Massive hybrid antenna array for millimeter-wave cellular communications

© 2002-2012 IEEE. A massive hybrid array consists of multiple analog subarrays, with each subarray having its digital processing chain. It offers the potential advantage of balancing cost and performance for massive arrays and therefore serves as an attractive solution for future millimeter-wave (mm- Wave) cellular communications. On one hand, using beamforming analog subarrays such as phased arrays, the hybrid configuration can effectively collect or distribute signal energy in sparse mm-Wave channels. On the other hand, multiple digital chains in the configuration provide multiplexing capability and more beamforming flexibility to the system. In this article, we discuss several important issues and the state-of-the-art development for mm-Wave hybrid arrays, such as channel modeling, capacity characterization, applications of various smart antenna techniques for single-user and multiuser communications, and practical hardware design. We investigate how the hybrid array architecture and special mm-Wave channel property can be exploited to design suboptimal but practical massive antenna array schemes. We also compare two main types of hybrid arrays, interleaved and localized arrays, and recommend that the localized array is a better option in terms of overall performance and hardware feasibility

A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications

Extremely large-scale multiple-input-multiple-output (XL-MIMO), which offers vast spatial degrees of freedom, has emerged as a potentially pivotal enabling technology for the sixth generation (6G) of wireless mobile networks. With its growing significance, both opportunities and challenges are concurrently manifesting. This paper presents a comprehensive survey of research on XL-MIMO wireless systems. In particular, we introduce four XL-MIMO hardware architectures: uniform linear array (ULA)-based XL-MIMO, uniform planar array (UPA)-based XL-MIMO utilizing either patch antennas or point antennas, and continuous aperture (CAP)-based XL-MIMO. We comprehensively analyze and discuss their characteristics and interrelationships. Following this, we examine exact and approximate near-field channel models for XL-MIMO. Given the distinct electromagnetic properties of near-field communications, we present a range of channel models to demonstrate the benefits of XL-MIMO. We further motivate and discuss low-complexity signal processing schemes to promote the practical implementation of XL-MIMO. Furthermore, we explore the interplay between XL-MIMO and other emergent 6G technologies. Finally, we outline several compelling research directions for future XL-MIMO wireless communication systems.Comment: 38 pages, 10 figure

Wearable Wireless Devices

No abstract available

Wearable Wireless Devices

No abstract available

Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks

Despite the basic premise that next-generation wireless networks (e.g., 6G) will be artificial intelligence (AI)-native, to date, most existing efforts remain either qualitative or incremental extensions to existing ``AI for wireless'' paradigms. Indeed, creating AI-native wireless networks faces significant technical challenges due to the limitations of data-driven, training-intensive AI. These limitations include the black-box nature of the AI models, their curve-fitting nature, which can limit their ability to reason and adapt, their reliance on large amounts of training data, and the energy inefficiency of large neural networks. In response to these limitations, this article presents a comprehensive, forward-looking vision that addresses these shortcomings by introducing a novel framework for building AI-native wireless networks; grounded in the emerging field of causal reasoning. Causal reasoning, founded on causal discovery, causal representation learning, and causal inference, can help build explainable, reasoning-aware, and sustainable wireless networks. Towards fulfilling this vision, we first highlight several wireless networking challenges that can be addressed by causal discovery and representation, including ultra-reliable beamforming for terahertz (THz) systems, near-accurate physical twin modeling for digital twins, training data augmentation, and semantic communication. We showcase how incorporating causal discovery can assist in achieving dynamic adaptability, resilience, and cognition in addressing these challenges. Furthermore, we outline potential frameworks that leverage causal inference to achieve the overarching objectives of future-generation networks, including intent management, dynamic adaptability, human-level cognition, reasoning, and the critical element of time sensitivity

Chaotic exploration and learning of locomotor behaviours

Recent developments in the embodied approach to understanding the generation of adaptive behaviour, suggests that the design of adaptive neural circuits for rhythmic motor patterns should not be done in isolation from an appreciation, and indeed exploitation, of neural-body-environment interactions. Utilising spontaneous mutual entrainment between neural systems and physical bodies provides a useful passage to the regions of phase space which are naturally structured by the neuralbody- environmental interactions. A growing body of work has provided evidence that chaotic dynamics can be useful in allowing embodied systems to spontaneously explore potentially useful motor patterns. However, up until now there has been no general integrated neural system that allows goal-directed, online, realtime exploration and capture of motor patterns without recourse to external monitoring, evaluation or training methods. For the first time, we introduce such a system in the form of a fully dynamic neural system, exploiting intrinsic chaotic dynamics, for the exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modelled as a network of neural oscillators which are coupled only through physical embodiment, and goal directed exploration of coordinated motor patterns is achieved by a chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organised dynamics each of which is a candidate for a locomotion behaviour. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states using its intrinsic chaotic dynamics as a driving force and stabilises the system on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced which results in an increased diversity of motor outputs, thus achieving multi-scale exploration. A rhythmic pattern discovered by this process is memorised and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronisation method. The dynamical nature of the weak coupling through physical embodiment allows this adaptive weight learning to be easily integrated, thus forming a continuous exploration-learning system. Our result shows that the novel neuro-robotic system is able to create and learn a number of emergent locomotion behaviours for a wide range of body configurations and physical environment, and can re-adapt after sustaining damage. The implications and analyses of these results for investigating the generality and limitations of the proposed system are discussed

OFDM Waveform Optimisation for Joint Communications and Sensing

Radar systems are radios to sense objects in their surrounding environment. These operate at a defined set of frequency ranges. Communication systems are used to transfer information between two points. In the present day, proliferation of mobile devices and the advancement of technology have led to communication systems being ubiquitous. This has made these systems to operate at the frequency bands already used by the radar systems. Thus, the communication signal interferes a radar receiver and vice versa, degrading performance of both systems. Different methods have been proposed to combat this phenomenon. One of the novel topics in this is the RF convergence, where a given bandwidth is used jointly by both systems. A differentiation criterion must be adopted between the two systems so that a receiver is able to separately extract radar and communication signals. The hardware convergence due to the emergence of software-defined radios also motivated a single system be used for both radar and communication. A joint waveform is adopted for both radar and communication systems, as the transmit signal. As orthogonal frequency-division multiplexing (OFDM) waveform is the most prominent in mobile communications, it is selected as the joint waveform. Considering practical cellular communication systems adopting OFDM, there often exist unused subcarriers within OFDM symbols. These can be filled up with arbitrary data to improve the performance of the radar system. This is the approach used, where the filling up is performed through an optimisation algorithm. The filled subcarriers are termed as radar subcarriers while the rest as communication subcarriers, throughout the thesis. The optimisation problem minimises the Cramer--Rao lower bounds of the delay and Doppler estimates made by the radar system subject to a set of constraints. It also outputs the indices of the radar and communication subcarriers within an OFDM symbol, which minimise the lower bounds. The first constraint allocates power between radar and communication subcarriers depending on their subcarrier ratio in an OFDM symbol. The second constraint ensures the peak-to-average power ratio (PAPR) of the joint waveform has an acceptable level of PAPR. The results show that the optimised waveform provides significant improvement in the Cramer--Rao lower bounds compared with the unoptimised waveform. In compensation for this, the power allocated to the communication subcarriers needs to be reduced. Thus, improving the performances of the radar and communication systems are a trade-off. It is also observed that for the minimum lower bounds, radar subcarriers need to be placed at the two edges of an OFDM symbol. Optimisation is also seen to improve the estimation performance of a maximum likelihood estimator, concluding that optimising the subcarriers to minimise a theoretical bound enables to achieve improvement for practical systems