Rate-splitting improves fairness, sum rate, and outage performance of additive white Gaussian noise (AWGN) single-input single-output (SISO) multiple-access channels (MAC) with low complexity and low latency. Rate-splitting requires neither time sharing nor joint encoding and decoding, making it suitable for time-sensitive applications. Although a rate-splitting scheme is proven to exist for any rate tuple in the rate region, an explicit MAC rate-splitting scheme has not been reported in the literature to date. Previously proposed approaches considered rate-splitting parameters as variables in sum rate and fairness optimization, and these previous formulations require intractable integer programming. In this paper, the problem of maximizing the sum rate and fairness is first formulated independently of the rate-splitting method. Then, a low complexity algorithm is proposed to obtain the rate-splitting scheme to achieve a given rate tuple. The algorithm can be extended to single-input-multiple-output (SIMO) MAC with known channels by adding minimum mean square error (MMSE) receivers for each virtual user. Examples are provided to illustrate the processing steps and complexity of the proposed algorithm.

This paper addresses interference management in downlink (DL) transmission of a multi-user multiple-input multiple-output (MU-MIMO) system in time division duplex (TDD) mode. A novel method of user selection is proposed based on a relaxed definition of spatial orthogonality, where the base station (BS) obtains channel state information (CSI) using pilot symbols in uplink (UL) transmission. Due to channel reciprocity in TDD mode, the BS selects user groups based on obtained CSI and maximum pairwise channel orthogonality for the DL transmission. Then, the BS employs linear beamformers including maximum ratio transmission (MRT) and zero forcing (ZF) to transmit the payload symbols for the users. Analytical and simulated results indicate the accuracy of the proposed method in terms of the achievable rate. Also for the proposed user selection method, the optimal training sequence length in UL channel is found to maximize sum rate among the selected users.

Rate splitting multiple access (RSMA), a recent generalization and merging of spatial division multiple access (SDMA) and superposition coding has exponential complexity. Hierarchical streams that transmit a subset of possible streams of encoded messages are proposed for rate splitting and analyzed. To reduce precoding complexity, RSMA with interference nulling (RSMA-IN), that nulls the portion of interference that is not decoded is investigated by formulating a sum rate maximization problem subject to a total power constraint. To solve this non-convex problem, a method that combines interference nulling and stream SNR maximization is proposed. A convex upper bound problem is formulated for the sum rate to compare existing algorithms, and conditions under which the upper bound problem is tight are determined. Taking advantage of the tight cases, we propose an upper bound aided (UBA) enhancement of existing precoding designs by exploiting optimal solutions in the tight cases. It is also shown that the existing augmented weighted mean square error (AWMSE) algorithms cannot converge to a local optimum in sum rate maximization. Simulation results indicate that the proposed approaches enhance performance over existing ones as well as approach their upper bounds in certain instances.

Densification is an essential paradigm for future wireless networks in the unlicensed millimeter-wave (mmWave) band. Despite the promises of multi-gigabit data rates in mmWave due to wide spectrum availability, high node density could lead to severe interference and channel contention that reduce spatial reuse and overall throughput performance. To that effect, this paper investigates an approach to improve spatial average of throughput by optimizing the carrier sensing thresholds that govern the effectiveness of the channel access protocols. We consider a network with two radio access technologies (RATs) coexisting in the unlicensed mmWave spectrum. Using stochastic geometry tools to model network density, channel access protocols and spatial statistical average of throughput, closed-form expressions are proposed for selecting the carrier sensing thresholds without requiring frequent channel sounding to obtain network information. Numerical results obtained through simulation demonstrate the effectiveness of optimizing the carrier sensing thresholds to account for node density, transmit power and the mmWave propagation characteristics.

Residual self-interference cancellation is an important practical requirement for realizing the full potential of full-duplex (FD) communication. Traditionally, the residual selfinterference is cancelled via digital processing at the baseband, which requires accurate knowledge of channel estimates of the desired and self-interference channels. In this work, we consider point-to-point FD communication and propose a superimposed signaling technique to cancel the residual self-interference and detect the data without estimating the unknown channels. We show that when the channel estimates are not available, data detection in FD communication results in ambiguity if the modulation constellation is symmetric around the origin. We demonstrate that this ambiguity can be resolved by superimposed signalling, i.e., by shifting the modulation constellation away from the origin, to create an asymmetric modulation constellation. We compare the performance of the proposed detection method to that of the conventional channel estimation-based detection method, where the unknown channels are first estimated and then the data signal is detected. Simulations show that for the same average energy over a transmission block, the bit error rate performance of the proposed detection method is better than that of the conventional method. The proposed method does not require any channel estimates and is bandwidth efficient.

A new decentralized hypothesis detection framework is developed, where local sensors are memoryless, receive independent observations, and minimum feedback from the fusion center. In addition to the standard criterion of minimizing detection delay under error probability constraints, an additional constraint on the number of communications between local sensors and the fusion center is introduced here. This communication metric is able to reflect both the cost of establishing communication links as well as overall energy consumption over time. The proposed detection scheme minimizes detection delay with constraints on both error probabilities and on the number of communications. Unfortunately, optimality requires derivation of time-delay distributions of reports to the fusion center and solution of nonlinear equations. Under certain approximations, a Poisson arrival model is shown to yield a tractable solution. The efficiency of the proposed algorithm is quantified analytically in comparison to centralized detection and is shown to be in agreement with simulation results.

The recently approved IEEE 802.11ac WLAN standard has dramatically boosted Wi-Fi capabilities through supporting more data streams, larger bandwidth and more efficient frame-aggregation, leading to significantly higher data rates. The multitude of data rates and MIMO configurations to choose from has made it crucial to develop efficient algorithms for rate adaptation and MIMO mode selection (spatial multiplexing versus spatial diversity) in order to realize the promised gains in data rate. At the MAC layer, the optimal level of frame aggregation needs to be chosen in order to maximize the throughput. The adaptation approach uses very limited feedback provided within the Wi-Fi packet acknowledgement protocol. In this paper, we develop a framework for jointly selecting the data rate, the MIMO mode and the frame aggregation configuration at the access point (AP) using subcarrier-level channel state information (CSI). We use approximate expressions for the BER based on the union bound for the first-event error probability in order to obtain approximate throughput expressions for the different rates and MIMO modes. Assuming A-MPDU aggregation, we also show that for each data rate and MIMO-mode pair, there is a corresponding optimal aggregation level, which we find analytically. Our numerical results reveal that the proposed joint selection scheme yields significant improvements in throughput.

Energy harvesting (EH) provides a means of greatly enhancing the lifetime of wireless sensor nodes. However, the randomness inherent in the EH process may cause significant delay for performing sensing operations and transmitting sensed information to the sink. Unlike most existing studies on the delay performance of EH sensor networks, where only the energy consumption of transmission is considered, we consider the energy costs of both sensing and transmission. Specifically, we consider an EH sensor that monitors some status property and adopts a harvest-then-use protocol to perform sensing and transmission. To comprehensively study the delay performance, we consider two complementary metrics and analytically derive their statistics: 1) update age-measuring the time taken from when information is obtained by the sensor to when the sensed information is successfully transmitted to the sink, i.e., how timely the updated information at the sink is, and 2) update cycle-measuring the time duration between two consecutive successful transmissions, i.e., how frequently the information at the sink is updated. Our results show that the consideration of sensing energy cost leads to an important tradeoff between the two metrics: more frequent updates result in less timely information available at the sink.

Realistic modeling of energy consumption is crucial for accurate performance analysis of wireless-powered sensor nodes. In this paper, we analyze the performance of wireless-powered sensor transmissions taking into account both the energy cost of sensing and transmission. We consider a sensor that is harvesting energy from an ambient radio-frequency (RF) signal and using this energy to perform sensing and transmission. Since energy harvesting is time-varying in nature, it introduces a delay in the sensor transmissions. We study two delay-related metrics, one measuring how frequent the sensed information is updated at the sink and the other measuring the time taken from the sensing operation to successful transmission of sensed information. We analytically characterize the statistical behavior of both metrics and find an important tradeoff between them. In particular, our results illustrate that more frequent update of sensed information at the sink increases the time taken from the sensing operation to successful transmission of sensed information.

Spectrum sensing constitutes a key functionality of a cognitive radio (CR), and sensing devices are required to detect a change in spectrum occupancy as quickly as possible. A new decentralized change detection framework is developed for cognitive wireless networks, where local sensors are memoryless, receive independent observations, and no feedback from the fusion center. In addition to traditional criteria of detection delay and false alarm rate, we introduce a new constraint: the number of communications between local sensors and the fusion center. This communication metric is able to reflect both the cost of establishing communication links as well as overall energy consumption over time. The proposed detection scheme minimizes detection delay with constraints on both false alarm rate and number of communications. Simulation results are investigated to explore the tradeoffs in parameter choices of the proposed algorithm.

