36 research outputs found
Performance Analysis of Discrete-Phase-Shifter IRS-aided Amplify-and-Forward Relay Network
As a new technology to reconfigure wireless communication environment by
signal reflection controlled by software, intelligent reflecting surface (IRS)
has attracted lots of attention in recent years. Compared with conventional
relay system, the relay system aided by IRS can effectively reduce the cost and
energy consumption, and significantly enhance the system performance. However,
the phase quantization error generated by IRS with discrete phase shifter may
degrade the receiving performance of the receiver. To analyze the performance
loss caused by IRS phase quantization error, based on the law of large numbers
and Rayleigh distribution, the closed-form expressions for the signal-to-noise
ratio (SNR) performance loss and achievable rate of the IRS-aided
amplify-and-forward (AF) relay network, which are related to the number of
phase shifter quantization bits, are derived under the line-of-sight (LoS)
channels and Rayleigh channels, respectively. Moreover, their approximate
performance loss closed-form expressions are also derived based on the Taylor
series expansion. Simulation results show that the performance losses of SNR
and achievable rate decrease with the number of quantization bits increases
gradually. When the number of quantization bits is larger than or equal to 3,
the SNR performance loss of the system is smaller than 0.23dB, and the
achievable rate loss is less than 0.04bits/s/Hz, regardless of the LoS channels
or Rayleigh channels
Joint Beamforming and Phase Shift Design for Hybrid-IRS-aided Directional Modulation Network
To make a good balance between performance, cost, and power consumption, a
hybrid intelligent reflecting surface (IRS)-aided directional modulation (DM)
network is investigated in this paper, where the hybrid IRS consists of passive
and active reflecting elements. To maximize the achievable rate, two
optimization algorithms, called maximum signal-to-noise ratio (SNR)-fractional
programming (FP) (Max-SNR-FP) and maximum SNR-equal amplitude reflecting (EAR)
(Max-SNR-EAR), are proposed to jointly design the beamforming vector and phase
shift matrix (PSM) of hybrid IRS by alternately optimizing one and giving
another. The former employs the successive convex approximation and FP methods
to derive the beamforming vector and hybrid IRS PSM, while the latter adopts
the maximum signal-to-leakage-noise ratio method and the criteria of phase
alignment and EAR to design them. Simulation results show that the rates
harvested by the proposed two methods are slightly lower than those of active
IRS with higher power consumption, which are 35 percent higher than those of no
IRS and random phase IRS, while passive IRS achieves only about 17 percent rate
gain over the latter. Moreover, compared to Max-SNR-FP, the proposed
Max-SNR-EAR method makes an obvious complexity degradation at the price of a
slight performance loss
Joint Power Allocation and Beamforming for Active IRS-aided Directional Modulation Network
To boost the secrecy rate (SR) of the conventional directional modulation
(DM) network and overcome the double fading effect of the cascaded channels of
passive intelligent reflecting surface (IRS), a novel active IRS-assisted DM
system with a power adjusting strategy between transmitter and active IRS is
proposed in this paper. Then, a joint optimization of maximizing the SR is cast
by alternately optimizing the power allocation (PA) factors, transmit
beamforming at the BS, and reflect beamforming at the active IRS, subject to
the power constraint at IRS. To tackle the formulated non-convex optimization
problem, a high-performance scheme of maximizing SR based on fractional
programming (FP) and successive convex approximation (SCA) (Max-SR-FS) is
proposed, where the FP and SCA methods are employed to optimize the PA factor
of confidential message and the PA factor of power allocated to the BS, and the
SCA algorithm is also utilized to design the transmit beamforming and phase
shift matrix of the IRS. To reduce the high complexity, a low-complexity
scheme, named maximizing SR based on derivative operation (DO) and general
power iterative (GPI) (Max-SR-DG), is developed, where the DO and methods of
the equal amplitude reflecting (EAR) and GPI are adopted to derive the PA
factors and IRS phase shift matrix, respectively. Simulation results show that
with the same power constraint, both the proposed schemes harvest about 12
percent and 70 percent rate gains over the equal PA and passive IRS schemes,
respectively
DOA Estimation for Hybrid Massive MIMO Systems using Mixed-ADCs: Performance Loss and Energy Efficiency
Due to the power consumption and high circuit cost in antenna arrays, the
practical application of massive multipleinput multiple-output (MIMO) in the
sixth generation (6G) and future wireless networks is still challenging.
