In this paper, we propose a novel generalized orthogonal chirp division multiplexing (GOCDM) communication system under doubly selective channels. The GOCDM waveform consists of modulated Zadoff-Chu sequences parameterized on the root index λ, which specifies the chirp rate. Thus, GOCDM subsumes OCDM as a special case (λ = 1) and inherits the full double-spreading feature. To deal with doubly selective channels, the optimal pilot chirp-assisted GOCDM is designed based on a basis expansion model. The optimal structure, placement and number of pilot symbols are proposed. Moreover, the optimal power allocation between pilot and data symbols is also derived. These optimal pilot parameters not only minimize the mean square error of the channel estimation, but also maximize a lower bound of the average channel capacity. Furthermore, it is possible to design the root index λ to achieve a GOCDM system with the optimal training to have higher bandwidth efficiency than the OCDM system. Simulation results corroborate the superior performance of the proposed design for GOCDM.

With the great development of wireless communications, Index Modulation (IM) has attracted significant attention due to its energy and spectrum efficiencies gained from utilizing additional transmission dimensions. In this paper, we propose a novel index modulation (IM) system, called adaptive subcarrier-index modulation (ASIM), which simultaneously enhances spectrum efficiency and error reliability over (enhanced) SIM systems through the introduced adaptive techniques. Moreover, its carefully designed adaptive framework enables flexible and efficient deployment across diverse Orthogonal Frequency Division Multiplexing (OFDM) environments and performance requirements. An advanced index subcarrier indicating methodology is proposed to distinguish between the index and regular subcarriers with high power efficiency. To better utilize channel state information (CSI) feedback, advanced adaptive power allocation methods, together with adaptive modulation, are incorporated into the ASIM system to significantly enhance performance. The proposed ASIM system is further developed with an adaptive framework and parameter selection to accommodate diverse transmission conditions and performance demands. Simulation results show that our ASIM OFDM system outperforms the existing OFDM and OFDM-SIM systems with lower error rates and higher transmission rates under various transmission conditions. Furthermore, simulations show that varying demands and priorities can be met through the introduced parameter adaptation, validating the adaptability of the proposed ASIM technique.

Orthogonal chirp division multiplexing (OCDM) has emerged as an attractive modulation scheme due to its double-spreading property in both the time and frequency domains. Meanwhile, it is well known that multiple-input multiple-output (MIMO) techniques enhance spatial diversity to combat channel fading. Thus, MIMO-OCDM is promising for reliable high rate wireless communications. When deploying MIMO-OCDM systems, most studies assume perfect channel knowledge at the receivers. However, channel estimation is crucial and should be considered in the system design. In this paper, we apply Alamouti code for MIMO-OCDM over frequency-selective fading channels. In order to facilitate the channel estimation, three pilot-aided transmission (PAT) schemes are proposed, where the pilots are inserted in the frequency, time, and Fresnel domains, respectively. The first two schemes enjoy higher bandwidth efficiency, while the third one is more resistant to burst interference. The channel estimation methods and the optimal PAT designs are developed accordingly for these PAT schemes. Simulation results corroborate the superior performance of the proposed MIMO-OCDM system and channel estimation methods.

Orthogonal chirp division multiplexing (OCDM) is a novel modulation scheme with increased spectral efficiency compared with conventional chirp spectrum spread systems. Meanwhile, it is well known that multiple-input multiple-output (MIMO) techniques can enhance diversity to combat channel fading. Thus, MIMO-OCDM is promising for reliable high-rate wireless communications. In MIMO-OCDM systems, channel estimation is crucial and challenging. In this paper, we apply the Alamouti technique to develop a MIMO-OCDM system and enable its spatial diversity under frequency-selective channels. In order to facilitate the channel estimation, a pilot-aided transmission (PAT) scheme is proposed. The pilots are designed in the Fresnel domain to inherit the double-spreading property of OCDM and be robust to burst interference. The corresponding channel estimator is provided to achieve the lower bound of the mean square error (MSE) of channel estimation. Simulation results verify the superior performance of the proposed channel estimation method for the pilot-aided MIMO-OCDM system.

In this paper, we investigate the carrier frequency offset (CFO) estimation problem in orthogonal chirp division multiplexing (OCDM) systems. We propose a transmission scheme by inserting consecutive null subchirps. A CFO estimator is developed to achieve a full acquisition range. We further demonstrate that the proposed transmission scheme not only helps to resolve CFO identifiability issues but also enables multipath diversity for OCDM systems. Simulation results corroborate our theoretical findings.

Multicarrier transmissions, such as orthogonal frequency/chirp division multiplexing (OF/CDM), offer high spectral efficiency and low complexity equalization in multipath fading channels at the cost of high peak-to-average power ratio (PAPR). High peak powers can occur randomly and may drive the power amplifier (PA) into saturation, resulting in non-linear distortion. In this work, we propose a novel tensor-train (TT) deep neural network (DNN) architecture combined with soft clipping to reduce the PAPR to a deterministic level and minimize in-band distortion, while also satisfying spectral mask constraints. The proposed solution requires modifications only at the transmitter and there is no loss of spectral efficiency. Employing the TT decomposition allows for significant reduction in parameters. Results show that the proposed solution allows for significant reduction in PAPR with minimal performance loss. Furthermore, an upper bound for the PAPR is also derived which allows for the prediction of the required input power back off (IBO) without extensive simulations and trial-and-error.

With the development of 5G, 6G, and beyond, wireless communications have taken a vital role in our daily life. The wide adoption of wireless devices and applications makes security of the wireless transmissions more critical. Many attacks aiming at wireless communications have been proposed (e.g., jamming attack). Orthogonal Frequency Division Multiplexing (OFDM) together with multi-input and multi-output (MIMO) design and adaptive modulation (AM) becomes an important technique to achieve high data rates. However, most of the attacks are not tailored for OFDM based systems. In this paper, we reveal a potential security issue of (MIMO-)OFDM-AM systems by developing three learning-based attack methods to (MIMO-) OFDM-AM systems. Based on the principle of AM, channel state information (CSI) is inferred from the modulation type of each subcarrier without any prior knowledge of the desired transmission. With the inferred CSI, we develop three adversarial attacking methods by maximizing error rate, minimizing capacity, or maximizing outage probability. Simulation results show that our learning-based method can detect the modulation type of each subcarrier in high accuracy and then successfully infer the CSI range. Furthermore, simulations also demonstrate that the proposed attacks cause severe performance degradation of (MIMO-)OFDM-AM systems (e.g., error rate, capacity) with fairly low power.

Radio tomographic imaging (RTI) has become a popular approach to reconstruct spatial loss fields (SLFs) in an area covered by a wireless network based on received signal strength (RSS) measurements. SLF images quantify the attenuation rate of the radio-frequency waves at each location in the network. The attenuation for the propagation path can be modeled as the 2-dimensional integral of SLF scaled by a weight function, which is the foundation of RTI techniques and makes the SLF reconstruction possible. In recent years, many methods, including machine-learning-based schemes, have been proposed to achieve more accurate SLF estimates. In this letter, we develop an attention neural network-augmented optimization SLF estimation scheme by taking advantage of deep learning and the traditional RTI technique. Our proposed method achieves the best reconstruction performance among the existing approaches.

Jointdetection and communication systems demonstrate their unique efficiencies in both spectrum and cost. In this letter, we propose a sonar-communication (SonarCom) system for underwater scenarios based on two multicarrier (MC) waveforms, which are orthogonal frequency division multiplexing (OFDM) and orthogonal chirp division multiplexing (OCDM) waveforms. Different types of waveforms provide flexibilities to deal with various underwater environments. Furthermore, a generalized likelihood ratio test (GLRT) is proposed for detection to deal with multipath channels. Time aliasing techniques are applied to leverage the signal structure. Moreover, a minimum mean square error (MMSE) equalizer is developed for communication to counter frequency-selective fading. Simulation results show that the GLRT detector for the SonarCom system outperforms the existing matched filter (MF). The OCDM scheme maintains better communication performance than the OFDM one.

The high peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) transmissions is susceptible to non-linear distortion caused by power amplifier (PA) saturation. In this work, we propose a novel technique, using residual neural networks and soft clipping, to deterministically limit the peak amplitude of the signal, thus lowering its PAPR and circumventing PA distortion. We show that the proposed solution is capable of significantly reducing PAPR and in-band distortion, while obeying a spectral mask. Furthermore, we show that the neural network is able to generalize for a range of peak amplitudes, thus eliminating the need to re-train the network when the requirement changes.

We propose a novel decentralized feature extraction approach in federated learning to address privacy-preservation issues for speech recognition. It is built upon a quantum convolutional neural network (QCNN) composed of a quantum circuit encoder for feature extraction, and a recurrent neural network (RNN) based end-to-end acoustic model (AM). To enhance model parameter protection in a decentralized architecture, an input speech is first up-streamed to a quantum computing server to extract Mel-spectrogram, and the corresponding convolutional features are encoded using a quantum circuit algorithm with random parameters. The encoded features are then down-streamed to the local RNN model for the final recognition. The proposed decentralized framework takes advantage of the quantum learning progress to secure models and to avoid privacy leakage attacks. Testing on the Google Speech Commands Dataset, the proposed QCNN encoder attains a competitive accuracy of 95.12% in a decentralized model, which is better than the previous architectures using centralized RNN models with convolutional features. We also conduct an in-depth study of different quantum circuit encoder architectures to provide insights into designing QCNN-based feature extractors. Neural saliency analyses demonstrate a correlation between the proposed QCNN features, class activation maps, and input spectrograms. We provide an implementation for future studies.

Underwater acoustic communications (UACs) are demanded in a wide range of marine applications. However, one of the main challenges for UACs is unexpected interferences from nearby acoustic activities, especially dynamic and short-period interferences (called shot interference), which are hard to detect, estimate, and mitigate over time- and frequency-selective acoustic channels. In this article, we propose a novel filter that mitigates the interference effects for both single-carrier and multi-carrier systems without any prior knowledge of the interference. An Adaptive Sliding Window Interference Detection (ASWID) algorithm is developed to detect the location and the number of interfered symbols. Meanwhile, a least trimmed squares (LTS) equalizer is presented for the shot interference estimation and mitigation based on robust regression. The proposed algorithms are evaluated through numerical simulations and real channel measurements. Our results show that the proposed designs can detect and mitigate shot interferences effectively for single-carrier and multi-carrier UAC systems.

Orthogonal chirp division multiplexing (OCDM) was recently introduced as a new multi-carrier scheme based on the chirp spread spectrum (CSS) and shown to be more robust to interference caused by insufficient guard intervals. However, a thorough analysis of OCDM for wireless channels has not been conducted. Thus, this paper investigates the performance of OCDM affected by different impairments that are typical in a wireless channel and shows that uncoded OCDM performs better than OFDM and similar to single carrier block transmissions in multipath channels. Building on previous results, this study also shows that OCDM is more robust to time-burst interference (TBI) than its competitors and to narrow band interference (NBI) than OFDM because of spreading. However, OCDM is shown to suffer because of the loss of orthogonality caused by carrier frequency offset (CFO) and is analytically shown to have the same peak-to-average power ratio (PAPR) as OFDM.

