Search Results (36)

Employing commercial off-the-shelf coherent optical transmission components and methods to design a continuous variable quantum key distribution (CVQKD) system is a promising trend of achieving QKD with high security key rate (SKR) and cost-effectiveness. In this paper, we explore a CVQKD system based on the widely used polarization division multiplexed (PDM) coherent optical transmission structure and pilot-aided digital signal processing methods. A simplified pilot-aided phase noise compensation scheme based on frequency division multiplexing (FDM) is proposed, which introduces less total excess noise than classical pilot-aided schemes based on time division multiplexing (TDM). In addition, the two schemes of training symbol (TS)-aided equalization are compared to find the optimal strategy for TS insertion, where the scheme based on block insertion strategy can provide the SKR gain of around 29%, 22%, and 15% compared with the scheme based on fine-grained insertion strategy at the transmission distance of 5 km, 25 km, and 50 km, respectively. The joint optimization of pilot-aided and TS-aided methods in this work can provide a reference for achieving a CVQKD system with a high SKR and low complexity in metropolitan-scale applications. Full article

Modern neuromorphic processors exhibit neuron densities that are orders of magnitude lower than those of the biological cortex, hindering the deployment of large-scale spiking neural networks (SNNs) on single chips. To bridge this gap, we propose HDNIP, a 40 nm high-density neuromorphic inference platform with a density-first architecture. By eliminating area-intensive on-chip SRAM and using 1280 compact cores with a time-division multiplexing factor of up to 8192, HDNIP integrates 10 million neurons and 80 billion synapses within a 44.39 ${\mathrm{mm}}^2$ synthesized area. This achieves an unprecedented neuron density of 225 k neurons/${\mathrm{mm}}^2$, over 100 times greater than prior art. The resulting bandwidth challenges are mitigated by a ReRAM-based near-memory computation strategy combined with input reuse, reducing off-chip data transfer by approximately 95%. Furthermore, adaptive TDM and dynamic core fusion ensure high hardware utilization across diverse network topologies. Emulator-based validation using large SNNs, demonstrates a throughput of 13 GSOP/s at a low power consumption of 146 mW. HDNIP establishes a scalable pathway towards single-chip, low-SWaP neuromorphic systems for complex edge intelligence applications. Full article

Time Division Multiplexing Passive Optical Networks (TDM-PONs) serve as a key enabler for the evolution of broadband access network infrastructure. As TDM-PONs adapt to support 6G networks, reducing energy consumption becomes increasingly critical. Sleep modes have been widely adopted as an effective energy-saving solution. However, their use can introduce delays that compromise performance. This issue becomes especially problematic in 6G PONs, where ultra-low latency and stringent service requirements leave minimal tolerance for delay-related inefficiencies. In this paper, we propose a novel sleep synchronization mechanism for both single and multiple TDM-PONs, allowing Optical Network Units (ONUs) to join one or more sleep/wake-up groups based on the service type and delay tolerance. Our practical design framework incorporates delay-based grouping and existing sleep modes to address the operational complexities of multi-PON systems while remaining fully compatible with current PON standards. The simulation results show that our approach satisfies the requirements of delay-sensitive traffic and achieves up to 37% energy savings. Compared to baseline methods such as adaptive scheduling and fixed-interval cyclic sleep, it offers a 15–20% improvement in the energy–delay trade-off. These results demonstrate the potential for near-term deployment of 6G PONs and lay the foundation for more advanced, delay-aware energy management strategies in next-generation optical access networks. Full article

This study designed and validated a dual-component beat-frequency quartz-enhanced photoacoustic spectroscopy (BF-QEPAS) gas detection system utilizing time-division multiplexing (TDM). By applying TDM to drive distributed feedback lasers, the system achieved the simultaneous detection of acetylene and methane. Its key innovation lies in exploiting the transient response of the quartz tuning fork (QTF) to acquire gas concentrations while concurrently capturing the QTF resonant frequency and quality factor in real-time. Owing to the short beat period and rapid system response, this approach significantly reduces time-delay constraints in time-division measurements, eliminating the need for periodic calibration inherent in conventional methods and preventing detection interruptions. The experimental results demonstrate minimum detection limits of 5.69 ppm for methane and 0.60 ppm for acetylene. Both gases exhibited excellent linear responses over the concentration range of 200 ppm to 4000 ppm, with the R² value for methane being 0.996 and for acetylene being 0.997. The system presents a viable solution for the real-time, calibration-free monitoring of dissolved gases in transformer oil. Full article

