This demonstration addresses a key open challenge in Open Radio Access Network (O-RAN) deployments: how to intelligently allocate Transport Network (TN) resources to ensure low-latency for mission-critical applications. The demo emulates a Smart Factory scenario where the time-sensitive control traffic of robotic arms competes with industrial camera broadband video streams. We propose an intelligent transport controller that combines network slicing, Adaptive Neuro-Fuzzy Inference System (ANFIS), and Federated Learning (FL) to dynamically prioritize traffic per slice. The architecture uses $\mathbf{P 4}$ switches for local queue monitoring and real-time resource scheduling. The integration with the O-RAN disaggregated stack is based on Open Air Interface (OAI). Experimental results demonstrate valuable load balancing and buffer occupation reduction in the O-RAN midhaul.

User-centric (UC) distributed massive multiple-input multiple-output (D-mMIMO), commonly called cell-free mMIMO, is an essential technology for ensuring more uniform coverage and higher spectral and energy efficiencies in next-generation communication systems. This paper investigates an alternative effective channel estimation method to address the issue of the lower channel hardening degree experienced by UC D-mMIMO systems. Specifically, this paper proposes a hybrid approach for effective channel estimation, allowing users to estimate their channels either through downlink pilot-based training, by using a blind algorithm, or by relying on statistical channel state information (CSI). The hybrid estimation method is compared with the case where each method is applied individually, as well as with the ideal case of perfect CSI. The analysis is conducted using various system parameter settings, such as the number of antennas and users, and it accounts for the presence of pilot contamination. Simulation results reveal that the proposed hybrid channel estimation algorithm is able to provide the best spectral efficiency performance in any network parameter configuration, while reducing the estimation normalized mean-square error compared to conventional statistical CSI.

This paper investigates the performance of scalable user-centric (UC) distributed massive multiple-input multiple-output (D-mMIMO) systems with multiple central processing units (CPUs), commonly called cell-free mMIMO. Specifically, a framework incorporating processing capacity and inter-CPU communication constraints is proposed. Two methods are presented for limiting the number of radio units (RUs) serving each user equipment (UE). The first method is performed by the CPUs, while the second one is implemented at the UEs and RUs. Both methods prevent the computational complexity (CC) for channel estimation and precoding signals from increasing with the number of RUs. The backhaul signaling demands are presented and modeled, and it is considered that each CPU can serve only a restricted number of UEs managed by other CPUs to mitigate inter-CPU communication. Two strategies to adjust the RU clusters according to the network implementations are also proposed. We compare the proposed approaches with a traditional scalable UC system. Simulation results reveal that the proposed techniques allow UC systems to keep their spectral efficiency (SE) under minor degradation while reducing the CC by 98% and improving energy efficiency (EE). Besides, managing inter-CPU communication controls backhaul traffic effectively, and RU cluster adjustments further reduce CC.

This paper investigates the performance of scalable user-centric (UC) distributed massive multiple-input multiple-output (D-mMIMO) systems, widely known in the literature as cell-free mMIMO, with limited processing capacity. Specifically, it is assumed that the computational complexity (CC) of performing channel estimation and precoding signals does not increase with the number of access points (APs). In this regard, it is considered that each user equipment (UE) can only be associated with a finite number of APs. Moreover, a method is proposed for adjusting the AP clusters according to the network implementation, i.e., centralized or distributed. We compare the proposed approaches with a scalable UC system that does not perform AP cluster adjustment and does not prevent the processing demands from growing with the number of APs. Simulation results reveal that UC systems can keep the spectral efficiency (SE) under minor degradation even if the processing capacity is limited, reducing the CC by up to 96%. Besides, the proposed method for adjusting the AP cluster leads to further reductions in CC.

This paper investigates the performance of downlink (DL) pilot-based training to estimate the effective channel in user-centric cell-free massive multiple-input multiple-output (MIMO) networks. An algorithm for DL pilot assignment is proposed based on the level of interference between each user equipment (UE). It is proposed a refinement method for access point (AP) selection that controls the maximum AP cluster size of UEs. The strategy aims to control the maximum number of APs serving each UE to reduce the disparities among the AP cluster sizes. DL pilot-based training is compared with the blind, perfect and statistical channel state information (CSI) methods, assuming different precoding techniques, AP selection schemes, and the presence of pilot contamination. Our results demonstrate the following: (i) the proposed DL pilot assignment algorithm outperforms the baseline solutions; (ii) the proposed AP selection refinement method can improve the energy efficiency up to 86.6% without compromising the spectral efficiency; and (iii) DL pilot-based estimation reduces the normalized mean-square error significantly compared with blind and statistical CSI methods.

