Towards ubiquitous radio access using nanodiamond based quantum receivers

The development of sixth-generation (6G) wireless communication systems demands innovative solutions to address challenges in the deployment of a large number of base stations and the detection of multi-band signals. Quantum technology, specifically nitrogen vacancy (NV) centers in diamonds, offers promising potential for the development of compact, robust receivers capable of supporting multiple users. For the first time, we propose a multiple access scheme using fluorescent nanodiamonds (FNDs) containing NV centers as nano-antennas. The unique response of each FND to applied microwaves allows for distinguishable patterns of fluorescence intensities, enabling multi-user signal demodulation. We demonstrate the effectiveness of our FNDs-implemented receiver by simultaneously transmitting two uncoded digitally modulated information bit streams from two separate transmitters, achieving a low bit error ratio. Moreover, our design supports tunable frequency band communication and reference-free signal decoupling, reducing communication overhead. Furthermore, we implement a miniaturized device comprising all essential components, highlighting its practicality as a receiver serving multiple users simultaneously. This approach paves the way for the integration of quantum sensing technologies in future 6G wireless communication networks.

IQ-aware precoding for atomic MIMO receivers

Leveraging the strong atom-light interaction, Rydberg atomic receivers significantly enhance the sensitivity of electromagnetic signal measurements, outperforming traditional antennas. Existing research primarily focuses on improving the architecture and signal detection algorithms of atomic receivers, while established signal processing schemes at the transmitter end have remained constant. However, these schemes fail to maximize the throughput of atomic receivers due to the nonlinearity of transmission model. To address this issue, we propose to design transmitter precoding in multiple-input multiple-output systems to achieve the capacity of atomic receivers. Initially, we harness a strong reference approximation to convert the nonlinear magnitude-detection model of atomic receivers into a linear real-part detector. Based on this approximation, we prove that the degree of freedom is min{Nr/2,Nt} for a MIMO system comprising an Nr-antenna atomic receiver and an Nt-antenna classic transmitter. To achieve the system capacity, we propose an IQ-aware fully digital precoding method. Unlike traditional complex-valued digital precoders that jointly manipulate the inphase and quadrature (IQ) symbols, our method employs four real matrices to independently precode the IQ baseband symbols, which is shown to be optimal for atomic receivers. Then, to eliminate the reliance on fully digital precoding architecture, we further explore IQ-aware hybrid precoding techniques. Our design incorporates a low-dimensional IQ-aware digital precoder and a high-dimensional complex analog precoder. Alternating minimization algorithms are proposed to produce IQ-aware hybrid precoders, with the objective of approaching the optimal IQ-aware fully digital precoder. Simulation results validate the superiority of proposed IQ-aware precoding methods over existing techniques in atomic MIMO communications.

A Survey on Integrated Sensing, Communication, and Computation

The forthcoming generation of wireless technology, 6G, promises a revolutionary leap beyond traditional data-centric services. It aims to usher in an era of ubiquitous intelligent services, where everything is interconnected and intelligent. This vision requires the seamless integration of three fundamental modules: Sensing for information acquisition, communication for information sharing, and computation for information processing and decision-making. These modules are intricately linked, especially in complex tasks such as edge learning and inference. However, the performance of these modules is interdependent, creating a resource competition for time, energy, and bandwidth. Existing techniques like integrated communication and computation (ICC), integrated sensing and computation (ISC), and integrated sensing and communication (ISAC) have made partial strides in addressing this challenge, but they fall short of meeting the extreme performance requirements. To overcome these limitations, it is essential to develop new techniques that comprehensively integrate sensing, communication, and computation. This integrated approach, known as Integrated Sensing, Communication, and Computation (ISCC), offers a systematic perspective for enhancing task performance. This paper begins with a comprehensive survey of historic and related techniques such as ICC, ISC, and ISAC, highlighting their strengths and limitations. It then explores the state-of-the-art signal designs for ISCC, along with network resource management strategies specifically tailored for ISCC. Furthermore, this paper discusses the exciting research opportunities that lie ahead for implementing ISCC in future advanced networks. By embracing ISCC, we can unlock the full potential of intelligent connectivity, paving the way for groundbreaking applications and services.

