Channel state information (CSI) feedback compression is a critical task in massive MIMO communication systems. Conventional approaches rely on either codebook-based quantization or autoencoder (AE) techniques. While AE-based methods offer improved reconstruction accuracy, they still exhibit limitations in compression efficiency and flexibility. This paper introduces a novel structured AE framework for efficient compression of a sequence of temporally correlated CSI samples. The proposed model imposes a multipart latent structure that decomposes each sequence into a common component, capturing long-term or shared features across samples, and a specific component, capturing short-term or sample-dependent variations. During feedback, the common representation is transmitted once per sequence, whereas the specific components are reported when scheduled or requested by the base station. This adaptive reporting strategy significantly reduces the overall feedback overhead while maintaining high reconstruction fidelity, offering an interpretable and scalable solution for next-generation FDD massive MIMO systems. Simulation results demonstrate that the proposed structured AE significantly outperforms the state-of-the-art CSI compression schemes, achieving notable reductions in feedback overhead while offering enhanced flexibility through adaptive and task-oriented reporting.

The introduction of massive MIMO (Multiple Input Multiple Output) communication systems enables base stations (BS) to perform beamforming for enhancing communication reliability. A typical key assumption, however, is the availability of accurate downlink channel state information (CSI). In practice, CSI estimation and reporting delays coupled with the process of channel aging result in the BS receiving outdated CSI information, which in turn impacts the system's spectral efficiency. To combat this latency, this paper develops efficient methods of CSI prediction that preemptively predict future downlink CSI based on historical data. We leverage the spatial and temporal correlation properties of the channel and use explicit feature extraction frameworks for both dimensions to accurately predict future CSI. We analyze combinations of spatial and temporal feature extractors in terms of a tradeoff between performance and latency. We evaluate the performance of the proposed prediction model in terms of proximity to the ground truth, prediction latency, and model footprint. Our experiments show that our method outperforms classical statistical methods as well as existing CSI prediction baselines.

The unprecedented increase in the number of wireless-connected devices requires novel solutions to improve the data rate at low latency. Reference signals overhead reduction is a powerful way to increase the data rates. However, excessive reduction in the number of reference signals degrades the channel estimation performance with potential negative impacts on the data rates. Toward this end, this paper proposes a machine learning-based approach that enables reference signal-free data channel demodulation. This new approach involves a repetition of part of the data channel symbols across the slot. Invoking canonical correlation analysis on the repeated data at the user side yields high-quality combiners that are used to recover both the repeated data blocks and the rest of the data symbols in the slot, without the need of traditional channel estimation. This paper also proposes two effective and principled strategies; one for repetition pattern selection as a function of the channel parameters and the other addresses performance in highly frequency selective channels. The proposed approach offers considerable gains in throughput performance and complexity reduction. Simulation results using a 3GPP NR link-level test bench, reveal the effectiveness of the proposed approach relative to the state-of-the-art methods.

Recent work has shown that repetition coding followed by interleaving induces signal structure that can be exploited to separate multiple co-channel user transmissions, without need for pilots or coordination/synchronization between the users. This is accomplished via a statistical learning technique known as canonical correlation analysis (CCA), which works even when the channels are time-varying. Previous analysis has established that it is possible to identify the user signals up to complex scaling in the noiseless case. This letter goes one important step further to show that CCA in fact yields the linear MMSE estimate of the user signals up to complex scaling, without using any explicit training. Instead, CCA relies only on the repetition and interleaving structure. This is particularly appealing in asynchronous ad-hoc and unlicensed setups, where tight user coordination is not practical.

In frequency division duplex (FDD) massive MIMO systems, each user equipment (UE) device typically feeds back downlink channel state information (CSI) to its serving base station (BS), which may then be utilized for beamforming and scheduling purposes. However, the large number of antennas increases the CSI signaling overhead in the uplink, thereby impacting the system's spectral efficiency. To address the feedback over-head problem, this paper proposes a low-complexity beam domain autoencoder that operates on low-dimensional beam domain channel samples. The key idea is to efficiently transform the channel from the antenna-domain to the beam-domain by selecting a small number of beams that preserve most of the channel information. To leverage the beam domain processor, we propose a beam selection method that enables high reconstruction quality with a small number of selected beams. The proposed framework offers a favorable balance between complexity, performance, and memory requirements. Simulations demonstrate the efficacy of the proposed approach in recovering the compressed channel at reduced overhead and complexity, relative to the state-of-the-art methods.

