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.

Joint Communication and Sensing (JCAS) is expected to play a critical role in next-generation wireless networks such as 6G. For complex sensing tasks, such as 3D pose estimation for virtual reality (VR) applications, accurate channel impulse response (CIR) or I/Q samples as well as processing using a neural network is required. Due to the higher bandwidth and antenna array sizes of future wireless networks, it is expected that offloading this data to a remote server for processing would require data rates in the order of 100s of Megabits per second, which is an unreasonable amount of overhead. Therefore it is necessary to preprocess the sensing data locally, and reduce the raw data to useful intermediary features, to mimimize the sensing data transmission overhead, especially when using multiple sensing devices. This paper proposes a method leveraging split inference to distribute neural networks across multiple devices, which achieves high accuracy while addressing the sensing data transfer bottleneck. We evaluate the performance of the proposed method in a VR gaming scenario, where mmWave Wi-Fi signals are used for 3D pose estimation. We show that split inference allows for reducing the communication overhead by three orders of magnitude compared to the centralised approach, while only losing 10% of accuracy. These results pave the way for future work, exploring highly distributed multi-static JCAS as a practical and efficient method of sensing.

This paper undertakes a holistic investigation of two fundamental trade-offs in monostatic OFDM integrated sensing and communication (ISAC) systems, namely, the time-frequency trade-off and the spatial trade-off, originating from the choice of modulation order for random data and the design of beamforming strategies, respectively. To counteract the elevated side-lobe levels induced by varying-amplitude data in high-order QAM signaling, we introduce a novel linear minimum mean-squared-error (LMMSE) estimator. We also provide a rigorous theoretical characterization of side-lobe levels achieved by the proposed LMMSE estimator and two benchmark schemes, proving its superiority for any modulation scheme and SNR level. Moreover, we explore spatial domain trade-offs through two ISAC transmission strategies: concurrent, employing joint beams, and time-sharing, using separate beams for sensing and communications not overlapping in time. Simulations demonstrate improved performance of the LMMSE estimator, especially in detecting weak targets in the presence of strong ones with high-order QAM, consistently yielding more favorable ISAC trade-offs than existing baselines under various modulation schemes, SNR conditions, RCS levels and transmission strategies. Additionally, we present experimental results to validate the effectiveness of the LMMSE estimator in reducing side-lobe levels, based on real-world measurements

Integrated Sensing And Communication (ISAC) systems are expected to perform accurate radar sensing while having minimal impact on communication. Ideally, sensing should only reuse communication resources, especially for spectrum which is contended by many applications. However, this poses a great challenge in that communication systems often operate on narrow subbands with low sensing resolution. Combining contiguous subbands has shown significant resolution gain in active localization. However, multiband ISAC remains unexplored due to communication subbands being highly sparse (non-contiguous) and affected by phase offsets that prevent their aggregation (incoherent). To tackle these problems, we design HiSAC, the first multiband ISAC system that combines diverse subbands across a wide frequency range to achieve super-resolved passive ranging. To solve the non-contiguity and incoherence of subbands, HiSAC combines them progressively, exploiting an anchor propagation path between transmitter and receiver in an optimization problem to achieve phase coherence. HiSAC fully reuses pilot signals in communication systems, applies to different frequencies, and can combine diverse technologies, e.g., 5G-NR and WiGig. We implement HiSAC on an experimental platform in the millimeter-wave unlicensed band and test it on objects and humans. Our results show it enhances the sensing resolution by up to 20 times compared to single-band processing while occupying the same spectrum.

mmWave communication has come up as the un-explored spectrum for 5G services. With new standards for 5G NR positioning, more off-the-shelf platforms and algorithms are needed to perform indoor positioning. An object can be accurately positioned in a room either by using an angle and a delay estimate or two angle estimates or three delay estimates. We propose an algorithm to jointly estimate the angle of arrival (AoA) and angle of departure (AoD), based only on the received signal strength (RSS). We use mm-FLEX, an experimentation platform developed by IMDEA Networks Institute that can perform realtime signal processing for experimental validation of our proposed algorithm. Codebook-based beampatterns are used with a uniquely placed multi-antenna array setup to enhance the reception of multipath components and we obtain an AoA estimate per receiver thereby overcoming the line-of-sight (LoS) limitation of RSS-based localization systems. We further validate the results from measurements by emulating the setup with a simple ray-tracing approach.

