Localization is a key feature of future Sixth Generation (6G) networks with foreseen accuracy requirements down to the millimeter level, to enable novel applications in the fields of telesurgery, high-precision manufacturing, and others. Currently, such accuracy requirements are only achievable with specialized or highly resource-demanding systems, rendering them impractical for more wide-spread deployment. In this paper, we present the first system that enables low-complexity and low-bandwidth absolute 3D localization with millimeter-level accuracy in generic wireless networks. It performs a carrier phase-based wireless localization refinement of an initial location estimate based on successive location-likelihood optimization across multiple bands. Unlike previous phase unwrapping methods, our solution is one-shot. We evaluate its performance collecting ∼ 350, 000 measurements, showing an improvement of more than one order of magnitude over classical localization techniques. Finally, we will open-source the low-cost, modular FR3 front-end that we developed for the experimental campaign.

Mobile networks are evolving rapidly, with 6G promising unprecedented capabilities in terms of data rates and ultra-low latencies. However, the development of testbed platforms for wireless experimentation has not kept pace. Existing platforms typically offer either end-to-end capabilities with low bandwidth or high bandwidth with limited or no real-time functionality. In this paper, we introduce HELIX, an experimentation platform with 6G scalable real-time capabilities. HELIX integrates a comprehensive physical layer subsystem with multi-numerology support alongside an advanced mixed software-hardware control unit responsible for interacting with the fronthaul network and dynamically configuring the functional split in real time. On the server side, we implement the necessary drivers and routines to enable seamless integration with O-RAN systems, thus facilitating open and end-to-end experimentation. We demonstrate the capabilities of HELIX through a variety of experiments at sub-6 GHz, 28 GHz, and 60 GHz frequencies. Notably, HELIX achieves data rates of up to 1200 Mbps using 256-QAM modulation with over 417 MHz of bandwidth, and end-to-end bidirectional latencies of 500 μs. We show advanced features, including the implementation of Integrated Sensing And Communication (ISAC), and discuss how the platform could be extended to support bandwidths of up to 1670 MHz.

Machine Learning (ML) is a promising tool to design wireless physical layer (PHY) components. It is particularly interesting for millimeter-wave (mm-wave) frequencies and above, due to the more challenging hardware design and channel environment at these frequencies. Rather than building individual ML-components, in this paper, we design an entire ML-enhanced mm-wave receiver for frequency selective channels. Our ML-receiver jointly optimizes the channel estimation, equalization, phase correction and demapper using Convolutional Neural Networks. We also show that for mm-wave systems, the channel varies significantly even over short timescales, requiring frequent channel measurements, and this situation is exacerbated in mobile scenarios. To tackle this, we propose a new ML-channel estimation approach that refreshes the channel state information using the guard intervals (not intended for channel measurements) that are available for every block of symbols in communication packets. To the best of our knowledge, our ML-receiver is the first work to outperform conventional receivers in general scenarios, with simulation results showing up to 7 dB gains. We also provide an experimental validation of the ML-enhanced receiver with a 60 GHz FPGA-based testbed with phased antenna arrays, which shows a throughput increase by a factor of up to 6 over baseline schemes in mobile scenarios.

With the advancement of the Internet of Things (IoT), many devices will be connected to the Internet, enabling digital twin and smart home applications. However, currently, these IoT devices are operating at lower frequency bands of the wireless spectrum, typically ranging from a few hundred MHz (such as RFID and LoRa) to a few GHz (such as BLE and WiFi). As a result, the current IoT devices not only place a huge strain on these bands, but also cannot benefit from the large bandwidth available in the higher frequencies of the spectrum such as mmWave bands. In this paper, our goal is to bring mmWave technology to existing IoT devices so they can benefit from the advantages this technology offers, such as high network capacity, low interference, and Space Division Multiple Access. To this end, we design mmPlug, a novel plug-and-play module which is simple and energy-efficient. mmPlug can be easily connected to the antenna port of any IoT device, enabling it to operate in the mmWave band. mm-Plug is compatible with different wireless technologies (such as WiFi, Lora, etc.) and does not require any modification to the circuit, firmware or communication protocols of the existing IoT devices. mmPlug achieves this by a novel design which can seamlessly be connected to the antenna port of the IoT device. We have implemented mmPlug on PCB and empirically evaluated its performance. Our results show that mmPlug enables existing IoT devices (such as WiFi and Lora) to operate at mmWave band while achieving accurate localization, uplink and downlink even when they are more than 30 m far from the access point.

Mm-wave communications use analog beamforming techniques, which steer the signal energy in a desired direction, to overcome the high path-loss at such frequencies. To determine the direction in which to steer, mm-wave standards such as IEEE 802.11ad specify beam training mechanisms for both access points as well as client stations. However, the overhead of the beam training limits scalability as the density of network deployments increases and mobile devices that require constantre-trainingare supported. We design SPIDER, a low-overhead beam-training mechanism where only access points actively participate in the training and stations perform passive compressive estimation of the angle-of-arrival. To this end, stations carry out phase-coherent measurements by switching through multiple receive beam patterns on a time-scale of tens of nanoseconds when receiving a packet preamble. Since no suitable testbed platforms exist that support such fast antenna reconfiguration, we design a high-performance, full-bandwidth FPGA-based testbed platform for flexible mm-wave experimentation, that we make available as open source. The performance analysis with this testbed shows that our algorithm achieves highly accurate angle estimation used to drive the beam steering decisions and reduces overhead by an order of magnitude compared to IEEE 802.11ad beam training.

The performance of wireless communication systems is evolving rapidly, making it difficult to build experimentation platforms that meet the hardware requirements of new standards. The bandwidth of current systems ranges from 160 MHz for IEEE 802.11ac/ax to 2 GHz for Millimeter-Wave (mm-wave) IEEE 802.11ad/ay, and they support up to 8 spatial MIMO streams. Mobile 5G and beyond systems have a similarly diverse set of requirements.