Showing papers on "Interference (wave propagation) published in 2020"

Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems

Abstract: Intelligent reflecting surface (IRS) has recently been envisioned to offer unprecedented massive multiple-input multiple-output (MIMO)-like gains by deploying large-scale and low-cost passive reflection elements. By adjusting the reflection coefficients, the IRS can change the phase shifts on the impinging electromagnetic waves so that it can smartly reconfigure the signal propagation environment and enhance the power of the desired received signal or suppress the interference signal. In this paper, we consider downlink multigroup multicast communication systems assisted by an IRS. We aim for maximizing the sum rate of all the multicasting groups by the joint optimization of the precoding matrix at the base station (BS) and the reflection coefficients at the IRS under both the power and unit-modulus constraint. To tackle this non-convex problem, we propose two efficient algorithms under the majorization–minimization (MM) algorithm framework. Specifically, a concave lower bound surrogate objective function of each user's rate has been derived firstly, based on which two sets of variables can be updated alternately by solving two corresponding second-order cone programming (SOCP) problems. Then, in order to reduce the computational complexity, we derive another concave lower bound function of each group's rate for each set of variables at every iteration, and obtain the closed-form solutions under these loose surrogate objective functions. Finally, the simulation results demonstrate the benefits in terms of the spectral and energy efficiency of the introduced IRS and the effectiveness in terms of the convergence and complexity of our proposed algorithms.

Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination

Abstract: The fifth generation of wireless communications (5G) promises massive increases in traffic volume and data rates, as well as improved reliability in voice calls. Jointly optimizing beamforming, power control, and interference coordination in a 5G wireless network to enhance the communication performance to end users poses a significant challenge. In this paper, we formulate the joint design of beamforming, power control, and interference coordination as a non-convex optimization problem to maximize the signal to interference plus noise ratio (SINR) and solve this problem using deep reinforcement learning. By using the greedy nature of deep Q-learning to estimate future rewards of actions and using the reported coordinates of the users served by the network, we propose an algorithm for voice bearers and data bearers in sub-6 GHz and millimeter wave (mmWave) frequency bands, respectively. The algorithm improves the performance measured by SINR and sum-rate capacity. In realistic cellular environments, the simulation results show that our algorithm outperforms the link adaptation industry standards for sub-6 GHz voice bearers. For data bearers in the mmWave frequency band, our algorithm approaches the maximum sum rate capacity, but with less than 4% of the required run time.

Long-Distance Free-Space Measurement-Device-Independent Quantum Key Distribution.

Abstract: Measurement-device-independent quantum key distribution (MDI-QKD), based on two-photon interference, is immune to all attacks against the detection system and allows a QKD network with untrusted relays. Since the MDI-QKD protocol was proposed, fiber-based implementations aimed at longer distance, higher key rates, and network verification have been rapidly developed. However, owing to the effect of atmospheric turbulence, MDI-QKD over a free-space channel remains experimentally challenging. Herein, by developing a robust adaptive optics system, high-precision time synchronization and frequency locking between independent photon sources located far apart, we realized the first free-space MDI-QKD over a 19.2-km urban atmospheric channel, which well exceeds the effective atmospheric thickness. Our experiment takes the first step toward satellite-based MDI-QKD. Moreover, the technology developed herein opens the way to quantum experiments in free space involving long-distance interference of independent single photons.

Quantum double-double-slit experiment with momentum entangled photons.

Abstract: Double-double-slit thought experiment provides profound insight on interference of quantum entangled particles. This paper presents a detailed experimental realisation of quantum double-double-slit thought experiment with momentum entangled photons and theoretical analysis of the experiment. Experiment is configured in such a way that photons are path entangled and each photon can reveal the which-slit path information of the other photon. As a consequence, single photon interference is suppressed. However, two-photon interference pattern appears if locations of detection of photons are correlated without revealing the which-slit path information. It is also shown experimentally and theoretically that two-photon quantum interference disappears when the which-slit path of a photon in the double-double-slit is detected.

