Multiple-input Multiple-output Processing Research Articles

Network-assisted full-duplex cell-free mmWave networks: Hybrid MIMO processing and multi-agent DRL-based power allocation

This paper investigates the network-assisted full-duplex (NAFD) cell-free millimeter-wave (mmWave) networks, where the distribution of the transmitting access points (T-APs) and receiving access points (R-APs) across distinct geographical locations mitigates cross-link interference, facilitating the attainment of a truly flexible duplex mode. To curtail deployment expenses and power consumption for mmWave band operations, each AP incorporates a hybrid digital–analog structure encompassing precoder/combiner functions. However, this incorporation introduces processing intricacies within channel estimation and precoding/combining design. In this paper, we first present a hybrid multiple-input multiple-output (MIMO) processing framework and derive explicit expressions for both uplink and downlink achievable rates. Then we formulate a power allocation problem to maximize the weighted bidirectional sum rates. To tackle this non-convex problem, we develop a collaborative multi-agent deep reinforcement learning (MADRL) algorithm called multi-agent twin delayed deep deterministic policy gradient (MATD3) for NAFD cell-free mmWave networks. Specifically, given the tightly coupled nature of both uplink and downlink power coefficients in NAFD cell-free mmWave networks, the MATD3 algorithm resolves such coupled conflicts through an interactive learning process between agents and the environment. Finally, the simulation results validate the effectiveness of the proposed channel estimation methods within our hybrid MIMO processing paradigm, and demonstrate that our MATD3 algorithm outperforms both multi-agent deep deterministic policy gradient (MADDPG) and conventional power allocation strategies.

Linear Frequency Modulation and Orthogonal Code Modulation for Co-Located Multiple-Input Multiple-Output High-Frequency Surface Wave Radar

A high-frequency surface wave radar (HFSWR) is the only sensor that provides inexpensive surveillance for up to 200 nautical miles (NM) of the exclusive economic zone in the 3–5 MHz band. However, because of its long wavelength, its angular resolution is low. Multiple-input multiple-output (MIMO) technology is an attractive method to improve angular resolution. This paper proposes MIMO waveforms and their processing that can be used in HFSWR systems. This dual modulation method applies linear frequency modulation to each pulse and orthogonal polyphase codes for a few consecutive pulses to enable MIMO processing. The proposed method can effectively remove the correlation of mutual interference and exhibits excellent performance in removing multiple-time-around echoes.

A Novel Approach to Unambiguous Doppler Beam Sharpening for Forward-Looking MIMO Radar

The ambiguity problem of targets in Doppler beam sharpening (DBS) with forward-looking radar is considered. While DBS is proposed earlier to improve the angular resolution of the radar while keeping the antenna aperture size limited, such a solution suffers from ambiguities in the case of targets positioned symmetrically with respect to the platform movement. To address this problem, an approach named unambiguous Doppler-based forward-looking multiple-input multiple-output (MIMO) radar beam sharpening scan (UDFMBSC) is proposed, based on the combination of MIMO processing and DBS. The performance of the proposed method is compared to existing approaches using simulated data with point-like and extended targets. The method is successfully verified using experimental data.

MIMO Processing With Linear Beat Interference Cancellation for Space Division Multiplexing Self-Homodyne Coherent Transmission

We investigate self-homodyne coherent transmission employing space division multiplexing (SDM), where a continuous wave laser source is divided to produce spatially multiplexed signals and a co-propagating pilot-tone (PT), which serves as a local oscillator for self-homodyne detection at the receiver side. The beat interference caused by the spatial coupling between PT and the signals during propagation is investigated through derivations and numerical modelling. Both conventional multiple-input–multiple-output (MIMO) and unreplicated parallel interference canceler (UPIC)-assisted MIMO suffer from the effects of beat interference and additional mode dependent loss introduced by PT-signal crosstalk, which limits the performance of SDM-based self-homodyne coherent systems. We propose two novel MIMO processing schemes employing digital conjugate input branches to handle the beat interference and demonstrate system performance improvement in both simulations and experiments. For a 240-Gbps self-homodyne coherent system transmitting mode-multiplexed QPSK signals over a 3-mode multimode fiber, after adapting the two enhanced MIMO schemes, simulations show a up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 4 dB improvement in PT-signal crosstalk level tolerance, and experiments show undistorted constellations as well as BER improvements from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 10^{-2}$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 10^{-5}$</tex-math></inline-formula> with PT-signal delay alignment after the mode demultiplexer.