In this work we present RAPID, the first joint communication and radar system based on next-generation IEEE 802.11ay WiFi networks operating in the 60 GHz band. Unlike existing approaches for human sensing at millimeter-wave frequencies, which rely on special-purpose radars, RAPID achieves radar-level sensing accuracy with IEEE 802.11ay access points, thus avoiding the burden of installing ad-hoc sensors. RAPID enables contactless human sensing applications, such as people tracking, Human Activity Recognition (HAR), and person identification without requiring modifications to the standard packet structure. Specifically, we leverage IEEE 802.11ay beam training to accurately localize and track multiple individuals within the same environment. Then, we propose a new way of using beam tracking to extract micro-Doppler signatures from the time-varying Channel Impulse Response (CIR) estimated fromreflectedpackets. Such signatures are fed to a deep learning classifier to perform HAR and person identification. RAPID is implemented on a cutting-edge IEEE 802.11ay-compatible FPGA platform with phased antenna arrays, and evaluated on a large dataset of CIR measurements. It is robust across different environments and subjects, and outperforms state-of-the-art sub-6 GHz WiFi sensing techniques. Using two access points, RAPID reliably tracks multiple subjects, reaching HAR and person identification accuracies of$94\%$and$90\%$, respectively.

Wideband millimeter-wave communication systems can be extended to provide radar-like sensing capabilities on top of data communication, in a cost-effective manner. However, the development ofjoint communication and sensingtechnology is hindered by practical challenges, such as occlusions to the line-of-sight path and clock asynchrony between devices. The latter introducestime-varyingtiming and frequency offsets that prevent the estimation of sensing parameters and, in turn, the use of standard signal processing solutions. Existing approaches cannot be applied to commonly used phased-array receivers, as they build on stringent assumptions about the multipath environment, and are computationally complex. We present JUMP, the first system enablingpracticalbistatic and asynchronous joint communication and sensing, while achieving accurate target tracking and micro-Doppler extraction in realistic conditions. Our system compensates for the timing offset by exploiting the channel correlation across subsequent packets. Further, it tracks multipath reflections and eliminates frequency offsets by observing the phase of a dynamically-selected static reference path. JUMP has been implemented on a 60 GHz experimental platform, performing extensive evaluations of human motion sensing, including non-line-of-sight scenarios. In our results, JUMP attains comparable tracking performance to a full-duplex monostatic system and similar micro-Doppler quality with respect to a phase-locked bistatic receiver.

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.

Millimeter-Wave (mmWave) WiFi can provide very low latency and multi-Gbps throughput, but real-world deployments usually do not achieve the theoretically feasible performance. One main source of inefficiency is the contention-based random channel access, as it requires omni-directional reception which limits performance. Additionally, carrier sensing at mmWave frequencies is highly unreliable, leading to reduced channel usage. In this paper, we present SIGNalling in the PHY Preamble (SIGNiPHY) for efficient directional communications, a solution that allows to embed user identity in the preamble of data packets. It allows for true early user identification and then immediately steering the beam towards the transmitter while receiving the physical layer preamble. SIGNiPHY enables directional reception in random access mmWave networks, and additionally helps to quickly filter unwanted packets. It does not affect any preamble functions and is backward-compatible with legacy stations. We implement SIGNiPHY on an FPGA-based mmWave testbed and show that it achieves 99.6% decoding accuracy even under very low SINR conditions. We also implement SIGNiPHY in ns-3 to evaluate large networks and show that it achieves throughput gains between 13% and 230% compared to different baseline schemes, due to the lower packet loss rate and improved spatial sharing.

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.

A well established method to detect and classify human movements using Millimeter-Wave (mmWave) devices is the time-frequency analysis of the small-scale Doppler effect (termed micro-Doppler) of the different body parts, which requires a regularly spaced and dense sampling of the Channel Impulse Response (CIR). This is currently done in the literature either using special-purpose radar sen-sors, or interrupting communications to transmit dedicated sensing waveforms, entailing high overhead and channel utilization. In this work we present SPARCS, an integrated human sensing and commu-nication solution for mmWave systems. SPARCS is the first method that reconstructs high quality signatures of human movement from irregular and sparse CIR samples, such as the ones obtained during communication traffic patterns. To accomplish this, we formulate the micro-Doppler extraction as a sparse recovery problem, which is critical to enable a smooth integration between communication and sensing. Moreover, if needed, our system can seamlessly inject short CIR estimation fields into the channel whenever communication traffic is absent or insufficient for the micro-Doppler extraction. SPARCS effectively leverages the intrinsic sparsity of the mmWave channel, thus drastically reducing the sensing overhead with re-spect to available approaches. We implemented SPARCS on an IEEE 802.11ay Software Defined Radio (SDR) platform working in the 60 GHz band, collecting standard-compliant CIR traces matching the traffic patterns of real WiFi access points. Our results show that the micro-Doppler signatures obtained by SPARCS enable a typical downstream application such as human activity recognition with more than 7 times lower overhead with respect to existing methods, while achieving better recognition performance.