Beyond 5G Wireless Localization with Reconfigurable Intelligent Surfaces

Abstract: 5G radio positioning exploits information in both angle and delay, by virtue of increased bandwidth and large antenna arrays. When large arrays are embedded in surfaces, they can passively steer electromagnetic waves in preferred directions of space. Reconfigurable intelligent surfaces (RIS), which are seen as a transformative 'beyond 5G' technology, can thus control the physical propagation environment. Whereas such RIS have been mainly intended for communication purposes so far, we herein state and analyze a RIS-aided downlink positioning problem from the Fisher Information perspective. Then, based on this analysis, we propose a two-step optimization scheme that selects the best RIS combination to be activated and controls the phases of their constituting elements so as to improve positioning performance. Preliminary simulation results show coverage and accuracy gains in comparison with natural scattering, while pointing out limitations in terms of low signal to noise ratio (SNR) and inter-path interference.

Performance of Network-Assisted Full-Duplex for Cell-Free Massive MIMO

Abstract: In this paper, the spectral efficiency of network assisted full-duplex communications (NAFD) in cell-free (CF) massive multiple-input multiple-output (MIMO) network with imperfect channel state information is investigated under spatial correlated channels. Based on large dimensional random matrix theory, the deterministic equivalents for the uplink sum-rate with minimum-mean-square-error receiver as well as the downlink sum-rate with zero-forcing and regularized zero-forcing beamforming are presented. Numerical results show that under various environmental settings, the deterministic equivalents are accurate in both a large-scale system and system with a finite number of antennas. It is also shown that with the downlink-to-uplink interference cancellation, the uplink spectral efficiency of CF massive MIMO with NAFD could be improved. The spectral efficiencies of NAFD with different duplex configurations such as in-band full-duplex, and half-duplex are compared. With the same total numbers of transmit and receive antennas, NAFD with half-duplex remote antenna units offers a higher spectral efficiency. To alleviate the uplink-to-downlink interference, a novel genetic algorithm based user scheduling strategy (GAS) is proposed. Simulation results show that the achievable downlink sum-rate by using the GAS is greatly improved compared to that by using the random user scheduling.

Broadband Acoustic Ventilation Barriers

Abstract: Conventional sound barriers impede airflow at the same time. Recent advances in acoustic metasurfaces provide a solution for air-permeable barriers utilizing the Fano-like interference. While the mechanism of Fano-like interference implies that such a realized device serves a narrow working frequency range around every destructive-interference frequency. Considering the fact that noise usually covers a wide frequency range, designing a broadband acoustic barrier is still a challenge. Here, we theoretically design a planar-profile and subwavelength-thickness (approximately $\ensuremath{\lambda}/8$) acoustic ventilation barrier prohibitive for sound in a broad range. Our design is a metasurface consisting of a central hollow orifice and two surrounding helical pathways with varying pitch. Thanks to the hornlike helical pathways, the response strength from the monopolar and dipolar modes of the system almost keeps balance in the frequency range of interest, leading to an effective blocking of more than $90\mathrm{%}$ of incident energy in the range of 900--1418 Hz. Experiments are conducted to validate the proposed design, whose results are consistent with the analytical predictions and simulations. The underlying working mechanism ensures the metasurface is capable of handling broadband sound coming from various directions. Our design has potential in air-permeable yet sound-proofing applications, such as simultaneously natural ventilation and noise reduction in green buildings.

Location Information Aided Multiple Intelligent Reflecting Surface Systems

Abstract: This paper proposes a novel location information aided multiple intelligent reflecting surface (IRS) systems. Assuming imperfect user location information, the effective angles from the IRS to the users are estimated, which is then used to design the transmit beam and IRS beam. Furthermore, closed-form expressions for the achievable rate are derived. The analytical findings indicate that the achievable rate can be improved by increasing the number of base station (BS) antennas or reflecting elements. Specifically, a power gain of order $N M^2$ is achieved, where $N$ is the antenna number and $M$ is the number of reflecting elements. Moreover, with a large number of reflecting elements, the individual signal to interference plus noise ratio (SINR) is proportional to $M$, while becomes proportional to $M^2$ as non-line-of-sight (NLOS) paths vanish. Also, it has been shown that high location uncertainty would significantly degrade the achievable rate. Besides, IRSs should be deployed at distinct directions (relative to the BS) and be far away from each other to reduce the interference from multiple IRSs. Finally, an optimal power allocation scheme has been proposed to improve the system performance.