Underwater acoustic channel (UAC) is featured as fast time-varying characteristic, and challenges the transmission designs. To countermine the time variation effect, we propose an adaptive design for orthogonal frequency division multiplexing (OFDM) transmission systems by utilizing the long-term stability of the second-order statistics of the channel state information (CSI). We derive the analytical expression of signal-to-interference-plus-noise-ratio (SINR) at each subcarrier to reach the target error performance based on the statistical information of the CSI. Thereafter, a new adaptive coding and bit-power loading algorithm with low computational complexity is proposed to pursuit the highest achievable bit rate with fixed error rate. The validity of SINR calculations and the effectiveness of the proposed adaptive algorithm are demonstrated under various conditions, wherein both simulated and measured channels have been tested.

Recent studies have highlighted adversarial examples as ubiquitous threats to the deep neural network (DNN) based speech recognition systems. In this work, we present a U-Net based attention model, UNetAt, to enhance adversarial speech signals. Specifically, we evaluate the model performance by interpretable speech recognition metrics and discuss the model performance by the augmented adversarial training. Our experiments show that our proposed U-NetAtimproves the perceptual evaluation of speech quality (PESQ) from 1.13 to 2.78, speech transmission index (STI) from 0.65 to 0.75, shortterm objective intelligibility (STOI) from 0.83 to 0.96 on the task of speech enhancement with adversarial speech examples. We conduct experiments on the automatic speech recognition (ASR) task with adversarial audio attacks. We find that (i) temporal features learned by the attention network are capable of enhancing the robustness of DNN based ASR models; (ii) the generalization power of DNN based ASR model could be enhanced by applying adversarial training with an additive adversarial data augmentation. The ASR metric on word-error-rates (WERs) shows that there is an absolute 2.22 % decrease under gradient-based perturbation, and an absolute 2.03 % decrease, under evolutionary-optimized perturbation, which suggests that our enhancement models with adversarial training can further secure a resilient ASR system.

Recent deep neural networks based techniques, especially those equipped with the ability of self-adaptation in the system level such as deep reinforcement learning (DRL), are shown to possess many advantages of optimizing robot learning systems (e.g., autonomous navigation and continuous robot arm control.) However, the learning-based systems and the associated models may be threatened by the risks of intentionally adaptive (e.g., noisy sensor confusion) and adversarial perturbations from real-world scenarios. In this paper, we introduce timing-based adversarial strategies against a DRL-based navigation system by jamming in physical noise patterns on the selected time frames. To study the vulnerability of learning-based navigation systems, we propose two adversarial agent models: one refers to online learning; another one is based on evolutionary learning. Besides, three open-source robot learning and navigation control environments are employed to study the vulnerability under adversarial timing attacks. Our experimental results show that the adversarial timing attacks can lead to a significant performance drop, and also suggest the necessity of enhancing the robustness of robot learning systems.

Designing an efficient difficulty control algorithm is an essential problem in Proof-of-Work (PoW) based blockchains because the network hash rate is randomly changing. This paper proposes a general difficulty control algorithm and provides insights for difficulty adjustment rules for PoW based blockchains. The proposed algorithm consists a two-layer neural network. It has low memory cost, meanwhile satisfying the fast-updating and low volatility requirements for difficulty adjustment. Real data from Ethereum are used in the simulations to prove that the proposed algorithm has better performance for the control of the block difficulty.

In high-mobility massive multiple-input multiple-output (MIMO) systems, Doppler shifts compensation can be combined with beamforming network to effectively suppress the channel time variation. The key of the beamforming network lies in the optimization of the common configurable amplitudes and phases (CCAP) parameter. In this paper, we reveal more insights of this approach by conducting the in-depth analysis. First, we demonstrate that the CCAP parameter optimizes the beamforming network to reduce channel time variation and approximates in a semi-sinusoidal form. Then, a scaling law between the asymptotic Doppler spread and the number of antennas M is derived, revealing that the asymptotic Doppler spread decreases at a rate of 1/M. We further prove that the optimal CCAP parameter obtained from Jakes' channel model can be directly applied to more general cases, while the inverse proportionality between the resulting asymptotic Doppler spread and the number of antennas remains valid. Numerical results confirm the correctness of the theoretical analysis.

This paper proposes a model-driven deep learning-based downlink channel reconstruction scheme for frequency division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The spatial non-stationarity, which is the key feature of the future extremely large aperture massive MIMO system, is considered. Instead of the channel matrix, the channel model parameters are learned by neural networks to save the overhead and improve the accuracy of channel reconstruction. By viewing the channel as an image, we introduce You Only Look Once (YOLO), a powerful neural network for object detection, to enable a rapid estimation process of the model parameters, including the detection of angles and delays of the paths and the identification of visibility regions of the scatterers. The deep learning-based scheme avoids the complicated iterative process introduced by the algorithm-based parameter extraction methods. A low-complexity algorithm-based refiner further refines the YOLO estimates toward high accuracy. Given the efficiency of model-driven deep learning and the combination of neural network and algorithm, the proposed scheme can rapidly and accurately reconstruct the non-stationary downlink channel. Moreover, the proposed scheme is also applicable to widely concerned stationary systems and achieves comparable reconstruction accuracy as an algorithm-based method with greatly reduced time consumption.

In this paper, we exploit the properties of mean absolute error (MAE) as a loss function for the deep neural network (DNN) based vector-to-vector regression. The goal of this work is two-fold: (i) presenting performance bounds of MAE, and (ii) demonstrating new properties of MAE that make it more appropriate than mean squared error (MSE) as a loss function for DNN based vector-to-vector regression. First, we show that a generalized upper-bound for DNN-based vector-to-vector regression can be ensured by leveraging the known Lipschitz continuity property of MAE. Next, we derive a new generalized upper bound in the presence of additive noise. Finally, in contrast to conventional MSE commonly adopted to approximate Gaussian errors for regression, we show that MAE can be interpreted as an error modeled by Laplacian distribution. Speech enhancement experiments are conducted to corroborate our proposed theorems and validate the performance advantages of MAE over MSE for DNN based regression.

In order to glean benefits from orthogonal frequency division multiplexing combined with index modulation (OFDM-IM) in the presence of strong Phase-Noise (PHN), in this paper, low-complexity joint PHN estimation and decoding methods are developed in the framework of message passing on a factor graph. Both the Wiener process and the truncated discrete cosine transform (DCT) expansion model are considered for approximating the PHN variation. Then based on these a factor graph is constructed for explicitly representing the joint estimation and detection problem. Taking full account of the sparse and structured a priori information arriving from the soft-in soft-out (SISO) decoder of a turbo receiver, a modified generalized approximate message passing (GAMP) algorithm is invoked for decoupling the frequency-domain symbols. In the decoupling step, mean field (MF) approximation is employed for solving the unknown nonlinear transform matrix problem imposed by PHN. Furthermore, merged belief propagation and MF (BP-MF) methods amalgamated both with sequential and parallel message passing schedules are introduced and compared to the proposed GAMP based algorithms in terms of their bit error ratio (BER) vs. complexity. Our simulation results demonstrate the efficiency of the proposed algorithms in the presence of both perfect and imperfect channel state information.

Consensus is one of the key problems in blockchains. There are many articles analyzing the performance of threat models for blockchains. But the network stability seems lack of attention, which in fact affects the blockchain performance. This paper studies the performance of a well adopted consensus algorithm, Raft, in networks with non-negligible packet loss rate. In particular, we propose a simple but accurate analytical model to analyze the distributed network split probability. At a given time, we explicitly present the network split probability as a function of the network size, the packet loss rate, and the election timeout period. To validate our analysis, we implement a Raft simulator and the simulation results coincide with the analytical results. With the proposed model, one can predict the network split time and probability in theory and optimize the parameters in Raft consensus algorithm.

Orthogonal chirp division multiplexing (OCDM) is a fairly new multi-carrier scheme that employs the discrete Fresnel transform (DFnT) to digitally modulate data onto orthogonal chirps. Most work surrounding OCDM has been done in the context of optical networks and single user wireless systems. Hence, there is no literature regarding multiple access in OCDM. This paper introduces a novel data multiplexing technique that leverages the properties of the DFnT to independently process smaller sub-blocks, thus enabling multiple access and low complexity equalization. Additionally, this paper employs a joint multi-user processing technique and compares its performance to the proposed scheme.

Discovering and exploiting the causality in deep neural networks (DNNs) are crucial challenges for understanding and reasoning causal effects (CE) on an explainable visual model. "Intervention" has been widely used for recognizing a causal relation ontologically. In this paper, we propose a causal inference framework for visual reasoning via docalculus. To study the intervention effects on pixel-level features for causal reasoning, we introduce pixel-wise masking and adversarial perturbation. In our framework, CE is calculated using features in a latent space and perturbed prediction from a DNN-based model. We further provide a first look into the characteristics of discovered CE of adversarially perturbed images generated by gradient-based methods1. Experimental results show that CE is a competitive and robust index for understanding DNNs when compared with conventional methods such as class-activation mappings (CAMs) on the Chest X-Ray-14 dataset for human-interpretable feature(s) (e.g., symptom) reasoning. Moreover, CE holds promises for detecting adversarial examples as it possesses distinct characteristics in the presence of adversarial perturbations.

Applying Machine Learning (ML) techniques to design and optimize computer architectures is a promising research direction. Optimizing the runtime performance of a Network-on-Chip (NoC) necessitates a continuous learning framework. In this work, we demonstrate the promise of applying reinforcement learning (RL) to optimize NoC runtime performance. We present three RL-based methods for learning optimal routing algorithms. The experimental results show the algorithms can successfully learn a near-optimal solution across different environment states.

In this letter, we examine orthogonality deficiency (od) of massive multi-input multi-output (mMIMO) channels. The Weibull distribution is adopted for modeling the fading envelopes, and various propagation channels are simulated by varying the Weibull parameters. By studying the statistics of od, we offer insights on the mMIMO size for linear detectors (LDs) to achieve desirable performance. By proposing an approximate distribution of od, we show that for various propagation channels, if the number of receive antennas exceeds a certain number while the number of transmit antennas is fixed, LDs achieve the same diversity as that of the maximum likelihood detector with high probability in practice. Theoretical analysis is validated by simulations.

Preamble detection is an important yet challenging task for underwater acoustic communications. The received preamble is distorted by unknown multipath propagation, severe Doppler scaling effect, various noise, and external interference in underwater scenarios. In this letter, we propose a cyclic feature detector using a Zadoff-Chu sequence by exploiting its cyclic features. The Doppler scale information is carried by these cyclic features. The proposed detector can bypass the requirement of channel information and is robust to carrier frequency offset. Moreover, it can deal with noise uncertainty and impulsive interference. Simulation and experimental results show that the proposed detector significantly outperforms the state of the art in detection performance.

