This paper presents a novel transparent amplifying intelligent surface (TAIS) architecture for uplink enhancement in indoor-To-outdoor mmWave communications. The TAIS is an amplifier-based transmissive intelligent surface that can refract and amplify the incident signal, instead of only refracting it with adjustable phase shift by most passive reconfigurable intelligent surfaces (RIS). With advanced indium tin oxide film and printing technology, TAIS can be fabricated on the windows without any visual effects. This paper primarily focuses on exploiting the TAIS-based architecture to boost the uplink spectral efficiency (SE) in indoor-To-outdoor mmWave communications. By jointly optimizing the TAIS's phase shift matrix and transmit power of the user equipment, the uplink SE can be maximized by exploiting the nonlinearity in the TAIS's amplification process. The key enabler is that we drive the optimal phase shift matrix that maximizes the SE and deduces its closed-form representation. The SE maximization is then proved to be transferred to the transmit power optimization problem. Another important enabler is that we design a low-complexity algorithm to solve the optimization problem using the difference of convex programming. Moreover, the asymptotic spectral efficiency under nonlinear amplification and power scaling law with infinitely large elements under both the sparse and rich scattering channel models are analyzed. Simulation results show that our proposed TAIS can increase the SE by up to 24.7% as compared to its alternative methods.

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This paper presents a novel transparent amplifying intelligent surface (TAIS) architecture for uplink enhancement in indoor-to-outdoor mmWave communications. The TAIS is an amplifier-based transmissive intelligent surface that can refract and amplify the incident signal, instead of only refracting it with adjustable phase shift by most passive reconfigurable intelligent surfaces (RIS). With advanced indium tin oxide film and printing technology, TAIS can be fabricated on the windows without any visual effects. This paper primarily focuses on exploiting the TAIS-based architecture to boost the uplink spectral efficiency (SE) in indoor-to-outdoor mmWave communications. By jointly optimizing the TAIS's phase shift matrix and transmit power of the user equipment, the uplink SE can be maximized by exploiting the nonlinearity in the TAIS's amplification process. The key point is that we drive the optimal phase shift matrix that maximizes the SE and deduces its closed-form representation. The SE maximization is then proved to be transferred to the transmit power optimization problem. Another important aspect is that we design a low-complexity algorithm to solve the problem using the difference of convex programming. Simulations show that our proposed TAIS can increase the SE by up to 32.6% as compared to its alternative methods.@en

This paper studies a promising use case of a private 5G network for the sports industry: wearable bodycams and sensors in a football match. This use case requires a reliable and dedicated massive MIMO network to provide uniform coverage with a high capacity in the whole pitch area. The coverage of co-located and distributed (cell-free) massive MIMO, differing in the configuration of base station antenna placement inside the stadium, are evaluated through ray tracing using a 3D stadium model and players' mobility dataset. The results give insight into how distributed antennas inside the stadium should be deployed to enhance the uniform coverage of moving wearable devices. Besides that, the uplink capacity performance and the impact of channel aging on the capacity are also evaluated. The results demonstrate the advantages and disadvantages of different base station antenna distribution strategies in terms of coverage, capacity and channel aging impact.@en

Autonomous flight for UAVs relies on visual information for avoiding obstacles and ensuring safe collision-free flight. In addition to visual clues, safe UAVs often need connectivity with the ground station. In this article, we study the synergies between vision and communications for edge-computing-enabled UAV flight. By proposing a framework of edge computing assisted autonomous flight (ECAAF), we illustrate that vision and communications can interact with and assist each other with the aid of edge computing and offloading, and further speed up UAV mission completion. ECAAF consists of three functionalities that are discussed in detail: edge computing for 3D map acquisition, radio map construction from the 3D map, and online trajectory planning. During ECAAF, the interactions of communication capacity, video offloading, 3D map quality, and channel state of the trajectory form a positive feedback loop. Simulation results verify that the proposed method can improve mission performance by enhancing connectivity. Finally, we conclude with some future research directions.

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In visible light communication (VLC), the quality of communication is primarily dominated by line-of-sight links. To ensure an appropriate link quality anywhere, beamsteering has been proposed where transmitters (TXs) dynamically steer their beams to create beamspots on the users. However, these highly dynamic TXs face the beam tracking problem and result in highly variable illumination. In this work, we propose BlendVLC, a cell-free network architecture to improve the mobility robustness of users by blending the beamspots from both steerable and fixed TXs. We solve the beam tracking by designing a centimeter-level visible light positioning algorithm empowered by a neural network. Relying on this location information, we formulate and solve an optimization problem on the beamspot blending, and design a fast and scalable heuristic for large networks. We build a proof-of-concept testbed as well as a simulator to evaluate BlendVLC. We show that it achieves superior performance compared to denser networks with fully fixed TXs. For example, in a large-scale VLC network of 8 m x 4 m, BlendVLC improves the average system throughput by 30%, while only requiring half the number of TXs.