Nanoscale neural network using non-linear spin-wave interference

Abstract: We demonstrate the design of a neural network, where all neuromorphic computing functions, including signal routing and nonlinear activation are performed by spin-wave propagation and interference. Weights and interconnections of the network are realized by a magnetic field pattern that is applied on the spin-wave propagating substrate and scatters the spin waves. The interference of the scattered waves creates a mapping between the wave sources and detectors. Training the neural network is equivalent to finding the field pattern that realizes the desired input-output mapping. A custom-built micromagnetic solver, based on the Pytorch machine learning framework, is used to inverse-design the scatterer. We show that the behavior of spin waves transitions from linear to nonlinear interference at high intensities and that its computational power greatly increases in the nonlinear regime. We envision small-scale, compact and low-power neural networks that perform their entire function in the spin-wave domain.

Reinforcement Learning-Based Downlink Interference Control for Ultra-Dense Small Cells

Abstract: The dense deployment of small cells in 5G cellular networks raises the issue of controlling downlink inter-cell interference under time-varying channel states. In this paper, we propose a reinforcement learning based power control scheme to suppress downlink inter-cell interference and save energy for ultra-dense small cells. This scheme enables base stations to schedule the downlink transmit power without knowing the interference distribution and the channel states of the neighboring small cells. A deep reinforcement learning based interference control algorithm is designed to further accelerate learning for ultra-dense small cells with a large number of active users. Analytical convergence performance bounds including throughput, energy consumption, inter-cell interference, and the utility of base stations are provided and the computational complexity of our proposed scheme is discussed. Simulation results show that this scheme optimizes the downlink interference control performance after sufficient power control instances and significantly increases the network throughput with less energy consumption compared with a benchmark scheme.

On the Error Rate of the LoRa Modulation With Interference

Abstract: LoRa is a chirp spread-spectrum modulation developed for the Internet of Things (IoT). In this work, we examine the performance of LoRa in the presence of both additive white Gaussian noise and interference from another LoRa user. To this end, we extend an existing interference model, which assumes perfect alignment of the signal of interest and the interference, to the more realistic case where the interfering user is neither chip- nor phase-aligned with the signal of interest and we derive an expression for the error rate. We show that the existing aligned interference model overestimates the effect of interference on the error rate. Moreover, we prove two symmetries in the interfering signal and we derive low-complexity approximate formulas that can significantly reduce the complexity of computing the symbol and frame error rates compared to the complete expression. Finally, we provide numerical simulations to corroborate the theoretical analysis and to verify the accuracy of our proposed approximations.

A MIMO Detector With Deep Learning in the Presence of Correlated Interference

Abstract: In this paper, we investigate the classical detection problem for vehicle networks with multiple antennas, by considering practical communication scenarios, where the interfering signals are correlated over time or frequency. In such cases, the conventional detector requires to estimate the joint distribution of the interfering signals, which imposes a huge computational complexity. To tackle this issue, we propose the joint use of a maximum likelihood detector (MLD) and a deep convolutional neural network (DCNN), where MLD is used to produce an initial detection result and DCNN improves the detection by exploiting the local correlation to suppress the interference. Furthermore, the improved DCNN is enhanced by devising the loss function through the cross-entropy of the detection, which can help to suppress the interfering signals and simultaneously force the residual interference to approach the Gaussian distribution. Simulation results are presented to verify the effectiveness of the proposed detector compared to the conventional one. The trained model and source code for this work are available at https://github.com/skypitcher/project_dcnnmld .