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.

Millimeter-wave systems not only provide high data rates and low latency, but the very large bandwidth also allows for highly accurate environment sensing. Such properties are extremely useful for smart factory scenarios. At the same time, reusing existing communication links for passive object localization is significantly more challenging than radar-based approaches due to the sparsity of the millimeter-wave multi-path environment and the weakness of the reflected paths compared to the line-of-sight path. In this paper, we explore the passive object localization accuracy that can be achieved with IEEE 802.11ad devices. We use commercial Access Points (APs) whereas the station design is based on a full-bandwidth 802.11ad compatible FPGA-based platform with a phased antenna array. The stations exploit the preamble of the beam training packets of the APs to obtain Channel Impulse Response (CIR) measurements for all antenna patterns. With this, we determine distance and angle information for the different multi-path components in the environment to passively localize a mobile object. We evaluate our system with multiple APs and a moving robot with a metallic surface. Our system operates in real-time and achieves 6.5cm mean error accuracy and sub-meter accuracy in 100% of the cases.

Millimeter-wave (mm-wave) communications, as any other emerging technology, require suitable experimentation platforms that allow validation and field tests in both academic and industry research environments. Existing platforms for mm-wave systems are based on Commercial-Off-The-Shelf (COTS) devices or expensive proprietary hardware platforms. In this paper we propose a mixed software-hardware testbed for mm-wave experimentation using Software Defined Radio (SDR) devices. Specifically, we design and implement the hardware processing blocks required to decode the preamble of frames that follow the structure of IEEE 802. Had compliant frames, working at a scaled-down bandwidth, along with their integration in X310 USRP devices using the RFNoC framework and 60GHz transceivers. The testbed is validated for different indoor channels with real-time Channel Impulse Response (CIR) measurements. The design exploits the maximum bandwidth for X310 devices while leaving enough FPGA logic space (≍ 60%), for further upgrades and extension of the system.

Millimeter-Wave (mm-wave) technology is increasingly being considered for mobile devices and use cases such as vehicular communication. This requires suitable experimentation platforms to support systems-oriented research to tackle the multitude of problems and challenges of mm-wave communications in such environments. To this end, we introduce mm-FLEX, a flexible and modular open platform with real-time signal processing capabilities that supports a bandwidth of 2 GHz and is compatible with mm-wave standard requirements. mm-FLEX integrates an FPGA-based baseband processor with full-duplex capabilities together with mm-wave RF front-ends and phased antenna arrays that are fully configurable from the processor in real-time. To demonstrate the capabilities of mm-FLEX, we implement a scalable, ultra-fast beam alignment mechanism for IEEE 802.11ad systems. It is based on compressive estimation of the signal's angle-of-arrival by means of switching through multiple receive beam patterns on a nano-second time-scale while receiving a packet preamble. Our implementation is open source and is made publicly available to the research community.

Machine learning is a highly promising tool to design the physical layer of wireless communication systems, but it usually requires that a channel model is known. As data rates increase and wireless transceivers become more complex, the wireless channel, hardware imperfections, and their interactions become more difficult to model and compensate explicitly. New machine learning schemes for the physical layer do not require an explicit model but implicitly learn the end-to-end link including channel characteristics and non-linearities of the system directly from the training data.

This paper presents a novel algorithm based on trellis min-max for decoding non-binary low-density parity-check (NB-LDPC) codes. This decoder reduces the number of messages exchanged between check node and variable node processors, which decreases the storage resources and the wiring congestion and, thus, increases the throughput of the decoder. Our frame error rate performance simulations show that the proposed algorithm has a negligible performance loss for high-rate codes with GF(16) and GF(32) and a performance loss smaller than 0.07 dB for high-rate codes over GF(64). In addition, a layered decoder architecture is presented and implemented on a 90-nm CMOS process for the following high-rate NB-LDPC codes: (2304, 2048) over GF(16), (837, 726) over GF(32), and (1536, 1344) over GF(64). In all cases, the achieved throughput is higher than 1 Gb/s.