Communications in high-mobility environments have received a lot of attention recently. In this paper, fast time-varying channels for massive multiple-input multiple-output (MIMO) systems are addressed. We derive the exact channel power spectrum density (PSD) for the uplink from a high-speed railway (HSR) to a base station (BS) and propose to further reduce the channel time variation via beamforming network optimization. A large-scale uniform linear array (ULA) is equipped at the HSR to separate multiple Doppler shifts in the angle domain through high-resolution transmit beamforming. Each branch comprises a dominant Doppler shift, which can be compensated to suppress the channel time variation, and we derive the channel PSD and the Doppler spread to assess the residual channel time variation. Interestingly, the channel PSD can be exactly expressed as the product of a pattern function and a beam-distortion function. The former reflects the impact of array aperture and is the converted radiation pattern of ULA, while the latter depends on the configuration of the beamforming directions. Inspired by the PSD analysis, we introduce a common configurable amplitudes and phases (CCAP) parameter to optimize the beamforming network, by partly removing the constant modulus quantized phase constraints of matched filter (MF) beamformers. In this way, the residual Doppler shifts can be ulteriorly suppressed, further reducing the residual channel time variation. The optimal CCAP parameter minimizing the Doppler spread is derived in a closed form. Numerical results are provided to corroborate both the channel PSD analysis and the superiority of the beamforming network optimization technique.

Underwater hostile channel conditions challenge video transmission designs. The current designs often treat video coding and transmission schemes as individual modules. In this study, we develop an adaptive transceiver with channel state information (CSI) by taking into account the importance of video components and channel conditions. The design is more effective than the traditional ones. However, in practical systems, perfect CSI may not be available. Therefore, we compare the imperfect CSI case with existing schemes, and validate the effectiveness of our design through simulations and measured channels in terms of a better peak signal-to-noise ratio and a higher video structural similarity index.

Faster-than-Nyquist (FTN) signaling has attracted a lot of attentions for the fifth-generation (5G) cellular communication systems. However, low-complexity receiver design for FTN signaling becomes challenging. In this paper, we develop frequency-domain joint channel estimation and decoding methods for FTN signaling transmitting systems over frequency-selective fading channels. To deal with the colored noise inherent in FTN signaling, we propose to approximate the corresponding autocorrelation matrix by a circulant matrix, the special eigenvalue decomposition of which facilitates an efficient fast Fourier transform operation and decoupling the noise in frequency domain. Through a specific partition of the received symbols, many independent estimates are obtained and combined to further improve the accuracy of the channel estimation and data detection. Moreover, instead of assuming the data symbols to be Gaussian random variables, a generalized approximated message passing-based equalization is developed and embedded in the turbo iterations between the channel estimation and the soft-in soft-out decoder. Simulation results show that the proposed algorithm outperforms the cyclic prefix-based and overlap-based frequency-domain equalization methods. With the proposed algorithms, FTN signaling reaches up to 67% higher transmission rate compared to the Nyquist counterpart without substantially consuming more transmitter energy per bit, and the overall complexities grow logarithmically with the length of the observations.

Universal filtered multi-carrier (UFMC) has been widely researched for 5G communication systems. In this paper, we propose a new UFMC receiver model and signaling structure to support asynchronous transmissions. Since the UFMC transmitter contains sub-band filters, we adopt equivalent filters at the receiver to separate target user signal over asynchronous multi-user transmissions. Moreover, we introduce a coarse timing synchronization algorithm in the presence of multiple timing offsets (TOs) over the complex additive white Gaussian noise (AWGN). Finally, simulation results show that the performance of the proposed system with asynchronous users is better than conventional 4G uplink system with synchronous users.

This paper explores the problem of localizing an emitter of radio frequency energy using a network of mobile receiver nodes, each taking measurements of the emitter's received signal strength (RSS). In this paper, we drop the assumption of receiver calibration, leading to biased measurements for each node. We model these bias effects as additive random variables for each receiver in the log-distance path loss model. We propose two novel estimators to handle these effects as modifications of the nonlinear least squares and the Gaussian particle filter algorithms. The estimators are augmented using the principle of variance least squares, in which the biases' effect on the data covariance is estimated online. These estimates inform subsequent iterations of the nonlinear algorithms. The path loss exponent and emitter power offset are likewise treated as unknowns. Our simulations show the performance improvement evident in various scenarios over the naive approaches, other contemporary algorithms, and with respect to the Cramér-Rao lower bound. We further show the efficacy of our approach through several experiments using real RSS data collected from a mobile network.

Call detail records (CDRs) collected at telecommunication networks have been well studied to reveal human behaviors such as voice service usage, contact regularities and mobility patterns. With the advances in big data technology, carriers can now collect, store and analyze more data, such as records of user mobile web activities, at scales much larger than CDRs. In this paper, we study 14 features extracted from both call and web records through statistical modeling. We also analyze the impact of locations on voice usage, and examine the correlations between the usage of voice and web services. The results can be used in user profiling, service usage prediction, and network dimensioning.

Determining the best fit statistical distribution with parameters for a dataset is a common task in many datadriven applications. Existing approaches to the problem usually combine parameter estimation and goodness-of-fit (GOF) tests or statistics, fitting the dataset to each of the candidate distributions and choosing the best one based on the GOF. With big data technologies collecting and storing more and more datasets, the efficiency of the existing solutions becomes a problem. In this paper, we decompose the model fitting task into two independent parts: selecting the distribution and estimating the parameters. We then treat the distribution selection as a classification problem. A classifying engine is proposed to efficiently determine the distribution for a dataset among a set of candidate distributions, after which parameter estimation can be performed. Our approach eliminates the need of estimating parameters for each candidate distribution and thus reduces complexity significantly.

Effective Lenstra-Lenstra-Lovász (ELLL) algorithm is a common low-complexity lattice reduction (LR) technique adopted in LR-aided successive interference cancellation (SIC) multiple-input multiple-output (MIMO) detectors. However, the original ELLL algorithm is undesirable for hardware implementation since the number and the sequence of ELLL iterations are not deterministic. To address these issues, some fixed-complexity ELLL (fcELLL) algorithms have recently been proposed. In this paper, we propose two new fcELLL algorithms to further improve the efficiency of existing fcELLL algorithms by eliminating unnecessary iterations, such that the fcEL-LL algorithm exhibits faster convergence and lower computational complexity. Compared to the existing fcELLL algorithms, simulations show that the proposed fcELLL algorithms can save up to 39%-48% complexity for 8 × 8 MIMO systems without performance loss.

Lenstra-Lenstra-Lovász (LLL) algorithm is a common technique for lattice reduction (LR) aided multiple-input multiple-output (MIMO) detectors. This paper presents the first VLSI implementation of a recently published Incremental fixed-complexity LLL algorithm (Incremental fcLLL) with fewer iterations than other existing fcLLL algorithms. We propose a modified Incremental fcLLL algorithm with simplified size reduction and Siegel condition as well as the designed two-angle complex Givens rotation for column swap, which eliminates all computationally intensive operations. By exploiting the proposed modified algorithm in a pipelined architecture, the implementation on a Xilinx Virtex-5 FPGA demonstrates a throughput of 26 cycles per matrix and near 8 M matrices per second, significantly outperforming the existing FPGA solutions.

This paper considers the problem of tomographic area mapping using radio frequency measurements gathered by a network of mobile nodes. Termed radio tomographic imaging, the technique has shown potential for object tracking, imaging static obstacles, and even through-wall imaging. Our approach addresses substantial issues for the practical implementation of such a system, namely, the mitigation of multipath signal effects and the characterization of a large number of uncalibrated network links. We propose a system that utilizes ultrawideband direct path signal strength measurements as a means of reducing the effects of the multipath fading. Furthermore, we address the estimation of unknown path loss and link bias parameters online through the framework of a linear mixed effects model. This permits the estimation of a static area map without a prohibitive calibration of these parameters prior to deployment, which is crucial in a network that may contain hundreds of links. Our model is posed as a convex optimization problem using the elastic net for regularization. Bayesian performance bounds are derived and our method shows positive results in simulation. We then demonstrate the efficacy of our solution on real tomographic data gathered from our cognitive spectrum operations testbed.

In MIMO wireless channels, channel estimation can provide a source of randomness common to sender and receiver that may be used to generate secret keys. These keys can provide information theoretically secure communication. Alternatively, a known MIMO channel allows transmitting artificial noise in the null space of the main channel to secure communication. In this paper, we show that both methods can be combined to further enhance secrecy. Symbols encrypted at the transmitter simultaneously act as message for the intended receiver and as noise for any potential eavesdroppers. We derive expressions for the minimum-guaranteeable secrecy rate as a function of number of symbols encrypted. Given at least one symbol can be encrypted, our proposed method outperforms the standard artificial noise scheme in terms of both secrecy rate and power expenditure. We first analyze the system assuming keys can be instantaneously extracted from channel measurements. We then derive bounds for rates assuming the overhead time required for key generation produces a Gaussian channel-estimation error.

In this paper, we consider the wire-tap channel model consisting of three users, namely, a transmitter, a legal receiver, and an eavesdropper, where the eavesdropper has possible unlimited centralized or distributed multiple antennas, while the legal user has just a single antenna. With this model, we study the realization of strict positive secrecy rate through Rayleigh flat fading channels using M-ary phase shift keying (PSK) and orthogonal space-time block code (OSTBC) based on channel reciprocal principle. We demonstrate that the information rate at the eavesdropper can be reduced to zero and a positive communication rate at the legal receiver can be realized if the transmitter employs a proper phase precoder based on the channel reciprocal, and uses OSTBC and M-ary PSK modulations for data transmissions. Application examples of space-time codes for positive secrecy rate are illustrated. As we have given the eavesdropper more capabilities than the legal receiver and thus captured a worst case scenario, any positive secrecy rate that is achieved serves as a lower bound on the secrecy capacity.

The Lenstra-Lenstra-Lovász (LLL) algorithm has been adopted as a lattice reduction (LR) technique for multiple-input multiple-output (MIMO) communications to improve performance with low complexity. However, implementing the LLL algorithm is still challenging due to slow convergence especially for large channel matrices. One of the main reasons is that the column swap may not happen in some LLL iterations, which leads to more iterations. To address it, some greedy LLL algorithms have recently been proposed, which only perform the iterations with column swaps. In this paper, we propose two efficient greedy LLL algorithms for various LR-aided MIMO detectors. First, both algorithms use a relaxed Lovász condition to search the candidate set of LLL iterations. Then, to select an LLL iteration in the candidate set each time, one is based on a relaxed decrescence of LLL potential, and the other takes the error performance of the LR-aided MIMO detectors into consideration. The proposed two algorithms are proved to collect full receive diversity in both LR-aided linear and successive-interference-cancellation detectors. Furthermore, compared to existing greedy LLL algorithms, simulations show that the proposed two algorithms not only converge faster but also exhibit much lower complexity while maintaining comparable error performance in LR-aided MIMO detectors.

Orthogonal frequency division multiplexing (OFDM) technique has been widely adopted in wireless systems, but it is known for being sensitive to synchronization errors. In this paper, we present timing and frequency synchronization algorithms for OFDM downlink transmissions using Zadoff-Chu sequences. We show that the current timing synchronization method employing Zadoff-Chu sequences is sensitive to carrier frequency offsets. To reduce this sensitivity, we develop conditions for the selection of appropriate Zadoff-Chu sequences. We then design a training sequence and propose joint signal detection, timing, and carrier frequency offset estimation algorithms for OFDM downlink transmissions. We show that the proposed schemes simplify the overall synchronization architecture and improve the performance compared to the existing schemes in the literature.