Dynamic Bandwidth Allocation (DBA) techniques enable Time Division Multiplexing Passive Optical Network (TDM-PON) systems to efficiently manage upstream bandwidth by allowing the centralized Optical Line Terminal (OLT) to coordinate resource allocation among distributed Optical Network Units (ONUs). Conventional DBA techniques struggle to adapt to dynamic traffic conditions, resulting in suboptimal performance under varying load scenarios. This work suggests a Collaborative Split Learning-Based DBA (CSL-DBA) framework that utilizes the recently emerging Split Learning (SL) technique between the OLT and ONUs for the objective of optimizing predictive traffic adaptation and reducing communication overhead. Instead of requiring centralized learning at the OLT, the proposed approach decentralizes the process by enabling ONUs to perform local traffic analysis and transmit only model updates to the OLT. This cooperative strategy guarantees rapid responsiveness to fluctuating traffic conditions. We show by extensive simulations spanning several traffic scenarios, including low, fluctuating, and high traffic load conditions, that our proposed CSL-DBA achieves at least 99% traffic prediction accuracy, with minimal inference latency and scalable learning performance, and it reduces communication overhead by approximately 60% compared to traditional federated learning approaches, making it a strong candidate for next-generation 6G-grade TDM-PON systems. Full article

To achieve commercial scalability, fiber-based quantum key distribution (QKD) systems must be integrated into existing optical communication infrastructures, rather than deployed exclusively on dedicated dark fibers. Integrating QKD into optical access networks (OANs) would be particularly advantageous, as these networks provide direct connectivity to end users for whom security is critical. Such integration can address the inherent security vulnerabilities in current OANs, which are primarily based on time-division multiplexing passive optical networks (TDM-PONs). However, integrating QKD into PONs poses significant challenges due to Raman noise and other detrimental effects induced by PON signals, which intensify as the launched power of PONs increases to support higher transmission speeds. In this study, we review recent advancements in both QKD and access network technologies, evaluate the technical feasibility of QKD-OAN integration, and propose cost-effective strategies to facilitate the widespread deployment of QKD in future access networks. Full article

The TDM Test Scheduler and TAM Optimization Toolkit is a novel, integrated, and user-friendly solution designed for engineers, researchers, and instructors working in the field of manufacturing tests. It effectively supports test planning for multicore, DVFS-based SoCs with multiple voltage islands, offering optimized solutions that minimize test costs while ensuring compliance with power and thermal constraints. The toolkit provides (a) a high-level language (HLL) for the intuitive representation of test processes, along with a smart syntax and logic checker for verification; (b) an advanced compilation and execution environment featuring two computationally efficient Time-Division Multiplexing (TDM)-specialized solvers; (c) a sophisticated Test Access Mechanism (TAM) optimization framework; (d) a customized visualization environment capable of depicting and animating power- and thermal-annotated test schedules; (e) a versatile testbed for educational and research activities. Full article

This paper presents a low-power and area-efficient chopper-stabilized low noise amplifier (CS-LNA) for in-pixel neural recording systems. The proposed CS-LNA can be used in a multi-channel architecture, in which the chopper mixers of the LNA are exploited to provide the time division multiplexing (TDM) of several channels, while reducing the flicker noise and rejecting the Electrode DC Offset (EDO). A detailed noise analysis including the effect of the chopper stabilization on flicker noise, and a design flow to optimize the trade-off between input-referred noise and silicon area are presented, and utilized to design the LNA. The adopted approach to reject the EDO allows to tolerate an input offset of ±50 mV, without appreciably affecting the CS-LNA performance, and does not require an additional DC Servo Loop (DSL). The proposed CS-LNA has been fabricated in a 0.13 μm CMOS process with an area of 0.0268 mm², consuming about 2 μA from a 0.8 V supply voltage. It achieves an integral noise of 4.19 μVrms (2.58 μVrms) from 1 to 7.5 kHz (from 300 to 7.5 kHz) and results in a noise efficiency factor (NEF) of 2.63 (1.62). Besides achieving a maximum gain of 38.67 dB with a tuning range of about 12 dB, the neural amplifier exhibits a CMRR of 67 dB. A comparison with the recent literature dealing with in-pixel amplifiers shows state-of-the-art performance. Full article