Two-photon interference: the Hong-Ou-Mandel effect

Abstract: Nearly 30 years ago, two-photon interference was observed, marking the beginning of a new quantum era. Indeed, two-photon interference has no classical analogue, giving it a distinct advantage for a range of applications. The peculiarities of quantum physics may now be used to our advantage to outperform classical computations, securely communicate information, simulate highly complex physical systems and increase the sensitivity of precise measurements. This separation from classical to quantum physics has motivated physicists to study two-particle interference for both fermionic and bosonic quantum objects. So far, two-particle interference has been observed with massive particles, among others, such as electrons and atoms, in addition to plasmons, demonstrating the extent of this effect to larger and more complex quantum systems. A wide array of novel applications to this quantum effect is to be expected in the future. This review will thus cover the progress and applications of two-photon (two-particle) interference over the last three decades.

Quantum Rotation Sensing with Dual Sagnac Interferometers in an Atom-Optical Waveguide.

Abstract: We describe a Sagnac interferometer suitable for rotation sensing, implemented using an atomic Bose-Einstein condensate confined in a harmonic magnetic trap. The atom wave packets are split and recombined by standing-wave Bragg lasers, and the trapping potential steers the packets along circular trajectories with a radius of 0.2 mm. Two conjugate interferometers are implemented simultaneously to provide common-mode rejection of noise and to isolate the rotation signal. With interference visibilities of about 50%, we achieve a rotation sensitivity comparable to Earth's rate in about 10 min of operation. Gyroscope operation was demonstrated by rotating the optical table on which the experiment was performed.

Determination of Electron Band Structure using Temporal Interferometry.

Abstract: We propose an all-optical method to directly reconstruct the band structure of semiconductors. Our scheme is based on the temporal Young's interferometer realized by high harmonic generation with a few-cycle laser pulse. As a time-energy domain interferometer, temporal interference encodes the band structure into the fringe in the energy domain. The relation between the band structure and the emitted harmonic frequencies is established. This enables us to retrieve the band structure from the spectrum of high harmonic generation with a single-shot measurement. Our scheme paves the way to study matters under ambient conditions and to track the ultrafast modification of band structures.

Rate-Splitting Multiple Access: A New Frontier for the PHY Layer of 6G

Abstract: In order to efficiently cope with the high throughput, reliability, heterogeneity of Quality-of-Service (QoS), and massive connectivity requirements of future 6G multi-antenna wireless networks, multiple access and multiuser communication system design need to depart from conventional interference management strategies, namely fully treat interference as noise (as commonly used in 4G/5G, MU-MIMO, CoMP, Massive MIMO, millimetre wave MIMO) and fully decode interference (as in Non-Orthogonal Multiple Access, NOMA). This paper is dedicated to the theory and applications of a more general and powerful transmission framework based on Rate-Splitting Multiple Access (RSMA) that splits messages into common and private parts and enables to partially decode interference and treat remaining part of the interference as noise. This enables RSMA to softly bridge and therefore reconcile the two extreme strategies of fully decode interference and treat interference as noise and provide room for spectral efficiency, energy efficiency and QoS enhancements, robustness to imperfect Channel State Information at the Transmitter (CSIT), and complexity reduction. This paper provides an overview of RSMA and its potential to address the requirements of 6G.

Frequency-Domain Quantum Interference with Correlated Photons from an Integrated Microresonator.

Abstract: Frequency encoding of quantum information together with fiber and integrated photonic technologies can significantly reduce the complexity and resource requirements for realizing all-photonic quantum networks. The key challenge for such frequency domain processing of single photons is to realize coherent and selective interactions between quantum optical fields of different frequencies over a range of bandwidths. Here, we report frequency-domain Hong-Ou-Mandel interference with spectrally distinct photons generated from a chip-based microresonator. We use four-wave mixing to implement an active ``frequency beam splitter'' and achieve interference visibilities of $0.95\ifmmode\pm\else\textpm\fi{}0.02$. Our work establishes four-wave mixing as a tool for selective high-fidelity two-photon operations in the frequency domain which, combined with integrated single-photon sources, provides a building block for frequency-multiplexed photonic quantum networks.