The problem of detecting an abrupt change in a sequence of independent and identically distributed (IID) random variables is addressed. Sequentially received samples are IID both before and after a single unknown change time. Unlike previous approaches to change detection that assume a known probability density function (PDF) for the observations at the start, the problem formulated here is to detect a change between two given PDFs in either direction, meaning that at any given time the number of hypotheses to be tracked is always twice the number of samples received. A Bayesian multiple hypothesis approach is proposed and shown to have the following properties: (i) unlike previous tests that operate with a threshold, the minimum-cost hypothesis is tracked through time, including that of no change. (ii) under an exponential delay cost function and suitable parameter choices, the proposed procedure's probability of detecting a change in the incorrect direction asymptotically vanishes with time, (iii) the method is recursive with constant computation per unit time, and (iv) error probabilities may be directly traded off with average delay. Performance results using simulation confirm the derived properties and also reveal that the additional average delay after a transient period corresponding to when the starting state is uncertain, compared to that of the optimal one-sided test, CUSUM, is modest.

We analyze the performance of multiple input/multiple output (MIMO) communications systems employing spatial multiplexing and zero-forcing detection (ZF). The distribution of the ZF signal-to-noise ratio (SNR) is characterized when either the intended stream or interfering streams experience Rician fading, and when the fading may be correlated on the transmit side. Previously, exact ZF analysis based on a well-known SNR expression has been hindered by the noncentrality of the Wishart distribution involved. In addition, approximation with a central-Wishart distribution has not proved consistently accurate. In contrast, the following exact ZF study proceeds from a lesser-known SNR expression that separates the intended and interfering channel-gain vectors. By first conditioning on, and then averaging over the interference, the ZF SNR distribution for Rician-Rayleigh fading is shown to be an infinite linear combination of gamma distributions. On the other hand, for Rayleigh-Rician fading, the ZF SNR is shown to be gamma-distributed. Based on the SNR distribution, we derive new series expressions for the ZF average error probability, outage probability, and ergodic capacity. Numerical results confirm the accuracy of our new expressions, and reveal effects of interference and channel statistics on performance.

This paper addresses training-based channel estimation in distributed amplify-and-forward (AF) multi-input multi-output (MIMO) multi-relay networks. To reduce channel estimation overhead and delay, a training algorithm that allows for simultaneous estimation of the entire MIMO cooperative network's channel parameters at the destination node is proposed. The exact Cramér-Rao lower bound (CRLB) for the problem is presented in closed-form. Channel estimators that are capable of estimating the overall source-relay-destination channel parameters at the destination are also derived. Numerical results show that while reducing delay, the proposed channel estimators are close to the derived CRLB over a wide range of signal-to-noise ratio values and outperform existing channel estimation methods. Finally, extensive simulations demonstrate that the proposed training method and channel estimators can be effectively deployed in combination with cooperative optimization algorithms to significantly enhance the performance of AF relaying MIMO systems in terms of average-bit-error-rate.

This paper considers a multi-access cooperative system uplink where multiple users access a base station (BS) through a multi-antenna relay. For a fixed relay beamformer, the optimal user beamformers are determined. Then when user beamformers are fixed, the optimal relay beamformer is derived. An iterative algorithm that jointly optimizes the user and relay beamformers is then proposed and proven to converge to a fixed point. Numerical results reveal that (i) convergence typically requires about three iterations, and (ii) performance of the iterative algorithm is very close to that of the user-relay MIMO-MAC channel and relay-BS MIMO upper bounds. An extension is proposed where multiple relays, serving as access points, co-exist with directly accessing users. While not guaranteeing a globally optimum solution to this non-convex problem, numerical results show that the performance of this extended iterative algorithm is very close to the MIMO MAC upper bound.

For distributed multi-user and multi-relay cooperative networks, the received signal may be affected by multiple timing offsets (MTOs) and multiple channels that need to be jointly estimated for successful decoding at the receiver. This paper addresses the design of optimal training sequences for efficient estimation of MTOs and multiple channel parameters. A new hybrid Cramer-Rao lower bound (HCRB) for joint estimation of MTOs and channels is derived. Subsequently, by minimizing the derived HCRB as a function of training sequences, three training sequence design guidelines are derived and according to these guidelines, two training sequences are proposed. In order to show that the proposed design guidelines also improve estimation accuracy, the conditional Cramer-Rao lower bound (ECRB), which is a tighter lower bound on the estimation accuracy compared to the HCRB, is also derived. Numerical results show that the proposed training sequence design guidelines not only lower the HCRB, but they also lower the ECRB and the mean-square error of the proposed maximum a posteriori estimator. Moreover, extensive simulations demonstrate that application of the proposed training sequences significantly lowers the bit-error rate performance of multi-relay cooperative networks when compared to training sequences that violate these design guidelines.

A new approach to relay and user selection is proposed using threshold-based transmission to reduce outage probability and save transmit power. By exploiting properties of dual-hop transmission using amplify-and-forward (AF) relaying, new threshold-based relay and user (TRU) selection protocols that employ one-bit channel state information (CSI) feedback are proposed for multiple-relay, multiple-user systems. These new protocols have the advantages of (1) economical feedback, and (2) optional adaptive power-saving transmission. Centralized selection (CS) and distributed selection (DS) policies as well as lower-complexity suboptimal selection schemes are investigated for downlink transmission. Exact expressions for outage probability, outage diversity and feedback requirement are provided to quantify the complexity/performance trade-offs. The analysis shows that selection threshold(s) can be chosen such that transmission is interrupted when system outage is certain, which allows power saving without impairing outage performance. These protocols are compared in terms of performance, energy-efficiency and system requirement to others that trade outage performance for additional energy saving and/or reduced complexity.

Receive antenna selection (AS) preserves the diversity benefits of multiple antennas with considerable reduction of hardware complexity and costs. We propose a receive AS method for time-varying channels which utilizes low-complexity Slepian subspace projection techniques. The proposed method uses Doppler bandwidth knowledge and takes into account practical limitations such as training, packetization and antenna switching. Results show that the proposed AS method outperforms ideal conventional systems with perfect channel state information (CSI) but no AS at the receiver and AS using the conventional Fourier estimation/prediction method. A closed-form expression for the symbol error probability (SEP) of M-ary phase-shift keying (MPSK) with receive AS is derived.

Single receive antenna selection (AS) allows single-input single-output (SISO) systems to retain the diversity benefits of multiple antennas with minimum hardware costs. We propose a single receive AS method for time-varying channels, in which practical limitations imposed by next-generation wireless standards such as training, packetization and antenna switching time are taken into account. The proposed method utilizes low-complexity subspace projection techniques spanned by discrete prolate spheroidal (DPS) sequences. It only uses Doppler bandwidth knowledge, and does not need detailed correlation knowledge. Results show that the proposed AS method outperforms ideal conventional SISO systems with perfect CSI but no AS at the receiver and AS using the conventional Fourier estimation/prediction method. A closed-form expression for the symbol error probability (SEP) of phase-shift keying (MPSK) with symbol-by-symbol receive AS is derived.

In cooperative networks, multiple carrier frequency offsets (MCFOs) and multiple timing offsets (MTOs) originate due to multiple distributed nodes. In this paper, algorithms for joint estimation of these parameters and channels in amplify-and-forward (AF) relaying networks are proposed. A new training model and transceiver structure at the relays for achieving synchronization throughout the network is devised. New exact closed-form expressions for the Cramér-Rao lower bounds (CRLBs) for the multi-parameter estimation problem are derived. An estimation method is proposed for jointly estimating MCFOs, MTOs, and channel gains at the destination based on space-alternating generalized expectation maximization (SAGE) and compared to a computationally-intensive least squares (LS) approach. The proposed estimator's performance is shown to be close to the CRLB at mid-to-high signal-to-noise ratio (SNR) resulting in significant cooperative performance gains in the presence of practical impairments.

A novel best-effort utility maximization scheme for video multicast with heterogeneous clients is proposed. Clients are assumed to have heterogeneous media playback requirements and channels with different capacities. The multicast employs H.264/SVC coded video which permits combined temporal, spatial, and amplitude scalability. The perceptual effects of the scalable video on clients with different terminal capabilities are modeled, and utilized in the optimization. For various scenarios with clients of different media adaptation capabilities, the proposed optimization of multi-scalable video multicasting reveals notable utility gains over single-scalable video multicasting. With a complexity independent of the number of clients, the proposed optimization scheme is suitable for large-scale multimedia multicast.

Receive antenna selection (AS) has been shown to maintain the diversity benefits of multiple antennas while potentially reducing hardware costs. However, the promised diversity gains of receive AS depend on the assumptions of perfect channel knowledge at the receiver and slowly time-varying fading. By explicitly accounting for practical constraints imposed by the next-generation wireless standards such as training, packetization and antenna switching time, we propose a single receive AS method for time-varying fading channels. The method exploits the low training overhead and accuracy possible from the use of discrete prolate spheroidal (DPS) sequences based reduced rank subspace projection techniques. It only requires knowledge of the Doppler bandwidth, and does not require detailed correlation knowledge. Closed-form expressions for the channel prediction and estimation error as well as symbol error probability (SEP) of M-ary phase-shift keying (MPSK) for symbol-by-symbol receive AS are also derived. It is shown that the proposed AS scheme, after accounting for the practical limitations mentioned above, outperforms the ideal conventional single-input single-output (SISO) system with perfect CSI and no AS at the receiver and AS with conventional estimation based on complex exponential basis functions.