Employing lowresolution analog-to-digital converters (ADCs) and hybrid analog
and digital (HAD) structure is two low-cost choice with acceptable performance
loss. In this paper, the combination of the mixedADC architecture and HAD
structure employed at receiver is proposed for direction of arrival (DOA)
estimation, which will be applied to the beamforming tracking and alignment in
6G. By adopting the additive quantization noise model, the exact closedform
expression of the Cramer-Rao lower bound (CRLB) for the HAD architecture with
mixed-ADCs is derived. Moreover, the closed-form expression of the performance
loss factor is derived as a benchmark. In addition, to take power consumption
into account, energy efficiency is also investigated in our paper. The
numerical results reveal that the HAD structure with mixedADCs can
significantly reduce the power consumption and hardware cost. Furthermore, that
architecture is able to achieve a better trade-off between the performance loss
and the power consumption. Finally, adopting 2-4 bits of resolution may be a
good choice in practical massive MIMO systems.Comment: 11 pages, 7 figure
Joint Beamforming and Phase Shift Design for Hybrid IRS and UAV-Aided Directional Modulation Networks
Recently, intelligent reflecting surfaces (IRSs) and unmanned aerial vehicles (UAVs) have been integrated into wireless communication systems to enhance the performance of airâground transmission. To balance performance, cost, and power consumption well, a hybrid IRS and UAV-assisted directional modulation (DM) network is investigated in this paper in which the hybrid IRS consisted of passive and active reflecting elements. We aimed to maximize the achievable rate by jointly designing the beamforming and phase shift matrix (PSM) of the hybrid IRS subject to the power and unit-modulus constraints of passive IRS phase shifts. To solve the non-convex optimization problem, a high-performance scheme based on successive convex approximation and fractional programming (FP) called the maximal signal-to-noise ratio (SNR)-FP (Max-SNR-FP) is proposed. Given its high complexity, we propose a low-complexity maximal SNR-equal amplitude reflecting (EAR) (Max-SNR-EAR) scheme based on the maximal signal-to-leakage-noise ratio method, and the criteria of phase alignment and EAR. Given that the active and passive IRS phase shift matrices of both schemes are optimized separately, to investigate the effect of jointly optimizing them to improve the achievable rate, a maximal SNR majorization-minimization (MM) (Max-SNR-MM) scheme using the MM criterion to design the IRS PSM is proposed. Simulation results show that the rates harvested by the three proposed methods were slightly lower than those of the active IRS with higher power consumption, which were 35% higher than those of no IRS and random phase IRS, while passive IRS achieved only about a 17% rate gain over the latter. Moreover, compared with the Max-SNR-FP, the proposed Max-SNR-EAR and Max-SNR-MM methods caused obvious complexity degradation at the price of slight performance loss
Two Low-Complexity Efficient Beamformers for an IRS- and UAV-Aided Directional Modulation Network
As excellent tools for aiding communication, an intelligent reflecting surface (IRS) and an unmanned aerial vehicle (UAV) can extend the coverage area, remove the blind area, and achieve a dramatic rate improvement. In this paper, we improve the secrecy rate (SR) performance of directional modulation (DM) networks using an IRS and UAV in combination. To fully explore the benefits of the IRS and UAV, two efficient methods are proposed to enhance the SR performance. The first approach computes the confidential message (CM) beamforming vector by maximizing the SR, and the signal-to-leakage-noise ratio (SLNR) method is used to optimize the IRS phase shift matrix (PSM), which is called Max-SR-SLNR. To reduce the computational complexity, the CM, artificial noise (AN) beamforming, and IRS phase shift design are independently designed in the following method. The CM beamforming vector is constructed based on the maximum ratio transmission (MRT) criteria along the channel from Alice-to-IRS, the AN beamforming vector is designed by null-space projection (NSP) on the remaining two channels, and the PSM of the IRS is directly given by the phase alignment (PA) method. This method is called the MRT-NSP-PA. The simulation results show that the SR performance of the Max-SR-SLNR method outperforms the MRT-NSP-PA method in the cases of small-scale and medium-scale IRSs, and the latter approaches the former in performance as the IRS tends to a larger scale
Investigation of two Fermi-LAT gamma-ray blazars coincident with high-energy neutrinos detected by IceCube
After the identification of the gamma-ray blazar TXS 0506+056 as the first
compelling IceCube neutrino source candidate, we perform a systematic analysis
of all high-energy neutrino events satisfying the IceCube realtime trigger
