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.

Accurate positioning is essential for meaningful data collection in underwater acoustic sensor networks (UASNs), and localization has become a fundamental technology that provides real-time position estimates for sensor nodes. However, due to the harsh underwater environment as well as the difficulties and high expenses in network maintenance, localization in UASNs has always been a challenging problem. Different from previous works that rely on fixed anchors (e.g., buoys), this article uses an autonomous underwater vehicle (AUV) as a mobile anchor and proposes a reinforcement-learning-based trajectory planning method that allows the AUV navigation to meet the localization requirements of all sensor nodes. Specifically, based on gridded scenarios, this work models the node position uncertainties with information entropy and formulates AUV trajectory planning as a process of reducing the entropy of the whole network. Moreover, a modified actor-critic-based deep deterministic policy gradient (DDPG) reinforcement learning algorithm is designed to shorten AUV trajectory on the premise of ensuring a certain localization accuracy for UASNs. Through various numerical comparisons, the advantages of the proposed method have been validated in terms of efficiency and localization accuracy.

This letter focuses on greedy approaches to select the most informative$k$sensors from$N$candidates to form a measurement submatrix that minimizes the estimation error. It is a submatrix selection problem. We refine conventional greedy sensor selection algorithms based on the square maximum-volume (SMV) submatrices finding method, particularly at their$n$th step, with$n$being the problem dimension. Our main idea is to increase the volume of the square measurement submatrix associated with the$n$sensors by iteratively swapping the selected and unselected sensors based on the dominant property of the maximum-volume submatrix. This simple refinement method ensures a square measurement matrix with increased volume, facilitating the subsequent greedy steps. It can be easily applied to existing greedy algorithms for performance improvement without increasing their complexity order. Numerical results demonstrate the effectiveness of the proposed refinement method in improving several popular greedy algorithms.

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.

The frame-based precoding oriented from the frame structure under the DVB-S2 standard for satellite communications leads to the multicast transmission in each user frame. This paper investigates the multicast-aware user framing/grouping problem to facilitate the frame-based precoding that demands users of high channel similarity in each group. We propose two alternative approaches to increase the intra-group channel similarity by taking into account the channels of all users already in the group when selecting the parallel users for it. One approach extracts the first principle component vector from the channel matrix constituted by current group members and uses it to measure the similarity to the ungrouped users for the selection of the next group member. The other one adds up the projections of the ungrouped user's channel to the channels of the current group members to measure the similarity. Numerical results demonstrate that the proposed two algorithms outperform a benchmark algorithm in various scenarios, verifying the effectiveness of exploiting the channel information of all group members to constitute multicast groups with high intra-group similarity.

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.

Underwater communications are fundamental techniques for the Internet of Underwater Things (IoUT) to establish information links among underwater devices. Recently, orthogonal chirp division multiplexing (OCDM) has drawn great attention in underwater communications due to its advantages in dealing with burst interference in both time and frequency domains. However, the harsh underwater channel conditions (e.g., multipath propagation, temporal variations, and significant Doppler effects) have not been systematically considered in current underwater OCDM systems. In this article, two transmission block structures (Structure A and Structure B) based on OCDM are designed, and a unified OCDM receiver framework is proposed under a single scale multipath lag (SSML) channel model. In the receiver framework, the Doppler scaling factor and carrier frequency offset (CFO) are sequentially compensated, and receiver algorithms that apply the separate or superimposed transmission features (for the pilot and data) of Structure A and Structure B are proposed, respectively. To be specific, a multipeak Doppler scaling factor estimation algorithm and a closed-form CFO estimator are designed for the received signal of Structure A. Moreover, a null symbol-based CFO estimation algorithm is presented for the received signals of Structure B. The effectiveness and advantages of the proposed methods are analyzed and validated through simulations and channel data by comparisons with existing methods under different conditions.