Resource Management for Low-latency Cooperative Fine-tuning of Foundation Models at the Network Edge

The emergence of large-scale foundation models (FoMo's) that can perform human-like intelligence motivates their deployment at the network edge for devices to access state-of-the-art artificial intelligence. For better user experiences, the pre-trained FoMo's need to be adapted to specialized downstream tasks through fine-tuning techniques. To transcend a single device's memory and computation limitations, we advocate multi-device cooperation within the device-edge cooperative fine-tuning (DEFT) paradigm, where edge devices cooperate to simultaneously optimize different parts of fine-tuning parameters within a FoMo. However, the parameter blocks reside at different depths within a FoMo architecture, leading to varied computation latency-and-memory cost due to gradient backpropagation-based calculations. The heterogeneous on-device computation and memory capacities and channel conditions necessitate an integrated communication-and-computation allocation of local computation loads and communication resources to achieve low-latency (LoLa) DEFT. To this end, we consider the depth-ware DEFT block allocation problem. The involved optimal block-device matching is tackled by the proposed low-complexity Cutting-RecoUNting-CHecking (CRUNCH) algorithm, which is designed by exploiting the monotone-increasing property between block depth and computation latency-and-memory cost. Next, the joint bandwidth-and-block allocation makes the problem more sophisticated. We observe a splittable Lagrangian expression through the transformation and analysis of the original problem, where the variables indicating device involvement are introduced. Then, the dual ascent method is employed to tackle this problem iteratively. Through extensive experiments conducted on the GLUE benchmark, our results demonstrate significant latency reduction achievable by LoLa DEFT for fine-tuning a RoBERTa model.

Mobile Edge Generation-Enabled Digital Twin: Architecture Design and Research Opportunities

A novel paradigm of mobile edge generation (MEG)-enabled digital twin (DT) is proposed, which enables distributed on-device generation at mobile edge networks for real-time DT applications. First, an MEG-DT architecture is put forward to decentralize generative artificial intelligence (GAI) models onto edge servers (ESs) and user equipments (UEs), which has the advantages of low latency, privacy preservation, and individual-level customization. Then, various single-user and multi-user generation mechanisms are conceived for MEG-DT, which strike trade-offs between generation latency, hardware costs, and device coordination. Furthermore, to perform efficient distributed generation, two operating protocols are explored for transmitting interpretable and latent features between ESs and UEs, namely sketch-based generation and seed-based generation, respectively. Based on the proposed protocols, the convergence between MEG and DT are highlighted. Considering the seed-based image generation scenario, numerical case studies are provided to reveal the superiority of MEG-DT over centralized generation. Finally, promising applications and research opportunities are identified.

Multi-beam Training for Near-field Communications in High-frequency Bands

In this paper, we study efficient multi-beam training design for near-field communications to reduce the beam training overhead of conventional single-beam training methods. In particular, the array-division based multi-beam training method, which is widely used in far-field communications, cannot be directly applied to the near-field scenario, since different sub-arrays may observe different user angles and there exist coverage holes in the angular domain. To address these issues, we first devise a new near-field multi-beam codebook by sparsely activating a portion of antennas to form a sparse linear array (SLA), hence generating multiple beams simultaneously by effective exploiting the near-field grating-lobs. Next, a two-stage near-field beam training method is proposed, for which several candidate user locations are identified firstly based on multi-beam sweeping over time, followed by the second stage to further determine the true user location with a small number of single-beam sweeping. Finally, numerical results show that our proposed multi-beam training method significantly reduces the beam training overhead of conventional single-beam training methods, yet achieving comparable rate performance in data transmission.

Waveforms for Computing Over the Air

Over-the-air computation (AirComp) leverages the signal-superposition characteristic of wireless multiple access channels to perform mathematical computations. Initially introduced to enhance communication reliability in interference channels and wireless sensor networks, AirComp has more recently found applications in task-oriented communications, namely, for wireless distributed learning and in wireless control systems. Its adoption aims to address latency challenges arising from an increased number of edge devices or IoT devices accessing the constrained wireless spectrum. This paper focuses on the physical layer of these systems, specifically on the waveform and the signal processing aspects at the transmitter and receiver to meet the challenges that AirComp presents within the different contexts and use cases.