The 5G mobile communication systems introduce multiples functional splits between base station elements, new transmission bands and a large number of antennas supporting beamforming. In this scenario, a viable strategy to avoid building penetration losses is to deploy the antenna elements indoor and use a fronthaul link to establish the connection between them and the rest of the radio access network. This work explores a 5G fronthaul scheme based on analog radio over coaxial cables, leveraging this existing fixed access infrastructure to facilitate 5G deployments. Results indicate that the fronthaul solution discussed here is able to support the high capacity requirement of 5G, with the additional benefits of low transmit power, low latency and low cost. The main novelty of this paper is to investigate the potential of this kind of fronthaul architecture considering both the physical layer and economic aspects.

Massive Beamforniing systems in next-generation Centralized Radio Access Network architectures can substantially increase the requirements on fronthaul interfaces. This paper proposes a novel and efficient model to reduce the overhead generated by the transfer of beamforming coefficients between base station elements. This approach creates a compressed version for said coefficients, in the beamspace domain, using a clustering strategy over sets (windows) of most powerful beams. The method can be extended for any antenna configuration and also parameterized to adjust the trade-off between radio performance and the fronthaul load reduction. Simulations in typical mobile outdoor scenarios show that the proposed method can present Signal-to-Interference-plus-Noise-Ratio performance comparable to the usual dimensionality reduction approach, i.e., beam selection, while generating lower fronthaul overhead.

The default medium access mechanism for Wi-Fi, Distributed Coordination Function (DCF), is a simple contention based protocol aimed at providing a fair distribution of resources among Wi-Fi nodes. However, DCF suffers from significant performance degradation in the presence of dense deployments. Hence, improving the performance of the Wi-Fi MAC layer is essential for efficient spectrum sharing among overlapping Basic Subscriber Set (OBSS) for next generation wireless networks. In this context, in this paper, we compare DCF with an existing Wi-Fi mechanisms, Power-Save Multi-Poll (PSMP) and Hybrid Coordination Function (HCF) Controlled Channel Access (HCCA), and show that, although standard scheduled access techniques enhance WiFi throughput, they also suffer a decrease on performance in dense deployments. As a starting point, we propose that scheduled access including contention-free channel access mechanisms should be considered in addition to DCF for dense deployments.

The increasing commercial success of Wi-Fi and the wireless communication industry forecast of exponential traffic growth for the next years indicate Wi-Fi dense deployments as scenarios more and more common in the future. Wi-Fi was not designed to operate in such scenarios. Because of its contention based channel access and backoff procedure, Wi-Fi presents low channel access efficiency and sensible performance degradation in terms of user throughput for dense deployments. This paper discusses the challenges of Wi-Fi operation in dense deployment scenarios and the benefits of a proposed adaptive transmit power mechanism. Wi-Fi performance is assessed by standard compliant simulations.

The recent development of regulatory policies that permit the use of TV bands spectrum on a secondary basis has motivated discussion about coexistence of primary (e.g. TV broadcasts) and secondary users (e.g. WiFi users in TV spectrum). However, much less attention has been given to coexistence of different secondary wireless technologies in the TV white spaces. Lack of coordination between secondary networks may create severe interference situations, resulting in less efficient usage of the spectrum. In this paper, we consider two of the most prominent wireless technologies available today, namely Long Term Evolution (LTE), and WiFi, and address some problems that arise from their coexistence in the same band. We perform exhaustive system simulations and observe that WiFi is hampered much more significantly than LTE in coexistence scenarios. A simple coexistence scheme that reuses the concept of almost blank subframes in LTE is proposed, and it is observed that it can improve the WiFi throughput per user up to 50 times in the studied scenarios.

The deployment of modern mobile systems has faced severe challenges due to the current spectrum scarcity. The situation has been further worsened by the development of different wireless technologies and standards that can be used in the same frequency band. Furthermore, the usage of smaller cells (e.g. pico, femto and wireless LAN), coexistence among heterogeneous networks (including amongst different wireless technologies such as LTE and Wi-Fi deployed in the same frequency band) has been a big field of research in the academy and industry. In this paper, we provide a performance evaluation of coexistence between LTE and Wi-Fi systems and show some of the challenges faced by the different technologies. We focus on a simulator-based system- level analysis in order to assess the network performance in an office scenario. Simulation results show that LTE system performance is slightly affected by coexistence whereas Wi-Fi is significantly impacted by LTE transmissions. In coexistence, the Wi-Fi channel is most often blocked by LTE interference, making the Wi-Fi nodes to stay on the LISTEN mode more than 96% of the time. This reflects directly on the Wi-Fi user throughput, that decreases from 70% to ≈100% depending on the scenario. Finally, some of the main issues that limit the LTE/Wi-Fi coexistence and some pointers on the mutual interference management of both the systems are provided.

