Explore the vast range of titles available.

This paper addresses altogether time and frequency synchronization in IEEE 802.11a orthogonal frequency division multiplexing (OFDM) wireless communication systems. The proposed algorithms have two main features: (i) they make use of an additional source of information available at IEEE 802.11a physical layer, on top of the usual ones typically adopted for synchronization such as training sequences. This additional source of information is provided by the higher layers of the communication protocol. In fact, when the carrier sense multiple access with collision avoidance (CSMA/CA) protocol is activated, the receiver is able to predict some parts of the SIGNAL field that are classically assumed unknown. Moreover, during the negotiation of the transmission medium reservation, the exchanged frames not only help the receiver to predict the SIGNAL field but also to obtain information about the channel state. (ii) Based on this property, we propose a joint MAP time and frequency synchronization algorithm using all available information. Finally, the time synchronization is fine tuned by means of a specific metric in the frequency domain that allows us to minimize the expectation of the transmission error function over all channel estimate errors. Simulation results compliant with the IEEE 802.11a standard in both indoor and outdoor environments show that the proposed algorithm drastically improves the performance in terms of synchronization failure probability and bit error ratio, compared to state-of-the-art algorithms.

This paper presents a 12-b successive approximation register (SAR) analog-to-digital converter (ADC) for biopotential sensing applications. To reduce the digital-to-analog converter (DAC) switching energy of the high-resolution ADC, we combine merged-capacitor-switching (MCS) and detect-and-skip (DAS) methods, successfully embedded in the subranging structure. The proposed method saves 96.7% of switching energy compared to the conventional method. Without an extra burden on the realization of the calibration circuit, we achieve mismatch calibration by reusing the on-chip DAC. The mismatch data are processed in the digital domain to compensate for the nonlinearity caused by the DAC mismatch. The ADC is realized using a 0.18 μm CMOS process with a core area of 0.7 mm2. At the sampling rate fS = 9 kS/s, the ADC achieves a signal-to-noise ratio and distortion (SINAD) of 67.4 dB. The proposed calibration technique improves the spurious-free dynamic range (SFDR) by 7.2 dB, resulting in 73.5 dB. At an increased fS = 200 kS/s, the ADC achieves a SINAD of 65.9 dB and an SFDR of 68.8 dB with a figure-of-merit (FoM) of 13.2 fJ/conversion-step.

This paper analyzes the bit error rate (BER) of multicarrier index keying—orthogonal frequency division multiplexing (MCIK-OFDM) with selection combining (SC) diversity reception. Particularly, we propose a generalized framework to derive the BER for both the low-complexity greedy detector (GD) and maximum likelihood (ML) detector. Based on this, closed-form expressions for the BERs of MCIK-OFDM with the SC using either the ML or the GD are derived in presence of the channel state information (CSI) imperfection. The asymptotic analysis is presented to gain helpful insights into effects of different CSI conditions on the BERs of these two detectors. More importantly, we theoretically provide opportunities for using the GD instead of the ML under each specific CSI uncertainty, which depend on the number of receiver antennas and the M-ary modulation size. Finally, extensive simulation results are provided in order to validate our theoretical expressions and analysis.

This paper proposes a novel spread spectrum and sub-carrier index modulation (SS-SIM) scheme, which is integrated to orthogonal frequency division multiplexing (OFDM) framework to enhance the diversity over the conventional IM schemes. Particularly, the resulting scheme, called SS-SIM-OFDM, jointly employs both spread spectrum and sub-carrier index modulations to form a precoding vector which is then used to spread an M-ary complex symbol across all active sub-carriers. As a result, the proposed scheme enables a novel transmission of three signal domains: SS and sub-carrier indices, and a single M-ary symbol. For practical implementations, two reduced-complexity near-optimal detectors are proposed, which have complexities less depending on the M-ary modulation size. Then, the bit error probability and its upper bound are analyzed to gain an insight into the diversity gain, which is shown to be strongly affected by the order of sub-carrier indices. Based on this observation, we propose two novel sub-carrier index mapping methods, which significantly increase the diversity gain of SS-SIM-OFDM. Finally, simulation results show that our scheme achieves better error performance than the benchmarks at the cost of lower spectral efficiency compared to classical OFDM and OFDM-IM, which can carry multiple M-ary symbols.

Herein, we present an energy efficient successive-approximation-register (SAR) analog-to-digital converter (ADC) featuring on-chip dual calibration and various accuracy-enhancement techniques. The dual calibration technique is realized in an energy and area-efficient manner for comparator offset calibration (COC) and digital-to-analog converter (DAC) capacitor mismatch calibration. The calibration of common-mode (CM) dependent comparator offset is performed without using separate circuit blocks by reusing the DAC for generating calibration signals. The calibration of the DAC mismatch is efficiently performed by reusing the comparator for delay-based mismatch detection. For accuracy enhancement, we propose new circuit techniques for a comparator, a sampling switch, and a DAC capacitor. An improved dynamic latched comparator is proposed with kick-back suppression and CM dependent offset calibration. An accuracy-enhanced bootstrap sampling switch suppresses the leakage-induced error <180 μV and the sampling error <150 μV. The energy-efficient monotonic switching technique is effectively combined with thermometer coding, which reduces the settling error in the DAC. The ADC is realized using a 0.18 μm complementary metal–oxide–semiconductor (CMOS) process in an area of 0.28 mm2. At the sampling rate fS = 9 kS/s, the proposed ADC achieves a signal-to-noise and distortion ratio (SNDR) of 55.5 dB and a spurious-free dynamic range (SFDR) of 70.6 dB. The proposed dual calibration technique improves the SFDR by 12.7 dB. Consuming 1.15 μW at fS = 200 kS/s, the ADC achieves an SNDR of 55.9 dB and an SFDR of 60.3 dB with a figure-of-merit of 11.4 fJ/conversion-step.