Reference signals overhead reduction has recently evolved as an effective solution for improving the system spectral efficiency. This paper introduces a new downlink data structure that is free from demodulation reference signals (DM-RS), and hence does not require any channel estimation at the receiver. The new proposed data transmission structure involves a simple repetition step of part of the user data across the different sub-bands. Exploiting the repetition structure at the user side, it is shown that reliable recovery is possible via canonical correlation analysis. This paper also proposes two effective mechanisms for boosting the CCA performance in OFDM systems; one for repetition pattern selection and another to deal with the severe frequency selectivity issues. The proposed approach exhibits favorable complexity-performance tradeoff, rendering it appealing for practical implementation. Numerical results, using a 3GPP link-level testbench, demonstrate the superiority of the proposed approach relative to the state-of-the-art methods.

The new applications enabled by the 5G new radio (NR) use cases require novel solutions capable of supporting high end-user data rates at low latency. To meet these needs, it is critical to have accurate channel state information at the receiver. This requires sending a large number of reference signals which scales up with the number of users and layers, thereby introducing significant overhead that eventually impacts the system's spectral efficiency. This paper introduces a new downlink data structure that is free from demodulation reference signals, and does not require channel estimation at the receiver. The proposed structure involves a repetition step of part of the user data across the time-frequency grid. Exploiting the repetition structure at the receiver, it is shown that reliable recovery is possible via canonical correlation analysis (CCA). This paper also proposes two effective mechanisms for optimizing the CCA performance in OFDM systems, one for repetition pattern selection and another to deal with the severe frequency selectivity issues. The proposed approach provides rigorous recovery guarantees, exhibits favorable complexity-performance tradeoff, and is shown to be robust to high interference scenarios, thus rendering it appealing for practical implementation. The paper provides simulation results, using a 3GPP link-level testbench, that demonstrate the feasibility of the proposed approach.

Planning and control for autonomous vehicles usually are hierarchical separated. However, increasing performance demands and operating in highly dynamic environments requires an frequent re-evaluation of the planning and tight integration of control and planning to guarantee safety. We propose an integrated hierarchical predictive control and planning approach to tackle this challenge. Planner and controller are based on the repeated solution of moving horizon optimal control problems. The planner can choose different low-layer controller modes for increased flexibility and performance instead of using a single controller with a large safety margin for collision avoidance under uncertainty. Planning is based on simplified system dynamics and safety, yet flexible operation is ensured by constraint tightening based on a mixed-integer linear programming formulation. A cyclic horizon tube-based model predictive controller guarantees constraint satisfaction for different control modes and disturbances. Examples of such modes are a slow-speed movement with high precision and fast-speed movements with large uncertainty bounds. Allowing for different control modes reduces the conservatism, while the hierarchical decomposition of the problem reduces the computational cost and enables real-time implementation. We derive conditions for recursive feasibility to ensure constraint satisfaction and obstacle avoidance to guarantee safety and ensure compatibility between the layers and modes. Simulation results illustrate the efficiency and applicability of the proposed hierarchical strategy.

The ever-growing demands on wireless connectivity, especially with the emergence of various data-intensive low-latency applications, require novel multiplexing solutions capable of reliably supporting high rates at low latency. Non-orthogonal time division duplex (TDD) coupled with multiuser detection can meet these emerging needs, provided that accurate channel state information is available. This paper proposes a new pilot-free TDD frame structure that allows designing highly effective multiuser decoders and precoders for uplink and downlink multicell systems, in an unsupervised manner. The key idea is that each user repeats and permutes its uplink data using a pre-assigned permutation code. Invoking canonical correlation analysis (CCA) at the serving BS on the two deinterleaved uplink blocks yields high quality CCA-based beamformers capable of both recovering the uplink and precoding the downlink user signals in a way that effectively mitigates interference. The paper includes a pilotless synchronization framework that leverages CCA to recover the timing and frequency offsets in an asynchronous multiuser MIMO setup, without using pilots. Simulations are used to study the performance of the proposed approach on a large-scale network with multiple users and cells, while laboratory experiments with a small-scale network of software radios are used to demonstrate that the approach works well in practice under common hardware imperfections.

Channel estimation in rapidly time-varying or short and bursty communication scenarios is costly in terms of both pilot overhead and co-channel interference. In recent work, it was shown that multipath delay-diversity can be exploited to detect multiple co-channel user signals, provided that the relative multipath delays for the different users are distinct, and the two multipath ‘taps’ of each user have roughly commensurate power. These requirements may not hold naturally, however, especially for relatively narrowband or short-range transmissions with small delay spread. As an alternative, this paper advocates using dual-antenna transmission in a manner that introduces artificial multipath and tight control of the power of the two channel taps, via baseband processing at the transmitter. The approach enjoys theoretical guarantees and affords simple decoding and accurate synchronization as a side bonus. Similar claims have been previously laid using packet repetition via a single transmit-antenna, but the dual-antenna artificial multipath scheme proposed herein doubles the transmission rate relative to packet repetition. Laboratory experiments using programmable radios are used to demonstrate successful operation of the proposed transmission scheme in practice.