Heterogeneous networks have the potential to improve coverage, throughput, and energy efficiency of wireless networks through the use of specialized cellular structures, in particular femtocells and macrocells. However, to reduce interference between different cells, ensure smooth hand-offs from cell to cell, and achieve seamless operation the overall network needs to be synchronized. In this paper a new distributed clock synchronization scheme for heterogeneous networks is proposed that employs the clock drift ratio (CDR) information available at user-equipments (UEs) to achieve synchronization between non-interacting femtocells and macrocells. Simulation results show that the proposed scheme can significantly reduce the clock drift between macrocells and femtocells and result in timing synchronization throughout the network without introducing significant overhead.

A construction of Grassmannian packings related to representation theory is applied to build implementation-friendly MIMO precoding codebooks when the number of transmit antennas is a power of a prime. Using the chordal distance as a metric, some of the corresponding packings appear to be optimal by meeting the othoplex bound. Also, by using only some of the codewords, smaller packings satisfying the equal power per antenna constraint may be found. Optimality with reference to this constraint may be shown by using a modification of the Conway-Hardin-Sloane's spherical embedding of the Grassmann manifold for equal per-antenna codebooks.

We investigate the density of codes in the complex Grassmann manifolds Gℂn,pequipped with the chordal distance. The density of a code is defined as the fraction of the Grassmannian covered by `kissing' balls of equal radius centered around the codewords. The kissing radius cannot be determined solely from the minimum distance, nonetheless upper and lower bounds as a function of minimum distance only are provided, along with the corresponding bounds on the density. This leads to a refinement of the Hamming bound for Grassmannian codes. Finally, we provide explicit bounds on code cardinality and minimum distance, notably a generalization of a bound on minimum distance previously proven only for line packing (p = 1).

We investigate the optimization of asynchronous multimedia multicast to heterogenous users. A priority encoding transmission (PET) based packetization scheme in combination with rateless coding has recently been proposed for multicasting. In previous work, a suboptimal greedy search algorithm is proposed to find the allocation of source symbols over different layers. However, the complexity of the algorithm is high for large numbers of user classes and the optimality of the algorithm is not proven. In this paper, we first show that the problem can be transformed to an equivalent convex optimization problem under the relaxation of integer constraints. In addition, a solution is found analytically for the optimal allocation. Numerical results demonstrate that the optimal analytical solution matches the results using the greedy search algorithm as well as results obtained using convex optimization software.

A spectrum sensing problem in which multiple sensors are used to detect an idle period in multiple channels is considered in this paper. By casting the problem using a partially observable Markov decision process (POMDP), a sequential detection scheme that minimizes the expected detection time and false alarm is described. Recent research shows that the POMDP formulation can be applied to spectrum sensing problems that sense multiple channels using only one sensor. This paper shows that this approach can be generalized to systems that incorporate an arbitrary number of sensors. Based on this more general procedure, this paper proposes two sequential detection schemes that exploit the additional sensors to reduce the detection time in the spectrum sensing system while maintaining its false alarm rate under a desired design constraint. The performances of the two detectors are investigated through Monte Carlo simulation.

Future wireless systems face challenges in supporting high-rate multimedia streaming with wide coverage area and at low power. In this paper we study cooperative MIMO systems in the context of imperfect channel estimation with per-user quality-of-service constraints with the objective of minimizing the sum transmission power at the source and relays. Simulation results quantify the different performance tradeoffs obtained in incorporating imperfect CSI into the problem formulation as a function of channel estimation accuracy.

The distributed nature of cooperative networks may result in multiple carrier frequency offsets (CFOs), which make the channels time varying and overshadow the diversity gains promised by collaborative communications. This paper seeks to address multiple CFO estimation using training sequences in space-division multiple access (SDMA) cooperative networks. The system model and CFO estimation problem for cases of both decode-and-forward (DF) and amplify-and-forward (AF) relaying are formulated and new closed-form expressions for the Cramer-Rao lower bound (CRLB) for both protocols are derived. The CRLBs are then applied in a novel way to formulate training sequence design guidelines and determine the effect of network protocol and topology on CFO estimation. Next, two computationally efficient iterative estimators are proposed that determine the CFOs from multiple simultaneously relaying nodes. The proposed algorithms reduce multiple CFO estimation complexity without sacrificing bandwidth and training performance. Unlike existing multiple CFO estimators, the proposed estimators are also accurate for both large and small CFO values. Numerical results show that the new methods outperform existing algorithms and reach or approach the CRLB at mid-to-high signal-to-noise ratio (SNR). When applied to system compensation, simulation results show that the proposed estimators significantly reduce average-bit-error-rate (ABER).

This paper considers multi-antenna downlink communication with base stations (BSs) that perform joint transmitter null-space decomposition (JT-decomp) and conventional single-cell linear precoding strategies under imperfect channel state information (CSI). Upper bounds for the rate loss of JT-decomp precoding and conventional cellular systems are first provided, which demonstrate that unlike conventional systems, the percentage rate loss of JT-decomp cooperative base station (BS) system decreases as signal-to-noise ratio (SNR) increases. This shows that JT-decomp precoding, in spite of the imperfect CSI, preserves its spatial multiplexing gain at high SNR. Accurate closed-form analytical expressions for the net ergodic achievable rate and the rate loss under erroneous CSI of the JT-decomp cooperative BS system are also derived.

Successful collaboration in cooperative networks require accurate estimation of multiple timing offsets. When combined with signal processing algorithms the estimated timing offsets can be applied to mitigate the resulting inter-symbol interference (ISI). This paper seeks to address timing synchronization in distributed multi-relay amplify-and-forward (AF) and decode-and-forward (DF) relaying networks, where timing offset estimation using a training sequence is analyzed. First, training sequence design guidelines are presented that are shown to result in improved estimation performance. Next, two iterative estimators are derived that can determine multiple timing offsets at the destination. The proposed estimators have a considerably lower computational complexity while numerical results demonstrate that they are accurate and reach or approach the Cramer-Rao lower bound (CRLB).

The distributed nature of cooperative networks may result in multiple carrier frequency offsets (CFOs), which make the channel time-varying and overshadow the performance gains promised by collaborative communications. However, much of the analysis in the area of cooperative communications assumes perfect synchronization. This paper seeks to address frequency synchronization in cooperative communication systems, where CFO estimation using a training sequence is analyzed. The Cramer-Rao lower bound (CRLB) for various cooperative protocols is derived. In the next step, we propose two computationally efficient iterative estimators based on the MUltiple SIgnal Characterization (MUSIC) algorithm, that can estimate the CFOs from all the relays simultaneously. Unlike existing multiple CFO estimation algorithms, the proposed estimators are effective and accurate in the presence of both large and small CFO values and numerical and simulation results show that both methods outperform the existing algorithms and reach the CRLB at mid-to-high signal to noise ratio.

We present an asynchronous and reliable multicast framework for a scalable multimedia system. An unequal error protection (UEP) transmission scheme employing a layered packetization structure and rateless codes is proposed. With this scheme, asynchronous data reception and guaranteed quality-of-service (QoS) can be achieved at the same time. Furthermore, a novel allocation algorithm is developed within the layered multicast framework, which can minimize the system cost in terms of the number of transmitted packets. Simulation results show that the proposed rateless-codes-based transmission scheme works well for asynchronous multicasting to heterogeneous receivers, and can be applied to a wide range of multimedia applications.

We study the design and optimization of unequal error protection (UEP) rateless codes for scalable multimedia multicasting. We formulate two general problems of optimizing UEP rateless code for multimedia multicasting to heterogenous users: one focusing on providing guaranteed quality of service (QoS) and the other focusing on providing best-effort QoS. A random interleaved rateless encoder design is proposed. Unlike previous designs, existing standardized raptor codes can be directly applied to this design without degrading performance. For each problem, optimal layer selection parameters are obtained either analytically or numerically. Numerical results demonstrate that the proposed optimized random interleaved UEP rateless code outperforms non-optimized rateless codes and recently proposed UEP rateless codes.

This paper proposes new selection schemes based on one-bit feedback for a system with multiple amplify-and-forward relays and users over Rayleigh fading channels. We introduce new distributed selection (DS), simplified DS (DSS), and centralized selection (CS) schemes that reduce outage and save transmit power using one-bit feedback. The CS scheme is optimal by selecting only the dual-hop link with equivalent end-to-end SNR above the outage threshold. DS and DSS schemes, on the other hand, require less feedback at the BS and employ a decentralized selection policy. The feedback bit in those schemes render the comparison result between the first- and second- hop SNR and an appropriate threshold. The chosen selection threshold in DS and DSS schemes allows the dual-hop links for which the system outage is certain to be discarded. In the three selection schemes, transmission is switched off to save power when there is no dual-hop link meeting the selection requirement. Closed-form expressions for the DS, DSS, and CS outage probabilities are derived. Numerical results reveal that, even though DSS is a simplified version of the DS scheme, they have similar outage performance. Additionally, DS and DSS are quite competitive with CS for a small number of relays relative to the number of users.