Millimeter-wave (mmWave) communication is able to provide high-speed data rates up to Gbps. However, mmWave communication is susceptible to fading and blockage by obstacles in indoor and urban areas owing to high path loss and directivity. To tackle these issues, the intelligent reflecting surface (IRS) is regarded as an effective technique that provides reflected signals to enhance system performance. However, designing the joint active and passive beamforming is challenging in the IRS-assisted mmWave communication systems owing to the time-varying channels. In this study, we propose a novel adaptive perturbation-based opportunistic beamforming design for IRS-assisted systems (IRS-APOBF) for mmWave channels. The proposed scheme employs adaptive perturbation to select the beamforming and phase-shift vectors that improve the system performance by enhancing the signal-to-noise ratio (SNR) of the received signal. Moreover, the proposed IRS-APOBF scheme can adapt to the time-varying channel conditions of IRS-assisted mmWave systems with the only requirement of SNR feedback from mobile stations, making it suitable for practical scenarios. The simulation outcomes reveal that the system throughput achieved by the IRS-APOBF scheme is significantly higher than that obtained via a conventional IRS-assisted opportunistic beamforming scheme, despite both schemes requiring limited feedback information.

Drone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on. Furthermore, as part of solution architectures, the use of Blockchain, Software Defined Networks (SDN), Machine Learning, and Fog/Edge computing are discussed as these are the most emerging technologies. Drones are highly resource-constrained devices and therefore it is not possible to deploy heavy security algorithms on board. Blockchain can be used to cryptographically store all the data that is sent to/from the drones, thereby saving it from tampering and eavesdropping. Various ML algorithms can be used to detect malicious drones in the network and to detect safe routes. Additionally, the SDN technology can be used to make the drone network reliable by allowing the controller to keep a close check on data traffic, and fog computing can be used to keep the computation capabilities closer to the drones without overloading them.

In a large-scale distributed machine learning system, coded computing has attracted wide-spread attention since it can effectively alleviate the impact of stragglers. However, several emerging problems greatly limit the performance of coded distributed systems. Firstly, an existence of colluding workers who collude results with each other leads to serious privacy leakage issues. Secondly, there are few existing works considering security issues in data transmission of distributed computing systems. Thirdly, the number of required results for which need to wait increases with the degree of decoding functions. In this paper, we design a secure and private approximated coded distributed computing (SPACDC) scheme that deals with the above-mentioned problems simultaneously. Our SPACDC scheme guarantees data security during the transmission process using a new encryption algorithm based on elliptic curve cryptography. Especially, the SPACDC scheme does not impose strict constraints on the minimum number of results required to be waited for. An extensive performance analysis is conducted to demonstrate the effectiveness of our SPACDC scheme. Furthermore, we present a secure and private distributed learning algorithm based on the SPACDC scheme, which can provide information-theoretic privacy protection for training data. Our experiments show that the SPACDC-based deep learning algorithm achieves a significant speedup over the baseline approaches.

In this paper, we investigate a joint computation offloading and target tracking in Integrated Sensing and Communication (ISAC)-enabled unmanned aerial vehicle (UAV) network. Therein, the UAV has a computing task that is partially offloaded to the ground UE for execution. Meanwhile, the UAV uses the offloading bit sequence to estimate the velocity of a ground target based on an autocorrelation function. The performance of the velocity estimation that is represented by Cramer-Rao lower bound (CRB) depends on the length of the offloading bit sequence and the UAV's location. Thus, we jointly optimize the task size for offloading and the UAV's location to minimize the overall computation latency and the CRB of the mean square error for velocity estimation subject to the UAV's budget. The problem is non-convex, and we propose a genetic algorithm to solve it. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

In this paper, we propose a deep learning-based signal detector called DuaIM-3DNet for dual-mode index modulation-based three-dimensional (3D) orthogonal frequency division multiplexing (DM-IM-3D-OFDM). Herein, DM-IM-3D- OFDM is a subcarrier index modulation scheme which conveys data bits via both dual-mode 3D constellation symbols and indices of active subcarriers. Thus, this scheme obtains better error performance than the existing IM schemes when using the conventional maximum likelihood (ML) detector, which, however, suffers from high computational complexity, especially when the system parameters increase. In order to address this fundamental issue, we propose the usage of a deep neural network (DNN) at the receiver to jointly and reliably detect both symbols and index bits of DM-IM-3D-OFDM under Rayleigh fading channels in a data-driven manner. Simulation results demonstrate that our proposed DNN detector achieves near-optimal performance at significantly lower runtime complexity compared to the ML detector.