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 problem of single-group multicast beamforming (SMBF) is well-known NP-hard. It motivates the pursuit of computationally efficient near-optimal solutions. Due to multicasting, the multicast group is bottlenecked by the user(s) with the minimum received signal-to-noise ratio (SNR). This paper provides an in-depth interpretation of the SMBF problem from the multicasting point of view and proposes to solve it in two steps: i) select the bottlenecking users by a machine learning approach based on determinantal point process (DPP), and ii) design the beamformer for the selected users. The DPP model jointly considers the magnitudes and directions of users’ channel vectors, and thus enables an efficient selection of the bottlenecking users. Moreover, for a specific channel model, the DPP model is only associated with network size and each takes a one-off training cost, thus can be used as a codebook. The proposed DPP-based subset selection is incorporated adaptively into two fast beamforming algorithms, i.e., the QR decomposition algorithm and the successive beamforming (SB) algorithm. They specifically design the beamformers for the selected users by leveraging channel orthogonalization therein. Numerical results demonstrate the superiority of the proposed QR-DPP and SB-DPP algorithms in terms of the performance-complexity compromise and their robustness to different scenarios.

The noncooperative target tracking is an important issue for the Internet of Underwater Things (IoUT). Autonomous underwater vehicles (AUVs) are preferred options to achieve the target tracking especially in anchor-free environments, where no equipments with known positions, named anchors, are deployed. The self-organized mobile network of multiple AUVs can localize and continuously monitor the target. Thus, in this article, we investigate the problem of the noncooperative target tracking using multiple AUVs in anchor-free environments. In the target tracking, AUVs play as references and their positions need to be estimated first. We propose a multi-AUV cooperative localization and target tracking (MCLTT) framework based on belief propagation (BP). Under MCLTT, BP-based underwater cooperative localization (BPUCL) and noncooperative mobile target tracking (NcMTT) algorithms are designed. Gaussian approximations are used to reduce communication costs among AUVs. The designed BPUCL alleviates the impact of the accumulated errors in the inertial measurements of AUVs and slows down the growth of the localization error. In NcMTT, model-free position prediction processes are proposed and a novel form of the particle-based BP message is designed using time-difference-of-arrival (TDOA) measurements. The simulation results validate the proposed algorithms by comparing with state-of-the-art methods.

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.

This letter is concerned with an asynchronous localization issue for underwater acoustic sensor networks (UASNs), subject to asynchronous clocks and stratification effects in physical channels. A novel unscented transform-based localization algorithm is proposed to estimate the positions of sensor nodes. Instead of linearizing the measurement equations, the proposed algorithm employs the unscented transform to compute the Jacobian matrix to reduce the linearization errors. Particularly, the ray-tracing approach is adopted to model the stratification effect. Moreover, the convergence analysis and Cramér-Rao lower bound for the algorithm are also provided. Simulation results show that the proposed algorithm can effectively improve the estimation accuracy as compared with the existing works.

Clock synchronization is required by most time-based localization methods in wireless sensor networks (WSNs). However, synchronization is often coupled with localization. Furthermore, the accuracy of anchor positions depends on several factors, and uncertainties may exist in the observed anchor positions. Thus, we propose a joint time and location estimation of target sensors using a single mobile anchor to reduce the deployment cost for WSNs. Taking anchor position uncertainties into account, we develop an expectation maximization (EM)-type method to solve the joint estimation problem. The simulation results verify the performance of the proposed EM method is superior than conventional methods, such as least squares (LS), weighted least squares (WLS) and generalized total least squares (GTLS) estimators.

In the urban transportation system, the unbalanced relationship between taxi demand and the number of running taxis reduces the drivers' income and the levels of passengers' satisfaction. With the help of vehicular global positioning system (GPS) data, the taxi demand distribution of city can be analyzed to provide advice for drivers. A clustering algorithm called Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is suitable for discovering demand hotspots. However, the execution efficiency is still a big challenge when DBSCAN is applied on big databases. In this paper, we propose an improved density-based clustering algorithm called Grid and Kd-tree for DBSCAN (GD-DBSCAN), which integrates partitioning method with kd-tree structure to improve the computational performance of DBSCAN. Furthermore, this algorithm can take advantages of multi cores and shared memory to parallelize related functions. The experiment shows GD- DBSCAN is efficient, it has an improvement of at least 10% in performance compared with DBSCAN.

