2,080 research outputs found
Development of Sustainable High-Strength Self-Consolidating Concrete Utilising Fly Ash, Shale Ash and Microsilica
With high flowability and passing ability, self-consolidating concrete (SCC) does not require compaction during casting and can improve constructability. The favourable properties of SCC have enabled its widespread adoption in many parts of the world. However, there are two major issues associated with the SCC mixes commonly used in practice. First, the cement content is usually at the high side. Since the production of cement involves calcination at high temperature and is an energy-intensive process, the high cement content imparts high embodied energy and carbon footprint to the SCC mixes. Besides, the exothermic reaction of cement hydration would cause high heat generation and early thermal cracking problem that would impair structural integrity and necessitate repair. Second, the strength is usually limited to around grade 60, which is considered as medium strength in nowadays achievable norm. With a view to develop sustainable high-strength self-consolidating concrete (HS-SCC), experimental research utilising fly ash (FA), shale ash (SA), and microsilica (MS) in the production of SCC has been conducted, as reported herein
DPSS-based Codebook Design for Near-Field XL-MIMO Channel Estimation
Future sixth-generation (6G) systems are expected to leverage extremely
large-scale multiple-input multiple-output (XL-MIMO) technology, which
significantly expands the range of the near-field region. While accurate
channel estimation is essential for beamforming and data detection, the unique
characteristics of near-field channels pose additional challenges to the
effective acquisition of channel state information. In this paper, we propose a
novel codebook design, which allows efficient near-field channel estimation
with significantly reduced codebook size. Specifically, we consider the
eigen-problem based on the near-field electromagnetic wave transmission model.
Moreover, we derive the general form of the eigenvectors associated with the
near-field channel matrix, revealing their noteworthy connection to the
discrete prolate spheroidal sequence (DPSS). Based on the proposed near-field
codebook design, we further introduce a two-step channel estimation scheme.
Simulation results demonstrate that the proposed codebook design not only
achieves superior sparsification performance of near-field channels with a
lower leakage effect, but also significantly improves the accuracy in
compressive sensing channel estimation.Comment: 6 pages, 5 figure
Integrated Sensing and Communication in Distributed Antenna Networks
In this paper, we investigate the resource allocation design for integrated
sensing and communication (ISAC) in distributed antenna networks (DANs). In
particular, coordinated by a central processor (CP), a set of remote radio
heads (RRHs) provide communication services to multiple users and sense several
target locations within an ISAC frame. To avoid the severe interference between
the information transmission and the radar echo, we propose to divide the ISAC
frame into a communication phase and a sensing phase. During the communication
phase, the data signal is generated at the CP and then conveyed to the RRHs via
fronthaul links. As for the sensing phase, based on pre-determined RRH-target
pairings, each RRH senses a dedicated target location with a synthesized
highly-directional beam and then transfers the samples of the received echo to
the CP via its fronthaul link for further processing of the sensing
information. Taking into account the limited fronthaul capacity and the
quality-of-service requirements of both communication and sensing, we jointly
optimize the durations of the two phases, the information beamforming, and the
covariance matrix of the sensing signal for minimization of the total energy
consumption over a given finite time horizon. To solve the formulated
non-convex design problem, we develop a low-complexity alternating optimization
algorithm which converges to a suboptimal solution. Simulation results show
that the proposed scheme achieves significant energy savings compared to two
baseline schemes. Moreover, our results reveal that for efficient ISAC in
wireless networks, energy-focused short-duration pulses are favorable for
sensing while low-power long-duration signals are preferable for communication.Comment: 8 pages, 5 figure
A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading
Computation offloading has become a popular solution to support
computationally intensive and latency-sensitive applications by transferring
computing tasks to mobile edge servers (MESs) for execution, which is known as
mobile/multi-access edge computing (MEC). To improve the MEC performance, it is
required to design an optimal offloading strategy that includes offloading
decision (i.e., whether offloading or not) and computational resource
allocation of MEC. The design can be formulated as a mixed-integer nonlinear
programming (MINLP) problem, which is generally NP-hard and its effective
solution can be obtained by performing online inference through a well-trained
deep neural network (DNN) model. However, when the system environments change
dynamically, the DNN model may lose efficacy due to the drift of input
parameters, thereby decreasing the generalization ability of the DNN model. To
address this unique challenge, in this paper, we propose a multi-head ensemble
multi-task learning (MEMTL) approach with a shared backbone and multiple
prediction heads (PHs). Specifically, the shared backbone will be invariant
during the PHs training and the inferred results will be ensembled, thereby
significantly reducing the required training overhead and improving the
inference performance. As a result, the joint optimization problem for
offloading decision and resource allocation can be efficiently solved even in a
time-varying wireless environment. Experimental results show that the proposed
MEMTL outperforms benchmark methods in both the inference accuracy and mean
square error without requiring additional training data
Sensing User's Activity, Channel, and Location with Near-Field Extra-Large-Scale MIMO
This paper proposes a grant-free massive access scheme based on the
millimeter wave (mmWave) extra-large-scale multiple-input multiple-output
(XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency,
high data rate, and high localization accuracy in the upcoming sixth-generation
(6G) networks. The XL-MIMO consists of multiple antenna subarrays that are
widely spaced over the service area to ensure line-of-sight (LoS)
transmissions. First, we establish the XL-MIMO-based massive access model
considering the near-field spatial non-stationary (SNS) property. Then, by
exploiting the block sparsity of subarrays and the SNS property, we propose a
structured block orthogonal matching pursuit algorithm for efficient active
user detection (AUD) and channel estimation (CE). Furthermore, different
sensing matrices are applied in different pilot subcarriers for exploiting the
diversity gains. Additionally, a multi-subarray collaborative localization
algorithm is designed for localization. In particular, the angle of arrival
(AoA) and time difference of arrival (TDoA) of the LoS links between active
users and related subarrays are extracted from the estimated XL-MIMO channels,
and then the coordinates of active users are acquired by jointly utilizing the
AoAs and TDoAs. Simulation results show that the proposed algorithms outperform
existing algorithms in terms of AUD and CE performance and can achieve
centimeter-level localization accuracy.Comment: Submitted to IEEE Transactions on Communications, Major revision.
Codes will be open to all on https://gaozhen16.github.io/ soo
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