We investigate the impact of fixed relay station deployment in a single frequency network (SFN) using orthogonal frequency-division multiplexing (OFDM). We provide semi-analytical methods for relay-based SFN performance evaluation. Monte Carlo simulations are performed to provide numerical calculation of multi-cell network performance. The performance metric is the cumulative distribution function of the signal-to-interference-plus-noise-ratio (SINR) for different user positions in the network. Common relaying methods for two-hop amplify-and-forward (A&F) relay networks are evaluated. The impact on the SINR is compared for different relaying gains, locations and densities of relays, as well as different transmission protocols, such as full-duplex (FD) and half-duplex (HD). Overall, taking into account their different rates and physical layer performances, FD appears to outperform HD. In addition to showing the benefit of relay deployment for enhancing network performance, our simulations show that the fixed and variable gains perform equally well.

New results on maximum mutual information design for multiple-input multiple-output (MIMO) systems are presented, assuming that both transmitter and receiver know only an estimate of the channel state as well as the transmit and receive correlation. Since an exact capacity expression is difficult to obtain for this case, a tight lower-bound on the mutual information between the input and the output of a MIMO channel has been previously formulated as a design criterion. However, in the previous literature, there has been no analytical expression of the optimum transmit covariance matrix for this lower-bound. Here it is shown that for the general case with channel correlation at both ends, there exists a unique and globally optimum transmit covariance matrix whose explicit expression can be conveniently determined. For the special case with transmit correlation only, the closed-form optimum transmit covariance matrix is presented. Interestingly, the optimal transmitters for the maximum mutual information design and the minimum total mean-square error design share the same structure, as they do in the case with perfect channel state information. Simulation results are provided to demonstrate the effects of channel estimation errors and channel correlation on the mutual information.

This paper extends square M×M linear dispersion codes (LDC) proposed by Hassibi and Hochwald to T×M non-square linear dispersion codes of the same rate M, termed uniform LDC, or U-LDC. This paper establishes a unitary property of arbitrary rectangular U-LDC encoding matrices and determines their connection to the traceless minimal nonorthogonality criterion for space-time codes. The U-LDC are then applied to rapid fading channels by constructing trace-orthonormal versions, or TON-U-LDC for 2L and 4L input symbols, where L is a positive integer. Compared to a variety of state-of-the-art codes, the proposed codes are found to perform well in both block and rapid fading channels. In rapid fading, the symbol-wise time diversity order of a T × M, TON-U-LDC for 2L input symbols is shown to be min (T,2M).

Beamforming and relaying are two methods of improving wireless system performance. Until now, these techniques have been considered separately. In this paper, a single source (basestation) with multiple antennas broadcasts to multiple single-antenna destinations through a network of single-antenna relays. A transmit precoder is first proposed to optimize the system for a set of fixed relay gains and phases (beamforming weights). Next, by straightforwardly generalizing results from the recent literature, these distributed relay beamforming weights are optimized for this transmit precoder. The process is performed iteratively, and both problems are solved using semidefinite relaxation. For the special case of a single destination, we also provide a closed-form solution for relay optimization using a fixed linear precoder at the transmitter. Simulation results quantify the tradeoffs between the number of antennas at the basestation versus the number of relays, in terms of total relay network transmit power savings.

In this paper, we study the tradeoffs between basestation beamforming and distributed beamforming in the uplink. Here, multiple single-antenna sources access a multiple-antenna destination (basestation) through a network of single-antenna relays. In order to determine the solution, we decouple the problem into that of optimizing a linear decoder at the destination and that of optimizing relay beamforming weights. We find the optimal linear decoder at the destination and the corresponding feasible SINR feasible upperbound when the number of receive antennas at the destination is no less than the number of relays. We then apply a recently developed semidefinite relaxation technique that optimizes the relay weights. The proposed algorithm iteratively optimizes the weight matrix at the relays and the linear decoder at the destination and is able to reach a fixed point in only a few iterations. In addition, the tradeoffs between basestation and relay network complexity are investigated.

In this paper we propose a new low feedback and low complexity scheme that adjusts the phases at the relays, thus allowing for the coherent combining of signals from multiple relays in a cooperative multi-input-multi-output (MIMO) system using space division multiple access (SDMA). It is assumed that the channel state information (CSI) is not fed back to the source and the relays and the case of imperfect CSI at the destination is also taken into consideration. In determining the parameters, conventional convex optimization schemes cannot be applied due to the non-convex nature of the function. A low complexity method is used to optimize the system that utilizes bisection search. Numerical calculations and BER simulations demonstrate a significant performance gain over conventional AF.

Assuming perfect channel state information (CSI), linear precoding/decoding for multiple-input multiple-output (MIMO) systems has been considered in the literature under various performance criteria, such as minimum total mean-square error (MSE), maximum mutual information, and minimum average bit error rate (BER). It has been shown that these criteria belong to a set of reasonable Schur-concave or Schur-convex objective functions of the diagonal entries of the system mean-square error (MSE) matrix. In this paper, assuming only the knowledge of channel mean and transmit correlation at both ends, a general theoretical framework is presented to derive the optimum precoder and decoder for MIMO systems using these objective functions. It is shown that for all these objective functions the optimum transceivers share a similar structure. Compared to the case with perfect CSI, a linear filter is added to both ends to balance the suppression of channel noise and the additional noise induced from channel estimation error. Simulation results are provided.

Joint linear minimum sum mean-squared error (referred to as MSMSE) transmitter and receiver (transceiver) optimization problems are formulated for multiuser MIMO systems under a sum power constraint assuming imperfect channel state information (CSI). Both the uplink and the dual downlink are considered. Based on the Karush-Kuhn-Tucker (KKT) conditions associated with both problems, a relation between the two problems is discovered, which is termed the uplinkdownlink duality in sum MSE under imperfect CSI. As a result, the MSMSEs in both links are the same and any admissible uplink design satisfying the KKT conditions can be translated for application to the downlink, and vice versa. Simulation results are provided to demonstrate the duality and show the impact of imperfect CSI.

An optimal admission control (AC) policy is proposed for a CDMA multiple antenna system, which can maximize the system throughput while simultaneously guaranteeing all quality-of-service (QoS) requirements in both physical and network layers. While previous research can only ensure the QoS requirements for appropriately chosen automatic-retransmission-request (ARQ) parameters, the proposed AC policy aims to ensure the QoS requirements for arbitrary ARQ parameters, which saves system resources. Numerical examples show that the proposed AC policy can achieve a significant performance gain in terms of network layer performance as well as system throughput.

A throughput-maximization call admission control (CAC) policy is proposed for CDMA beamforming systems in which the QoS requirements in both physical and network layers can be guaranteed. While the existing cross-layer CAC policies rely on a separate reduced-outage-probability (ROP) algorithm to guarantee the physical layer QoS requirement, which adds to system complexity and reduces spectral efficiency, the proposed CAC policy can maintain arbitrary outage probability constraints as well as all the other QoS requirements without the aid of any ROP algorithm. The optimal CAC policy, obtained by formulating a constrained semi-Markov decision process (SMDP), is able to optimize the overall system throughput across different layers. Numerical examples demonstrate that the proposed policy is capable of achieving a significant performance gain, in terms of lowered blocking and outage probabilities as well as increased system throughput.

To improve performance of orthogonal frequency division multiplexing (OFDM) for fading channels, this paper proposes increasing frequency and time diversity using linear dispersion codes (LDC-OFDM). Methods of LDC-OFDM processing are proposed for both zero-padding (ZP) and cyclic-prefix (CP) type guard intervals. A two-step-estimation (TSE) decoding strategy is proposed that decouples symbol estimation from LDC decoding. This paper analyzes the upper bound diversity order of LDC-CP-OFDM, which is equal to the full diversity order available in the channels. A criterion for full frequency-time diversity design is derived, a rate-one code is provided and performance is examined through simulations. This paper also investigates LDC-CP-OFDM and LDC-ZP-OFDM performance under imperfect channel estimation and low complexity receiver structures, respectively. In addition, TSE is shown to have performance close to that of full complexity one-step estimation (OSE).

Computationally scalable and accurate estimation, prediction, and simulation of wireless communication channels is critical to the development of more adaptive transceiver algorithms. Previously, the application of autoregressive moving average (ARMA) modeling to fading processes has been complicated by ill-conditioning and nonlinear parameter estimation. This correspondence presents a numerically stable and accurate method to synthesize ARMA rational approximations of correlated Rayleigh fading processes from more complex higher order representations. Here, the problem is decomposed into autoregressive (AR) model matching followed by linear system identification. Performance is compared to that of AR, inverse discrete Fourier transform, and sum of sinusoids techniques. Also, for the first time, the finite-precision performance of different methods is compared.