Manifold Discriminative Learning Inspired Hybrid Beamforming for Millimeter-Wave Massive MIMO Systems

Millimeter-wave (mmWave) massive MIMO (multiple-input multiple-output) is a promising technology as it provides significant beamforming gains and interference reduction capabilities due to the large number of antennas. However, mmWave massive MIMO is computationally demanding, as the high antenna count results in high-dimensional matrix operations when conventional MIMO processing is applied. Hybrid precoding is an effective solution for the mmWave massive MIMO systems to significantly decrease the number of radio frequency (RF) chains without an apparent sum-rate loss. In this paper, we propose user clustering hybrid precoding to enable efficient and low-complexity operation in high-dimensional mmWave massive MIMO, where a large number of antennas are used in low-dimensional manifolds. By modeling each user set as a manifold, we formulate the problem as clustering-oriented multi-manifolds learning. The manifold discriminative learning seek to learn the embedding low-dimensional manifolds, where manifolds with different user cluster labels are better separated, and the local spatial correlation of the high-dimensional channels within each manifold is enhanced. Most of the high-dimensional channels are embedded in the low-dimensional manifolds by manifold discriminative learning, while retaining the potential spatial correlation of the high-dimensional channels. The nonlinearity of high-dimensional channel is transformed into global and local nonlinearity to achieve dimensionality reduction. Through proper user clustering, the hybrid precoding is investigated for the sum-rate maximization problem by manifold quasi conjugate gradient methods. The high signal to interference plus noise ratio (SINR) is achieved and the computational complexity is reduced by avoiding the conventional schemes to deal with high-dimensional channel parameters. Performance evaluations show that the proposed scheme can obtain near-optimal sum-rate and considerably higher spectral efficiency than some existing solutions

Analysis of an optical SDM ring network model for applying MIMO processing

Optical multiple-input multiple-output (MIMO) technology is introduced into optical space division multiplexing (SDM) communication as an effective solution for the crosstalk issue. Unfortunately, this application is limited because all the channel information over the transmission link must be transmitted to MIMO receivers for complete crosstalk compensation, which indicates difficulty in applying MIMO to the transmission path via optical switches. We newly propose that one of the solutions to this limitation is a ring network because all channel information can be maintained in the ring. In particular, a typical ring network model comprising optical add-drop multiplexers (OADMs) and multicore fibers (MCFs) is introduced, and the ability of MIMO to compensate for capacity degradation because of the inter-core crosstalk is investigated. Moreover, the protection function, which is essential for the ring network, is also evaluated under the fiber and fiber core failures. As per the simulation results, although the protection operation affects the transmission capacity, the capacity loss is suppressed in the event of a failure to < 5%. We believe that these results provide useful information for designing SDM-based optical networks.

Deep Unfolded Extended Conjugate Gradient Method for Massive MIMO Processing with Application to Reciprocity Calibration

In this paper, we consider deep unfolding the standard iterative conjugate gradient (CG) algorithm to solve a linear system of equations. Instead of being adjusted with known rules, the parameters are learned via backpropagation to yield the optimal results. However, the proposed unfolded CG (UCG) is extended wherein a scalar parameter is substituted by a matrix-parameter to augment the degrees of freedom per layer. Once the training is completed, the UCG has revealed to require far a smaller number of layers than the number of iterations needed using the standard iterative CG. It is also shown to be very robust to noise and outperforms the standard CG in low signal to noise ratio (SNR) region. A key merit of the proposed approach is the fact that no explicit training data is dedicated to the learning phase as the optimization process relies on the residual error which is not explicitly expressed as a function of the desired data. As an example, the proposed UCG is applied to solve the reciprocity calibration problem encountered in massive MIMO (Multiple-Input Multiple-Output) systems.

Joint Bernoulli Filtering and MIMO Processing for Detection of Moving Targets in Shallow Ocean Environments

A joint multiple-input multiple-output (MIMO) processing framework is proposed to exploit spatial diversity and moving cues for the enhancement of target detection in a shallow ocean environment. Orthogonal signals are transmitted to illuminate different aspects of a target and beamforming operation is carried out over the received data for estimating target bearing. The target range is achieved by a replica correlation integrator where the beamformer outputs are matched with transmitted signals. After meanshift clustering algorithm is carried out over the bearing-range spectrums to generate the clutter centers, the potential trajectories of targets can be tracked by an improved Bernoulli filter with inputs of these centers. The at-sea experimental results have shown the effectiveness of the joint processing framework in MIMO detection of moving targets.