Folded-tapered multimode-no-core fiber sensor for simultaneous measurement of refractive index and temperature

Abstract: In this paper, a refractive index (RI) and temperature sensor based on a folded-tapered multimode-no-core (FTMN) fiber structure is proposed and experimentally demonstrated. The FTMN has an additional Mach-Zehnder interferometer (MZI), which is introduced in the folded-tapered multimode (FTM) fiber. And with the inherent multimode interference (MMI) and the previously mentioned MZI as foundation, a composite interference is successfully established. This synthetic composite interference greatly improves the performance of traditional optical fiber RI sensing in the low RI range. The experimental results demonstrate that a maximum sensitivity of 1191.5 nm/RIU within a linear RI ranging from 1.3405 to 1.3497 can be achieved, which is greater than the traditional modal interferometer structure. Furthermore, the temperature sensitivities at interference dips A and B are 0.0648 nm/°C and 0.0598 nm/°C, respectively. By monitoring the wavelength shifts of interference dips A and B, the sensor can simultaneously measure RI and temperature to overcome the temperature induced cross-sensitivity.

PARAFAC-Based Channel Estimation for Intelligent Reflective Surface Assisted MIMO System

Abstract: Intelligent reflective surface (IRS) is an emergent technology for future wireless communications. It consists of a large 2D array of passive scattering elements that control the electromagnetic properties of radio-frequency waves so that the reflected signals add coherently at the intended receiver or destructively to reduce co-channel interference. The promised gains of IRS-assisted communications depend on the accuracy of the channel state information. In this paper, we propose two novel channel estimation methods for an IRS-assisted multiple-input multiple-output (MIMO) communication system. Assuming a structured time-domain pattern of pilots and IRS phase shifts, we show that the received signal follows a parallel factor (PARAFAC) tensor model that can be exploited to estimate the involved communication channels in closed-form or iteratively. Numerical results corroborate the effectiveness of the proposed channel estimation methods and highlight the involved tradeoffs.

Rate-Splitting Multiple Access: A New Frontier for the PHY Layer of 6G

Abstract: In order to efficiently cope with the high throughput, reliability, heterogeneity of Quality-of-Service (QoS), and massive connectivity requirements of future 6G multi-antenna wireless networks, multiple access and multiuser communication system design need to depart from conventional interference management strategies, namely fully treat interference as noise (as commonly used in 4G/5G, MU-MIMO, CoMP, Massive MIMO, millimetre wave MIMO) and fully decode interference (as in Non-Orthogonal Multiple Access, NOMA). This paper is dedicated to the theory and applications of a more general and powerful transmission framework based on Rate-Splitting Multiple Access (RSMA) that splits messages into common and private parts and enables to partially decode interference and treat remaining part of the interference as noise. This enables RSMA to softly bridge and therefore reconcile the two extreme strategies of fully decode interference and treat interference as noise and provide room for spectral efficiency, energy efficiency and QoS enhancements, robustness to imperfect Channel State Information at the Transmitter (CSIT), and complexity reduction. We give an overview of RSMA and its potential to address the requirements of 6G. This paper provides an overview of RSMA and its potential to address the requirements of 6G.

Cellular Clustering-Based Interference-Aware Data Transmission Protocol for Underwater Acoustic Sensor Networks

Abstract: Recently, the development of three-dimensional interference-aware data transmission methods has attracted the attention of scholars due to the increased interest in exploiting and studying the underwater acoustic sensor networks (UASNs). In this paper, an interference aware data transmission protocol based on cellular clustering architecture is proposed. The protocol involves two steps. The first one is an inter-cell time division multiple access (TDMA) scheduling, which reduces acoustic interference by restricting simultaneous data transmission via adjacent routing paths; and the second one is an intra-cell hierarchical routing, which targets efficient data collection in the submarine and reliable data transmission from the seabed to the surface. Moreover, a novel Ekman spiral-based low-cost location prediction method and a void hole recovery scheme are adopted in each step to support the practicability of proposed protocol. Theoretical analysis and simulations indicate that the proposed protocol has advantages related to the quality of service (QoS) of UASNs because the signal interference is significantly mitigated.