criteria. We find one additional known gamma-ray source, the blazar GB6
J1040+0617, in spatial coincidence with a neutrino in this sample. The chance
probability of this coincidence is 30% after trial correction. For the first
time, we present a systematic study of the gamma-ray flux, spectral and optical
variability, and multi-wavelength behavior of GB6 J1040+0617 and compare it to
TXS 0506+056. We find that TXS 0506+056 shows strong flux variability in the
Fermi-LAT gamma-ray band, being in an active state around the arrival of
IceCube-170922A, but in a low state during the archival IceCube neutrino flare
in 2014/15. In both cases the spectral shape is statistically compatible () with the average spectrum showing no indication of a significant
relative increase of a high-energy component. While the association of GB6
J1040+0617 with the neutrino is consistent with background expectations, the
source appears to be a plausible neutrino source candidate based on its
energetics and multi-wavelength features, namely a bright optical flare and
modestly increased gamma-ray activity. Finding one or two neutrinos originating
from gamma-ray blazars in the given sample of high-energy neutrinos is
consistent with previously derived limits of neutrino emission from gamma-ray
blazars, indicating the sources of the majority of cosmic high-energy neutrinos
remain unknown.Comment: 22 pages, 11 figures, 2 Table
Multi-messenger observations of a binary neutron star merger
On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transientâs position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta
Multi-messenger Observations of a Binary Neutron Star Merger
On 2017 August 17 a binary neutron star coalescence candidate (later
designated GW170817) with merger time 12:41:04 UTC was observed through
gravitational waves by the Advanced LIGO and Advanced Virgo detectors.
The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray
burst (GRB 170817A) with a time delay of ⌠1.7 {{s}} with respect to
the merger time. From the gravitational-wave signal, the source was
initially localized to a sky region of 31 deg2 at a
luminosity distance of {40}-8+8 Mpc and with
component masses consistent with neutron stars. The component masses
were later measured to be in the range 0.86 to 2.26 {M}ÈŻ
. An extensive observing campaign was launched across the
electromagnetic spectrum leading to the discovery of a bright optical
transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC
4993 (at ⌠40 {{Mpc}}) less than 11 hours after the merger by the
One-Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The
optical transient was independently detected by multiple teams within an
hour. Subsequent observations targeted the object and its environment.
Early ultraviolet observations revealed a blue transient that faded
within 48 hours. Optical and infrared observations showed a redward
evolution over âŒ10 days. Following early non-detections, X-ray and
radio emission were discovered at the transientâs position ⌠9
and ⌠16 days, respectively, after the merger. Both the X-ray and
radio emission likely arise from a physical process that is distinct
from the one that generates the UV/optical/near-infrared emission. No
ultra-high-energy gamma-rays and no neutrino candidates consistent with
the source were found in follow-up searches. These observations support
the hypothesis that GW170817 was produced by the merger of two neutron
stars in NGC 4993 followed by a short gamma-ray burst (GRB 170817A) and
a kilonova/macronova powered by the radioactive decay of r-process
nuclei synthesized in the ejecta.</p
Enhanced-rate Iterative Beamformers for Active IRS-assisted Wireless Communications
Compared to passive intelligent reflecting surface (IRS), active IRS is
viewed as a more efficient promising technique to combat the double-fading
impact in IRS-aided wireless network. In this paper, in order to boost the
achievable rate of user in such a wireless network, three enhanced-rate
iterative beamforming methods are proposed by designing the amplifying factors
and the corresponding phases at active IRS. The first method, maximizing the
simplified signal-to-noise ratio (Max-SSNR) is designed by omitting the
cross-term in the definition of rate. Using the Rayleigh-Ritz (RR) theorem,
Max-SSNR-RR is proposed to iteratively optimize the norm of beamforming vector
and its associated normalized vector. In addition, generalized maximum ratio
reflection (GMRR) is presented with a closed-form expression, which is
motivated by the maximum ratio combining. To further improve rate, maximizing
SNR (Max-SNR) is designed by fractional programming (FP), which is called
Max-SNR-FP. Simulation results show that the proposed three methods make an
obvious rate enhancement over Max-reflecting signal-to-noise ratio (Max-RSNR),
maximum ratio reflection (MRR), selective ratio reflecting (SRR), equal gain
reflection (EGR) and passive IRS, and are in increasing order of rate
performance as follows: Max-SSNR-RR, GMRR, and Max-SNR-FP