Compressive-Sensing-Aided MIMO Radar Enabling Multi-Functional and Compact Sensors in Air Scenarios Using Optimized Antenna Arrays

We address the problem of direction-of-arrival (DoA) estimation for air targets using a compact, multi-functional radar sensor. In order to enhance the angular resolution of such sensors while exploiting the sparseness of typical air scenarios, we consider the combination of a multiple-input multiple-output (MIMO) radar approach with suitable compressive-sensing (CS) techniques. In particular, we investigate the combination of MIMO processing for two-dimensional (2D) antenna arrays with CS-based angular processing for three-dimensional (3D) target localization and 2D DoA estimation in azimuth and elevation. We analyze the benefits of randomized antenna element positions in one and two dimensions and devise optimized array geometries for practicable aperture sizes. In particular, we take physical side constraints into account, such as the smallest realizable/ desired element spacing as well as area restrictions for antenna placement within the overall aperture. The aperture area reserved by this means could be used to accommodate additional hardware components enabling a multi-functional sensor approach. Extensive computer simulation results for different 3D target scenarios illustrate the advantages of our CS-based compact MIMO radar approach with randomized antenna elements compared to fully randomized arrays, given the same number of physical antenna elements.

Analog Radio-over-Fiber-Aided Optical-Domain MIMO Signal Processing for High-Performance Low-Cost Radio Access Networks

Wireless communication is facing an ever increasing thirst for high-quality data transmission. However, this imposes high demands on the radio access networks (RANs), where optical fiber has been widely used for both the backhaul and fronthaul. However, the explosive escalation of wireless tele-traffic tests the limits of the RAN's revenue vs. cost trade-off, which motivates low-cost designs. Hence, we present a cost-efficient but high-performance radio over fiber (RoF)-aided RAN concept. We commence by introducing this motivation, followed by a brief introduction to analog RoF (A-RoF) and digitized RoF (D-RoF), as well as to wireless multiple-input multiple-output (MIMO) techniques. Then we present the centralized RAN (C-RAN) concept relying on A-RoF-aid-ed MIMO signal processing, where the MIMO signal is carried by fiber and processed optically in a central unit. Subsequently, we focus our attention on a C-RAN small cell application followed by our performance vs. cost analysis of the A-RoF system compared to that of its baseband counterparts, demonstrating that the A-RoF design is capable of reducing the RAN's total cost while meeting the Third Generation Partnership Proj-ect's requirements.

Widely Distributed Photonics-Based Dual-Band MIMO Radar for Harbour Surveillance

A new architecture for a widely distributed dual-band coherent multiple-input multiple-output (MIMO) radar system is illustrated, and its implementation and testing are reported. The system consists in a central unit where radar signals are coherently generated and detected, which serves multiple remote sensors connected over transparent WDM optical network. Every remote node operates coherently both in the S- and X-band, and is displaced over distances of several kilometers, allowing to monitor a scene under different angles of view. All the remote sensors share the same oscillator and digital signal processing unit, both located in the central office, allowing to perform centralized raw data fusion on the acquired signals. By virtue of the system coherence, the system takes advantage of the coherent MIMO processing strategy to offer a superior spatial resolution, which is even magnified by the dual-band approach.