Indoor localization has been actively researched in recent years due to the increasing demand for location-awareness services. However, to balance localization accuracy and system cost is always a challenge for indoor localization systems. Radio frequency identification (RFID) is a promising technology to achieve both goals, because of its reasonable cost and reliability. In this paper, we propose a novel RFID indoor localization system based on angle of arrival (AoA) and phase of arrival (PoA) methods. This system leverages RFID's two experimental signal diffusion characteristics to estimate AoA. One is that the interrogation zone is constrained in a lobe, and only in this area the tag can be queried. The second is that there exits a stable pattern of received signal strength (RSS) on angle changes. We use the two features to find a general area and to pinpoint the AoA consecutively. This effectively narrows the sampling zone (where signal needs to be sampled), and helps to reduce computational complexity. In addition, we reduce the multipath effect on range estimation by determining the AoA and rotating the reader into the direction of the target. Moreover, we exploit two signals with a slightly different frequency to eliminate the phase ambiguity issue. Our system takes only one reader and achieves mean accuracy of 23 cm. The simplicity and effectiveness of our system make it convenient to be used in practice.

In this demo, an efficient and reliable wireless link is designed for mobile video surveillance systems. In the link, the idea of cognitive radio is utilized and an adaptive channel switching mechanism is employed to avoid unpredictable interferences. Packets pipelining and accumulative acknowledgement (ACK) is proposed based on the stop-and-wait ARQ protocol to improve the communication efficiency of the link. Moreover, an integration design of the ACK packet is used to piggyback different kinds of control messages. With the efficient and reliable wireless link, a cognitive radio prototype is developed for video transmission between the telerobot and teleoperator. The video information from the telerobot can be transmitted back to the teleoperator quickly and reliably even under channel interferences. The telerobot can also be controlled timely and accurately. A video demo shows the whole story and performance.

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.

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.

In this paper, we propose a clock skew calibration method for ranging applications using an ultra-wideband (UWB) signal. The clock skew is one of the main error sources in time-of-arrival (TOA) based UWB ranging, since a long ranging signal is required to obtain a sufficiently high signal-to-noise ratio (SNR). Therefore, the clock skew calibration is essential for accurate TOA ranging. We propose to estimate the clock skew in the frequency domain to take full advantage of the periodic property of the ranging signal, which allows the proposed method to reach super-resolution. Simulation results corroborate the efficiency of the proposed method.

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.

We consider the problem of node localization given partial pairwise distance measurements. Current solutions first complete the missing distances and then apply the classical multidimensional scaling (MDS) algorithm. Instead, we extend the classical MDS to a setup where the sensor network is composed of a fully connected group of nodes that communicate with each other (e.g., beacons), and a group of nodes that cannot communicate with each other, but each one of them communicates with each node in the first group. The positions of all nodes are unknown. We localize the fully connected nodes by exploiting their distance measurements to the disconnected nodes. At the same time, the positions of the disconnected nodes are obtained up to a translation relative to the positions of the connected nodes. Recovering this translation, can be obtained with an additional step. Simulation results show that the proposed algorithm outperforms current MDS-like solutions to the problem.

In this paper, we derive the Cramér-Rao bound (CRB) for range estimation, which does not only exploit the range information in the time delay, but also in the amplitude of the received signal. This new bound is lower than the conventional CRB that only makes use of the range information in the time delay. We investigate the new bound in an additive white Gaussian noise (AWGN) channel with attenuation by employing both narrowband (NB) signals and ultra-wideband (UWB) signals. For NB signals, the new bound can be 3dB lower than the conventional CRB under certain conditions. However, there is not much difference between the new bound and the conventional CRB for UWB signals. Further, shadowing effects are added into the data model. Several CRB-like bounds for range estimation are derived to take these shadowing effects into account.

In the design of a packet-oriented impulse-radio UWB communication system, the main challenge at the receiver is to have a fast synchronization to the coded pulses, along with a detection of the message. We consider schemes that are straightforward to implement in practical systems and propose two methods to realize the synchronization algorithms: a serial and a parallel method. The algorithms for synchronization and demodulation are implemented in a receiver prototype based on an FPGA.