Lenstra-Lenstra-Lovász (LLL) lattice reduction technique has been applied to multiple-input multiple-output (MIMO) detectors to collect full diversity while enjoying low complexity. However, the original LLL algorithm has variable complexity, which is not desirable in hardware implementation. To solve this problem, some fixed-complexity LLL (fcLLL) algorithms have recently been proposed by using the fixed-column traverse strategy with limited number of LLL iterations. The existing fcLLL algorithms are designed to process each column with equal priority, which is not optimized in terms of error performance and complexity. In this paper, we propose an enhanced fcLLL algorithm with a novel column traverse strategy by allocating priorities to columns based on the characteristics of LLL and MIMO detection. In addition, we propose an improved termination criterion without sacrificing the error performance in the proposed fcLLL algorithm. Simulations show that our proposed fcLLL algorithm converges faster than LLL and existing fcLLL algorithms, and yields better error performance than the LLL and existing fcLLL algorithms when the maximum number of LLL iterations is fixed. Furthermore, in large MIMO systems, our proposed fcLLL algorithm exhibits significant complexity advantage, saving about 90% LLL iterations in average compared to the existing fcLLL algorithms for a 128× 128 MIMO system with 64-QAM.

Nowadays, there are many events reported by News Media everyday, which contains a massive number of news. People are getting more and more interested in understanding how an event evolves after it happens. News related to the same event or similar events usually has more common entities and stronger topic correlations, which is a new perspective to study news event. Due to the complexity of event evolving process, event visualization has been a big challenge for a long time.

High accuracy and low cost are challenging requirements for localization in wireless sensor networks (WSNs). The radio interferometric positioning system (RIPS) proposed inaims to meet both requirements at the same time. However, it is vulnerable to channel fading, and suffers from the noise aggravation due to the square operation. In this paper, we propose a dual-tone radio interferometric positioning system (DRIPS) using undersampling techniques, named uDRIPS. Our proposed methodology is immune to flat fading effects, and avoids the amplification of measurement noise by directly undersampling the received signal. Furthermore, the time-of-arrival (TOA) information is extracted from the phases of the received dual-tone signals in the uDRIPS. As a result, it is able to localize an asynchronous target with the help of synchronous anchors (nodes with known positions). Moreover, we investigate the integer ambiguity problem due to phase wrapping, and develop a localization algorithm to estimate the unknowns alternatively. Simulation results corroborate the efficiency of our proposed algorithm.

Most existing differential receivers for ultra-wideband (UWB) communications employ Gaussian approximation for multiple access interference (MAI). However, for a system with strong interference caused by a small number of active users, significant performance degradation is found for differential UWB receivers due to the impreciseness of Gaussian approximation on impulsive MAI. In this paper, we propose new differential UWB receivers based on the generalized Gaussian (GG) distribution, which subsumes Gaussian distribution and Laplace distribution as special cases. Numerical results show that the GG distribution approximates MAI well. In addition, we show that GG-based multiple-symbol differential receivers can be formulated as the same form as the conventional multiple-symbol differential receivers, which can be efficiently solved using existing algorithms. Simulations are conducted to demonstrate the superior performance of the proposed receivers to that of the existing differential UWB receivers when the interference caused by a small number of active users is strong.

With the aim of extending the coverage and improving the performance of impulse radio ultra-wideband (UWB) systems, this paper focuses on developing a novel single differential encoded decode and forward (DF) non-cooperative relaying scheme (NCR). To favor simple receiver structures, differential noncoherent detection is employed which enables effective energy capture without any channel estimation. Putting emphasis on the general case of multi-hop relaying, we illustrate an original algorithm for the joint power allocation and path selection (JPAPS), minimizing an approximate expression of the overall bit error rate (BER). In particular, after deriving a closed-form power allocation strategy, the optimal path selection is reduced to a shortest path problem on a connected graph, which can be solved without any topology information with complexity O(N3), N being the number of available relays of the network. An approximate scheme is also presented, which reduces the complexity to O(N2) while showing a negligible performance loss, and for benchmarking purposes, an exhaustive-search based multi-hop DF cooperative strategy is derived. Simulation results for various network setups corroborate the effectiveness of the proposed low-complexity JPAPS algorithm, which favorably compares to existing AF and DF relaying methods.

In this paper, we propose the new incremental relaying scheme using network modulation (NeMo) concept in OFDM-based systems. We first introduce how to integrate NeMo to OFDM incremental relay and show how both cooperative diversity and frequency diversity can be achieved on incremental relaying schemes. From the simulation results, it is confirmed that NeMo enables OFDM incremental relay to enjoy the frequency-selective diversity from grouping and subcarrier interleaving and to partially obtain the cooperative diversity gain for all the symbols, which is different from the conventional incremental relaying method. By increasing the NeMo modulator size, it is shown that the diversity gain is also increased causing the performance improvement in term of bit error rate.

Multiple-input multiple-output (MIMO) techniques provide high data rates but the optimal maximum likelihood (ML) detector exhibits high complexity. Recently lattice reduction (LR) aided detectors have been proposed to achieve near-ML performance with low complexity. In this paper, we develop a LR-aided complex K-best algorithm which reduces the complexity of the existing sphere decoding based K-best algorithm. Then we provide the fixed-point design of the LR-aided K-best MIMO receiver for both coded and uncoded systems. The architecture selection of each sub-module is developed and a simulation-based wordlength optimization procedure is proposed. Simulations show that the fixed-point results can keep bit error rate degradation within 0.2dB under 8 × 8 256-QAM MIMO systems.

As the usage of wireless devices rapidly increases, mobile phones have become an important aid for people to maintain social relationships. Analysis on cellular networks through mobile communication records, especially when such records contain temporal and spatial information, can potentially unveil fundamental laws that govern the dynamics of social networks. In this paper, we use call detail records (CDRs) collected from a nation-wide cellular network in North America for more than one month. We analyze contact patterns over one million pairs of people using real call data as looking into pairwise call records for each pair. First, we investigate the impacts of social relationships on contacts and discover that family members show different characteristics from non-family pairs in terms of contact regularity or duration. Next, we characterize inter-contact time and contact duration using finite mixture models of Gaussian, Lognormal, and Gamma distributions for each pair. Our mixture models for pairwise communication patterns of cellular users capture and demonstrate burstiness, periodicity, and inhomogeneity of human communication.

With the introduction of manifold assumption, Laplacian Support Vector Machine (LapSVM) has advantages over the traditional SVM classifiers. However the dual solution of LapSVM is still a major barrier on the further application of LapSVM. Primal optimization is a promising solution to this problem. In this paper, we introduce a novel primal Laplacian Support Vector Machine with Precondition Conjugate Gradient method (PCG) to the problem of hyperspectral images classification which is one type of primal optimization solution. To prove the effectiveness of the proposed method, we apply it into the hyperspectral image data set Indian Pine. The experiment results show higher accuracy and better generalization ability than dual strategy.

From security perspective, a friendly relay may help to keep the confidential messages from being eavesdropped, while an untrusted relay may intentionally eavesdrop the messages when relaying. This paper studies the secure beamforming for multiple-input multiple-output (MIMO) two-way communications, where two source nodes exchange information with the help of an untrusted relay node. The relay adopts amplify-and-forward (AF) strategy and acts as both an essential helper and a potential eavesdropper. Our goal is to maximize the secrecy sum rate of the bidirectional links by jointly optimizing the source and relay beamformers. For the two-phase two-way relay scheme, we first derive the optimal structure of the relay beamformer and then propose an iterative algorithm to jointly optimize the source and relay beamformers. Then, a comprehensive study on the asymptotical performance is conducted by letting the source and relay powers approach zero or infinity. In particular, we show that when all powers approach infinity, the two-way relay scheme achieves the maximum secrecy rate if the transceiver beamformers are designed such that the received signals at the relay can be aligned to be parallel.

Radio interferometric positioning systems (RIPS) are recently proposed for low-complexity and high-accuracy localization. However, the original RIPS involves four nodes (two transmitters and two receivers) for a ranging session, and requires stringent time synchronization upon two receivers. In this paper, an asynchronous radio interferometric positioning system (ARIPS) is developed with larger positioning ranges. In ARIPS, two anchors (nodes with known positions) transmit two slightly different dual-tone signals. The differences of the two dual-tone signals create two low-frequency differential signals at the target receiver. The phase differences of the differential signals bear the time-difference-of-arrival (TDOA) information, i.e., the distance information. We develop two new methods to estimate the TDOA with and without accurate knowledge of the frequencies of the differential signals, respectively. By switching the pairs of the anchor nodes, several TDOAs can be obtained and thus the location of the target node can be estimated. The proposed ARIPS is robust to carrier frequency offsets (CFOs) and random phases due to asynchronous oscillators, and increases the resolving range limit due to the well-known integer ambiguity issue. Simulation results illustrate the performance of the proposed ARIPS.

Large multi-input multi-output (MIMO) systems with tens or hundreds of antennas have shown great potential for next generation of wireless communications to support high spectral efficiencies. However, due to the non-deterministic polynomial hard nature of MIMO detection, large MIMO systems impose stringent requirements on the design of reliable and computationally efficient detectors. Recently, lattice reduction (LR) techniques have been applied to improve the performance of low-complexity detectors for MIMO systems without increasing the complexity dramatically. Most existing LR algorithms are designed to improve the orthogonality of channel matrices, which is not directly related to the error performance. In this paper, we propose element-based lattice reduction (ELR) algorithms that reduce the diagonal elements of the noise covariance matrix of linear detectors and thus enhance the asymptotic performance of linear detectors. The general goal is formulated as solving a "shortest longest vector reduction" or a stronger version, "shortest longest basis reduction," both of which require high complexity to find the optimal solution. Our proposed ELR algorithms find sub-optimal solutions to the reductions with low complexity and high performance. The fundamental properties of the ELR algorithms are investigated. Simulations show that the proposed ELR-aided detectors yield better error performance than the existing low-complexity detectors for large MIMO systems while maintaining lower complexity.

Lattice-reduction (LR)-aided linear detectors (LDs) have shown great potentials for wireless communications due to their low complexity and high performance. However, most of the existing LR algorithms do not directly aim at minimizing the asymptotic error performance of LDs, which is dominated by the shortest longest vector (SLV) in the dual space. To find sub-optimal solutions to the SLV reduction, element-based lattice reduction (ELR) algorithms were recently proposed by performing column-addition operations. In this paper, we propose improved ELR algorithms (called ELR+) by performing generalized column-addition operations. We find that the problem that minimizes a basis vector by a generalized column-addition operation can be formulated as a closest vector problem (CVP). By solving the CVP that minimizes the longest basis vector for each basis update, the proposed ELR+algorithms find sub-optimal solutions to the SLV reduction problem with high performance. Simulations illustrate that the proposed ELR+algorithms show superior performance relative to the state-of-the-art LRs for linear detection, including Korkin-Zolotarev reductions.

