35 research outputs found

    D4.2 Intelligent D-Band wireless systems and networks initial designs

    Get PDF
    This deliverable gives the results of the ARIADNE project's Task 4.2: Machine Learning based network intelligence. It presents the work conducted on various aspects of network management to deliver system level, qualitative solutions that leverage diverse machine learning techniques. The different chapters present system level, simulation and algorithmic models based on multi-agent reinforcement learning, deep reinforcement learning, learning automata for complex event forecasting, system level model for proactive handovers and resource allocation, model-driven deep learning-based channel estimation and feedbacks as well as strategies for deployment of machine learning based solutions. In short, the D4.2 provides results on promising AI and ML based methods along with their limitations and potentials that have been investigated in the ARIADNE project

    Quasi-Synchronous Random Access for Massive MIMO-Based LEO Satellite Constellations

    Full text link
    Low earth orbit (LEO) satellite constellation-enabled communication networks are expected to be an important part of many Internet of Things (IoT) deployments due to their unique advantage of providing seamless global coverage. In this paper, we investigate the random access problem in massive multiple-input multiple-output-based LEO satellite systems, where the multi-satellite cooperative processing mechanism is considered. Specifically, at edge satellite nodes, we conceive a training sequence padded multi-carrier system to overcome the issue of imperfect synchronization, where the training sequence is utilized to detect the devices' activity and estimate their channels. Considering the inherent sparsity of terrestrial-satellite links and the sporadic traffic feature of IoT terminals, we utilize the orthogonal approximate message passing-multiple measurement vector algorithm to estimate the delay coefficients and user terminal activity. To further utilize the structure of the receive array, a two-dimensional estimation of signal parameters via rotational invariance technique is performed for enhancing channel estimation. Finally, at the central server node, we propose a majority voting scheme to enhance activity detection by aggregating backhaul information from multiple satellites. Moreover, multi-satellite cooperative linear data detection and multi-satellite cooperative Bayesian dequantization data detection are proposed to cope with perfect and quantized backhaul, respectively. Simulation results verify the effectiveness of our proposed schemes in terms of channel estimation, activity detection, and data detection for quasi-synchronous random access in satellite systems.Comment: 38 pages, 16 figures. This paper has been accepted by IEEE JSAC SI on 3GPP Technologies: 5G-Advanced and Beyond. Copyright may be transferred without notice, after which this version may no longer be accessibl

    On the feasibility and applications of in-band full-duplex radios for future wireless networks

