GI-Free Pilot-Aided Channel Estimation for Affine Frequency Division Multiplexing Systems

The recently developed affine frequency division multiplexing (AFDM) can achieve full diversity in doubly selective channels, providing a comprehensive sparse representation of the delay-Doppler domain channel. Thus, accurate channel estimation is feasible by using just one pilot symbol. However, traditional AFDM channel estimation schemes necessitate the use of guard intervals (GI) to mitigate data-pilot interference, leading to spectral efficiency degradation. In this paper, we propose a GI-free pilot-aided channel estimation algorithm for AFDM systems, which improves spectral efficiency significantly. To mitigate the interference between the pilot and data symbols caused by the absence of GI, we perform joint interference cancellation, channel estimation, and signal detection iterately. Simulation results show that the bit error rate (BER) performance of the proposed method can approach the ideal case with perfect channel estimation.

Impact of spatial transformations on landscape features of CEC2022 basic benchmark problems

When benchmarking optimization heuristics, we need to take care to avoid an algorithm exploiting biases in the construction of the used problems. One way in which this might be done is by providing different versions of each problem but with transformations applied to ensure the algorithms are equipped with mechanisms for successfully tackling a range of problems. In this paper, we investigate several of these problem transformations and show how they influence the low-level landscape features of a set of 5 problems from the CEC2022 benchmark suite. Our results highlight that even relatively small transformations can significantly alter the measured landscape features. This poses a wider question of what properties we want to preserve when creating problem transformations, and how to fairly measure them.

A Low-Complexity Range Estimation with Adjusted Affine Frequency Division Multiplexing Waveform

Affine frequency division multiplexing (AFDM) is a recently proposed communication waveform for time-varying channel scenarios. As a chirp-based multicarrier modulation technique it can not only satisfy the needs of multiple scenarios in future mobile communication networks but also achieve good performance in radar sensing by adjusting the built-in parameters, making it a promising air interface waveform in integrated sensing and communication (ISAC) applications. In this paper, we investigate an AFDM-based radar system and analyze the radar ambiguity function of AFDM with different built-in parameters, based on which we find an AFDM waveform with the specific parameter c2 owns the near-optimal time-domain ambiguity function. Then a low-complexity algorithm based on matched filtering for high-resolution target range estimation is proposed for this specific AFDM waveform. Through simulation and analysis, the specific AFDM waveform has near-optimal range estimation performance with the proposed low-complexity algorithm while having the same bit error rate (BER) performance as orthogonal time frequency space (OTFS) using simple linear minimum mean square error (LMMSE) equalizer.

A Low-Complexity Radar System Based on Affine Frequency Division Multiplexing Modulation

Affine frequency division multiplexing (AFDM) is a recently proposed communication waveform for time-varying channel scenarios. As a chirp-based multicarrier modulation technique, by adjusting the built-in parameters of this waveform, it can not only adapt to the needs of multiple scenarios in future mobile communication networks but also achieve good performance in radar sensing, making it a promising air interface waveform in integrated sensing and communication (ISAC) applications. In this paper, we investigate an AFDM-based radar system and analyze the radar ambiguity function of AFDM with different built-in parameters, based on which we find an AFDM waveform with the specific $c_2$ owns the near-optimal time-domain ambiguity function. Then a low-complexity algorithm based on matched filtering for high-precision target range estimation is proposed for this specific AFDM waveform. Through simulation and analysis, the specific AFDM waveform has near-optimal range estimation performance with the proposed low-complexity algorithm while having the same bit error rate (BER) performance as orthogonal time frequency space (OTFS) under the practical linear minimum mean square error (LMMSE) detector.

Error Performance of Coded AFDM Systems in Doubly Selective Channels

Affine frequency division multiplexing (AFDM) is a strong candidate for the sixth-generation wireless network thanks to its strong resilience to delay-Doppler spreads. In this letter, we investigate the error performance of coded AFDM systems in doubly selective channels. We first study the conditional pairwise-error probability (PEP) of AFDM system and derive its conditional coding gain. Then, we show that there is a fundamental trade-off between the diversity gain and the coding gain of AFDM system, namely the coding gain declines with a descending speed with respect to the number of separable paths, while the diversity gain increases linearly. Moreover, we propose a near-optimal turbo decoder based on the sum-product algorithm for coded AFDM systems to improve its error performance. Simulation results verify our analyses and the effectiveness of the proposed turbo decoder, showing that AFDM outperforms orthogonal frequency division multiplexing (OFDM) and orthogonal time frequency space (OTFS) in both coded and uncoded cases over high-mobility channels.