Code-multiplexed transmitted reference (CM-TR) and code-shifted reference (CSR) have recently drawn attention in the field of ultra-wideband communications mainly because they enable noncoherent detection without requiring either a delay component, as in transmitted reference, or an analog carrier, as in frequency-shifted reference, to separate the reference and data-modulated signals at the receiver. In this paper, we propose a generalized code-multiplexing (GCM) system based on the formulation of a constrained mixed-integer optimization problem. The GCM extends the concept of CM-TR and CSR while retaining their simple receiver structure, even offering better bit-error-rate performance and a higher data rate in the sense that more data symbols can be embedded in each transmitted block. The GCM framework is further extended to the cases when peak power constraint is considered and when inter-frame interference exists, as typically occurs in high data-rate transmissions. Numerical simulations performed over demanding wireless environments corroborate the effectiveness of the proposed approach.

This paper proposes a novel cooperative approach for two-hop amplify-and-forward (A&F) relaying that exploits both the signal forwarded by the relay and the one directly transmitted by the source in impulse-radio ultra-wideband (IR-UWB) systems. Specifically, we focus on a non-coherent setup employing a double-differential encoding scheme at the source node and a single differential demodulation at the relay and destination. The log-likelihood ratio based decision rule is derived at the destination node. A semi-analytical power allocation strategy is presented by evaluating a closed-form expression for the effective signal to noise ratio (SNR) at the destination, which is maximized by exhaustive search. Numerical simulations show that the proposed system outperforms both the direct transmission with single differential encoding and the non-cooperative multi-hop approach in different scenarios.

Existing attempts to improve the performance of AdaBoost on imbalanced datasets have largely been focused on modifying its weight updating rule or incorporating sampling or cost sensitive learning techniques. In this paper, we propose to tackle the challenge from a novel perspective. Initially, the dataset is over-sampled and the standard AdaBoost is applied to create a series of base classifiers. Next, the weights of the classifiers are further retrained by Genetic Algorithms (GAs) or comparable optimization techniques where more targeted performance measures such as G-mean and F-measure can be directly used as the objective function. Consequently, unlike other indirect solutions, this sampling + reweighting strategy can purposefully tune AdaBoost towards a certain performance measure of interest with only moderate computational overhead. Experimental results on ten benchmark datasets show that this strategy can reliably boost the performance of AdaBoost and has consistent superiority over EasyEnsemble, which is a competent ensemble method for class imbalance learning.

Wireless MIMO relay networks have received significant interests in recent years. However, most MIMO relay techniques require high complexity equalizers and error free forwarding at the relay to collect diversity. In this paper, we propose high-rate and low-complexity decode-and-forward (DF) MIMO relay designs that achieve cooperative diversity, receive diversity or joint diversity. We introduce a power-scaling strategy for MIMO relays to enable cooperative diversity. Zero-forcing equalizers are employed at both relay and destination to maintain low-complexity. We also develop a channel-controlled ARQ scheme to collect receive diversity. Through mathematical analysis, we prove the diversity order achieved by each proposed design and numerical simulations confirm the theoretical claims.

Clock synchronization is essential for a large number of applications ranging from performance measurements in wired networks to data fusion in sensor networks. Existing techniques are either limited to undesirable accuracy or rely on specific hardware characteristics that may not be available in certain applications. In this paper, we examine the clock synchronization problem in networks where nodes lack the high-accuracy oscillators or programmable network interfaces some previous protocols depend on. This paper derives a general model for clock offset and skew and demonstrates its application to real clock oscillators. We design an efficient algorithm based on this model to achieve high synchronization accuracy. This algorithm applies the Kalman filter to track the clock offset and skew. We demonstrate the performance advantages of our schemes through extensive simulations and real clock oscillator measurements.

For multiple-input multiple-output communication systems that employ four or more transmit and four or more receive antennas, symbol detection remains a challenge in communication systems research. Lattice-reduction-aided detectors are attractive solutions to this problem because these detectors achieve the same diversity as the maximum-likelihood detector while exhibiting lower complexity. Current applications of lattice-reduction-aided detectors involve executing a lattice reduction algorithm to completion and then utilizing this result in the subsequent symbol detection. In this article, however, we examine the possibility of partially executing the lattice reduction algorithm. We first demonstrate using a hypothetical lattice-reduction-aided detector that early termination of lattice reduction algorithms is possible in the context of MIMO detection. Encouraged by these results, we develop and introduce incremental lattice reduction, which utilizes a practical early termination condition. We then apply this idea to develop a joint symbol detection and lattice reduction algorithm that is based on the Lenstra, Lenstra, Lovasz algorithm and successive interference cancellation. An evaluation using a spatial correlation channel model demonstrates that the proposed algorithm effectively distributes the lattice reduction processing over the length of each received packet. This behavior naturally enables the relaxation of throughput and latency requirements of lattice reduction algorithm hardware realizations.

Relay networks with amplify-and-forward (AF) protocol have attracted attention due to its high performance and low implementation complexity at the relay. To collect full cooperative diversity, the AF relay networks are facing two main issues. One is that the channel state information (CSI) of the source to relay link (i.e., two-hop information) is needed at the destination. The other concern is that the power scaling factor (PSF) and output signals at the relay are unbounded. These two issues make AF less practical in resource constrained networks, e.g., blind and peak power constrained relays. In this paper, we reveal the sufficient and necessary conditions on the PSF for the maximum ratio combining (MRC) receiver with two-hop CSI to achieve full cooperative diversity. Furthermore, we also provide necessary conditions on the PSF design so that MRC with one-hop CSI still collects full cooperative diversity. Based on these design criteria, we show a practical peak power constrained AF strategy so that neither the relay nor the destination node needs the source-relay CSI, yet full cooperative diversity can still be achieved.

A gap between theory and practice in signals and systems courses is often reported at many universities as a key problem in recruiting signals and systems students. On the other hand, instructors often cite a lack of fundamental understanding in mathematics as an issue in this course. Students seem to be discontent with some of the abstraction of the signals and systems courses. In this work-in-progress paper, we describe a new pathways concept we introduced to address these problems by introducing in-depth discussions, several applications and hands-on exercises.

The question in the title is relevant when considering lattice-reduction-aided MIMO detectors, which achieve the same diversity as the maximum-likelihood detector while exhibiting lower complexity. In this paper we examine if all basis updates, which account for the largest complexity contribution in the Lenstra, Lenstra, Lovasz lattice reduction algorithm, are necessary for lattice reduction in the context of MIMO detection. We first provide an abstract answer to this question in the form of an idealized experiment that demonstrates the potential for a large reduction in the number of basis updates even when spatial correlation is present. Encouraged by these results, we seek a practical answer to this question by formulating a joint lattice reduction and symbol detection algorithm based on successive interference cancellation. Experimental results of the proposed method demonstrate that on average only 10% to 25% of basis updates are necessary on average depending on the degree of spatial correlation. Therefore, the answer to the question in the title is an encouraging no.

We configure the multiple node interference (MNI) on OFDMA mesh networks and analyze the feature of this MNI as a closed form in terms of timing misalignment between multiple nodes. Based on our analysis, we propose some new timing adjustment techniques to mitigate MNI and verify the difference and superiority of the proposed techniques compared to the ones adopted by cellular OFDMA systems.

In this paper, we propose time-based localization approaches for asynchronous wireless sensor networks (WSNs), where not only clock skews but also clock offsets are present at all nodes. We first propose a joint synchronization and localization approach using the two-way ranging (TWR) protocol. Furthermore, a novel ranging protocol, namely asymmetric trip ranging (ATR), is employed and a two-step joint synchronization and localization approach is developed. As a result, we achieve efficient closed-form least-squares (LS) estimators. We compare these two proposed approaches. More over, simulation results corroborate the efficiency of our time-based localization schemes.

Wireless relay networks have caught a lot of attention recently due to their potential to enhance performance by combating fading effects. Numerous strategies have been proposed in the literature to enable cooperative diversity. Among them, decode-and-forward protocol has been well adopted because of its low implementation complexity. However, most decode-and-forward based schemes assume that the relay node has perfect error detection or has feedback information from the destination. In this paper, we design power profiles at the relay which enable diversity at the destination without any feedback or coding assumptions at the relay and source. Furthermore, the diversity orders enabled by any general power profile are quantified. We then summarize necessary and sufficient conditions on the power profiles to guarantee full diversity at the destination. The theoretical analysis is corroborated with numerical simulations.

Wireless communication systems are increasingly adopting orthogonal frequency division multiplexing (OFDM) to enable efficient high-data rate transmissions. Such systems often employ pilot symbols to estimate wireless channels, and null subcarriers on band edges to reduce adjacent channel interference. Recent work has focused on designing the pilot sequence to improve channel estimation performance. In this paper, we present a new pilot design for OFDM systems based on a arbitrary-order polynomial parameterization of the pilot subcarrier indices. We show our design achieves better performance than existing methods. Theoretical models and simulated performance demonstrate the increased accuracy of our design.

Optimizing the indices and values of free subcarriers (FSs) can significantly reduce the peak-to-average power ratio (PAR) of orthogonal frequency division multiplexing (OFDM) signals. In this paper, a numerical method is proposed to analyze the average PAR reduction performance of the FS optimization method. Different PAR reduction performance is revealed for different FS index assignments and the optimal single FS index can be determined by the proposed approach.

In this paper, we propose a novel amplify-and-forward relaying strategy for a two-hop single-relay network by incorporating an intentional peak power limit (IPPL). We show that the proposed strategy is not only practical for peak power constrained relay networks, but also helps to simplify the system at both relay and destination. With the proposed IPPL strategy, neither the relay nor the destination node needs the channel state information for the source-relay link, and full cooperative diversity can still be achieved.

This paper proposes a new adaptive algorithm to improve the least mean square (LMS) performance for the sparse system identification in the presence of the colored inputs. The l1norm penalty on the filter coefficients is incorporated into the quadratic LMS cost function to improve the LMS performance in sparse systems. Different from the existing algorithms, the adaptive filter coefficients are updated in the transform domain (TD) to reduce the eigenvalue spread of the input signal correlation matrix. Correspondingly, the l1norm constraint is applied to the TD filter coefficients. In this way, the TD zero-attracting LMS (TD-ZA-LMS) and TD reweighted-zero-attracting LMS (TD-RZA-LMS) algorithms result. Compared to ZA-LMS and RZA-LMS algorithms, the proposed TD-ZA-LMS and TD-RZA-LMS algorithms have been proven to have the same steady-state behavior, but achieve faster convergence rate with non-white system inputs. Effectiveness of the proposed algorithms is demonstrated through computer simulations.

MIMO systems have caught a lot of attentions mainly because of the boost of the information rate. However, so far the existing results only consider continuous signals at the receiver side. The effect of detectors on the mutual information has not been addressed. In this paper, we study the mutual information between the transmitted discrete signal and the quantized signal given by maximum-likelihood or zero-forcing detectors over MIMO channels. To differentiate from the mutual information without quantization step, we refer the one here as post-detection mutual information (PMI). The approximations for PMI with MLD or ZFD and the closed form of the PMI with ZFD are derived. We show that the reliable data rates that can be transmitted through the MIMO channels with MLD or ZFD is actually reduced by the presence of quantization. The approximations proposed in the paper match well with the simulation results in the mid to high SNR region.