Interference and Coverage Analysis in Coexisting RF and Dense TeraHertz Wireless Networks

Abstract: This letter develops a stochastic geometry framework to characterize the statistics of the downlink interference and coverage probability of a typical user in a coexisting terahertz (THz) and radio frequency (RF) network. We first characterize the exact Laplace Transform (LT) of the aggregate interference and coverage probability of a user in a THz-only network. Then, for a coexisting RF/THz network, we derive the coverage probability of a typical user considering biased received signal power association (BRSP). The framework can be customized to capture the performance of a typical user in various network configurations such as THz-only, opportunistic RF/THz, and hybrid RF/THz. In addition, asymptotic approximations are presented for scenarios where the intensity of THz BSs becomes large or molecular absorption coefficient in THz approaches to zero. Numerical results demonstrate the accuracy of the derived expressions and extract insights related to the significance of the BRSP association compared to the conventional reference signal received power (RSRP) association in the coexisting network.

A Block Bi-Diagonalization-Based Pre-Coding for Indoor Multiple-Input-Multiple-Output-Visible Light Communication System

Abstract: Visible Light Communication (VLC) is a promising field in optical wireless communications, which uses the illumination infrastructure for data transmission. The important features of VLC are electromagnetic interference-free, license-free, etc. Additionally, Multiple-Input-Multiple-Output (MIMO) techniques are enabled in the VLC for enhancing the limited modulation bandwidth by its spectral efficiency. The data transmission through the MIMO-VLC system is corrupted by different interferences, namely thermal noise, shot noise and phase noise, which are caused by the traditional fluorescent light. In this paper, an effective precoding technique, namely Block Bi-Diagonalization (BBD), is enabled to mitigate the interference occurring in the indoor MIMO-VLC communications. Besides, a Quadrature Amplitude Modulation (QAM) is used to modulate the signal before transmission. Here, the indoor MIMO-VLC system is developed to analyze the communication performance under noise constraints. The performance of the proposed system is analyzed in terms of Bit Error Rate (BER) and throughput. Furthermore, the performances are compared with three different existing methods such as OAP, FBM and NRZ-OOK-LOS. The BER value of the proposed system of scenario 1 is 0.0501 at 10 dB, which is less than that of the FBM technique.

Temperature-Compensated Refractive Index Measurement Using a Dual Fabry–Perot Interferometer Based on C-Fiber Cavity

Abstract: We demonstrate a simple-to-fabricate, temperature-compensated refractive index sensor using a dual Fabry-Perot optical fiber interferometer using C-fiber. The sensor device is formed by combining two types of in-fiber interferometers, including a C-fiber Fabry-Perot interferometer and a single mode fiber (SMF) Fabry-Perot (FP) interferometer. The C-fiber is a silica capillary with an open side, which allows external liquid to directly enter the internal hole. The C-fiber interferometer is sensitive to external refractive index (1704 nm/RIU) as well as temperature (-0.196 nm/°C) due to the thermo-optic effect and thermal expansion of the C-fiber cavity, while the SMF interferometer is only sensitive to ambient temperature (0.0118 nm/°C). Thus, temperature-compensated refractive index measurement can be achieved by examining the phase shift responses of the two FP interference peaks with transfer matrix approach, solving the problem of temperature sensitivity of RI sensors due to the relatively large thermo-optic coefficient of colloidal materials and aqueous samples.

Analysis and optimization of an Intelligent Reflecting Surface-assisted System with Interference.

