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In this article, a stepped-carrier orthogonal frequency-division multiplexing (OFDM) system with a nonlinear hopping pattern is developed for joint radar and communication (RadCom). In the proposed OFDM-based RadCom system, the carrier frequencies of OFDM symbols for multiple RadCom nodes are orthogonally switched to avoid interference while also maximizing the signal-to-radar interference ratio (SRIR). Differently from conventional linear stepped-carrier OFDM radar that does not consider the communication performance, we first develop a subband allocation method that considers both the radar and the communication performance for multiple RadCom nodes. Channel state information (CSI) of multiple nodes is shared with a centralized server, which then computes the subband allocation strategy that gives the maximum SRIR. Furthermore, a distributed subband allocation strategy based on stateless Q-learning is also proposed, which is suitable when the RadCom nodes do not share their CSI. We also develop the radar signal processing algorithm for range and velocity estimation when the stepped-carrier OFDM waveforms are transmitted through multiple subbands with a nonlinear hopping pattern. Through computer simulations, we confirm that the proposed stepped-carrier OFDM with the nonlinear hopping pattern exhibits a higher achievable sum rate than conventional linear stepped OFDM while not sacrificing the radar performance of target parameter estimation. [ABSTRACT FROM AUTHOR]

In this work, we have developed a neural network (NN) model that can analyze enrichment from depleted (0.2%) to low enriched uranium (4.5%) when UO2 waste with very low radioactivity was contained in a 1-L Marinelli beaker, even when the measurement time is as short as 10 s using a low-resolution detector. The average count rate was about 3800 cps. Measurement of uranium enrichment is necessary for quantitative analysis of uranium radioactivity for disposal of uranium waste. Previously studied uranium enrichment methods (infinite thickness (IT) method, peak ratio (PR) method, and relative-efficiency (RE) method) are difficult to use for field measurement due to many limitations of the algorithms. Among existing methods, the RE method is accurate but requires a long measurement time; there is also a limitation in which a high-resolution detector is essential. In this work, we proposed a model to predict uranium enrichment using a low-resolution detector and an artificial NN model. Furthermore, we validated the results of the NN models using an explainable AI algorithm and principal component analysis (PCA). When the measurement time was less than 60 s, the existing method failed to analyze uranium enrichment, but the proposed model can predict enrichment of uranium within 5% of relative error when 5 g of uranium powder was mixed with various waste (ash, soil, and concrete). [ABSTRACT FROM AUTHOR]

In this article, to get a high-resolution radar image with distributed frequency modulated continuous waveform multiple-input–multiple-output (FMCW MIMO) radar, Bayesian matching pursuit (BMP)-based imaging methods are proposed, in which the received signals at the distributed FMCW MIMO radars are reformulated in terms of the (azimuth, range) patches in the image region of interest and the maximum a posterior (MAP) estimator that can estimate the azimuth angles and ranges of multiple targets is then derived. For a single FMCW MIMO radar, we first propose the BMP-based imaging, in which the support vector (indicating the presence of targets in the radar image patches) and the associated coefficients (the reflection coefficients of the associated targets) are iteratively updated. Because MAP estimation needs the combinatorial search over all possible candidates to find the nonzero elements in the support vector, an approximated MAP estimation method is newly proposed. In addition, by extending the BMP-based imaging for a single FMCW MIMO radar, we develop a new distributed BMP-based imaging method when multiple FMCW MIMO radars are spatially distributed. Through the simulation, we confirm that the proposed BMP-based radar imaging methods outperform the conventional backprojection method or the orthogonal matching pursuit-based method with slightly increased computational complexity. [ABSTRACT FROM AUTHOR]

In this article, we consider a distributed antenna system to enable the downlink ultrareliable and low-latency communications (URLLC) for the industrial Internet of Things. To satisfy the low latency, the short packet length is exploited and accordingly, the achievable rate under the finite blocklength codes is considered in the analysis of the packet loss probability in downlink URLLC distributed antenna system. Specifically, the packet loss probability is approximated as a sum of the channel OP and the finite blocklength coding error probability. By using a stochastic geometry approach, we analytically derive the packet loss probability in terms of the system parameters (i.e., the densities of the DA ports and the receiving sensor nodes, the number of receive antennas, and the packet size/duration, etc.), when the sensor nodes and the DA ports are randomly distributed in a given area. From the analytic results, we show how to optimize the short blocklength coding error probability to minimize the overall packet loss probability. Furthermore, we also derive the required DA port density to satisfy the packet loss probability constraint for a given distributed antenna system parameters, which gives us a useful insight into the design of downlink distributed antenna system for URLLC. [ABSTRACT FROM AUTHOR]

