6 research outputs found
Enhancing Spectrum Sensing via Reconfigurable Intelligent Surfaces: Passive or Active Sensing and How Many Reflecting Elements are Needed?
Cognitive radio has been proposed to alleviate the scarcity of available
spectrum caused by the significant demand for wideband services and the
fragmentation of spectrum resources. However, sensing performance is quite poor
due to the low sensing signal-to-noise ratio, especially in complex
environments with severe channel fading. Fortunately, reconfigurable
intelligent surface (RIS)-aided spectrum sensing can effectively tackle the
above challenge due to its high array gain. Nevertheless, the traditional
passive RIS may suffer from the ``double fading'' effect, which severely limits
the performance of passive RIS-aided spectrum sensing. Thus, a crucial
challenge is how to fully exploit the potential advantages of the RIS and
further improve the sensing performance. To this end, we introduce the active
RIS into spectrum sensing and respectively formulate two optimization problems
for the passive RIS and the active RIS to maximize the detection probability.
In light of the intractability of the formulated problems, we develop a
one-stage optimization algorithm with inner approximation and a two-stage
optimization algorithm with a bisection method to obtain sub-optimal solutions,
and apply the Rayleigh quotient to obtain the upper and lower bounds of the
detection probability. Furthermore, in order to gain more insight into the
impact of the RIS on spectrum sensing, we respectively investigate the number
configuration for passive RIS and active RIS and analyze how many reflecting
elements are needed to achieve the detection probability close to 1. Simulation
results verify that the proposed algorithms outperform existing algorithms
under the same parameter configuration, and achieve a detection probability
close to 1 with even fewer reflecting elements or antennas than existing
schemes
Underlay Drone Cell for Temporary Events: Impact of Drone Height and Aerial Channel Environments
Providing seamless connection to a large number of devices is one of the
biggest challenges for the Internet of Things (IoT) networks. Using a drone as
an aerial base station (ABS) to provide coverage to devices or users on ground
is envisaged as a promising solution for IoT networks. In this paper, we
consider a communication network with an underlay ABS to provide coverage for a
temporary event, such as a sporting event or a concert in a stadium. Using
stochastic geometry, we propose a general analytical framework to compute the
uplink and downlink coverage probabilities for both the aerial and the
terrestrial cellular system. Our framework is valid for any aerial channel
model for which the probabilistic functions of line-of-sight (LOS) and
non-line-of-sight (NLOS) links are specified. The accuracy of the analytical
results is verified by Monte Carlo simulations considering two commonly adopted
aerial channel models. Our results show the non-trivial impact of the different
aerial channel environments (i.e., suburban, urban, dense urban and high-rise
urban) on the uplink and downlink coverage probabilities and provide design
guidelines for best ABS deployment height.Comment: This work is accepted to appear in IEEE Internet of Things Journal
Special Issue on UAV over IoT. Copyright may be transferred without notice,
after which this version may no longer be accessible. arXiv admin note: text
overlap with arXiv:1801.0594
A DRL Approach for RIS-Assisted Full-Duplex UL and DL Transmission: Beamforming, Phase Shift and Power Optimization
In this work, a two-stage deep reinforcement learning (DRL) approach is
presented for a full-duplex (FD) transmission scenario that does not depend on
the channel state information (CSI) knowledge to predict the phase-shifts of
reconfigurable intelligent surface (RIS), beamformers at the base station (BS),
and the transmit powers of BS and uplink users in order to maximize the
weighted sum rate of uplink and downlink users. As the self-interference (SI)
cancellation and beamformer design are coupled problems, the first stage uses a
least squares method to partially cancel self-interference (SI) and initiate
learning, while the second stage uses DRL to make predictions and achieve
performance close to methods with perfect CSI knowledge. Further, to reduce the
signaling from BS to the RISs, a DRL framework is proposed that predicts
quantized RIS phase-shifts and beamformers using times fewer bits than the
continuous version. The quantized methods have reduced action space and
therefore faster convergence; with sufficient training, the UL and DL rates for
the quantized phase method are and better than the continuous
phase method respectively. The RIS elements can be grouped to have similar
phase-shifts to further reduce signaling, at the cost of reduced performance
Advanced channel coding techniques using bit-level soft information
In this dissertation, advanced channel decoding techniques based on bit-level soft information are studied. Two main approaches are proposed: bit-level probabilistic iterative decoding and bit-level algebraic soft-decision (list) decoding (ASD).
In the first part of the dissertation, we first study iterative decoding for high density parity check (HDPC) codes. An iterative decoding algorithm, which uses the sum product algorithm (SPA) in conjunction with a binary parity check matrix adapted in each decoding iteration according to the bit-level reliabilities is proposed. In contrast to the common belief that iterative decoding is not suitable for HDPC codes, this bit-level reliability based adaptation procedure is critical to the conver-gence behavior of iterative decoding for HDPC codes and it significantly improves the iterative decoding performance of Reed-Solomon (RS) codes, whose parity check matrices are in general not sparse. We also present another iterative decoding scheme for cyclic codes by randomly shifting the bit-level reliability values in each iteration. The random shift based adaptation can also prevent iterative decoding from getting stuck with a significant complexity reduction compared with the reliability based parity check matrix adaptation and still provides reasonable good performance for short-length cyclic codes.
In the second part of the dissertation, we investigate ASD for RS codes using bit-level soft information. In particular, we show that by carefully incorporating bit¬level soft information in the multiplicity assignment and the interpolation step, ASD can significantly outperform conventional hard decision decoding (HDD) for RS codes with a very small amount of complexity, even though the kernel of ASD is operating at the symbol-level. More importantly, the performance of the proposed bit-level ASD can be tightly upper bounded for practical high rate RS codes, which is in general not possible for other popular ASD schemes.
Bit-level soft-decision decoding (SDD) serves as an efficient way to exploit the potential gain of many classical codes, and also facilitates the corresponding per-formance analysis. The proposed bit-level SDD schemes are potential and feasible alternatives to conventional symbol-level HDD schemes in many communication sys-tems
Aeronautical engineering: A cumulative index to a continuing bibliography
This bibliography is a cumulative index to the abstracts contained in NASA SP-7037 (197) through NASA SP-7037 (208) of Aeronautical Engineering: A Continuing Bibliography. NASA SP-7037 and its supplements have been compiled through the cooperative efforts of the American Institute of Aeronautics and Astronautics (AIAA) and the National Aeronautics and Space Administration (NASA). This cumulative index includes subject, personal author, corporate source, foreign technology, contract, report number, and accession number indexes