Cooperative networks allow the nodes relaying each other's messages to enhance the transmission reliability over wireless fading channels by achieving cooperative diversity. Among the various relaying protocols, the amplify-and-forward (AF) strategy is well studied for its simplicity. However, to collect the cooperative diversity, there are two main issues that the AF protocol is facing. One is that the channel state information (CSI) of the source-to-relay link (i.e., two-hop CSI) is needed at the destination. The other concern is that the power scaling factor (PSF) and output signals at the relay are unbounded. These two issues make AF less practical in resource constrained networks, e.g., blind and peak power constrained relays. In this paper, we reveal the necessary and sufficient conditions on designing the PSF for the maximum ratio combining (MRC) receiver at the destination with two-hop (TH) CSI to achieve full cooperative diversity. Furthermore, we also provide the necessary conditions on the PSF design so that MRC with only one-hop (OH) CSI still collects full cooperative diversity. These designs make AF strategies more general and practical. The theoretical analysis is corroborated by numerical simulations.

Recently, multi-symbol based transmitted reference system has drawn attention for UWB communications because of its high performance without estimating the channel explicitly. The maximum-likelihood (ML) detector for multi-symbol transmitted reference system has been proposed to jointly detect the multi-symbol and yields a considerable improvement compared with conventional single symbol detector. However, the high computational complexity incurred by ML detectors hinders their applications to practical systems. In this paper, we propose a polynomial-time (in worse case) approximation near-ML detector based on semi-definite programming (SDP). Simulation results demonstrate that the proposed SDP detector provides almost the same BER performance as the ML detectors and is robust to the multi-access interference.

Frequency-domain (FD) adaptive filters are able to reduce the numerical complexity by using the overlap-and-save implementation method compared to time-domain (TD) ones. Similar to the TD algorithms, the selection of step size trades off between the convergence rate and steady state performance. In this letter, we propose a bin-wise block-varying step-size control method for the FD least-mean-square (LMS) algorithm. It achieves both fast convergence rate and low steady state error. In addition, compared to the TD step-size control method, the proposed method performs better when the filter input is a non-white process. Effectiveness of the proposed algorithm is demonstrated through computer simulations.

Lattice reduction (LR) techniques have been introduced to enhance the performance of linear equalizers by collecting diversity with low complexity for many transmission systems. Although LR-aided linear detectors may collect the same diversity as that collected by the maximum-likelihood (ML) detector, there still exists a performance gap between LR-aided and ML equalizers. One approach to fill this gap is to adopt soft-output detectors. Soft-output detectors enable iterative decoding when error-control codes are employed for a system. In this paper, we propose three LR-aided soft-output detectors with different methods to generate the candidate lists. We compare the performance and complexity of our algorithms with the existing alternatives and show that our methods achieve better performance with lower complexity. The performance-complexity tradeoffs of our proposed algorithms are also studied. Iterative decoders are performed based on these soft-output detectors in the simulations to validate the effectiveness of our algorithms.

Lattice reduction (LR) has been applied to linear equalizers and decision feedback equalizers to improve their performance. Recently, Seysen's algorithm (SA) has been proposed as an alternative LR method to the well-documented LLL algorithm. In this paper, we first provide a tree-search implementation method of SA which enables flexible performance-complexity trade-offs. Based on this tree structure, SA-aided hard-output and soft-output detectors are proposed to enhance performance. The diversity and complexity of the proposed methods are also studied. Thorough comparisons of SA and other LR algorithms are provided. Our analysis of complexity and numerical simulations provide insights that are useful to system designers.

Lattice reduction-aided equalization techniques have emerged as a low-complexity method to achieve the same diversity as maximum likelihood detectors. We address the VLSI implementation of these LR-aided equalizers by modifying the CLLL algorithm from a fixed-point hardware perspective. We then apply the modified algorithm together with additional micro-architecture and operation scheduling enhancements to create an updated CLLL processor. Finally, through BER simulations and FPGA synthesis results we demonstrate the suitability of our CLLL processor for integration into a 64-QAM MIMO system.

In multiple-access multicarrier systems, it is possible to serve multiple users at high data rates from a single base station. In this paper, we propose the multi-channel partial transmit sequences (MCPTS) method for multiple-access OFDM peak-to-average power ratio (PAR) reduction. By applying the PTS phase rotations to each access channel separately and assuming that the rotations can be detected by each user as a channel fading, MCPTS avoids two major disadvantages of standard PTS: i) the phase rotations can be detected without side information, and ii) the set of possible phase rotation values does not need to be finite. In addition, an iterative method of joint MCPTS and power allocation is proposed to minimize the average BER. The results show a significant BER improvement.

Step size and tap length play critical roles in balancing the complexity and steady-state performance of an adaptive filter. For an impulse response with an exponential power decay profile, which models a wide range of practical systems, such as an acoustic echo path, this paper proposes a new variable step-size and tap-length least mean square (LMS) algorithm. In each iteration, the optimal step-size and tap-length are derived by minimizing the mean square deviation (MSD) between the true and the estimated filter coefficients. The proposed algorithm performs better in terms of both convergence rate and steady-state performance than the existing ones. Effectiveness of the proposed algorithm is demonstrated through computer simulations.

Orthogonal frequency division multiplexing (OFDM) is an effective technique to deal with frequency-selective channels since it facilitates low complexity equalization and decoding. Many existing OFDM designs successfully exploit the multipath diversity offered by frequency-selective channels. However, most of them require maximum likelihood (ML) or near-ML detection at the receiver, which is of high complexity. On the other hand, empirical results have shown that linear detectors have low complexity but offer inferior performance. In this paper, we analytically quantify the diversity orders of linear equalizers for linear precoded OFDM systems, and prove that they are unable to collect full diversity. To improve the performance of linear equalizers, we further propose to use a lattice reduction (LR) technique to help collect diversity. The LR-aided linear equalizers are shown to achieve maximum diversity order (i.e., the one collected by the ML detector), but with low complexity that is comparable to that of conventional linear equalizers. The theoretical findings are corroborated by simulation results.

Abstract Target localization and tracking are two of the critical tasks of sensor networks in many applications. Conventional localization and tracking techniques developed for wireless systems that rely on direction‐of‐arrival (DOA) or time‐of‐arrival (TOA) information are not suitable for low‐power sensors with limited computation and communication capabilities. In this paper, we propose a low‐complexity and energy‐efficient method for target localization and tracking in noisy binary sensor networks, where the sensors can only perform binary detection, and the physical links are characterized by additive white Gaussian noise (AWGN) channels. The proposed method is based on known spatial topology. An efficient wake‐up strategy is used to activate a particular group of sensors for cooperative localization and tracking. We analyze the localization error probability and tracking miss probability in the presence of prediction errors. Simulation results validate the theoretic analysis and demonstrate the effectiveness of the proposed approach. Copyright © 2008 John Wiley & Sons, Ltd.

In this paper we apply a new joint synchronization-pilot sequence (JSPS) optimization design technique to multiple transmitter OFDM systems. One objective in the design of the JSPS for MISO transmissions is to eliminate the requirement for the different preamble fields used for coarse and fine synchronization and channel training. In this work, independent JSPSs are designed for each transmitter in a multiple antenna system, but are transmitted simultaneously from each antenna. Consequently, JSPSs can potentially reduce the overhead of multiple antenna preamble transmissions (improving bandwidth efficiency). The objective of this paper is to determine the performance advantages of joint carrier frequency offset (CFO) and channel estimation using only the pilot portion of the JSPS. By jointly optimizing the position and power of each pilot across the transmitter antennas in Rayleigh fading channels with CFO, we show ≫ 15 dB SNR improvement in the performance of the channel estimator.

Linear acoustic echo cancellers are widely employed to enhance the quality of telecommunication systems. However, low-cost audio components may generate significant nonlinear distortions in the acoustic echo path, which degrades the performance of adaptive linear acoustic echo cancellers (AECs), thus motivating the nonlinear AEC research. In this paper, we propose to use a postfilter to further attenuate the nonlinear residual echo following a linear AEC. The postfilter design is based on the power spectral density (PSD) of the nonlinear residual echo. By modeling the nonlinearity in the system using a basis expansion form, the PSD estimate becomes independent of the length of the room impulse response. The performance of the proposed algorithm is illustrated by computer simulations.

A Hammerstein-Wiener system consists of a linear time invariant subsystem sandwiched between two memoryless nonlinear blocks as is the case of an acoustic system with a nonlinear loudspeaker and a nonlinear microphone. We propose to model the memoryless nonlinear blocks of the Hammerstein-Wiener system using a linear combination of nonlinear basis functions, and concentrate on the task of parameter estimation for the nonlinear blocks. An adaptive algorithm is proposed using a pseudo magnitude squared coherence (PMSC) function-based criterion. The proposed method carries out nonlinearity identification without knowing the linear block in the Hammerstein-Wiener system. This is particularly useful for nonlinear acoustic echo cancellation (NAEC) applications, where dealing with the linear and nonlinear blocks together can be computationally challenging due to the long room impulse response. Numerical examples are provided to illustrate the performance of the proposed method.

Diversity order is widely adopted as a critical metric on the reliability of wireless transmissions over fading channels. Numerous designs have been proposed to collect the diversity from different types of channels. However, since the simulators and practical systems can only afford finite bits to represent the real/complex numbers, the definition of diversity needs to be revisited. In this paper, we thoroughly investigate the effects of finite-bit representation on the statistical properties of the channel and the diversity with different transmitters/receivers. We show that finite-bit representation may cause diversity loss relative to the infinite precision representation. For different transmission systems, the diversity loss follows different patterns as the number of bits decreases. Furthermore, we find that the number of non-vanishing eigenvalues plays an important role in quantizing the diversity for infinite lattice/constellation. Numerical examples verify our theoretical findings.

Clock synchronization across a network is essential for a large number of applications ranging from wired network measurements to data fusion in sensor networks. Earlier techniques are either limited to undesirable accuracy or rely on specific hardware characteristics that may not be available for certain systems. In this work, we examine the clock synchronization problem in resource-constrained networks such as wireless sensor networks where nodes have limited energy and bandwidth, and also lack the high accuracy oscillators or programmable network interfaces some previous protocols depend on. This paper derives a general model for clock offset and skew and demonstrates its applicability. We design efficient algorithms based on this model to achieve high synchronization accuracy given limited resources. These algorithms apply the Kalman filter to track the clock offset and skew, and adaptively adjust the synchronization interval so that the desired error bounds are achieved. We demonstrate the performance advantages of our schemes through extensive simulations obeying real-world constraints.

