6G Wireless Communications in 7-24 GHz Band: Opportunities, Techniques, and Challenges

The sixth generation (6G) wireless communication nowadays is seeking a new spectrum to inherit the pros and discard the cons of sub-6 GHz, millimeter-wave (mmWave), and sub-terahertz (THz) bands. To this end, an upper mid-band, Frequency Range (FR) spanning from 7 GHz to 24 GHz, also known as FR3, has emerged as a focal point in 6G communications. Thus, as an inexorable prerequisite, a comprehensive investigation encompassing spectrum utilization and channel modeling is the first step to exploit potential applications and future prospects of using this FR in the 6G ecosystem. In this article, we provide FR3 deployment insights into emerging technologies including non-terrestrial network (NTN), massive multi-input multi-output (mMIMO), reconfigurable intelligent surface (RIS), and joint communications and sensing (JCAS). Furthermore, leveraging ray-tracing simulations, our investigation unveils the channel characteristics in FR3 are close to those in the sub-6 GHz band. The analysis of RIS-aided communication shows a higher spectral efficiency achieved in FR3 compared to other FRs when using the same RIS size. Finally, challenges and promising directions are discussed for FR3-based communication systems.Comment: 7 pages, 5 figures, 1 tabl

Sub-quadratic scaling real-space random-phase approximation correlation energy calculations for periodic systems with numerical atomic orbitals

The random phase approximation (RPA) as formulated as an orbital-dependent, fifth-rung functional within the density functional theory (DFT) framework offers a promising approach for calculating the ground-state energies and the derived properties of real materials. Its widespread use to large-size, complex materials is however impeded by the significantly increased computational cost, compared to lower-rung functionals. The standard implementation exhibits an $\mathcal{O}(N^4)$-scaling behavior with respect to system size $N$. In this work, we develop a low-scaling RPA algorithm for periodic systems, based on the numerical atomic orbital (NAO) basis-set framework and a localized variant of the resolution of identity (RI) approximation. The rate-determining step for RPA calculations -- the evaluation of non-interacting response function matrix, is reduced from $\mathcal{O}(N^4)$ to $\mathcal{O}(N^2)$ by just exploiting the sparsity of the RI expansion coefficients, resultant from localized RI (LRI) scheme and the strict locality of NAOs. The computational cost of this step can be further reduced to linear scaling if the decay behavior of the Green's function in real space can be further taken into account. Benchmark calculations against existing $\textbf k$-space based implementation confirms the validity and high numerical precision of the present algorithm and implementation. The new RPA algorithm allows us to readily handle three-dimensional, closely-packed solid state materials with over 1000 atoms. The algorithm and numerical techniques developed in this work also have implications for developing low-scaling algorithms for other correlated methods to be applicable to large-scale extended materials

GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest

Instruction tuning large language model (LLM) on image-text pairs has achieved unprecedented vision-language multimodal abilities. However, their vision-language alignments are only built on image-level, the lack of region-level alignment limits their advancements to fine-grained multimodal understanding. In this paper, we propose instruction tuning on region-of-interest. The key design is to reformulate the bounding box as the format of spatial instruction. The interleaved sequences of visual features extracted by the spatial instruction and the language embedding are input to LLM, and trained on the transformed region-text data in instruction tuning format. Our region-level vision-language model, termed as GPT4RoI, brings brand new conversational and interactive experience beyond image-level understanding. (1) Controllability: Users can interact with our model by both language and spatial instructions to flexibly adjust the detail level of the question. (2) Capacities: Our model supports not only single-region spatial instruction but also multi-region. This unlocks more region-level multimodal capacities such as detailed region caption and complex region reasoning. (3) Composition: Any off-the-shelf object detector can be a spatial instruction provider so as to mine informative object attributes from our model, like color, shape, material, action, relation to other objects, etc. The code, data, and demo can be found at https://github.com/jshilong/GPT4RoI.Comment: Code has been released at https://github.com/jshilong/GPT4Ro

Out-of-Band Information Aided mmWave/THz Beam Search: A Spatial Channel Similarity Perspective

The transition to higher frequency bands, e.g., millimeter-wave (mmWave) and terahertz (THz), will be capitalized on the long term for future wireless communications. One of challenges relates to rapid establishment of a mmWave/THz link with low beam training overhead due to highly directional transmission. A promising solution is to take advantage of the coexistence of sub-6 GHz, mmWave, and THz networks and to use out-of-band spatial information for enabling fast beam search. The success depends on the spatial similarity of radio channels across different frequencies. In this article we promote a feasibility study of low-frequency spatial information assisted high-frequency beam search from a radio channel point of view. We develop multi-band channel similarity measure of desired beam directions extracted from radio channels, which are obtained by filtering propagation paths by different beampatterns at different frequencies. Measurement- and ray-tracing-based evaluations across multiple frequencies and environments are performed, which prove the usability of out-of-band information aided beam search strategy in line-of-sight (LOS) dominated scenario and even in non-LOS scenario. Finally, we discuss the challenges associated with exploiting spatial channel similarity