Abstract: In this paper, we study an intelligent reflecting surface (IRS)-assisted system where a multi-antenna base station (BS) serves a single-antenna user with the help of a multi-element IRS in the presence of interference generated by a multi-antenna BS serving its own single-antenna user. The signal and interference links via the IRS are modeled with Rician fading. To reduce phase adjustment cost, we adopt quasi-static phase shift design where the phase shifts do not change with the instantaneous channel state information (CSI). Maximum Ratio Transmission (MRT) is adopted at the two BSs to enhance the receive signals at their own users. First, we obtain a tractable expression of the ergodic rate. Then, we maximize the ergodic rate with respect to the phase shifts, corresponding to a non-convex optimization problem. We obtain a globally optimal solution under certain system parameters, and propose an iterative algorithm based on parallel coordinate descent (PCD), to obtain a stationary point under arbitrary system parameters. Finally, we numerically verify the analytical results and demonstrate the notable gains of the proposed solutions. To the best of our knowledge, this is the first work that studies the analysis and optimization of the ergodic rate of an IRS-assisted system in the presence of interference.

Quantum synchronization on the IBM Q system

Abstract: The authors show a quantum effect in synchronization, namely, interference-based quantum synchronization blockade. Their results show that state-of-the-art quantum computers enable the study of realistic dissipative quantum systems.

Mutual Successive Interference Cancellation Strategies in NOMA for Enhancing the Spectral Efficiency of CoMP Systems

Abstract: The densification of mobile networks should enable the fifth generation (5G) mobile networks to cope with the ever increasing demand for higher rate traffic, reduced latency, and improved reliability. The large scale deployment of small cells and distributed antenna systems in heterogeneous environments will require more elaborate interference mitigating techniques to increase spectral efficiency and to help unlock the expected performance leaps from the new network topologies. Coordinated multi-point (CoMP) is the most advanced framework for interference management enabling the cooperation between base stations to mitigate inter-cell interference and boost cell-edge user performance. In this paper, we study the combination of CoMP with mutual SIC, an interference cancellation technique based on power-domain non-orthogonal multiple access (NOMA) that enables multiplexed users to simultaneously cancel their corresponding interfering signals. A highly efficient inter-cell interference cancellation scheme is then devised, that can encompass several deployment configurations and coordination techniques. The obtained results prove the superiority of this approach compared to conventional NOMA-CoMP systems.

Decoherence in Molecular Electron Spin Qubits: Insights from Quantum Many-Body Simulations.

Abstract: Quantum states are described by wave functions whose phases cannot be directly measured but which play a vital role in quantum effects such as interference and entanglement. The loss of the relativ...

Nonlinear interference in crystal superlattices.

Abstract: Nonlinear interferometers with correlated photons hold promise to advance optical characterization and metrology techniques by improving their performance and affordability. These interferometers offer subshot noise phase sensitivity and enable measurements in detection-challenging regions using inexpensive and efficient components. The sensitivity of nonlinear interferometers, defined by the ability to measure small shifts of interference fringes, can be significantly enhanced by using multiple nonlinear elements, or crystal superlattices. However, to date, experiments with more than two nonlinear elements have not been realized, thus hindering the potential of nonlinear interferometers. Here, we build a nonlinear interferometer with up to five nonlinear elements, referred to as superlattices, in a highly stable and versatile configuration. We study the modification of the interference pattern for different configurations of the superlattices and perform a proof-of-concept gas sensing experiment with enhanced sensitivity. Our approach offers a viable path towards broader adoption of nonlinear interferometers with correlated photons for imaging, interferometry, and spectroscopy. Ultra-sensitive interferometric measurements can be made by using correlated photons generated in multiple nonlinear crystals, researchers in Singapore have shown. Correlated photons, linked through a form of quantum entanglement, are useful for studying materials in detection challenging spectral ranges. For example, when infrared photons are affected by striking a sample, the same changes will happen to their correlated photons of visible light, which are easier to detect. Anna Paterova and Leonid Krivitsky at A*STAR in Singapore developed an interferometer that generates correlated photons using up to five nonlinear elements which form crystal superlattices – periodic layers of different materials. Compared to previous systems that used only two nonlinear elements, the new system allowed the researchers to observe smaller changes in interference patterns, and therefore showed greater sensitivity when detecting carbon dioxide gas in tests.