless multipath propagation, motivate well multirate wideband code-division multiple-access (CDMA) systems. Unlike most existing continuous-time symbol-periodic and multipath-free studies, the present paper develops an all-digital block-precoded filter-bank framework capable of encompassing single- or multirate transceivers for asynchronous or quasi-synchronous CDMA transmissions through multipath channels. Thanks to symbol blocking and through appropriate design of user codes, the resulting generalized multicarrier (GMC) CDMA system not only subsumes known multicarrier CDMA variants, but also equips them with flexible multirate capabilities. It is computationally simple, and guarantees symbol recovery regardless of the (possibly unknown) FIR multipath channels in both downlink and uplink setups. Simulations corroborate that the novel GMC-CDMA system outperforms existing multirate alternatives in the presence of asynchronism and multipath, and illustrate the feasibility of recovering blindly multirate transmissions received through unknown frequency-selective channels in the uplink. The performance of GMC-CDMA system in UMTS channels is also simulated and compared with existing multirate schemes.

Relying on symbol blocking and judicious design of user codes, this paper builds on a generalized multicarrier (GMC) quasi-synchronous CDMA system capable of multiuser interference (MUI) elimination and intersymbol interference (ISI) suppression with guaranteed symbol recovery, regardless of the wireless frequency-selective channels. GMC-CDMA provides a unifying framework for multicarrier (MC) CDMA systems and offers flexibility in full load (maximum number of users allowed by the available bandwidth) as well in reduced load settings. Analytic evaluation and simulations illustrate that GMC-CDMA outperforms competing MC-CDMA alternatives especially in the presence of uplink multipath channels.

The potential increase in capacity along with the need to provide multimedia services and cope with multiuser interference (MUI) and intersymbol interference (ISI) arising due to wireless multipath propagation, motivate well multirate wideband code-division multiple-access (CDMA) systems. This paper develops an all-digital block-spread filterbank framework capable of encompassing single- or multi-rate transceivers for asynchronous or quasi-synchronous CDMA transmissions through multipath channels. Thanks to symbol blocking and through appropriate design of user codes, the resulting generalized multi-carrier (GMC) CDMA system guarantees symbol recovery irrespective of the (possibly unknown) FIR multipath channels in both downlink and uplink setups with low complexity FIR receivers. Simulations corroborate that the novel GMC-CDMA system outperforms existing multirate alternatives in the presence of asynchronism and multipath.

Suppression of multiuser interference (MUI) and mitigation of multipath effects constitute major challenges in the design of third-generation wireless mobile systems. Most wide-band and multicarrier uplink code-division multiple-access (CDMA) schemes suppress MUI statistically in the presence of unknown multipath. For fading resistance, they all rely on transmit- or receive-diversity and multichannel equalization based on bandwidth-consuming training sequences or self-recovering techniques at the receiver end. Either way, they impose restrictive and difficult to check conditions on the finite-impulse response channel nulls. Relying on block-symbol spreading, we design a mutually-orthogonal usercode-receiver (AMOUR) system for quasi-synchronous blind CDMA that eliminates MUI deterministically and mitigates fading regardless of the unknown multipath and the adopted signal constellation. AMOUR converts a multiuser CDMA system into parallel single-user systems regardless of multipath and guarantees identifiability of users' symbols without restrictive conditions on channel nulls in both blind and nonblind setups. An alternative AMOUR design called Vandermonde-Lagrange AMOUR is derived to add flexibility in the code assignment procedure. Analytic evaluation and preliminary simulations reveal the generality, flexibility, and superior performance of AMOUR over competing alternatives.

Suppression of multiuser interference (MUI) and mitigation of multipath effects constitute major challenges in the design of third-generation wireless mobile systems. Most wideband and multicarrier uplink CDMA schemes suppress MUI statistically in the presence of unknown multipath. For fading resistance, they all rely on transmit- or receive-diversity and multichannel equalization based on bandwidth-consuming training or blind techniques. Either way, they impose restrictive and difficult to check conditions on the FIR channel nulls. Relying on symbol blocking, we design a mutually-orthogonal usercode-receiver (AMOUR) system for quasi-synchronous blind CDMA that eliminates MUI deterministically and mitigates fading irrespective of the unknown multipath and the adopted signal constellation. Analytic evaluation and preliminary simulations reveal the generality, flexibility, and superior performance of AMOUR over competing alternatives.