Hybrid beamforming has evolved as a promising technology that offers the balance between system performance and design complexity in mmWave MIMO systems. Existing hybrid beamforming methods either impose unit-modulus constraints or a codebook constraint on the analog precoders/combiners, which in turn results in a performance-overhead tradeoff. This paper puts forth a tensor framework to handle the wideband hybrid beamforming problem, with Vandermonde constraints on the analog precoders/combiners. The proposed method strikes the balance between performance, overhead and complexity. Numerical results on a 3GPP link-level test bench reveal the efficacy of the proposed approach relative to the codebook-based method while attaining the same feedback overhead. Moreover, the proposed method is shown to achieve comparable performance to the unit-modulus approaches, with substantial reductions in overhead.

Hybrid beamforming provides a cost effective strategy towards practical deployment of massive multiple-input multiple-output (MIMO) systems. Since the hybrid precoder-combiner evaluation requires channel state information, the computation is performed at the receiver and the evaluated precoders are communicated back to the transmitter. This transmission overhead associated with the precoder feedback can be large when the number of transmit antennas is high. In this letter, we study the optimization associated with hybrid beamforming in limited feedback systems. We propose an efficient solution for the optimization under a codebook constraint and prove that the proposed solution is globally optimal when the codebook is orthonormal. The proposed approach is also shown to provide superior performance compared to existing approaches in a limited feedback setup. Further, it exhibits a computational complexity far lower than existing alternatives, making it feasible for deployment in real, resource constrained systems.

The unprecedented growth in wireless Internet-of-Things and WiFi devices has renewed interest in mechanisms for efficient spectrum reuse. Existing schemes require some level of primary-secondary coordination, cross-channel state estimation and tracking, or activity detection– which complicate implementation. For low-power short-range secondary communication, the main impediment is strong and time-varying (e.g., intermittent) interference from the primary system. This paper proposes a practical underlay scheme that permits reliable secondary communication in this regime. The secondary transmitter merely has to send its signal twice, at very low power - a few dBs above the noise floor, but far below the primary’s interference. Exploiting the repetition structure, reliable and computationally efficient recovery of the secondary signal is possible via canonical correlation analysis (CCA). Experiments using a software radio testbed reveal that, for a secondary user with only two receive antennas, reliable detection of the secondary signal is possible for signal to interference plus noise ratio (SINR) in the range of −20 to −40 dB. The approach works with unknown time-varying channels, digital or analog modulation, it is immune to carrier frequency offset, and, as a side-benefit, it provides means for accurate synchronization of the secondary user even at very low SINR.

Designing predictive controllers for systems with computationally limited embedded hardware, e.g. for autonomous vehicles, requires solving an optimization problem in real-time taking the vehicle dynamics and constraints into account. Furthermore, often the controller needs to be available in explicit form for verification and validation purposes and should only exploit the available output measurement. We propose an approximation of a special nonlinear model predictive output-feedback formulation considering the infinite-horizon case. The main idea is to offline derive an approximated explicit solution of the underlying Hamilton–Jacobi–Bellman equation. The resulting feedback control law is polynomial in terms of the measurements and estimated parameters. Therefore, the online evaluation can be efficiently implemented. The optimal control law is parameterized in terms of the varying parameters which can be updated/learned online. We provide a proof of convergence and existence of the explicit solution and compare the proposed approximated nonlinear controller to a finite-horizon nonlinear model predictive controller considering the control of a quadcopter subject to wind disturbances.

The spectrum underlay concept promises enhanced spectrum utilization without disturbing legacy / licensed or scientific primary users, so long as their interference constraints can be met. Existing underlay schemes assume that both the primary signal to secondary interference plus noise ratio, and the secondary signal to primary interference plus noise ratio can be high enough at the primary and secondary receiver, respectively. Even if the cross-network channel state information is available at the secondary users, these two conflicting requirements are hard to achieve simultaneously in practice. This work proposes a practical data-driven approach that allows a pair of secondary users to reliably communicate in underlay mode while keeping the interference at the primary receiver close to its noise floor. The secondary transmitter merely has to transmit its signal twice, at very low power - above the noise floor, but well below the primary’s interference. It is shown here that reliable detection of the secondary signal is possible via canonical correlation analysis (CCA). Theoretical and experimental results reveal the remarkable detection performance of the proposed CCA-based approach, which does not require any cross-network coordination, or even channel state information.