Coherent Dual-Band Radar-Over-Fiber Network With VCSEL-Based Signal Distribution

Today, the advent of radar sensing in applications requiring ever higher precision, such as automotive and industrial monitoring, needs the development of systems with superior resolution, stability and accuracy on 2D/3D spaces. Multiple-input–multiple-output (MIMO) radar systems have been studied for more than twenty years, proving to achieve excellent resolutions by merging simultaneous observations from sparse multiple radars. In addition, long-term frequency/phase coherence among distributed signals to and from remote and widely distributed radar frontends, enables a centralized architecture where, in a common base station (BS) site, all the received signals can be coherently processed in synergy, allowing to further improve detection, localization, and imaging capabilities. Radio-over-fiber (RoF) solutions represent an enabling technology for guaranteeing coherence among all radar signals coherently generated in the BS and distributed/collected to/from the radar heads by means of optical fiber links. Despite the unprecedented frequency-agility provided by photonics-based radiofrequency (RF) up-/down-conversion in the optical domain, for applications requiring massive low-cost production and power efficiency, such as advanced driver-assistance systems, it might be convenient to transmit and receive conventionally generated RF signals, and to limit the use of photonics, as in RoF, to RF signal distribution through cheap high-capacity optical links, in order to avoid EM interference, minimize loss, signal distortion and link encumbrance, still keeping the coherence among distributed signals. In this work, we present a MIMO radar-over-fiber (RaoF) network, where a common BS transmits and receives RF signals through optical standard single-mode fiber (SSMF), to and from two remote radar transmitters (TXs) and four receivers (RXs), all remote with respect to the BS and distributed on a 3 m-long baseline, and employing cheap patch antennas. Electro-optic conversion is achieved through direct modulation (DM) of low-cost, power-effective, and high-speed vertical-cavity surface-emitting lasers (VCSELs) working in the 1.3 µm-wavelength regime. Such a wavelength ensures propagation over SSMF with negligible distortions up to the km-range. The system is tested in a down-scaled indoor scenario and an RF carrier of 8.5 GHz is used, due to the limited frequency response of the employed VCSELs. System performance is evaluated with 1.4 GHz-bandwidth signals, comparing non-coherent and coherent signal elaboration. In addition, dual-band operation in the same RF region is tested, confirming that VCSEL-based optical links are a viable way to guarantee phase coherence among the distributed signals and among multiple frequency bands. Coherent MIMO processing also confirm the potential to achieve superior range and cross-range resolution than non-coherent data fusion.

Detecting WDM visible light signals by a single multi-color photodiode with MIMO processing

For the first time, to the best of our knowledge, we experimentally demonstrate that multiple-input-multiple-output (MIMO) processing allows using a single photodiode to detect simultaneously a wavelength-division multiplexing (WDM) visible light communications (VLC) signal. The photodiode has a triple junction, and when it is illuminated by a WDM signal, the junctions produce inherently three photocurrents that are unusable for detecting any of the WDM signals. However, by means of linear MIMO processing, we are able to recover the transmitted signals exactly. Bit error rate measurements confirm the effectiveness of the proposed solution. This opens a new scenario for practical WDM-VLC systems.

Underwater Communication Employing High-Sensitive Magnetic Field Detectors

Magnetic communication is receiving significant interest in RF-challenging environments. Particularly in underwater environments and underground wireless sensor networks, magnetic communication is an emerging research area. In this paper, a new approach for magnetic underwater communication is presented and evaluated. In this approach, the receiver coil of a conventional magnetic induction communication system is replaced by a high-sensitive low-noise wideband magnetic field sensor. This concept enables a good detection sensitivity and, under certain conditions, an extended communication range. Most magnetic field sensors are small compared to equivalent search coils and offer a high bandwidth. Hence, they can be assembled in order to provide multiple-input multiple-output processing. Based on suitable channel modeling, trade-offs between system parameters are analyzed and the channel capacity is derived. Analytical results are supported by a prototype implementation. Potential application scenarios are studied, where emphasis is on mobile applications.

Bending and twisting effects on multicore fiber differential group delay.

In this paper we provide the theoretical and experimental evaluation of fiber bending and twisting effects on the group delay performance of a homogeneous 7-core fiber. We have experimentally evaluated the differential group delay between the central and outer cores for different curvature radii and twisting conditions, demonstrating that fiber twisting counteracts the degradation introduced by the curvature itself. These findings are generally applicable to time-sensitive application areas such as radio-over-fiber distribution and microwave photonics signal processing in fiber-wireless access networks, as well as high-capacity long-haul digital communications where digital multiple-input multiple-output processing may be required.

Empirical Rates Characterization of Wearable Multi-Antenna Terminals for First-Responders

Empirical characterization of the achievable rates for a wearable multi-antenna terminal shows the potential advantages of deploying a large number of antennas at the user end. We focus on the challenges and requirements of the broadband communication in future emergency communication systems, specifically addressing the outdoor–to–indoor propagation scenario, where the first responder is within an underground area such as a garage or basement. The measurement campaign undertaken characterizes the flat fading multiple-input multiple-output (MIMO) channel matrices at 3.5 GHz for a maximum of $M=30$ antennas deployed at the base station (BS), and $N=12$ wearable antennas at the user. The achievable rates are obtained for two transmission strategies that account for the different levels of channel knowledge. In both cases, all the MIMO processing is carried out at the BS.