    Get PDF
    Due to the continuous increase of the demands for the wireless network’s capacity, in-band full-duplex (IBFD) has recently become a key research topic due to its potential to double spectral efficiency, reduce latency, enhance emerging applications, etc., by transmitting and receiving simultaneously over the same channel. Meanwhile, many studies in the literature experimentally demonstrated the feasibility of IBFD radios, which leads to the belief that it is possible to introduce IBFD in the standard of the next-generation networks. Therefore, in this thesis, we timely study the feasibility of IBFD and investigate its advantages for emerging applications in future networks. In the first part, we investigate the interference suppression methods to maximize the IBFD gain by minimizing the effects of self-interference (SI) and co-channel interference (CCI). To this end, we first study a 3-step self-interference cancellation (SIC) scheme. We focus on the time domain-based analog canceller and nonlinear digital canceller, explaining their rationale, demonstrating their effectiveness, and finding the optimal design by minimizing the residual effects. To break the limitation of conventional electrical radio frequency (RF) cancellers, we study the photonic-assisted canceller (PAC) and propose a new design, namely a fiber array-based canceller. We propose a new low-complexity tuning algorithm for the PAC. The effectiveness of the proposed fiber array canceller is demonstrated via simulations. Furthermore, we construct a prototype of the fiber array canceller with two taps and carry out experiments in real-world environments. Results show that the 3-step cancellation scheme can bring the SI close to the receiver's noise floor. Then, we consider the multiple-input multiple-output (MIMO) scenarios, proposing to employ hybrid RF-digital beamforming to reduce the implementation cost and studying its effects on the SIC design. Additionally, we propose a user allocation algorithm to reduce the CCI from the physical layer. A heterogeneous industrial Internet of Things (IIoT) scenario is considered, while the proposed algorithm can be generalized by modifying the parameters to fit any other network. In the second part, we study the beamforming schemes for IBFD multi-cell multi-user (IBFD-MCMU) networks. The transceiver hardware impairments (HWIs) and channel uncertainty are considered for robustness. We first enhance zero-forcing (ZF) and maximum ratio transmission and combining (MRTC) beamforming to be compatible with IBFD-MCMU networks in the presence of multi-antenna users. Then, we study beamforming for SIC, which is challenging for MCMU networks due to the limited antennas but complex interference. We propose a minimum mean-squared error (MMSE)-based scheme to enhance the SIC performance while minimizing its effects on the sum rate. Furthermore, we investigate a robust joint power allocation and beamforming (JPABF) scheme, which approaches the performance of existing optimal designs with reduced complexity. Their performance is evaluated and compared through 3GPP-based simulations. In the third part, we investigate the advantages of applying IBFD radios for physical layer security (PLS). We focus on a channel frequency response (CFR)-based secret key generation (SKG) scheme in MIMO systems. We formulate the intrinsic imperfections of IBFD radios (e.g., SIC overheads and noise due to imperfect SIC) and derive their effects on the probing errors. Then we derive closed-form expressions for the secret key capacity (SKC) of the SKG scheme in the presence of a passive eavesdropper. We analyze the asymptotic behavior of the SKC in the high-SNR regime and reveal the fundamental limits for IBFD and half-duplex (HD) radios. Based on the asymptotic SKC, numerical results illustrate that effective analog self-interference cancellation (ASIC) is the basis for IBFD to gain benefits over HD. Additionally, we investigate essential processing for the CFR-based SKG scheme and verify its effectiveness via simulations and the National Institute of Standards and Technology (NIST) test. In the fourth part, we consider a typical application of IBFD radios: integrated sensing and communication (ISAC). To provide reliable services in high-mobility scenarios, we introduce orthogonal time frequency space (OTFS) modulation and develop a novel framework for OTFS-ISAC. We give the channel representation in different domains and reveal the limitations and disadvantages of existing ISAC frameworks for OTFS waveforms and propose a novel radar sensing method, including a conventional MUSIC algorithm for angle estimation and a delay-time domain-based range and velocity estimator. Additionally, we study the communication design based on the estimated radar sensing parameters. To enable reliable IBFD radios in high-mobility scenarios, a SIC scheme compatible with OTFS and rapidly-changing channels is proposed, which is lacking in the literature. Numerical results demonstrate that the proposed ISAC waveform and associated estimation algorithm can provide both reliable communications and accurate radar sensing with reduced latency, improved spectral efficiency, etc

    Analog-Domain Suppression of Strong Interference Using Hybrid Antenna Array.

    Full text link
    The proliferation of wireless applications, the ever-increasing spectrum crowdedness, as well as cell densification makes the issue of interference increasingly severe in many emerging wireless applications. Most interference management/mitigation methods in the literature are problem-specific and require some cooperation/coordination between different radio frequency systems. Aiming to seek a more versatile solution to counteracting strong interference, we resort to the hybrid array of analog subarrays and suppress interference in the analog domain so as to greatly reduce the required quantization bits of the analog-to-digital converters and their power consumption. To this end, we design a real-time algorithm to steer nulls towards the interference directions and maintain flat in non-interference directions, solely using constant-modulus phase shifters. To ensure sufficient null depth for interference suppression, we also develop a two-stage method for accurately estimating interference directions. The proposed solution can be applicable to most (if not all) wireless systems as neither training/reference signal nor cooperation/coordination is required. Extensive simulations show that more than 65 dB of suppression can be achieved for 3 spatially resolvable interference signals yet with random directions

    Indoor Positioning and Navigation

    Get PDF
    In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot

    Angle-of-Arrival Estimation Using Difference Beams in Localized Hybrid Arrays.