Duality between the multi-antenna multi-user uplink and the downlink has been discovered in terms of sum rate, capacity region, signal-to-interference-plus-noise-ratio (SINR) region or normalized mean-squared error (MSE) region. Previous work on duality has assumed perfect channel knowledge. However, channel estimation is never perfect in practice. In this paper, channel estimation error as well as antenna correlation at the base station (BS) is taken into account. A multi-user system with multiple antennas at the BS and with single-antenna users is studied. Joint detection and transmission are used in the uplink and the downlink, respectively. It is analytically shown that with imperfect channel state information (CSI), under the same sum power constraint, the achievable SINR regions or the normalized MSE regions in both links are the same, as in the case with perfect CSI. Monte Carlo simulation results and discussions are also provided to complement the analysis.

Adaptive transceivers play an important role in wireless communications and the design of MIMO systems. Therefore models that enable simulation of dynamic and time varying channels in a computationally scalable fashion are extremely valuable. Previously, the application of auto regressive moving average (ARMA) modeling to fading processes has been complicated by ill-conditioning and nonlinear parameter estimation. This paper presents a numerically stable and accurate method to synthesize ARMA rational approximations of correlated Rayleigh fading processes from more complex higher order representations. The resulting ARMA synthesis is then used in the generation of dynamic MIMO channel realizations with time varying Doppler at significantly lower computational complexities.

A novel cross-layer call admission control policy is proposed for a general CDMA beamforming system. In contrast to previously proposed call admission control (CAC) policies which focus on large systems and specific receiver structures, in this paper we aim to design an optimal CAC policy in a more general CDMA beamforming system. The effects due to mutual coupling and multi-path scattering are also taken into account. Based on this more realistic signal propagation model, an optimal semi-Markov decision process (SMDP)-based CAC policy is proposed, which is then evaluated and compared with single antenna systems. Numerical examples demonstrate that compared with the case in single antenna systems, our proposed CAC policy for beamforming systems is capable of achieving a significant performance gain, while simultaneously guaranteeing quality-of-service (QoS) requirements.

This paper presents a new result on minimum total mean squared error (MSE) joint transceiver design for multiple-input multiple-output (MIMO) systems, with imperfect channel state information (CSI) at both ends and subject to a total power constraint. The channel knowledge here is the channel mean and transmit correlation information. The joint design is formulated into an optimization problem, to which the closed-form optimum solution is found. The optimum precoder consists of a linear filter, a matrix which collects effective channel eigenmodes, and a diagonal power allocation matrix. The linear filter balances the suppression of channel noise and the additional noise induced by channel estimation error. Given the precoder, the optimum decoder is simply a linear minimum MSE (LMMSE) data estimator. The performance degradations due to imperfect channel estimation and/or transmit correlation are demonstrated by simulation results. The relation between the minimum total MSE design and the maximum mutual information design is determined as well, under the above mentioned imperfect CSI.

Precoding for multiple-input multiple-output (MIMO) spatial multiplexing generally requires high feedback overhead and/or high-complexity processing. Simultaneous reduction in transmitter complexity and feedback overhead is proposed by imposing a diagonal structural constraint to precoding, i.e., power allocation. Minimum bit-error rate (MBER) is employed as the optimization criterion, and an approximate MBER (AMBER) power-allocation algorithm is proposed for a variety of receivers, including zero-forcing (ZF), successive interference cancellation (SIC), and ordered SIC (OSIC). While previously proposed precoding schemes either require ZF equalization for MBER, or use a minimum mean-squared error (MMSE) criterion, we provide a unified MBER solution to power allocation for ZF, SIC, and OSIC receiver structures. Improved error-rate performance is shown both analytically and by simulation. Simulation results also indicate that SIC and OSIC with AMBER power allocation offer superior performance over previously proposed MBER precoding with ZF equalization, as well as over MMSE precoding/decoding. Performance under noisy channels and power feedback is analyzed. A modified AMBER algorithm that mitigates error propagation in interference cancellation is developed. Compared with existing precoding methods, the proposed schemes significantly reduce both transmit processing complexity and feedback overhead, and improve error-rate performance

In typical mobile wireless scenarios, signals are received with power azimuth angle spectrum (p.a.s.) of variable azimuth angle spread (AS). Therefore, conventional maximum average signal-to-noise ratio beamforming (BF) or maximal-ratio combining (MRC) may not necessarily be effective in terms of performance or signal processing complexity. A newer, more flexible, approach, called maximal-ratio eigen-combining (MREC) is analyzed and found to generalize BF and MRC. For imperfectly-known channels we study both suboptimal and optimal eigen-/combining. MREC-based analysis is shown to simplify MRC performance investigations for correlated channel gains. For MPSK signals, we present average error probability (AEP) expressions for MREC, BF, and MRC, that are new or generalizations of our previous work. Furthermore, we propose a performance-complexity tradeoff criterion (PCTC) for MREC-receiver adaptation to changing AS. Numerical evaluations for typical urban scenarios with realistic Laplacian p.a.s. of random AS demonstrate that PCTC-based MREC is an interesting alternative to BF and MRC, for smart antenna arrays

A new call admission control (CAC) algorithm based on an approximated power control feasibility condition is proposed for the uplink of a cellular system. While power control feasibility can be expressed in closed-form for the case of single antennas, an approximated power control feasibility condition is required for multiple antenna systems. This approximation, however, increases outage probability in the physical layer. In this paper, we investigate the mitigation of the outage problem in the context of cross-layer performance, and propose CAC algorithms for code-division multiple access (CDMA) beamforming systems. More specifically, these new methods achieve a network-layer performance gain, in terms of low connection delay and low blocking probability, while simultaneously guaranteeing physical-layer performance in terms of outage probability. In this paper, several reduced-outage-probability (ROP) algorithms are proposed. Numerical examples illustrating the performance and properties of these ROP are presented and compared.

High-rate space-time-frequency block codes (STFBC) are promising for achieving high bandwidth efficiency, low overhead and latency. Recently, a class of low-complexity STFBC methods based on two stages of complex diversity coding (CDC) have been proposed, known as double linear dispersion STFC(DLD-STFC). This paper investigates two issues related to the performance improvement of high-rate STFCs. First, it is shown that the two CDC stages of DLD-STFC can be interchanged. Two new diversity concepts for analysis of 3-dimensional DLD-STFC are introduced: per dimension diversity order and per dimension symbol-wise diversity order. A sufficient condition for DLD-STFC to achieve full symbol-wise diversity order is provided despite the existence of two CDC stages. Second, the gain obtainable in combining CDC with forward error correction (FEC) for STFC designs is quantified. Through simulations, it is shown that STFC based on the proper combination of CDC and FEC may outperform a variety of other STFC combinations, especially in spatially correlated channels. Further, the choice of the mapping from FEC to DLD-STFC may significantly impact system performance.

This paper proposes a general coordinate-interleaving method for block-based space-time codes or linear dispersion codes, called space-time coordinate interleaving linear dispersion codes (ST-CILDC), which enables not only symbol-level diversity but also coordinate-level diversity for high rate block-based space-time code design. This paper analyzes the upper bound diversity order and provides the analysis results of the upper bound statistical diversity order and average diversity order for ST-CILDC systems. Compared with conventional ST-LDC systems, ST-CILDC systems may show either almost doubled average diversity order or extra coding advantage in time varying channels. With trivial extra complexity over ST-LDC systems, ST-CILDC systems maintain the diversity performance in quasi-static block fading channels, and significantly improve the diversity performance in rapid fading channels

Linear dispersion coded orthogonal frequency division multiplexing (LDC-OFDM) has recently been proposed to improve joint frequency and time diversity. This paper investigates whether LDC are able to support joint frequency and time diversity for single-carrier block communications in time-varying frequency selective fading channels, and proposes linear dispersion coded cyclic-prefix single-carrier modulation (LDC- CP-SCM), which utilizes LDC across multiple CP-SCM blocks. LDC-CP-SCM uses a layered two-stage LDC decoding strategy, and is thus backwards-compatible to CP-SCM systems. This paper analyzes the diversity properties of LDC-CP-SCM, and provides a sufficient condition for LDC-CP-SCM to maximize all available joint frequency and time diversity gain and coding gain. For the LDC considered, simulations show that with and without carrier frequency offset (CFO) effects, LDC-CP-SCM may outperform both CP-SCM and LDC-CP-OFDM in time- varying frequency selective channels. This paper also shows that LDC-CP-SCM with forward error correction (FEC) may outperform CP-SCM with FEC over time.