TrimCaching: Parameter-sharing AI Model Caching in Wireless Edge Networks

Next-generation mobile networks are expected to facilitate fast AI model downloading to end users. By caching models on edge servers, mobile networks can deliver models to end users with low latency, resulting in a paradigm called edge model caching. In this paper, we develop a novel model placement scheme, called parameter-sharing model caching (TrimCaching). TrimCaching exploits the key observation that a wide range of AI models, such as convolutional neural networks or large language models, can share a significant proportion of parameter blocks containing reusable knowledge, thereby improving storage efficiency. To this end, we formulate a parameter-sharing model placement problem to maximize the cache hit ratio in multi-edge wireless networks by balancing the fundamental tradeoff between storage efficiency and service latency. We show that the formulated problem is a submodular maximization problem with submodular constraints, for which no polynomial-time approximation algorithm exists. To overcome this challenge, we study an important special case, where a small fixed number of parameter blocks are shared across models, which often holds in practice. In such a case, a polynomial-time algorithm with $\left(1-\epsilon\right)/2$-approximation guarantee is developed. Subsequently, we address the original problem for the general case by developing a greedy algorithm. Simulation results demonstrate that the proposed TrimCaching framework significantly improves the cache hit ratio compared with state-of-the-art content caching without exploiting shared parameters in AI models.

Over-the-Air Fusion of Sparse Spatial Features for Integrated Sensing and Edge AI over Broadband Channels

The 6G mobile networks are differentiated from 5G by two new usage scenarios - distributed sensing and edge AI. Their natural integration, termed integrated sensing and edge AI (ISEA), promised to create a platform for enabling environment perception to make intelligent decisions and take real-time actions. A basic operation in ISEA is for a fusion center to acquire and fuse features of spatial sensing data distributed at many agents. To overcome its communication bottleneck due to multiple access by numerous agents over hostile wireless channels, we propose a novel framework, called Spatial Over-the-Air Fusion (Spatial AirFusion), which exploits radio waveform superposition to aggregate spatially sparse features over the air. The technology is more sophisticated than conventional Over-the-Air Computing (AirComp) as it supports simultaneous aggregation over multiple voxels, which partition the 3D sensing region, and across multiple subcarriers. Its efficiency and robustness are derived from exploitation of both spatial feature sparsity and multiuser channel diversity to intelligently pair voxel-level aggregation tasks and subcarriers to maximize the minimum receive SNR among voxels under instantaneous power constraints. To optimally solve the mixed-integer Voxel-Carrier Pairing and Power Allocation (VoCa-PPA) problem, the proposed approach hinges on two useful results: (1) deriving the optimal power allocation as a closed-form function of voxel-carrier pairing and (2) discovering a useful property of VoCa-PPA that dramatically reduces the solution-space dimensionality. Both a low-complexity greedy algorithm and an optimal tree-search based approach are designed for VoCa-PPA. Extensive simulations using real datasets show that Spatial AirFusion achieves significant error reduction and accuracy improvement compared with conventional AirComp without awareness of spatial sparsity.

Digital Over-the-Air Computation: Achieving High Reliability via Bit-Slicing

6G mobile networks aim to realize ubiquitous intelligence at the network edge via distributed learning, sensing, and data analytics. Their common operation is to aggregate high-dimensional data, which causes a communication bottleneck that cannot be resolved using traditional orthogonal multi-access schemes. A promising solution, called over-the-air computation (AirComp), exploits channels' waveform superposition property to enable simultaneous access, thereby overcoming the bottleneck. Nevertheless, its reliance on uncoded linear analog modulation exposes data to perturbation by noise and interference. Hence, the traditional analog AirComp falls short of meeting the high-reliability requirement for 6G. Overcoming the limitation of analog AirComp motivates this work, which focuses on developing a framework for digital AirComp. The proposed framework features digital modulation of each data value, integrated with the bit-slicing technique to allocate its bits to multiple symbols, thereby increasing the AirComp reliability. To optimally detect the aggregated digital symbols, we derive the optimal maximum a posteriori detector that is shown to outperform the traditional maximum likelihood detector. Furthermore, a comparative performance analysis of digital AirComp with respect to its analog counterpart with repetition coding is conducted to quantify the practical signal-to-noise ratio (SNR) regime favoring the proposed scheme. On the other hand, digital AirComp is enhanced by further development to feature awareness of heterogeneous bit importance levels and its exploitation in channel adaptation. Lastly, simulation results demonstrate the achivability of substantial reliability improvement of digital AirComp over its analog counterpart given the same channel uses.