Multi-input multi-output (MIMO) systems equipped with multiple antennas have well documented merits in combating fading and enhancing data rates. MIMO V-BLAST transmission is a widely adopted method to achieve high spectral efficiency and low-complexity implementation. When the maximum likelihood (ML) or near-ML detector is employed, receive diversity is collected for MIMO V-BLAST systems to enhance the performance. However, because of its exponential complexity, ML detector may be infeasible for practical systems when the number of antennas and/or the constellation size is large. On the other hand, linear equalizers have much lower complexity but come with inferior performance. In this paper, we analytically quantify the diversity order of linear detectors for MIMO V-BLAST systems. Then, we adopt low-complexity complex lattice-reduction (LR) aided linear equalizers for V-BLAST systems to improve the performance and prove that LR-aided linear equalizers collect the same diversity order as that exploited by the ML detector but with much lower complexity. Relative to the existing real LR-aided equalizers, we illustrate that the complex LR further reduces the complexity while keeping the same performance. Simulation results corroborate our theoretical claims.

Linear equalizers (LEs) have been widely adopted for practical systems due to their low computational complexity. However, it is also well known that LEs provide inferior performance relative to a maximum-likelihood equalizer (MLE) or other near-MLEs, because LEs usually cannot collect the diversity order enabled by the transmitter and at the same time they lose mutual information. More importantly, unlike MLE or near-MLEs, the performance of LEs has not been well quantified. This hinders more general applications of LEs in wireless systems. In this paper, we reveal a fundamental link between a channel parameter—orthogonality deficiency$(od)$of the channel matrix—and the diversity and capacity of LEs. We identify that when the$od$of channel matrix has an upper bound strictly less than$1$, the same diversity order as that of MLEs is collected by LEs and the outage capacity loss relative to MLEs is also a constant over signal-to-noise ratio (SNR). These results can be applied to designing a framework for hybrid equalizers. Furthermore, by studying the statistical properties of the$od$and comparing the complexity of different equalizers, we show that hybrid equalizers can trade off between performance and complexity by tuning the channel matrix$od$. The theoretical analysis is corroborated by computer simulations.

Diversity techniques are important in combating the deleterious effects of channel fading. To enjoy diversity, both transmitter and receiver have to be designed appropriately. Many existing designs have exploited different flavors of diversity by employing maximum likelihood (ML) or near-ML detectors at the receiver, which is of high complexity. On the other hand, empirical results have shown that linear equalizers (LEs) offer inferior performance but come with low complexity. Unfortunately, the diversity collected by general LEs has not been quantified, which reduces the attraction of LEs in theoretical and practical aspects. In this paper, we reveal a fundamental yet simple condition that determines whether LEs can achieve the same diversity order as an ML equalizer does. The condition is based on the orthogonal deficiency (od) of channel matrix. Based on the distribution of the channel od, we propose a framework of hybrid equalizers which guarantees maximum diversity while trading off complexity for coding gains. The theoretical findings are verified by simulation results.

Target localization and tracking are two of the critical tasks of sensor networks in many applications. Conventional localization and tracking techniques developed for wireless systems that rely on direction-of-arrival or time-of-arrival information, are not suitable for low-power sensors with limited computation and communication capabilities. In this paper, we propose a low complexity and energy efficient localization and tracking method for binary sensor networks in noisy environments, where the sensors can only perform binary detection, and the physical links are characterized by additive white Gaussian noise channels. The proposed method is based on known spatial topology. An efficient wake-up strategy is used to activate a particular group of sensors for cooperative localization and tracking. We analyze the localization error probability and tracking miss probability in the presence of prediction errors. Simulation results validate the theoretic analysis and demonstrate the effectiveness of the localization and tracking mechanism.

Routing in wireless networks confronts more diverse and more rapidly varying characteristics associated with wireless links. Several recent protocols have tried to exploit cooperative diversity to deal with these channel characteristics, but they suffer from large overhead costs due to the need for collaboration among candidate nodes of next hop. We propose a novel routing protocol that is able to realize cooperative diversity without exchanging information among candidate nodes. Our protocol divides the routing decision between the transmitter and the receivers: the transmitter decides the direction and an angle spread to broadcast the packet; each receiver, without communicating each other, decides whether to start a timer whose duration depends on the strength of the channel state information. The node with shortest timer transmits first and thus becomes the transmitter for the next hop. Thus, a multi-hop route is found in a noncooperative way where the communication overhead is minimal, and we show that cooperative diversity is collected. The performance characteristics and design parameters of this protocol are first theoretically analyzed, and then examined in a realistic network setting through simulations.

A substantially long round trip time (RTT) in space channel hurts TCP interactions between the sending and receiving ends. The consultative committee for space data systems (CCSDS) file delivery protocol (CFDP) is a new international standard developed to meet a comprehensive set of deep space file transfer requirements. There is a need in NASA to have controlled, experimental evaluation of CFDP performance in space, especially on its effectiveness in coping with long channel delay. This paper presents an experimental evaluation of channel delay impact on throughput performance of CFDP over LEO-satellite link, GEO-satellite link, and cislunar communication link in comparison with the widely deployed TCP and a space transport protocol, SCPS-TP. The experimental results show that CFDP/TCP in the deferred NAK mode is much more effective than other two stacks in the simulated point-to- point space communication links, especially when operated with a long link delay, a high BER and channel-rate asymmetry. We have found that CFDP/TCP is less sensitive, and FTP/TCP and SCPS-FP/SCPS-TP are highly sensitive, to the increase of link delay.

Load balancing is an important issue in sensor networks especially when geographic routing is employed since in this case, a node often forwards its packet to a certain neighbor. As a result, nodes located at the intersection of multiple routes to the base station tend to be placed under great stress and drain its energy quickly. This can potentially lead to disconnection of the network. Current solutions to this problem fail to take the traffic load at nodes into account and can potentially forward packets toward the heavy trafficked region and create collisions and network congestion. In this paper, we propose a load balancing scheme, namely traffic adaptive routing (TAR), which works with any geographic routing protocols. TAR relieves the aforementioned issues by having a node evaluate the level of traffic at its neighbors and decide where to forward the packet based on the distance to the base station and the traffic load. Through simulations, we have shown that when compared against the existing load balancing techniques, TAR is able to deliver 10% more packets to the base station using approximately the same amount of energy due to the reduction of the collisions and congestion.

A SpeechWeb is a collection of hyperlinked applications, which are accessed remotely by speech browsers running on end-user devices. Links are activated through spoken commands. Despite the fact that protocols and technologies for creating and deploying speech applications have been readily available for several years, we have not seen the development of a Public-Domain SpeechWeb. In this paper, we show how freely available software and commonly used communication protocols can be used to change this situation.

Geographical routing protocols are promising in wireless sensor networks because of their ability to discover a sub-optimal route without the help of a global state. Existing geographical routing protocols can guarantee packet delivery if the network topology after planarization remains connected. However, face routing used in non-flooding geographical routing algorithms usually results in a large number of hops, which not only reduces network efficiency, but may also decrease delivery rate when packets are subject to the constraint of time to live (TTL). In this paper, we present a path shortening algorithm that reduces the number of hops of geographical routing by exploiting the channel listening capability of wireless nodes. We investigate the impact of the algorithm on the delivery rate under the constraint of TTL. Simulation results show that in average the path shortening algorithm can reduce as much as 80% hops on the routes obtained by existing geographical protocols such as GPSR and GOAFR+in a critical network density region, and the improvement of packet delivery rate is up to 50% with a maximum of three retransmissions without increasing TTL.

In this work we propose a new adaptive channel shortening technique for doubly selective (time-varying frequency-selective) Orthogonal Frequency Division Multiplexing (OFDM) channels. OFDM is considered to be a very bandwidth efficient wireless transmission technique (due to its tightly packed orthogonal subcarrier structure) for multi-path channels. Transmissions in multipath channels exhibiting long delay spread channels, however, require an excessively long cyclic prefix (CP) to prevent inter-symbol interference (ISI) with OFDM, and thus undesirably reduce the bandwidth and power efficiency of the information transmission. Channel shortening equalization (CSE) is used to shorten the channel delay spread to a certain length (less than the CP length) so that ISI is minimized. Our technique adapts to channel variation and provides a significant bit error rate (BER) improvement in performance over non-adaptive techniques. We show that the adaptive technique improves the demodulated BER by greater than a factor of 10, with diversity order 1, in a mobile long delay spread (frequency-selective) channel, while the non-adaptive method exhibits a BER floor at 10-1

Differential encoding is known to simplify receiver implementation because it by-passes channel estimation. Relying on linear constellation precoders designed in closed-from for coherent multi-antenna systems, we derive a non-constant modulus space-time differential scheme that enables diversity, does not sacrifice rate, and more interestingly, it allows for constellation designs with many degrees of freedom. Performance merits of this scheme are analyzed and compared with existing differential designs. Simulations corroborate our theoretical findings.

Geographical routing is powerful for its ability to discover route to the destination without the help of global state. However, detours usually occur when the packet reaches a local minimum. In this case, the network topology has to be reduced to a planar graph and recovery schemes such as face routing are needed. However, face routing may create a large number of hops on a planar graph. When multiple packets are generated for the same destination, such a large number of hops tends to consume more energy. In this paper, a simple yet effective path pruning strategy is proposed to reduce the excessive number of hops caused by the detouring mode of geographical routing protocols. The path pruning algorithm finds routing shortcuts by exploiting the channel listening capability of wireless nodes, and is able to reduce a large number of hops with the help of little state information passively maintained by a subset of nodes on the route. The average hop count of the proposed algorithm is compared to those of existing geographical routing algorithms and the benchmark shortest path algorithm. Simulation results show that in average the path pruning algorithm can reduce as much as 80% of hops on the routes obtained by greedy perimeter stateless routing (GPSR) and greedy other adaptive face routing+(GOAFR+) in a critical network density range

Carrier frequency offset (CFO) estimation for orthogonal frequency division multiplexing (OFDM) has caught attention as OFDM systems have become widely adopted in recent years. In this paper, we design a novel double differential codec with low computational complexity. Our design bypasses CFO and channel estimation and is easy to be implemented at both transmitter and receiver. It also guarantees full multipath diversity, and reduces the peak-to-average power ratio from the number of subcarriers to the channel order. In addition, it is robust to CFO drifting. The closed form of the performance for our design is derived for OFDM transmissions over frequency-selective channels with CFO. Thorough simulation results corroborate our claims.

Differential encoding is known to simplify receiver complexity because it by-passes channel estimation. However, over rapidly fading wireless channels, extra transceiver modules are necessary to enable differential transmission. Relying on a basis expansion model for time and frequency selective (doubly-selective) channels, we derive such a generalized block differential (BD) codec that achieves the maximum Doppler and multi-path diversity gains, while affording low-complexity maximum likelihood (ML) decoding. We further show that existing BD systems over frequency-selective or time-selective channels follow as special cases of our novel system. Simulations corroborate our theoretical analysis.

Differential encoding is known to simplify receiver implementation because it by-passes channel estimation. However, over rapidly fading wireless channels, extra transceiver modules are necessary to enable differential transmission. Relying on a basis expansion model for time and frequency selective (doubly selective) channels, we derive such a generalized block-differential (BD) codec and prove that it achieves maximum Doppler and multipath diversity gains, while affording low-complexity maximum-likelihood decoding. We further show that existing BD systems over frequency-selective or time-selective channels follow as special cases of our novel system. Simulations using the widely accepted Jakes' model corroborate our theoretical analysis.