Multiple-Input Multiple-Output (MIMO) radar enjoys the advantages of a high degree of freedom and relatively large virtual aperture, so it has various forms of applications in several aspects such as remote sensing, autonomous driving and radar imaging. Among all multiplexing schemes, Time-Division Multiplexing (TDM)-MIMO radar gains a wide range of interests, as it has a simple and low-cost hardware system which is easy to implement. However, the time-division nature of TDM-MIMO leads to the dilemma between the lower Pulse Repetition Interval (PRI) and more transmitters, as the PRI of a TDM-MIMO system is proportional to the number of transmitters while the number of transmitters significantly affects the resolution of MIMO radar. Moreover, a high PRI is often needed to obtain unambiguous imaging results for MIMO-SAR 3D imaging applications on a fast-moving platform such as a car or an aircraft. Therefore, it is of vital importance to develop an algorithm which can achieve unambiguous TDM-MIMO-SAR 3D imaging even when the PRI is low. Inspired by the motion compensation problem associated with TDM-MIMO radar imaging, this paper proposes a novel imaging algorithm which can utilize the phase shift induced by the time-division nature of TDM-MIMO radar to achieve unambiguous MIMO-SAR 3D imaging. A 2D-Compressed Sensing (CS)-based method is employed and the proposed method, which is called HPC-2D-FISTA, is verified by simulation data. Finally, a real-world experiment is conducted to show the unambiguous imaging ability of the proposed method compared with the ordinary matched-filter-based method. The effect of velocity error is also analyzed with simulation results. Full article

In this paper, a multichannel FIR filter design based on the Time Division Multiplex (TDM) approach that incorporates one multiply and add unit, regardless of the variable coefficient length and varying channels, by associating the resource sharing doctrine is suggested. A multiplier based on approximate distributed arithmetic (DA) circuits is employed for effective resource optimization. Although no explicit multiplication was conducted in this realization, the radix-8 and radix-4 Booth algorithms are utilized in the DA framework to curtail and optimize the partial products (PPs). Furthermore, the input stream is truncated with an erratum mending unit to roughly construct the partial products. For an aggregation of PPs, an approximate Wallace tree is taken into consideration to further minimize hardware expenses. Consequently, the suggested design’s latency, utilized area, and power usage are largely reduced. The Xilinx Vertex device is expedited, given the synthesis of the suggested multichannel realization with 16 taps, which is simulated using the Verilog formulary. It is observed that the filter structure with one channel produced the desired results, and the system’s frequency can support up to 429 MHz with a reduced area. Utilizing TSMC 180 nm CMOS technology and the Cadence RC compiler, cell-level performance is also achieved. Full article

Mobile technology is currently trending toward supporting multiple communication standards on a single device. This means that some reconfigurable techniques must be the foundation of their design. The two essential requirements of channel filters are minimized complexity and reconfigurability. In this research, a novel extension of Frequency Response Masking (FRM) was investigated by employing Time Division Multiplexing (TDM)-based single Multiply and Accumulate (MAC) architecture using the principle of resource sharing to realize multiple sharp filter responses from a single prototype constant group delay low pass filter. This paper uses a single multiply and add units regardless of the quantity of channels and taps. The suggested reconfigurable filter was synthesized on technology based on 0.18-µm CMOS and put into practice. Further trials were carried out on Virtex-II 2v3000ff1152-4 FPGA device. The outcomes revealed that the suggested channel filter, which was synthesized using FPGA, provides 21.36% of the area curtail and 14.88% of power scaling down on average and put into practice using ASIC provides 5.18% of the area reduction and 9.08% of power scaling down on average. Full article