A two-input multiple-output (TIMO) system represents an important special case of multiple-input multiple-output (MIMO) systems and occurs in practical scenarios where there are limitations on cost and/or space to install more antennas, in MIMO with transmit antenna selection which selects two out of multiple transmit antennas and turns MIMO into TIMO, or in cooperative communications with two single-antenna mobiles sharing their antennas. In this paper, minimum bit error rate (MBER) transmit optimization for TIMO spatial multiplexing systems is investigated. Approximate MBER transmit power allocation for a variety of receiver structures is proposed. Transmit beamforming schemes using 4-ary pulse-amplitude-modulation (4-PAM) and quaternary phase-shift-keying (QPSK) pre-mixing are also proposed, which eliminate error floors in ill-conditioned TIMO channels. It is shown both analytically and by numerical simulations that the proposed schemes offer superior performance over existing schemes. Essentially, the proposed transmit optimization provides a simple and efficient means to utilize partial channel state information at the transmitter

This paper investigates increasing space, time, and frequency diversity through linear dispersion codes (LDC) in MIMO-OFDM wireless fading channels. Two new types of block-based high-rate space-time-frequency (STF) codes: (1) double linear dispersion space-time-frequency-coding (DLD-STFC), and (2) linear dispersion space-time-frequency-coding (LD-STFC) are proposed. In addition to double LDC encoding, DLD-STFC uses three-stage LDC decoding. The LD-STFC, on the other hand, requires only one LDC procedure across multiple OFDM subcarriers, OFDM blocks and multiple antennas. Both DLD-STFC and LD-STFC are backwards compatible to uncoded MIMO-OFDM systems. Comparison to an extension of a recently proposed LDC-OFDM to MIMO systems, called MIMO-LDC-OFDM, is made in which a single LDC-OFDM codeword is mapped to one transmit antenna. This paper discusses diversity properties of these STF block based designs. An error union bound analysis provides further insights, including more restrictive LDC code design criteria. Compared to other methods of similar complexity, simulations reveal that the bit error rate (BER) performance of full-rate DLD-STFC offers superior performance.

Power allocation for multiple-input multiple-output (MIMO) spatial multiplexing systems is investigated. Minimization of bit error rate (MBER) is employed as the optimization criterion. MBER power allocation schemes for a variety of receiver structures, including zero-forcing (ZF), successive interference cancellation (SIC), and ordered SIC (OSIC), are proposed. It is shown that the newly proposed MBER power allocation schemes improve error rate performance. Simulation results indicate that SIC and OSIC with MBER power allocation outperform MBER precoding for ZF equalization as well as minimum mean squared-error (MMSE) preceding/decoding. Performance under noisy channels and power feedback is analyzed. A modified algorithm that mitigates error propagation in interference cancellation is developed. Compared to existing precoding methods, the proposed schemes significantly reduce both processing complexity and feedback overhead, and improve error rate performance.

Existing performance comparisons of single-carrier (SC) and multicarrier transmission are mainly based on simulations. In this letter, we present a framework to compare the performance of cyclic prefix (CP)-based SC block transmission and orthogonal frequency-division multiplexing (OFDM) in frequency-selective fading channels. Minimum mean-squared error, equal-gain, and approximate minimum bit-error rate power-allocation schemes for CP-OFDM are considered. Performance of these methods is compared analytically and verified by simulations.

Space diversity is an effective method to combat fading and cochannel interference (CCI) in wireless systems. In this paper, outage performances of several diversity schemes, including a practical variation of maximal-ratio combining that does not require signal-to-noise ratios at different antennas, equal-gain combining, and selection combining, are compared analytically for an interference-limited environment in a Rayleigh fading channel. Our analysis provides insight into the performance of diversity schemes in the presence of CCI as well as assesses the impact of cochannel interferer power distributions.

Eigenbeamforming, herein referred to as maximal-ratio eigen-combining (MREC), was recently proposed as an alternative to maximum average signal-to-noise ratio beamforming (Max-ASNR BF) and maximal-ratio combining (MRC) in antenna array systems. An analysis of MREC is undertaken and an average error probability (AEP) expression is obtained for BPSK modulation and Rayleigh fading when the channel gains may be imperfectly-known and partially correlated. The analysis is further specialized to pilot-symbol-aided channel estimation, to allow an analytical performance assessment of smart antenna arrays (SAAs) employing MREC in realistic scenarios with angle-of-arrival (AOA) dispersion. Numerical results show that MREC may significantly outperform Max-ASNR BF and MRC in imperfect conditions.

High rate linear dispersion codes (LDC) for space time channels can support arbitrary numbers of transmit and receive antennas. In contrast to the one-to-one transformations used in interleaving, these codes disperse data in linear combinations over space and time. To improve performance of orthogonal frequency division multiplexing (OFDM) for wireless fading channels, this paper investigates increasing frequency and time diversity using LDC. To overcome the requirement of constant channel gains over an entire LDC time interval, a new decoding algorithm for a special subclass of LDC is proposed. The newly proposed LDC-OFDM linearly disperses data over both time and frequency, i.e., over multiple subcarriers and OFDM blocks. Simulations show the bit error rate (BER) performance of rate-one LDC-OFDM with zero padding is superior to that of uncoded OFDM with zero padding. Further, compared to uncoded OFDM, LDC-OFDM may have improved performance without increasing the peak-to-average power ratio (PAPR).

We compare cyclic prefix (CP) based single and multicarrier block transmission schemes in frequency-selective fading channels. Analytical comparison shows that at moderate-to-high signal-to-noise ratio (SNR), the uncoded error rate performance of multicarrier transmission is inferior to that of single carrier. We propose new minimum bit error rate (MBER) power loading algorithms for multicarrier transmission. It is also shown that a simpler approximate MBER (AMBER) power loading method has performance close to that of the optimum MBER scheme. Performance of a variety of methods is compared analytically and verified by simulations.

In this paper, we propose a novel matched-type filter parallel code acquisition scheme to detect up-link short-code wideband CDMA multipath signals. A tree-structured parallel adaptive network of RAKE fingers is created for all possible delay shifts and users. Employing a sequential detection scheme, only viable fingers are used to generate the output decision statistics. Monte Carlo computer simulation and numerical calculations have been carried out to study the performance of the algorithm in single-path and multi-path channels corrupted with co-channel interference (CCI) and additive white Gaussian noise (AWGN). Both simulation and numerical calculations demonstrate that the proposed algorithm can, at the expense of signal combining loss, significantly reduce the computation complexity and latency of the receiver. Additional signal combining loss is also quantified when the channel estimation error from weighted multislot averaging (WMSA) is taken into account.

Equal gain combining (EGC) diversity has performance close to that of maximal ratio combining but at lower implementation complexity. We present a new outage performance analysis for EGC in mobile cellular radio systems that are limited by cochannel interference and undergo Rayleigh fading. We utilize a new model where interfering signals add in amplitude and phase across antenna array elements. In addition, the interfering signals may each have a different power. In comparing our analysis to an existing method, we find that: 1) as much as 1.5 dB difference in signal-to-interference ratio may exist at the same probability of outage; 2) the existing method can lead to overly optimistic outage performance prediction in certain situations.

Knowledge of the velocity of a mobile terminal is useful for a variety of radio resource management functions being contemplated for future wireless communications systems. Currently, the radial component of velocity of a mobile terminal can be inferred from estimating the maximum Doppler fading bandwidth. Unfortunately, the practicality of current methods are limited since they require an estimate of the signal-to-noise ratio (SNR) of the link. In this paper, we propose two novel autocorrelation function (ACF) based velocity estimators. These estimation methods are then extended to estimate mobile velocity without requiring knowledge of the SNR of the link. Monte-Carlo simulation of the proposed estimators are provided and compared to that of Sampath and Holtzman [1].

We propose a stable and reduced-complexity detection method for the Bell Labs layered space-time (BLAST) coding system. The existing iterative nulling and cancellation algorithm for BLAST has high computational complexity and requires repeated matrix pseudo-inverse calculation which may lead to numerical instability. A square-root algorithm was proposed by other researchers to reduce complexity and improve numerical stability. In this paper, to further reduce complexity, we modify the decorrelating decision-feedback CDMA multiuser detection method and apply it to BLAST. Similar to the square-root algorithm, numerical stable unitary transformations are performed on the Cholesky-decomposed matrices to reorder the detection and cancellation steps. However, our method exploits a symmetry property in the triangularization process, which may further improve the numerical stability and reduce computational complexity over that of the square-root algorithm. In the simulation results, the impact of the reordering on performance is demonstrated.

We investigate a new robust multiuser signal detector for asynchronous code-division multiple-access uplink channels under delay mismatch. We first formulate a robust decorrelating detector by dividing each user into two virtual users with rectangular chip pulse shapes. To increase the system capacity, a multistage version of the robust decorrelating detector is derived, which can achieve capacity of up to M/(M+1) of the spreading factor, where M is the observation block length. We further propose a robust successive interference cancellation (SIC) implementation. The proposed robust SIC detector adds only a residual error estimation procedure onto the standard SIC detector, so its computational complexity is of the same order of that of the SIC. Performance is investigated via analysis and simulation. Computer simulation results showed that our proposed robust SIC detector outperforms the conventional decorrelating detector when delay estimation error is present, and its performance is close to that of the decorrelating detector with perfect time-delay information. Finally, we generalize the robust SIC detector to the case of nonrectangular chip pulse shapes.