Comparison of Kramer–Kronig receiver and one-stage SSBI mitigation algorithm in twin-SSB direct detection transmission systems enabled by MIMO processing

Abstract In this paper, we demonstrate a spectrally efficient twin-SSB single carrier system based on multiple-input multiple-output (MIMO) processing. MIMO processing based on training sequence aided and decision-directed least mean square (LMS) is applied to mitigate the crosstalk introduced by non-ideal optical filters in receiver. The number of taps used in MIMO is evaluated. In addition, performance of Kramers–Kronig receiver and one-stage signal–signal beat interference (SSBI) mitigation algorithm is compared. 264 Gb/s per polarization in C-band transmission over 80 km SSMF with a net bit rate of 220 Gb/s using 88 GSa/s DACs is experimentally demonstrated with KK receiver.

DMD-Unmanaged Long-Haul SDM Transmission Over 2500-km 12-Core × 3-Mode MC-FMF and 6300-km 3-Mode FMF Employing Intermodal Interference Canceling Technique

We demonstrate differential mode delay (DMD) unmanaged 2500-km 12-core × 3-mode multicore few-mode fiber (MC-FMF) and 6300-km 3-mode FMF transmission. Mode-division multiplexed signals exhibit different transmission behavior depending on excited spatial modes in the presence of mode-relevant physical phenomena including DMD and mode-dependent loss (MDL). In weakly coupled FMF transmission over long distance where overall DMD and MDL grow almost linearly with fiber length, these phenomena restrict achievable information capacity and/or transmission reach. This paper presents a newly developed transmission scheme and a multiple-input multiple-output (MIMO) signal processing technique that are designed for achieving long-haul FMF transmission. Permutating spatial channels cyclically at each span induced quasi-strongly coupled transmission regime and hence significantly suppressed DMD-induced pulse broadening. Layered MIMO signal processing comprising multiple stages performing successive signal detection and intermodal interference canceling mitigated MDL impact with more than 2-dB Q-factor improvement. The combined use of these techniques enabled us to achieve the first-ever transoceanic-class long-haul FMF transmission.

RoF Data Transmission Using Four Linearly Polarized Vector Modes of a Polarization Maintaining Elliptical Ring Core Fiber

We experimentally investigate the feasibility of transmission of radio-frequency (RF) signals over a 900 m polarization-maintaining elliptical ring core fiber. No multiple-input–multiple output processing is used to recover the RF signals carried by different modes; we recover the 16 quadratic-amplitude modulation orthogonal frequency-division multiplexing RF signals with the same techniques used for single-mode fibers. For the first time, we report transmission of four RF streams over four channels in few-mode fibers. Also, for the first time, we transmit RF signals over two polarizations of a mode in few-mode fibers and successfully recover data in both polarizations without polarization tracking or digital signal processing to separate polarizations. Furthermore, we examine the impact of fiber bending on crosstalk among channels. We show that even under severe bending, the polarization states remain separated and the RF streams transmitted on polarization states of a mode could be recovered with low-power penalty.

Analog Radio-Over-Fiber Supported Increased RAU Spacing for 60 GHz Distributed MIMO Employing Spatial Diversity and Multiplexing

The improvements in coverage through spatial diversity and increased data rates through spatial multiplexing using a distributed multiple-input multiple-output (MIMO) system are important targets for future wireless communications. Here, the appropriate separation of remote antenna units (RAUs) at several user locations in a millimeter-wave system is demonstrated. An analog radio-over-fiber (RoF) fronthaul is used to achieve flexible spacing of distributed RAUs and transports two Gb/s data streams over 2.2 km of fiber and up to 8 m of 60 GHz wireless transmission distance. A performance comparison is performed between single-input single-output and MIMO operation using different antenna spacing and transmission distance. Results show that the wider RAU spacing enabled by the RoF distribution provides improved results at longer distances, for both spatial diversity and for spatial multiplexing. Verification of a method for measuring each channel coefficient individually and using subsequent MIMO processing on these coefficients enables an extension to the results showing the feasibility of 30 m indoor transmission.