H2-Mapping: Real-time Dense Mapping Using Hierarchical Hybrid Representation

Constructing a high-quality dense map in real-time is essential for robotics, AR/VR, and digital twins applications. As Neural Radiance Field (NeRF) greatly improves the mapping performance, in this paper, we propose a NeRF-based mapping method that enables higher-quality reconstruction and real-time capability even on edge computers. Specifically, we propose a novel hierarchical hybrid representation that leverages implicit multiresolution hash encoding aided by explicit octree SDF priors, describing the scene at different levels of detail. This representation allows for fast scene geometry initialization and makes scene geometry easier to learn. Besides, we present a coverage-maximizing keyframe selection strategy to address the forgetting issue and enhance mapping quality, particularly in marginal areas. To the best of our knowledge, our method is the first to achieve high-quality NeRF-based mapping on edge computers of handheld devices and quadrotors in real-time. Experiments demonstrate that our method outperforms existing NeRF-based mapping methods in geometry accuracy, texture realism, and time consumption. The code will be released at: https://github.com/SYSU-STAR/H2-MappingComment: Accepted by IEEE Robotics and Automation Letter

ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box

Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects across video frames. Detection boxes serve as the basis of both 2D and 3D MOT. The inevitable changing of detection scores leads to object missing after tracking. We propose a hierarchical data association strategy to mine the true objects in low-score detection boxes, which alleviates the problems of object missing and fragmented trajectories. The simple and generic data association strategy shows effectiveness under both 2D and 3D settings. In 3D scenarios, it is much easier for the tracker to predict object velocities in the world coordinate. We propose a complementary motion prediction strategy that incorporates the detected velocities with a Kalman filter to address the problem of abrupt motion and short-term disappearing. ByteTrackV2 leads the nuScenes 3D MOT leaderboard in both camera (56.4% AMOTA) and LiDAR (70.1% AMOTA) modalities. Furthermore, it is nonparametric and can be integrated with various detectors, making it appealing in real applications. The source code is released at https://github.com/ifzhang/ByteTrack-V2.Comment: Code is available at https://github.com/ifzhang/ByteTrack-V2. arXiv admin note: text overlap with arXiv:2110.06864; substantial text overlap with arXiv:2203.06424 by other author

Alternate furrow irrigation for maize production in an arid area

Abstract A new irrigation method for maize production was designed and tested for yield and water use ef®ciency (WUE). A ®eld experiment was conducted in an arid area, with seasonal rainfall of 80 mm, over 2 years (1997 and 1998). Irrigation was applied through furrows in three ways: alternate furrow irrigation (AFI), ®xed furrow irrigation (FFI), and conventional furrow irrigation (CFI). AFI means that one of the two neighboring furrows was alternately irrigated during consecutive watering. FFI means that irrigation was ®xed to one of the two neighboring furrows. CFI was the conventional way where every furrow was irrigated during each watering. Each irrigation method was further divided into three sub-treatments with different irrigation amounts: 45, 30 and 22.5 mm water at each application. Results showed that root development was signi®cantly enhanced by AFI treatment. Primary root numbers, total root dry weight, and root density were all higher in AFI than in FFI and CFI treatments. Less irrigation signi®cantly reduced the total root dry weight and plant height in both FFI and CFI treatments but not as substantially with AFI treatments. The most surprising result was that AFI maintained high grain yield with up to 50% reduction in irrigation amount, while FFI and CFI all showed a substantial decrease in yield with reduced irrigation. As a result, WUE for irrigated water was substantially increased. We conclude that AFI is a way to save water in arid areas where maize production relies heavily on repeated irrigation.

The association between type 2 diabetes and pulmonary cavitation revealed among IGRA-positive tuberculosis patients

The co-occurrence of tuberculosis (TB) and diabetes mellitus (DM) presents a significant obstacle to TB eradication. Pulmonary cavitation can occur in severe cases of TB, particularly in patients with DM. From 1 May 2014 through 30 June 2019, we conducted a cross-sectional study of 1,658 smear- or culture-confirmed pulmonary TB (PTB) patients at the Second Department of Pulmonary Medicine and Tuberculosis, Shenzhen, China. A total of 861 participants who satisfied the criteria (chest CT scan for cavitation, interferon-gamma release assay (IGRA), diagnosis of diabetes mellitus), with the median age of 36.7 years, 63.6% of male, 79.7% IGRA positive, 13.8% with diabetes, and 40.8% with pulmonary cavitation, were included in the study. The association between diabetes and pulmonary cavitation was confirmed in these TB patients (adjusted OR, 2.54; 95% CI, 1.66–3.94; p < 0.001). No associations were observed between diabetes and IGRA, as well as between lung cavitary and IGRA. Based on the criteria of IGRA+/–, pulmonary cavitation+/–, and DM+/–, the further analysis with univariate and multivariate logistic regression were conducted in six subgroups. The significant association between diabetes and pulmonary cavitation was further confirmed in the IGRA+ subgroup (adjusted OR, 3.07; 95% CI, 1.86–5.16; p < 0.001) but not observed in IGRA- individuals. This observation suggests that different immunological mechanisms of pulmonary cavitary/DM may be employed in IGRA+ TB patients from IGRA- TB patients