Successive interference cancellation (SIC) is a low-complexity multiuser detection method for DS/CDMA systems. If the channel is AWGN or slowly varying, averaging the channel amplitude estimates over multiple bits can greatly improve performance. However, the error propagation associated with a hard-limiter decision rule prevents SIC from achieving the performance improvement from averaging. We propose a new soft-decision rule to be used with amplitude averaging, which combines linear and hard decision rules. A performance within 0.4 dB of the single-user bound is obtained. We also made the above soft-decision SIC robust to time delay errors.

We investigate an improved multiuser detector for asynchronous CDMA uplink channels when there exist timing delay estimation errors. We first formulate a robust decorrelating detector with a capacity limit of 50%. To increase the system capacity, we further propose a robust successive interference cancellation (SIC) implementation. The proposed robust SIC detector adds only a residual error estimation procedure onto the standard SIC detector. Computer simulation results show that its performance and capacity are very close to those of the ideal decorrelating detector which assumes perfect knowledge of time delay.

We investigate multiuser signal detection with a base-station antenna array for a synchronous DS-CDMA uplink using nonorthogonal codes in Rayleigh fading channels. We have developed a new formulation for a spatial-temporal decorrelating detector using the maximum-likelihood criteria. The detector is shown to be near-far resistant. We propose to implement the spatial-temporal decorrelating receiver iteratively by applying the space-alternating generalized expectation-maximization (SAGE) algorithm. Simulation results show that the SAGE-based decorrelating receiver significantly outperforms the conventional single-user receiver and with performance close to that of a spatial-temporal decorrelating receiver with known channel parameters. We have observed that adding base-station antennas can actually improve convergence of the proposed iterative receiver.

In spotlight-mode synthetic aperture radar (SAR), a long integration time is required to achieve a high cross-range resolution. Unfortunately, target motion during this long integration time will lead to undesirable smeared SAR images. The effect of subaperture integration time (SIT) on motion compensation for spotlight-mode SAR is investigated. Subaperture images are used to estimate the target velocity via object's centroid measurements. The estimated velocity is used to achieve motion compensation. The target is assumed to have a constant translational motion. The results demonstrate how the accuracy of motion compensation depends on the SIT and target velocity. With the analysis in this paper, we can optimize the choice of the SIT.

A recent neural clustering scheme called "probabilistic winner-take-all (PWTA)" is applied to image segmentation. It is demonstrated that PWTA avoids underutilization of clusters by adapting the form of the cluster-conditional probability density function as clustering proceeds. A modification to PWTA is introduced so as to explicitly utilize the spatial continuity of image regions and thus improve the PWTA segmentation performance. The effectiveness of PWTA is then demonstrated through the segmentation of airborne synthetic aperture radar (SAR) images of ocean surfaces so as to detect ship signatures, where an approach is proposed to find a suitable value for the number of clusters required for this application. Results show that PWTA gives high segmentation quality and significantly outperforms four other segmentation techniques, namely, 1) K-means, 2) maximum likelihood (ML), 3) backpropagation network (BPN), and 4) histogram thresholding.

We investigate multi-user signal detection with a base-station antenna array for synchronous CDMA Rayleigh fading uplink channels. We have developed a discrete-time model that enables the formulation of a spatial-temporal decorrelating detector using the maximum-likelihood criterion. The detector is shown to be near-far resistant. We implement the spatial-temporal decorrelating receiver iteratively by applying either the expectation-maximization (EM) algorithm or the space alternating generalized expectation-maximization (SAGE) algorithm. Out of the two receiver structures obtained, the SAGE-based decorrelating receiver outperforms the EM-based decorrelating receiver and converges to the performance of the spatial-temporal decorrelating receiver with known channel parameters.

This paper proposes a variation of the standard backpropagation (BP) algorithm that is particularly suitable for training neural networks utilized in multiresolution image classification. The aim is to solve the problem of determining which image resolutions to utilize as network inputs. The approach is to have a mix of image resolutions as network inputs and then apply the proposed BP variant. This variant not only solves the image classification problem, but also removes redundant multiresolution network inputs aiming at attaining only those necessary for classification. Removal of redundant inputs is based on that during the backward phase of BP training the network input units whose inputs are extracted from shared image space inhibit one another. Experimental results that demonstrate the viability of the proposed BP variant are presented.

In video communication systems based on motion-compensated predictive coding, transmission errors cause spatial and temporal distortion propagation during; the reconstruction of the video sequence at the receiver. Two commonly used techniques to stop error propagation are (1) periodic refreshing by intra-frame coding and (2) retransmission. However, frequent intra-frame refreshing may be expensive in band-limited applications such as wireless video transmission. On the other hand, retransmission causes additional delay which may be intolerable in real-time applications. We present a novel video coding mode which we call transmitter receiver identical reference frame (TRIRF) based inter-frame coding. Under the assumption of the existence of a feedback channel, TRIRF-frame coding constructs a new type of reference frame from the correctly received data which is made identical both at the receiver and the transmitter. Motion estimation and compensation are based on the TRIRF-frame. Simulations show that TRIRF-frame coding prevents error propagation as effectively as intra-coding but with improve compression efficiency. We also propose a packetization scheme for the encoded video bit streams which enables rapid resynchronization of the decoder.

We propose a new algorithm for estimating the interference-plus-noise covariance matrix for beamforming in a cellular CDMA system in a fading channel. The method uses direct PN sequence signal cancellation. We show in theory that our method outperforms that of [1,2] for finite input data. The results, confirmed by simulation, show that we get improved DOA estimates and SINR with lower computational requirements.

We investigate the application of an existing beamforming weight estimation method to the IS-95 reverse link and propose an extension to the algorithm which yields dramatically improved accuracy. An error analysis presents the statistical parameters of the various estimators. This includes an eigenvector perturbation analysis and the derivation of the Cramer-Rao lower bound for this situation. Sample results are used to demonstrate the close agreement between observed and predicted values, thereby justifying the use of the analytical derivations to reduce simulation complexity in cell capacity evaluation studies.

We introduce a novel adaptive digital beamforming algorithm for application in cellular CDMA communications systems. A noniterative rank-one signal subspace eigenstructure update (NR1SSU) algorithm enables low-complexity recursive processing of beamforming weights. A PN chip-level IS-95 CDMA reference model has been used to test the beamforming algorithm via simulation. Our results show that accurate beamforming can be achieved to moving mobiles at one update per IS-95 frame of 192 data bits.

This paper develops a two-stage approach for the identification of ship targets in airborne synthetic aperture radar (SAR) imagery representing open ocean scenes. The first stage of the developed approach segments the SAR image using a novel neural clustering scheme, called "probabilistic winner-take-all (PWTA)". As for the second stage, it employs a backpropagation (BP) neural network to classify ships that may be found in the segmented SAR image. Experimental results are presented. These results demonstrate that the developed two-stage ship-identification approach is successful in automatically interpreting the SAR imagery even in the presence of confusing ships and natural clutter.

We present a novel approach to find a sub-optimal image segmentation for video coding using a minimum description length (MDL) cost function. We describe the theory necessary to derive an appropriate cost function based on probabilistic models for the image prediction error and motion parameters. A key feature of our approach is the use the MDL cost function to incorporate global bit-rate minimization into the criterion for region segmentation. We apply this theory to the design of a bit-stream level interframe motion-compensated video coding system using a quad-tree optimisation procedure with variable order motion modeling. A computationally feasible approach is illustrated which could potentially be implemented in real-time with appropriate hardware.

A noniterative method using optical flow to recover the translation direction of a moving camera has been previously proposed by Heeger-Jepson (1992). In this paper we present a detailed explanation of the bias in this algorithm and compare methods for eliminating this bias, as well as presenting a comprehensive error analysis. This analysis includes a necessary modification to the Cramer-Rao lower bound (CRLB), We propose a simple iterative modification to the algorithm which produces unbiased translation direction estimates that approach the CRLB. Numerical results are used to compare the various techniques on synthetic and real image sequences.

A linear method using optical flow to recover the translation direction of a moving camera has been previously presented. This algorithm produced a biased estimate and various compensation techniques have been proposed. The results of an error analysis of the different approaches containing expressions for the expected mean vectors and covariance matrices of the translation estimates are presented. This analysis includes a necessary modification to the Cramer-Rao lower bound which specifies the minimum possible covariance matrix for the different estimators. The analytical results were verified experimentally using simulations and real images.

Pyramidal vector quantization (PVQ), with its robust fixed-rate property, has been applied to two-dimensional (2-D) image coding. We extend the PVQ scheme to three-dimensional (3-D) subband video coding and apply it to shadowed mobile satellite channels. The Canadian geostationary mobile satellite channel has been modeled as shadowed Rician fading. The proposed scheme has efficient image compression while exhibiting graceful performance degradation in fading and shadowing as encountered in the mobile satellite channel. This ensures consistent visual quality for video signals transmitted over a shadowed fading channel with minimum delay and system complexity. Compared to the 2-D subband PVQ algorithm, the 3-D scheme can achieve 1.0-2.0 dB PSNR improvement or, equivalently, 21-34% bit rate reduction.