This research advances millimeter-wave (mmWave) altimetry for unmanned aerial systems (UASs) by optimizing performance metrics within the constraints of inexpensive automotive radars. Leveraging the software-defined architecture, this study encompasses the intricacies of frequency modulated continuous waveform (FMCW) design for three distinct stages of UAS flight: cruise, landing approach, and touchdown within a signal processing framework. Angle of arrival (AoA) estimation, traditionally employed in terrain mapping applications, is largely unexplored for UAS radar altimeters (RAs). Time-division multiplexing multiple input–multiple output (TDM-MIMO) is an efficient method for enhancing angular resolution without compromising the size, weight, and power (SWaP) characteristics. Accordingly, this work argues the potential of AoA estimation using TDM-MIMO to augment situational awareness in challenging landing scenarios. To this end, two corner cases comprising landing a small-sized drone on a platform in the middle of a water body are included. Likewise, for the touchdown stage, an improvised rendition of zoom fast Fourier transform (ZFFT) is investigated to achieve millimeter (mm)-level range accuracy. Aptly, it is proposed that a mm-level accurate RA may be exploited as a software redundancy for the critical weight-on-wheels (WoW) system in fixed-wing commercial UASs. Each stage is simulated as a radar scenario using the specifications of automotive radar operating in the 77–81 GHz band to optimize waveform design, setting the stage for field verification. This article addresses challenges arising from radial velocity due to UAS descent rates and terrain variation through theoretical and mathematical approaches for characterization and mandatory compensation. While constant false alarm rate (CFAR) algorithms have been reported for ground detection, a comparison of their variants within the scope UAS altimetry is limited. This study appraises popular CFAR variants to achieve optimized ground detection performance. The authors advocate for dedicated minimum operational performance standards (MOPS) for UAS RAs. Lastly, this body of work identifies potential challenges, proposes solutions, and outlines future research directions. Full article

For time division multiplexing multiple input multiple output (TDM MIMO) millimeter wave radar, the measurement of target range, velocity and other parameters depends on the phase of the received Intermediate Frequency (IF) signal. The coupling between range and velocity phases occurs when measuring moving targets, leading to inevitable errors in calculating range and velocity from the phase, which in turn affects measurement accuracy. Traditional two-dimensional fast fourier transform (2D FFT) estimation errors are particularly pronounced at high velocity, significantly impacting measurement accuracy. Additionally, due to limitations imposed by the Nyquist sampling theorem, there is a restricted range for velocity measurements that can result in aliasing. In this study, we propose a method to address the coupling of range and velocity based on the original signal as well as a method for velocity compensation to resolve aliasing issues. Our research findings demonstrate that this approach effectively reduces errors in measuring ranges and velocities of high-velocity moving targets while efficiently de-aliasing velocities. Full article

A large-aperture radar composed of a multiple-input-multiple-output (MIMO) planar array can complete 3D through-wall imaging (TWI), but the simultaneous work of the multiple transceiver channels leads to difficulties in designing the hardware. In engineering, multiple transceiver channels are usually realized by time-division multiplexing (TDM) in MIMO radar, which is called TDM MIMO radar. A time delay will be introduced when the channels are switched, which will cause high sidelobes and position deviation in the 3D imaging of moving targets, also known as range migration. This paper proposes a motion compensation algorithm based on sparse array, designed to eliminate range migration in moving targets in 3D TWI scenes. In the proposed algorithm, the coincident array elements of the equivalent array are used as the compensation channels to calculate the position difference of the target, which can correct the remaining MIMO channels. The proposed algorithm is compared with no compensation, and the reference-channel-based motion compensation algorithm (RCMCA). According to the simulation and experimental results, the proposed motion compensation algorithm can effectively eliminate sidelobes, and keep the position deviation within 0.30 m in the 3D TWI of moving targets under the TDM MIMO radar, without increasing the system complexity. Full article

Radars have been widely deployed in cars in recent years, for advanced driving assistance systems. The most popular and studied modulated waveform for automotive radar is the frequency-modulated continuous wave (FMCW), due to FMCW radar technology’s ease of implementation and low power consumption. However, FMCW radars have several limitations, such as low interference resilience, range-Doppler coupling, limited maximum velocity with time-division multiplexing (TDM), and high-range sidelobes that reduce high-contrast resolution (HCR). These issues can be tackled by adopting other modulated waveforms. The most interesting modulated waveform for automotive radar, which has been the focus of research in recent years, is the phase-modulated continuous wave (PMCW): this modulated waveform has a better HCR, allows large maximum velocity, permits interference mitigation, thanks to codes orthogonality, and eases integration of communication and sensing. Despite the growing interest in PMCW technology, and while simulations have been extensively performed to analyze and compare its performance to FMCW, there are still only limited real-world measured data available for automotive applications. In this paper, the realization of a 1 Tx/1 Rx binary PMCW radar, assembled with connectorized modules and an FPGA, is presented. Its captured data were compared to the captured data of an off-the-shelf system-on-chip (SoC) FMCW radar. The radar processing firmware of both radars were fully developed and optimized for the tests. The measured performances in real-world conditions showed that PMCW radars manifest better behavior than FMCW radars, regarding the above-mentioned issues. Our analysis demonstrates that PMCW radars can be successfully adopted by future automotive radars. Full article