Spectrum sharing is a powerful alternative to deal with the exponential increase on the wireless communication capacity demand. In this context, the coexistence of two of the most prominent wireless technologies today, Long Term Evolution (LTE) and Wi-Fi, is an important research topic. In the most common Wi-Fi network operation, the Distributed Coordination Function (DCF), communication nodes access the channel only if the interference level is below a certain threshold. Then, Wi-Fi operation is severely affected when in coexistence with LTE. This paper proposes the use of LTE uplink (UL) power control to improve LTE/Wi-Fi coexistence. With the introduction of an additional factor to the conventional LTE UL power control, a controlled decrease of LTE UL transmit powers is carried out according to interference measurements, giving opportunity to Wi-Fi transmissions. The proposed LTE UL power control with interference aware power operating point is a flexible tool to deal with the trade-off between LTE and Wi-Fi performances in coexistence, since it is able to set different LTE/Wi-Fi coexistence configurations with the choice of a single parameter. Simulation results show that the proposed approach can provide similar or better performance for both LTE and Wi-Fi networks than a previously proposed interference avoidance mechanism.

Location Based Services can generate variety of new commercial applications and create new streams of revenue. However, there is currently no simple indoor positioning method for low-end devices which have no Wi-Fi or GPS capability. This paper presents a novel and simple audio-based localization system to provide location-aware content for low-end mobile devices, i.e., a location solution that uses acoustic waves to determine the position of the users and/or the content associated to it. In this solution, Audio Beacons send tones which are read by mobile devices for localization. These tones are generated in a barely audible frequency range, that can be hardly perceived by humans. The tones of the Audio Beacons are associated with an identification code that is linked to a location specific content. The solution requires no special hardware at the mobile device side, and a simple localization infrastructure, which can be deployed with low cost loudspeakers. Real experiments demonstrate the effectiveness of the proposed system, and show that the codes can be identified with distances as large as 20 m from the Audio Beacon.

Current mobile devices continuously estimate their locations, allowing users to “check in”, find nearby friends and interests, and determine routes to their destinations. While underlying satellite, cell, and WiFi-based positioning systems can return an accurate and meaningful position in many cases, extending them to work energy-efficiently, particularly indoors, remains an open problem. In this work, we study energy-efficient and robust human-scale motion classifiers and their use in room-grain, collaborative indoor positioning systems. Previous work on improving energy-efficiency in positioning systems has assumed sensor input from an energy-cheaper alternative: using an accelerometer in lieu of GPS, for example. Unfortunately, even these alternative sensors are not practical for everyday use because of their own energy consumption, at least when sampled continuously. After studying what accelerometer sampling rates are feasible, we compare six methods for motion classification, two of which are new. We find that the existing simple statistical methods are not sufficiently robust with respect to different kinds of movement and different users, because the thresholds between movement and non-movement are too tight. In contrast we find that the two new, more sophisticated models, one based on Page-Hinkley statistics, and the other inspired by the Discrete Fourier Transform, provide a clearer differentiation between the two states. Only the Page-Hinkley-based one is as energy-efficient as the simple statistical methods, however. Through a WiFi geolocation system that relies on motion detection, we show how the choice of the underlying motion classifier can have a significant impact on user-perceived performance.

We describe the design, implementation, and evaluation of Molé, a mobile organic localization engine. Unlike previous work on crowd-sourced WiFi positioning, Mole uses a hierarchical name space. By not relying on a map and by being more strict than uninterpreted names for places, Molé aims for a more flexible and scalable point in the design space of localization systems. Molé employs several new techniques, including a new statistical positioning algorithm to differentiate between neighboring places, a motion detector to reduce update lag, and a scalable “cloud”-based fingerprint distribution system. Molé's localization algorithm, called Maximum Overlap (MAO), accounts for temporal variations in a place's fingerprint in a principled manner. It also allows for aggregation of fingerprints from many users and is compact enough for on-device storage. We show through end-to-end experiments in two deployments that MAO is significantly more accurate than state-of-the-art Bayesian-based localizers. We also show that non-experts can use Molé to quickly survey a building, enabling room-grained location-based services for themselves and others.

This paper intends to propose a novel handover algorithm to balance the load of the layers in a multi-reuse scenario. Each layer works independently from each other and it has its own scheduling process, coverage and mobile stations attached. The algorithm proposed balances the resources among layers by moving mobile stations from one to another layer according their QoS requirements and channel condition. Results show that it can increase the cell coverage and still keeping a satisfactory quality for the VoIP calls. Effective spectral efficiency also increased when comparing with single tri-sectorized reuse 1 scenario. The algorithm is also prepared to avoid ping-pong handovers and to work for any number of layers.

A receiver for a MIMO OFDM communication system using direct conversion transceivers is proposed. The novel receiver takes advantage of the I/Q imbalance inherent to the direct conversion operation. It can be shown that the I/Q imbalance in multicarrier communication systems creates an additional frequency diversity. By adequately exploiting such diversity at the receiver, the overall performance can be considerably improved. Simulation results considering a MIMO OFDM system using V-BLAST show that, when exploiting the I/Q imbalance, gains of up to 5 dB at a BER of 10-4can be obtained with respect to the ideal system, without I/Q imbalance.