In this paper we propose an algorithm for tracking the phase and amplitude of frequency nonselective fading channels. In contrast to Irvine-McLane's (see IEEE Journal on Selected Areas in Communications, vol.10, no.8, p.1289-1299, 1992) symbol-aided plus decision-directed (SADD) tracker which mainly uses training symbols for channel estimation, the new algorithm exploits data-bearing-bearing symbol correlation of fading channels. This algorithm combines decision feedback and adaptive linear prediction (DFALP) by using tentative coherent detection and the least mean square (LMS) algorithm. Both simulation results and theoretical analysis demonstrate that the performance of this algorithm compares favorably with the SADD algorithm in terms of decision delay, mean-square error and bit-error-rate. The algorithm is also applied to a system similar to EIA/TIA recommendation IS-54 and has achieved 3 dB gain using the same channel coding and similar interleaving.>

There are increasing demands for ultralow bit-rate video image transmission. We present a new formulation of moving object segmentation and motion estimation to quantify the potential gain in using object-oriented motion compensation in image sequence coding. Motivated by real-valued parameter estimation required in object-oriented motion-compensated video source coding, a framework motivated by Rissanen's (1983) minimum description length (MDL) principle is proposed to more tightly couple motion estimation and object segmentation algorithms to the overall objective of minimizing source bit rate. A new objective function is constructed, and a suboptimal procedure to segment and estimate moving objects in a scene is proposed. Each object is represented by chain-coded block boundaries, affine motion parameters, and motion-compensated prediction error. A number of experimental comparisons between block- and object-oriented coding schemes suggests a significant potential coding gain using object-oriented motion-compensated coding.

Presents a new formulation of moving object segmentation and motion estimation to quantify the potential gain in using object-oriented motion compensation in image sequence coding. Motivated by Rissanen's (1983) minimum description length (MDL) principle that allows unified treatment of coding model complexity and parameter values, an objective function is constructed in terms of an ideal coding length function for each motion-compensated frame. Based on this new objective function, a procedure to segment and estimate moving objects in a scene is proposed. Each object in the scene is represented by its boundaries, motion parameters, and motion-compensated prediction error (MCPE). A number of experimental comparisons between block-oriented and object oriented coding schemes suggest that significant potential coding gain using the new MDL-based criterion is possible.>

This paper proposes a new procedure for quickest detection of an abrupt change in a random sequence, where a change is known to occur with probability one. Applications include on-line speech segmentation, edge detection in image processing, and communications channel monitoring. In contrast to Shiryayev's (1963) problem formulation, prior knowledge of a change-time distribution is not required. The optimality criterion considered is similar to Shiryayev's, except that the expected delay is to be minimized subject to both overall false alarm probability and false alarm average run length constraints under this criterion, theoretical study shows that the new procedure approximates an optimal but unrealizable Bayesian procedure, particularly for small signal changes or for low probability of false alarm. Simulations confirm that under such conditions, the new procedure compares favorably with Page's (1954) CUSUM, an optimized moving-window fixed-sample-size (FSS) procedure, and a special ease of the Girshick-Rubin-Shiryayev (1952) procedure.>

Local motion measurement errors are used to guide the global smoothing process in order to preserve motion-field discontinuities. A field-smoothing algorithm based on matching-error weighting is proposed. The added computation is minimal, since it uses byproducts of the local measurement process. The error-weighting functional provides significantly improved motion field estimates, as measured by motion-compensated interpolation performance. However, the mean-square reconstruction error is somewhat higher than that obtained by performing the much more computationally expensive stochastic optimization.

A unified decision-theoretic framework for automating the establishment of feature point correspondences in a temporally dense sequence of images is discussed. The approach extends a recent sequential detection algorithm to guide the detection and tracking of object feature points through an image sequence. The resulting extended feature tracks provide robust feature correspondences, for the estimation of three-dimensional structure and motion, over an extended number of image frames.>

Bayesian analysis is used to show that Wald's sequential probability ratio test with varying thresholds is optimal for the nonstationary situation, where the observed samples are independent but not identically distributed. Some important properties useful for the design of the test thresholds are discussed. Wald's lower bound, generalized to the nonstationary situation, is also presented. The results have important applications in situations where the observed signal is time-varying. such as in radar signal processing, image processing, and spread spectrum communications.>

Well-known procedures exist for sequentially detecting abrupt changes in the distribution of a stochastic process with independent and identically distributed (IID) samples. An analysis is made of the asymptotic optimality of a class of such procedures by comparing expected delay as the false alarm rate is sufficiently small. The criterion used as minimum expected delay, while previously the procedures were found to be optimal in the sense of minimizing only an upper bound of the expected delay. It is proved that Page's (1954) repeated sequential probability ratio test (SPRT) procedure is asymptotically optimal in view of this more practically useful criterion.>

A practical algorithm for quickest detection of time-varying arbitrary one-parameter changes in a sequence of independent random variables is developed. The amplitude of the parameter need not to be known. This model can be applied to the problem of coherent detection of sampled sinusoidal signals of known frequency, but unknown phase and amplitude. The tests are designed according to a maximum allowable false alarm rate. Expressions that predict algorithm performance, in terms of average detection time are obtained. Simulation results show the scheme has improved performance over E.S. Page's (1954) quickest-detection procedure in the detection of sampled sinusoids of known frequency (and unknown amplitude and phase) in white Gaussian noise.>

Two-dimensional robust autoregressive parameter estimation is performed on image data using an iteratively reweighted least squares (IRLS) procedure which explicitly identifies the model outliers. In practice, these outliers often arise from nonhomogeneous image structures. An initial least median of squares estimate is used to obtain a more robust version of IRLS. Both versions of the IRLS algorithm are tested experimentally on synthetic and real image data. A whiteness measure, based on a two-dimensional version of the Box and Pierce portmanteau test, serves as a useful performance evaluator. The experimental results demonstrate that the robust parameter estimators can offer significant improvement over the classical least-squares estimator on image data that deviates from the autoregressive model. These results have potential applications in image processing, including image coding and object detection.>

The mapping of multistage hypothesis testing (MHT)-based algorithms to two-dimensional mesh architectures is discussed. How the interprocessor communication overhead varies as a function of the grain-size of the architecture is discussed. The speedup achievable by a processor mesh relative to a single processor is calculated as a function of the cost of interprocessor communication. It is shown that the smaller the mesh grain-size, the greater the speedup of the MHT-based algorithm, even for very expensive interprocessor communication costs.>

Given a time sequence of digital images of a high-noise environment, the authors address the problem of detecting pixel-sized, barely discernible moving objects whose position and trajectories are unknown. The sequences may be temporally sparse and contain significant frame-to-frame drifting background clutter as caused by relative motion between the sensor array and natural terrain, ocean, or clouds. A general, two-step approach is presented. First, time correlation and space-varying background structure are removed. Second, a large, dense set of pixel-sized space-time trajectories are hypothesized and tested in the innovations sequence. The search space is organized into a tree structure. A sequential statistical technique, multistage hypothesis testing, optimized for the innovations model, is used to test the multiple hypotheses and prune the tree-structured list of candidate trajectories.>

The detection of small, low-contrast, moving objects in a time sequence of digital images is addressed. Since object positions and velocities are unknown, a large number of candidate trajectories, organized into a tree-structure, are hypothesized at each pixel. At each 'root' image pixel, trajectory extensions are mapped to tree nodes. Pixels along a trajectory are tested sequentially for a shift in mean intensity using multistage hypothesis testing (MHT). The MHT is designed according to prespecified error probabilities. Exact, closed-form expressions for MHT test performance are derived and then applied to predicting the algorithm's computation and memory requirements. Under a Gaussian white noise background assumption it is shown theoretically that over 4000 candidate trajectories per pixel are tested using an average of only 30 additions and threshold comparisons.>

Two point sets {pi} and {p'i}; i = 1, 2,..., N are related by p'i = Rpi + T + Ni, where R is a rotation matrix, T a translation vector, and Ni a noise vector. Given {pi} and {p'i}, we present an algorithm for finding the least-squares solution of R and T, which is based on the singular value decomposition (SVD) of a 3 × 3 matrix. This new algorithm is compared to two earlier algorithms with respect to computer time requirements.

The relationship between the geometry of a stereo camera setup and the accuracy in obtaining three-dimensional position information is of great practical importance in many imaging applications. Assuming a point in a scene has been correctly identified in each image, its three-dimensional position can be recovered via a simple geometrical method known as triangulation. The probability that position estimates from triangulation are within some specified error tolerance is derived. An ideal pinhole camera model is used and the error is modeled as known spatial image plane quantization. A point's measured position maps to a small volume in 3-D determined by the finite resolution of the stereo setup. With the assumption that the point's actual position is uniformly distributed inside this volume, closed form expressions for the probability distribution of error in position along each coordinate direction (horizontal, vertical, and range) are derived. Following this, the probability that range error dominates over errors in the point's horizontal or vertical position is determined. It is hoped that the results presented will have an impact upon both sensor design and error modeling of position measuring systems for computer vision and related applications.