In this paper, we propose novel resource allocation algorithms for ultra-reliable and low-latency communication (URLLC) in distributed antenna systems (DASs) and discuss the effectiveness of the receive antenna deployment and multiple access strategies in fulfilling the stringent reliability and latency requirements. We analyze achievable rates and reliabilities of the DASs with orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) by taking into account the influence of the latency constraint and the non-identical channel distributions of the distributed antennas. Based on the performance analysis, we propose novel resource allocation algorithms for fulfilling the stringent performance requirement in OMA and NOMA. The numerical simulation results provide insightful information for understanding the impacts of multiple access strategy and antenna deployment on the spectral efficiency under the reliability and latency constraints. In particular, NOMA makes it possible to deal with more transmit nodes than OMA, and more dispersed antenna deployment facilitates to achieve a target URLLC performance with less spectrum resource as the ratio of transmit nodes to receive antennas increases. Consequently, the analysis and simulation results provide the meaningful information on the network design for mission-critical applications. [ABSTRACT FROM AUTHOR]

Radar signals scattered by an operating jet engine contain a unique signature that can be used to classify the type of engine, and therefore the aircraft. We present a new algorithm based on the maximum likelihood estimation (MLE) principle that can identify the number of blades of the rotors in the jet engine from the returned radar signal. Unlike the conventional algorithms, the proposed algorithm avoids the estimation/inspection of the autocorrelation of radar signal and utilizes the property of jet engine modulation (JEM) harmonics together with the successive interference cancellation (SIC) technique in the multi-dimensional search, which saves about 66.7% computing time compared to the autocorrelation based algorithm (specifically, the empirical mode decomposition (EMD) based algorithm). The proposed algorithm is assessed with synthetic data as well as real radar data using a mock-up jet engine. Specifically, by evaluating the mean-squared-errors and Cramer-Rao lower bound, we verify that the proposed SIC algorithm performs better than previously known jet engine-classification algorithms. [ABSTRACT FROM AUTHOR]

NAND flash storage has become a standard choice in consumer electronics and is gaining popularity in enterprise systems due to its superior performance and low-power consumption. While its cost disadvantage is rapidly fading thanks to multibit cell technologies and 3-D stacking architectures, the challenge of limited endurance is still lingering and is expected to become more daunting as bits-per-cell continues to increase. In this article, we propose a novel flash translation layer (FTL) design named AxFTL (Approximate FTL) that extends the lifetime of NAND flash storage for error-tolerant applications. For error-tolerant data, AxFTL adopts shallow erase that lowers erase voltage to reduce the erase-induced wearing at the cost of an increased error rate. AxFTL manages multiple groups of blocks by error rates and allocates them according to the error tolerance of write requests. The key components of AxFTL include error tolerance-aware garbage collection and wear leveling schemes that manage the blocks with different error rates with minimal overhead. We implement AxFTL in an SSD simulator for the evaluation of the lifetime improvement and the actual allocation of the blocks. For application-level evaluation, we apply AxFTL to compressed video storage and evaluate the quality of video playback. Our experimental results show that AxFTL greatly improves the lifetime of NAND flash storage by 61% while maintaining a high structural similarity (SSIM) of 0.86 as compared to the conventional FTL. [ABSTRACT FROM AUTHOR]

Recurrent Neural Networks (RNNs) spend most of their execution time performing matrix-vector multiplication (MV-mul). Because the matrices in RNNs have poor reusability and the ever-increasing size of the matrices becomes too large to fit in the on-chip storage of mobile/IoT devices, the performance and energy efficiency of MV-mul is determined by those of main-memory DRAM. Therefore, computing MV-mul within DRAM draws much attention. However, previous studies lacked consideration for the matrix sparsity, the power constraints of DRAM devices, and concurrency in accessing DRAM from processors while performing MV-mul. We propose a main-memory architecture called MViD, which performs MV-mul by placing MAC units inside DRAM banks. For higher computational efficiency, we use a sparse matrix format and exploit quantization. Because of the limited power budget for DRAM devices, we implement the MAC units only on a portion of the DRAM banks. We architect MViD to slow down or pause MV-mul for concurrently processing memory requests from processors while satisfying the limited power budget. Our results show that MViD provides 7.2× higher throughput compared to the baseline system with four DRAM ranks (performing MV-mul in a chip-multiprocessor) while running inference of Deep Speech 2 with a memory-intensive workload. [ABSTRACT FROM AUTHOR]