Carrier frequency offset (CFO) estimation for orthogonal frequency-division multiplexing (OFDM) has caught attention as OFDM systems have become widely adopted in recent years. In this paper, we design a novel double-differential (DD) codec with low computational complexity. Our design bypasses CFO and channel estimation, and is easy to implement at both transmitter and receiver. It also guarantees full multipath diversity, and reduces the peak-to-average power ratio from the number of subcarriers to the channel order. In addition, it is robust to CFO drifting. The closed form of the performance for our design is derived for OFDM transmissions over frequency-selective channels with CFO. Thorough simulation results corroborate our claims.

We design pilot-symbol-assisted modulation for carrier frequency offset (CFO) and channel estimation in orthogonal frequency-division multiplexing transmissions over multi-input multi-output frequency-selective fading channels. The CFO and channel-estimation tasks rely on null-subcarrier and nonzero pilot symbols that we insert and hop from block to block. Because we separate CFO and channel estimation from symbol detection, the novel training patterns lead to further decoupled CFO and channel estimators. The performance of our algorithms is investigated analytically, and then compared with an existing approach by simulations.

High data rates give rise to frequency-selective propagation effects. Space-time multiplexing and/or coding offer attractive means of combating fading and boosting capacity of multi-antenna communications. As the number of antennas increases, channel estimation becomes challenging because the number of unknowns increases, and the power is split at the transmitter. Optimal training sequences have been designed for flat-fading multi-antenna systems or for frequency-selective single transmit antenna systems. We design a low-complexity optimal training scheme for block transmissions over frequency-selective channels with multiple antennas. The optimality in designing our training schemes consists of maximizing a lower bound on the ergodic (average) capacity that is shown to be equivalent to minimizing the mean square error of the linear channel estimator. Simulation results confirm our theoretical analysis that applies to both single- and multicarrier transmissions.

Demand for high data rate leads to frequency-selective propagation effects, whereas carrier frequency-offsets and Doppler effects induced by mobility introduce time-selectivity in wireless links. These fading channels, once acquired, offer joint multipath-Doppler diversity gains. In addition, space-time multiplexing and/or coding offer attractive means of combating fading, and boosting capacity of multi-antenna communications. As the number of antennas increases, channel estimation becomes challenging because the number of unknowns increases, and the power is split at the transmitter. Optimal training sequences have so far been designed for flat-fading and frequency-selective multi-antenna systems. In this paper, we design a low complexity optimal training scheme for block transmissions over time-and frequency (a.k.a. doubly)- selective channels with multiple antennas. The optimality in designing our training schemes consists of maximizing a lower bound on the ergodic (average) capacity that is shown to be equivalent to minimizing the mean-square error of the linear channel estimator. Simulation results confirm our theoretical analysis which applies to both single- and multi-carrier transmissions.

Rapidly fading channels provide Doppler-induced diversity, but are also challenging to estimate. To bypass channel estimation, we derive two novel block differential (BD) codecs. Relying on a basis expansion model for time-varying channels, our differential designs are easy to implement, and can achieve the maximum possible Doppler diversity. The first design (BD-I) relies on a time-frequency duality, based on which we convert a time-varying channel into multiple frequency-selective channels, and subsequently into multiple flat-fading channels using orthogonal frequency-division multiplexing. Combined with a group partitioning scheme, BD-I offers flexibility to trade off decoding complexity with performance. Our second block differential design (BD-II) improves the bandwidth efficiency of BD-I at the price of increased complexity at the receiver, which relies on decision-feedback decoding. Simulation results corroborate our theoretical analysis, and compare with competing alternatives.

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

Abstract A full‐diversity full‐rate (FDFR) multi‐antenna system was developed recently, enabling uncoded layered space‐time (LST) transmissions to achieve full‐diversity (Nt) and full‐rate (Ntsymbols per channel use) simultaneously, for any number of transmit antennasNtand receive antennasNr. In this paper, we investigate the performance of a coded FDFR design obtained by concatenating an error control coding (ECC) module and FDFR module with a random interleaver in between. Turbo decoding is performed at the receiver. WithRcdenoting the ECC rate,dminthe minimum Hamming distance of the ECC, andMthe constellation size, an overall transfer rate ofRcNtlog2Mbits per channel use and a full diversity orderdminNtNrare achieved. Different ECC choices are considered. Approximate analysis reveals that multi‐stream ECC and single‐stream ECC make no difference when convolutional codes with long frame length and near‐optimal MIMO decoding schemes are adopted. Without sacrificing rate, the coded FDFR system improves error performance compared with coded V‐BLAST, when relatively weak codes are used. AsNrincreases, even strong codes such as rate 1/2 turbo codes can benefit from FDFR. Specifically, 1.5–2 dB gain over coded V‐BLAST is obtained in a 2 × 2 antenna setup when convolutional codes or rate 3/4 turbo codes are used; 0.5 dB gain is offered in a 2 × 5 setup when rate 1/2 turbo codes are used. Coded FDFR also outperforms a 16‐QAM Alamouti coded scheme by 1 dB when convolutional codes are used. The price paid is increased complexity. Copyright © 2004 John Wiley & Sons, Ltd.

We design pilot symbol assisted modulation for carrier frequency offset (CFO) and channel estimation in orthogonal frequency division multiplexing (OFDM) transmissions over multiinput multioutput (MIMO) frequency-selective fading channels. By separating CFO and channel estimation from symbol detection, the novel training patterns lead to low-complexity CFO and channel estimators. The performance of our algorithms is investigated analytically, and then compared with an existing approach by simulations.

Rapidly fading channels provide Doppler-induced diversity, but are also challenging to estimate. To by-pass channel estimation, we derive a novel block differential codec. Relying on a basis expansion model for time-varying channels, our block differential design is easy to implement, and achieves the maximum possible Doppler diversity. Simulation results corroborate our theoretical analysis.

We derive a novel block differential codec to bypass estimation of rapidly fading channels. Based on a basis expansion model for time-varying channels, our differential design is easy to implement, and achieves the maximum possible Doppler diversity. Combined with a group partitioning scheme, the differential scheme provides also flexibility to tradeoff decoding complexity with performance. Simulation results corroborate our theoretical analysis.

High data rates and multipath propagation give rise to frequency-selectivity of wireless channels, while carrier frequency offsets and mobility-induced Doppler shifts introduce time-selectivity in wireless links. The resulting time- and frequency-selective (or doubly selective) channels offer joint multipath-Doppler diversity gains. Relying on a basis expansion model of the doubly selective channel, we prove that the maximum achievable multipath-Doppler diversity order is determined by the rank of the correlation matrix of the channel's expansion coefficients, and is multiplicative in the effective degrees of freedom that the channel exhibits in the time and frequency dimensions. Interestingly, it turns out that time-frequency reception alone does not guarantee maximum diversity, unless the transmission is also designed judiciously. We design such block precoded transmissions. The corresponding designs for frequency-selective or time-selective channels follow as special cases, and thorough simulations are provided to corroborate our theoretical findings.

We propose novel space-time multipath (STM) coded multi-antenna transmissions over frequency-selective Rayleigh fading channels. We develop STM coded systems that guarantee the maximum possible space-multipath diversity without rate loss for any number of transmit-antennae, and with large coding gains within the class of linearly coded systems. By incorporating subchannel grouping, we also enable desirable tradeoffs between performance and complexity. The merits of our design are confirmed by corroborating simulations, and comparisons with existing approaches.

We rely on a Basis Expansion Model (BEM) for the channel, to design three Space-Time-Doppler (SID) codecs that enable maximum diversity gains, when block transmissions undergo time-selective fading effects. Within the constraints of each maximum-diversity design, it is also possible to achieve maximum coding gain, at least when the BEM parameters are i.i.d. The theoretical results are corroborated by simulation results and the BEM is validated.

High data rates give rise to frequency-selective propagation, while carrier frequency-offsets and mobility-induced Doppler shifts introduce time-selectivity in wireless links. To mitigate the resulting time- and frequency-selective (or doubly-selective) channels, an optimal training strategy is designed in this paper for block transmissions over doubly-selective channels, relying on a basis expansion channel model. The optimality in designing our PSAM parameters consists of maximizing a tight lower bound on the average channel capacity, that is also shown to be equivalent to the minimization of the minimum mean-square channel estimation error. Numerical results corroborate our theoretical designs.

Carrier frequency-offsets and mobility-induced Doppler shifts introduce time-selectivity in wireless links. Doppler-RAKE receivers have been developed for collecting the resulting diversity gains only for spread-spectrum systems. Relying on a basis expansion model of time-selective channels, we find that the maximum achievable Doppler diversity is determined by the rank of the correlation matrix of the channel's expansion coefficients. We also prove that RAKE reception can not collect maximum diversity gains, unless the transmission is appropriately designed. Finally, we design such block precoded transmissions to ensure maximum diversity gains, and provide thorough simulations to corroborate our theoretical findings.

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

This paper proposes a novel decoding scheme for Alamouti's (see IEEE J. Select. Areas Commun., vol.16, p.1451-1458, 1998) space-time (ST) coded transmissions over time-selective fading channels that arise due to Doppler shifts and carrier frequency offsets. Modeling the time-selective channels as random processes, we employ Kalman filtering for channel tracking in order to enable ST decoding with diversity gains. Computer simulations confirm that the proposed scheme exhibits robustness to time-selectivity with a few training symbols.

Abstract The quest for reliable high‐rate wireless links motivates fading‐resilient and bandwidth‐efficient communication systems capable of capitalizing on available forms of diversity. This tutorial focuses on linear complex field (LCF) coding—a powerful tool that complements the traditional Galois field (GF) coding, in enabling diversity transmissions over single‐ and multi‐antenna fading channels. Performance and capacity analyses are provided, along with systematic guidelines for constructing LCF encoders, and options available to the designer for selecting LCF decoders. Emphasis is placed on high‐performance and high‐rate designs, and the emerging performance‐rate‐complexity trade‐offs. Copyright © 2002 John Wiley & Sons, Ltd.

The many advantages responsible for the widespread application of OFDM are primarily limited by its sensitivity to carrier frequency-offsets. Most of the literature dealing with this problem focuses on data-aided frequency-offset estimation only. In this contribution, several non-data-aided frequency-offset and channel estimation schemes are developed for OFDM transmissions over frequency-selective channels. Timing offset is also accounted for because it is incorporated as a pure delay in the unknown channel. The resulting blind estimation methods include subspace based deterministic or statistical algorithms. Simulations illustrate the performance tradeoffs of these schemes.

The ability of orthogonal frequency-division multiplexing systems to mitigate frequency-selective channels is impaired by the presence of carrier frequency offsets (CFOs). In this paper, we investigate identifiability issues involving high-resolution techniques that have been proposed for blind CFO estimation based on null subcarriers. We propose new approaches that do not suffer from the lack of identifiability and adopt adaptive algorithms that are computationally feasible. The performance of these techniques in relation to the location of the null subcarriers is also investigated via computer simulations and compared with the modified Cramer-Rao bound.