80 research outputs found
Wirelessly Powered Backscatter Communication Networks: Modeling, Coverage and Capacity
Future Internet-of-Things (IoT) will connect billions of small computing
devices embedded in the environment and support their device-to-device (D2D)
communication. Powering this massive number of embedded devices is a key
challenge of designing IoT since batteries increase the devices' form factors
and battery recharging/replacement is difficult. To tackle this challenge, we
propose a novel network architecture that enables D2D communication between
passive nodes by integrating wireless power transfer and backscatter
communication, which is called a wirelessly powered backscatter communication
(WP-BackCom) network. In the network, standalone power beacons (PBs) are
deployed for wirelessly powering nodes by beaming unmodulated carrier signals
to targeted nodes. Provisioned with a backscatter antenna, a node transmits
data to an intended receiver by modulating and reflecting a fraction of a
carrier signal. Such transmission by backscatter consumes orders-of-magnitude
less power than a traditional radio. Thereby, the dense deployment of
low-complexity PBs with high transmission power can power a large-scale IoT. In
this paper, a WP-BackCom network is modeled as a random Poisson cluster process
in the horizontal plane where PBs are Poisson distributed and active ad-hoc
pairs of backscatter communication nodes with fixed separation distances form
random clusters centered at PBs. The backscatter nodes can harvest energy from
and backscatter carrier signals transmitted by PBs. Furthermore, the
transmission power of each node depends on the distance from the associated PB.
Applying stochastic geometry, the network coverage probability and transmission
capacity are derived and optimized as functions of backscatter parameters,
including backscatter duty cycle and reflection coefficient, as well as the PB
density. The effects of the parameters on network performance are
characterized.Comment: 28 pages, 11 figures, has been submitted to IEEE Trans. on Wireless
Communicatio
Coexistence Designs of Radar and Communication Systems in a Multi-path Scenario
The focus of this study is on the spectrum sharing between multiple-input
multiple-output (MIMO) communications and co-located MIMO radar systems in
multi-path environments. The major challenge is to suppress the mutual
interference between the two systems while combining the useful multi-path
components received at each system. We tackle this challenge by jointly
designing the communication precoder, radar transmit waveform and receive
filter. Specifically, the signal-to-interference-plus-noise ratio (SINR) at the
radar receiver is maximized subject to constraints on the radar waveform,
communication rate and transmit power. The multi-path propagation complicates
the expressions of the radar SINR and communication rate, leading to a
non-convex problem. To solve it, a sub-optimal algorithm based on the
alternating maximization is used to optimize the precoder, radar transmit
waveform and receive filter iteratively. Simulation results are provided to
demonstrate the effectiveness of the proposed design
Fast Neighbor Discovery for Wireless Ad Hoc Network with Successive Interference Cancellation
Neighbor discovery (ND) is a key step in wireless ad hoc network, which
directly affects the efficiency of wireless networking. Improving the speed of
ND has always been the goal of ND algorithms. The classical ND algorithms lose
packets due to the collision of multiple packets, which greatly affects the
speed of the ND algorithms. Traditional methods detect packet collision and
implement retransmission when encountering packet loss. However, they does not
solve the packet collision problem and the performance improvement of ND
algorithms is limited. In this paper, the successive interference cancellation
(SIC) technology is introduced into the ND algorithms to unpack multiple
collision packets by distinguishing multiple packets in the power domain.
Besides, the multi-packet reception (MPR) is further applied to reduce the
probability of packet collision by distinguishing multiple received packets,
thus further improving the speed of ND algorithms. Six ND algorithms, namely
completely random algorithm (CRA), CRA based on SIC (CRA-SIC), CRA based on SIC
and MPR (CRA-SIC-MPR), scan-based algorithm (SBA), SBA based on SIC (SBA-SIC),
and SBA based on SIC and MPR (SBA-SIC-MPR), are theoretically analyzed and
verified by simulation. The simulation results show that SIC and MPR reduce the
ND time of SBA by 69.02% and CRA by 66.03% averagely.Comment: 16 pages, 16 figure
Iterative Signal Processing for Integrated Sensing and Communication Systems
Integrated sensing and communication (ISAC), with sensing and communication
sharing the same wireless resources and hardware, has the advantages of high
spectrum efficiency and low hardware cost, which is regarded as one of the key
technologies of the fifth generation advanced (5G-A) and sixth generation (6G)
mobile communication systems. ISAC has the potential to be applied in the
intelligent applications requiring both communication and high accurate sensing
capabilities. The fundamental challenges of ISAC system are the ISAC signal
design and ISAC signal processing. However, the existing ISAC signal has low
anti-noise capability. And the existing ISAC signal processing algorithms have
the disadvantages of quantization errors and high complexity, resulting in
large energy consumption. In this paper, phase coding is applied in ISAC signal
design to improve the anti-noise performance of ISAC signal. Then, the effect
of phase coding method on improving the sensing accuracy is analyzed. In order
to improve the sensing accuracy with low-complexity algorithm, the iterative
ISAC signal processing methods are proposed. The proposed methods improve the
sensing accuracy with low computational complexity, realizing energy efficient
ISAC signal processing. Taking the scenarios of short distance and long
distance sensing into account, the iterative two-dimensional (2D) fast Fourier
transform (FFT) and iterative cyclic cross-correlation (CC) methods are
proposed, respectively, realizing high sensing accuracy and low computational
complexity. Finally, the feasibility of the proposed ISAC signal processing
methods are verified by simulation results
Sensing as a Service in 6G Perceptive Networks: A Unified Framework for ISAC Resource Allocation
In the upcoming next-generation (5G-Advanced and 6G) wireless networks,
sensing as a service will play a more important role than ever before.
Recently, the concept of perceptive network is proposed as a paradigm shift
that provides sensing and communication (S&C) services simultaneously. This
type of technology is typically referred to as Integrated Sensing and
Communications (ISAC). In this paper, we propose the concept of sensing quality
of service (QoS) in terms of diverse applications. Specifically, the
probability of detection, the Cramer-Rao bound (CRB) for parameter estimation
and the posterior CRB for moving target indication are employed to measure the
sensing QoS for detection, localization, and tracking, respectively. Then, we
establish a unified framework for ISAC resource allocation, where the fairness
and the comprehensiveness optimization criteria are considered for the
aforementioned sensing services. The proposed schemes can flexibly allocate the
limited power and bandwidth resources according to both S&C QoSs. Finally, we
study the performance trade-off between S&C services in different resource
allocation schemes by numerical simulations
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