This paper investigates the use of beamforming millimeter-wave (mmWave) repeaters in wireless industrial control systems. We study deployment of mmWave in a factory floor with several production lines and a multitude of devices that periodically receive packets from the controller via a hub access point (AP). We propose to use beamforming repeaters as an alternative to the so-called multi-transmission reception point (multi-TRP) technology, where wireless fronthaul links from the hub TRP to the repeaters substitute the bulky wired links to the different TRPs. The proposed wireless TRP is then demonstrated to extend communication coverage across the factory floor, while improving end-to-end link reliability via over-the-air combining of the signal from the repeater and the hub TRP. We formulate the optimization problem of associating TRPs and beams to each user for the objective of minimizing the overall scheduling latency while satisfying reliable communication to all users. To tackle such a problem, we propose a low-complexity greedy algorithm, which through extensive simulations is shown to significantly reduce system- level scheduling latency compared to the existing schemes in the literature. In our simulations, the effects of most objects on radio wave propagation are accurately modeled using a ray-tracing tool.

Improving the uplink quality of service for users located around the boundaries between cells is a key challenge in LTE systems. Relying on power control, existing approaches throttle the rates of cell-center users, while multi-user detection requires accurate channel estimates for the cell-edge users, which is another challenge due to their low received signal-to-noise ratio (SNR). Utilizing the fact that cell-edge user signals are weak but common (received at roughly equal power) at different base stations (BSs), this paper establishes a connection between cell-edge user detection and generalized canonical correlation analysis (GCCA). It puts forth a GCCA-based method that leverages selective BS cooperation to recover the cell-edge user signal subspace even at low SNR. The cell-edge user signals can then be extracted from the resulting mixture via algebraic signal processing techniques. The paper includes theoretical analysis showing why GCCA recovers the correct subspace containing the cell-edge user signals under mild conditions. The proposed method can also identify the number of cell-edge users in the system, i.e., the common subspace dimension. Simulations reveal significant performance improvement relative to various multiuser detection techniques. Cell-edge detection performance is further studied as a function of how many / which BSs are selected, and it is shown that using the closest three BS is always the best choice.

Safety critical control problems often require the availability of fallback strategies, in case of failure of the main control scheme, sensors or actuators. Those controllers should provide safe operation or emergency shut down of the system under all circumstance. They should also be able to operate subject to reduced information, and limited computation power. We propose a verifiable and efficiently implementable output feedback controller based on an approximated explicit solution of a constrained optimal control problem. The control law is derived by solving an infinite horizon optimal control problem utilizing Al'brekht's Method to obtain power series expansions. The feedback control law is a polynomial in terms of the measurements and estimated parameters, thus the online evaluation can be done efficiently. We provide conditions for convergence and existence of the optimal control law and the corresponding value function. Simulation results for the control of a non-linear quadcopter example show the effectiveness of the proposed strategy.

Reliable detection and accurate estimation of weak targets and their Doppler frequencies is a challenging problem in MIMO radar systems. Reflections from such targets are often overpowered by those from stronger nearby targets and clutter. Considering a 3-D data model where the coherent processing interval comprises multiple pulses, a novel weak target detection and estimation approach is proposed in this paper. The proposed method is based on creating partially overlapping spatial beams, and performing canonical correlation analysis (CCA) in the resulting beamspace. It is shown that if a target is present in the overlap sector, then its Doppler profile can be reliably estimated via beamspace CCA, even if hidden under much stronger interference from nearby targets and clutter. Numerical results are included to validate this theoretical claim, demonstrating that the proposed Beamspace Canonical Correlation (BCC) method yields considerable performance improvement over existing approaches.

In frequency division duplex (FDD) massive MIMO systems, each user equipment (UE) device is required to frequently feed back downlink channel state information to its serving base station (BS), resulting in huge uplink overhead. This paper considers the problem of downlink channel feedback in practical wideband massive MIMO systems operating in the FDD mode. The starting point is to leverage the spatial structure of the BS antenna array to reshape the channel matrix in tensor form, and then bring in orthonormal Tucker (also known as multilinear / higher-order SVD) factorization to approximate the channel tensor on the UE side, and feed the obtained factors back to the BS. The resulting decomposition results in a significant reduction in the number of parameters that needs to be fed back from each UE to its serving BS, without sacrificing channel estimation accuracy. Furthermore, this paper exploits a recently-developed sampling/compression mechanism that efficiently handles the considered problem. Using this approach, each UE is only required to feed back a small number of samples/measurements from the channel tensor, thus shifting the computational burden of tensor decomposition/compression to the BS which naturally possesses higher computational capabilities compared to the UE. Experimental results using channels constructed from a practical ray-tracing simulator demonstrate the superiority of the proposed tensor-based approaches relative to the state-of-the-art.