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We exploit a dense cell-free mmWave network where User Equipments (UEs) are served by multiple highly directional beams provided by multiple Base Stations (BSs) simultaneously. Such multi-beam scenarios can either offer high spectral efficiency when different information is transmitted through each beam or a diversity gain when each beam transmits the same information. However, this increased spectral efficiency or diversity gain costs a more complex network association phase. A UE requires finding multiple nearby serving BSs and determining the optimal beam pair for each one. Thus, an efficient association process is urgently needed. In this work, we propose a UE-initiated association method for dense cell-free mmWave networks. We design an efficient beam training mechanism with multiple BSs using hybrid beamforming. We evaluate the proposed association method under different network configurations. The simulation results show that compared to traditional solutions, our proposed association method can lead to maximally 100% faster beam training and reduce energy consumption by up to 77%. The proposed UE-initiated association method is also scalable to the number of RF chains and antennas at BSs and UEs, making it very suitable for dense cell-free networks.@en

Visible Light Communication (VLC) has attracted significant attention over the past decade. Although numerous research studies have been performed to improve the data rate of VLC links, an important fact has been largely neglected: human bodies that host VLC receivers could block their Line-of-Sight (LOS) downlinks, and thus, degrade the system performance greatly. In this paper, we propose a system that can significantly improve the robustness for VLC networks by avoiding performance degradation due to blockage. A novel user-in-the-loop mechanism is designed in which users (including human bodies and VLC receivers) are guided by the network to rotate themselves to improve the system performance and the individual user experience. Both our simulation and experimental results demonstrate that the proposed user-in-the-loop mechanism can improve the system throughput and user fairness on average by 48% and 14%, respectively. For individual users, the average gain in throughput can reach up to 135%. Furthermore, to make the system more practical, two lightweight heuristics are designed and implemented which can achieve similar gains while reducing the computational complexity by 99%.@en

We propose a concept of coverage-bounding and 'visual' wireless communication-HODOR 1-to secure the Internet of Things (IoT). Coverage-bounding means the communication coverage is controlled accurately in 3-dimensions. 'Visual' implies that the communication coverage and process are visible to user, representing an important and user-friendly side-channel for se-curing IoT. HODOR can provide secure wireless communication both psychologically (visible to users) and technically (nodes only communicate with each other within their delimited coverage). It can benefit IoT applications for secure wireless communications, especially those that demand secure interactions in proximity.

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A massive MIMO network can serve ten's of users simultaneously. However, in dense scenarios the users are potentially closely-spaced, potentially resulting in substantial inter-user interference. Scheduling can overcome this by selecting the users that lead to the highest combined spectral efficiency. As scheduling comes with a significant pilot overhead, an alternative strategy could minimize user correlation by distributing the antenna elements in space. In this paper, we propose a comprehensive system study including antenna topology and distribution, user scheduling and pilot overhead reduction. Our user scheduling and pilot reduction algorithms are evaluated using system level simulations relying on indoor line-of-sight channel measurements from a 64 antenna base station at 2.61GHz. To have a thorough evaluation of the proposed algorithm, we consider four different antenna topologies, including co-located and distributed placement of the base station arrays. Our evaluation shows that in a conference room with 64 densely deployed users, our proposed low complexity algorithm can improve the spectral efficiency by at least 14% compared to random user selection with the best antenna distribution strategy. Finally, our results show that by relying on channel hardening, we reduce the pilot overhead by 3.2 \times.

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LED luminaries are now deployed densely in indoor areas to provide uniform illumination. Visible Light Communication (VLC) can also benefit from this dense LED infrastructure. In this paper, we propose DenseVLC, a cell-free massive MIMO networking system enabled by densely distributed LEDs, that forms different beamspots to simultaneously serve multiple receivers. This is a cell-free system, as there is no notion of autonomous cells and transmitters cooperate to jointly serve the users. Given a power budget for communication, DenseVLC assigns the power budget among the distributed LEDs to optimize the system throughput and user fairness. We formulate an optimization problem to derive the optimal policy for the power allocation. Our insights from the optimal policies allow us to simplify DenseVLC's system design and propose a heuristic algorithm that can reduce the complexity by 99.96%. Besides, we propose a novel synchronization method using non-line-of-sight VLC to synchronize all the transmitters that will form a beamspot to serve the same receiver. We implement DenseVLC with off-the-shelf devices, solve practical challenges in the system design, and evaluate it with extensive and realistic experiments in a system of 36 transmitters and 4 receivers in an area of\:\text{m}\,\,\times 3\:\text{m}$. Our results show that DenseVLC can improve the average system throughput by 45%, or improve the average power efficiency by 2.3 times, while maintaining the requirement for uniform illumination. Finally, we demonstrate that DenseVLC is robust against blockage.

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