    Full text link
    Angle-of-arrival (AoA) estimation in localized hybrid arrays suffers from phase ambiguity owing to its localized structure and vulnerability to noise. In this letter, we propose a novel phase shift design, allowing each subarray to exploit difference beam steering in two potential AoA directions. This enables the calibration of cross-correlations and an enhanced phase offset estimation between adjacent subarrays. We propose two unambiguous AoA estimation schemes based on the even and odd ratios of the number of antennas per subarray N to the number of different phase shifts per symbol K (i.e., N/K), respectively. The simulation results show that the proposed approach greatly improves the estimation accuracy as compared to the state of the art when the ratio N/K is even

    Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond

    Get PDF
    As the standardization of 5G solidifies, researchers are speculating what 6G will be. The integration of sensing functionality is emerging as a key feature of the 6G Radio Access Network (RAN), allowing for the exploitation of dense cell infrastructures to construct a perceptive network. In this IEEE Journal on Selected Areas in Commmunications (JSAC) Special Issue overview, we provide a comprehensive review on the background, range of key applications and state-of-the-art approaches of Integrated Sensing and Communications (ISAC). We commence by discussing the interplay between sensing and communications (S&C) from a historical point of view, and then consider the multiple facets of ISAC and the resulting performance gains. By introducing both ongoing and potential use cases, we shed light on the industrial progress and standardization activities related to ISAC. We analyze a number of performance tradeoffs between S&C, spanning from information theoretical limits to physical layer performance tradeoffs, and the cross-layer design tradeoffs. Next, we discuss the signal processing aspects of ISAC, namely ISAC waveform design and receive signal processing. As a step further, we provide our vision on the deeper integration between S&C within the framework of perceptive networks, where the two functionalities are expected to mutually assist each other, i.e., via communication-assisted sensing and sensing-assisted communications. Finally, we identify the potential integration of ISAC with other emerging communication technologies, and their positive impacts on the future of wireless networks

    Link-level simulator for 5G localization

    Full text link
    Channel-state-information-based localization in 5G networks has been a promising way to obtain highly accurate positions compared to previous communication networks. However, there is no unified and effective platform to support the research on 5G localization algorithms. This paper releases a link-level simulator for 5G localization, which can depict realistic physical behaviors of the 5G positioning signal transmission. Specifically, we first develop a simulation architecture considering more elaborate parameter configuration and physical-layer processing. The architecture supports the link modeling at sub-6GHz and millimeter-wave (mmWave) frequency bands. Subsequently, the critical physical-layer components that determine the localization performance are designed and integrated. In particular, a lightweight new-radio channel model and hardware impairment functions that significantly limit the parameter estimation accuracy are developed. Finally, we present three application cases to evaluate the simulator, i.e. two-dimensional mobile terminal localization, mmWave beam sweeping, and beamforming-based angle estimation. The numerical results in the application cases present the performance diversity of localization algorithms in various impairment conditions

    Energy Efficient Massive MIMO and Beamforming for 5G Communications

    Get PDF
    Massive multiple-input multiple-output (MIMO) has been a key technique in the next generation of wireless communications for its potential to achieve higher capacity and data rates. However, the exponential growth of data traffic has led to a significant increase in the power consumption and system complexity. Therefore, we propose and study wireless technologies to improve the trade-off between system performance and power consumption of wireless communications. This Thesis firstly proposes a strategy with partial channel state information (CSI) acquisition to reduce the power consumption and hardware complexity of massive MIMO base stations. In this context, the employment of partial CSI is proposed in correlated communication channels with user mobility. By exploiting both the spatial correlation and temporal correlation of the channel, our analytical results demonstrate significant gains in the energy efficiency of the massive MIMO base station. Moreover, relay-aided communications have experienced raising interest; especially, two-way relaying systems can improve spectral efficiency with short required operating time. Therefore, this Thesis focuses on an uncorrelated massive MIMO two-way relaying system and studies power scaling laws to investigate how the transmit powers can be scaled to improve the energy efficiency up to several times the energy efficiency without power scaling while approximately maintaining the system performance. In a similar line, large antenna arrays deployed at the space-constrained relay would give rise to the spatial correlation. For this reason, this Thesis presents an incomplete CSI scheme to evaluate the trade-off between the spatial correlation and system performance. In addition, the advantages of linear processing methods and the effects of channel aging are investigated to further improve the relay-aided system performance. Similarly, large antenna arrays are required in millimeter-wave communications to achieve narrow beams with higher power gain. This poses the problem that locating the best beam direction requires high power and complexity consumption. Therefore, this Thesis presents several low-complexity beam alignment methods with respect to the state-of-the-art to evaluate the trade-off between complexity and system performance. Overall, extensive analytical and numerical results show an improved performance and validate the effectiveness of the proposed techniques
    corecore