This paper presents multi-user (MU) linear precoding methods for massively distributed antenna system (DAS) with polarized antennas, where mobile stations (MSs) are spatially grouped and the distributed antenna (DA) ports with a polarized-antenna are connected to a central base station (BS). While the (slowly-changing) long-term channel state information (CSI) (i.e., large scale fading and polarization) can be accurately estimated at the transmit-side, the short-term CSI (instantaneous channel response) is available at the transmit-side with quantization errors due to the limited feedback. Accordingly, the utilization of a large number of DA ports is not always beneficial due to the imperfect short-term CSI at the transmitter (CSIT). In this paper, we first derive the asymptotic achievable rate and signal-to-leakage-and-noise ratio (SLNR) for the MU linear precoding in the massive DAS, which depends on the long-term CSIT and the number of feedback bits, and propose a transmit DA port selection based on the asymptotic SLNR performance. Here, the number of selected DA ports can be also optimized according to the long-term CSIT and the number of feedback bits. Furthermore, we also propose a polarization selection strategy at each DA port (equipped with multi-polarized antennas) so as to exploit the polarization diversity. Through the simulation, we confirm that the proposed MU precoding with the DA port selection outperforms the conventional precoding schemes for the DAS and, furthermore, the proposed polarization selection strategy achieves a rate performance comparable to the DAS with a (single polarized antenna) node density twice as large. [ABSTRACT FROM AUTHOR]

In this paper, we propose a millimeter-wave-based cooperative backhaul scheme for a mobile X-haul distributed unit (XDU), whose line of sight for backhaul data reception is not always guaranteed in an X-haul network. Since the received signal at the mobile XDU experiences severe path loss than other stationary XDUs, the proposed scheme selects one of stationary wireless XDUs as a relay for assisting the mobile XDU. The relaying XDU selection takes into account not only the location information, but also the load information of wireless XDUs. We derive the outage probability of the mobile XDU and show that the proposed scheme outperforms the conventional relay selection scheme without taking account of the traffic load on relaying nodes. [ABSTRACT FROM AUTHOR]

For jointly estimating angle-Doppler information, two-dimen-sional multiple signal classification (MUSIC) is often applied, which requires an exhaustive grid search over angle-Doppler domain. To mitigate the computational complexity and enhance the estimation performance, we modify two-dimensional MUSIC in three steps. First, instead of using two-dimensional spectral search, it is converted to two consecutive polynomial root-finding problems. Second, the degree of the polynomial in the first root-finding stage is undesirably high in general; hence, we approximate it to a lower order polynomial through discrete Fourier transform. Finally, further enhancement can be achieved by having the closest approximation of null-spectrum function by assigning unequal weight coefficients. Cramer–Rao bound is derived for joint angle-Doppler estimation as a benchmark in our performance analysis. [ABSTRACT FROM AUTHOR]

This paper investigates simultaneous wireless information and power transfer (SWIPT) in the ad hoc network, where access points (APs) with multiple antennas can transfer energy as well as information using a common spectrum resource. The receiving nodes decode the incoming information data (information decoding, ID) and/or harvest the RF energy (energy harvesting, EH), where two different receiving node architectures—time switching (TS) and power splitting (PS)—are considered. By using a stochastic geometry approach, we aim to analyze the transmission capacity and the harvested energy per unit area as performance measures of the SWIPT in the ad hoc network. From the analytical results, we can derive the optimal node densities of ID nodes and EH nodes for TS architectures and, furthermore, optimize the power splitting ratio jointly with the node density for the PS architectures such that the harvested energy per unit area is maximized when the target transmission capacity is given. The proposed node density optimization gives us a useful insight into the design of SWIPT-based ad hoc network, where we can show how densely the APs/nodes can be deployed in a given area and furthermore, find that the ad hoc network with PS receiving nodes outperforms that with TS receiving nodes for a large target transmission capacity, and vice versa. [ABSTRACT FROM AUTHOR]

This paper studies precoder designs for simultaneous wireless information and power transfer (SWIPT) in multi-input multi-output (MIMO) channels, where a transmitter sends information to information decoding (ID) users while satisfying the minimum energy requirement of energy harvesting users. In contrast to the previous designs focused only on maximum information rate (MIR), we propose a more general and simpler solution using the weighted minimum mean squared error (WMMSE) criterion. To solve the SWIPT-WMMSE problem which is generally non-convex, we suggest two different design schemes, separate and joint designs. Interestingly, it is shown that the joint design achieves optimal performance with a single initial point and a few iterations, while the separate design needs a large number of iterations and initial points to approach the optimum. Based on the observation, we propose a simple closed-form solution, which is shown to achieve near optimal performance with reduced complexity. The derived solution can be adopted in various pragmatic applications of MIMO communications, such as the MMSE, quality-of-service, equal error designs, as well as the MIR by adjusting the weight matrix. We also confirm that our design strategies are a great use for managing co-channel interference in multiple ID-user scenarios. Finally, simulation results demonstrate the efficiency of the proposed MIMO-SWIPT framework. [ABSTRACT FROM PUBLISHER]