Nonbinary LDPC codes outperform their binary counterparts in different scenarios. However, they require a considerable increase in complexity, especially in the check-node (CN) processor, for high-order Galois fields (GFs) higher than GF(16). To overcome this drawback, we propose an approximation for the trellis min-max algorithm that allows us to reduce the number of exchanged messages between the CN and the variable node compared with previous proposals from the literature. On the other hand, we reduce the complexity in the CN processor, keeping the parallel computation of messages. We implemented a layered scheduled decoder, based on this algorithm, in a 90-nm CMOS technology for the (837, 723) NB-LDPC code over GF(32) and the (1536, 1344) over GF(64), achieving an area saving of 16% and 36% for the CN and 10% and 12% for the whole decoder, respectively. The throughput is 1.07 and 1.26 Gb/s, which outperforms the state of the art of high-rate decoders with the high GF order from the literature.

A high-speed non-binary LDPC decoder based on Trellis Min-Max algorithm with layered schedule is presented. The proposed approach compresses the check-node output messages into a reduced set, decreasing the number of messages sent to the variable node. Additionally, the memory resources from the layered architecture are reduced. The proposed decoder was implemented for the (2304,2048) NB-LDPC code over GF(16) on a Virtex-7 FPGA and in a 90 nm CMOS process. Our implementation outperforms state-of-the-art NB-LDPC decoder implementations for both technologies, achieving a throughput of 630 and 965 Mbps, respectively.

Nonbinary low-density parity-check (NB-LDPC) codes have become an efficient alternative to their binary counterparts in different scenarios, such as moderate codeword lengths, high-order modulations, and burst error correction. Unfortunately, the complexity of NB-LDPC decoders is still too high for practical applications, especially for the check node (CN) processing, which limits the maximum achievable throughput. Although a great effort has been made in the recent literature to overcome this disadvantage, the proposed decoders are still not ready for high-speed implementations for high-order fields. In this paper, a simplified trellis min-max algorithm is proposed, where the CN messages are computed in a parallel way using only the most reliable information. The proposed CN algorithm is implemented using a horizontal layered schedule. The overall decoder architecture has been implemented in a 90-nm CMOS process for a (N = 837 and K = 726) NB-LDPC code over GF(32), achieving a throughput of 660 Mb/s at nine iterations based on postlayout results. This decoder increases hardware efficiency compared with the existing recent solutions for the same code.

In this brief, a method for compressing the messages between check nodes and variable nodes is proposed. This method is named compressed nonbinary message passing (CNBMP). CNBMP reduces the number of messages exchanged between one check node and the connected variable nodes from dc x q to 5 × q, and its application has a high impact on the performance of the decoder: the storage and routing areas are reduced and the throughput is increased. Unlike other methods, CNBMP does not introduce any approximation or modification in the information and the processed operations are exactly the same as those of the original decoders; hence, no performance degradation is introduced. To demonstrate its advantages, an architecture applying this CNBMP to the Trellis Min-Max algorithm was derived showing that most of the storage resources were also reduced from dc× q to 5 × q. This architecture was implemented for a (837 726) nonbinary low-density parity-check code using a 90-nm CMOS technology reaching a throughput of 981 Mb/s with an area of 10.67 mm2, which is 3.9 more efficient than the best solution found in the literature.

In this paper, we present a robust approach to estimate communications channel in OFDM systems exploiting the sparsity of the channel impulse response (CIR) commonly found in multi-path channels. The CIR is found as the solution of a regularized optimization problem where we minimize the least absolute deviation of a residual signal while, at the same time, encourage sparsity in the solution by an ℓ0pseudo-norm regularization term. The proposed approach reduces to estimate iteratively each tap of the CIR using a complex median based operator followed by a relevance test that forces sparsity in the solution. A blanking filter at the front end of the receiver is used to further mitigate the impact of the impulsive noise. Extensive simulations show that the proposed approach performs better than conventional approaches for AWGN channels and for situations where the additive noise follows a heavier-than-Gaussian tail distribution.