A Vision-Centric Approach for Static Map Element Annotation

The recent development of online static map element (a.k.a. HD Map) construction algorithms has raised a vast demand for data with ground truth annotations. However, available public datasets currently cannot provide high-quality training data regarding consistency and accuracy. To this end, we present CAMA: a vision-centric approach for Consistent and Accurate Map Annotation. Without LiDAR inputs, our proposed framework can still generate high-quality 3D annotations of static map elements. Specifically, the annotation can achieve high reprojection accuracy across all surrounding cameras and is spatial-temporal consistent across the whole sequence. We apply our proposed framework to the popular nuScenes dataset to provide efficient and highly accurate annotations. Compared with the original nuScenes static map element, models trained with annotations from CAMA achieve lower reprojection errors (e.g., 4.73 vs. 8.03 pixels).

A Phase-Coded Time-Domain Interleaved OTFS Waveform with Improved Ambiguity Function

Integrated sensing and communication (ISAC) is a significant application scenario in future wireless communication networks, and sensing is always evaluated by the ambiguity function. To enhance the sensing performance of the orthogonal time frequency space (OTFS) waveform, we propose a novel time-domain interleaved cyclic-shifted P4-coded OTFS (TICP4-OTFS) with improved ambiguity function. TICP4-OTFS can achieve superior autocorrelation features in both the time and frequency domains by exploiting the multicarrier-like form of OTFS after interleaved and the favorable autocorrelation attributes of the P4 code. Furthermore, we present the vectorized formulation of TICP4-OTFS modulation as well as its signal structure in each domain. Numerical simulations show that our proposed TICP4-OTFS waveform outperforms OTFS with a narrower mainlobe as well as lower and more distant sidelobes in terms of delay and Doppler-dimensional ambiguity functions, and an instance of range estimation using pulse compression is illustrated to exhibit the proposed waveform\u2019s greater resolution. Besides, TICP4-OTFS achieves better performance of bit error rate for communication in low signal-to-noise ratio (SNR) scenarios.

Locate and Beamform: Two-dimensional Locating All-neural Beamformer for Multi-channel Speech Separation

Recently, stunning improvements on multi-channel speech separation have been achieved by neural beamformers when direction information is available. However, most of them neglect to utilize speaker's 2-dimensional (2D) location cues contained in mixture signal, which limits the performance when two sources come from close directions. In this paper, we propose an end-to-end beamforming network for 2D location guided speech separation merely given mixture signal. It first estimates discriminable direction and 2D location cues, which imply directions the sources come from in multi views of microphones and their 2D coordinates. These cues are then integrated into location-aware neural beamformer, thus allowing accurate reconstruction of two sources' speech signals. Experiments show that our proposed model not only achieves a comprehensive decent improvement compared to baseline systems, but avoids inferior performance on spatial overlapping cases.

An Overview of Resource Allocation in Integrated Sensing and Communication

Integrated sensing and communication (ISAC) is considered as a promising solution for improving spectrum efficiency and relieving wireless spectrum congestion. This paper systematically introduces the evolutionary path of ISAC technologies, then sorts out and summarizes the current research status of ISAC resource allocation. From the perspective of different integrated levels of ISAC, we introduce and elaborate the research progress of resource allocation in different stages, namely, resource separated, orthogonal, converged, and collaborative stages. In addition, we give in-depth consideration to propose a new resource allocation framework from a multi-granularity perspective. Finally, we demonstrate the feasibility of our proposed framework with a case of full-duplex ISAC system.

Overview and Performance Analysis of Various Waveforms in High Mobility Scenarios

In the high-mobility scenarios of next-generation wireless communication systems (beyond 5G/6G), the performance of orthogonal frequency division multiplexing (OFDM) deteriorates drastically due to the loss of orthogonality between the subcarriers caused by large Doppler frequency shifts. Various emerging waveforms have been proposed for fast time-varying channels with excellent results. In this paper, we classify these waveforms from the perspective of their modulation domain and establish a unified framework to provide a comprehensive system structure comparison. Then we analyze bit error rate (BER) performance of each waveform in doubly selective channels. Through the discussions on their complexity and compatibility with OFDM systems, we finally give the candidate waveform suggestions.