We present a non-iterative downlink precoding approach for distributed massive multiple-input multiple output systems (DmMIMO) where users are served by overlapping clusters of transmission/reception points (TRP) and channel estimates for links outside the clusters are available. In contrast to traditional cellular systems, each user is served by its own cluster of transmission points and a DmMIMO TRP could be part of multiple clusters. We also propose a power control algorithm that ensures per site power constraints are satisfied when the proposed precoding approach is used. The algorithm extends straightforwardly to the multiple receive antenna case. Extensive simulation results are presented for various cluster sizes and inter-site distances for a dense urban environment with channels generated using ray tracing. Results show that spectral efficiency comparable to massive MIMO can be achieved for a dense deployment of small cells each with only a small number of antennas with our DmMIMO scheme without the need for extensive coordination between many access points.

In this paper, we consider a joint beam tracking and pattern optimization problem for massive multiple input multiple output (MIMO) systems in which the base station (BS) selects a beamforming codebook and performs adaptive beam tracking taking into account the user mobility. A joint adaptation scheme is developed in a two-phase reinforcement learning framework which utilizes practical signaling and feedback information. In particular, an inner agent adjusts the transmission beam index for a given beamforming codebook based on short-term instantaneous signal-to-noise ratio (SNR) rewards. In addition, an outer agent selects the beamforming codebook based on long-term SNR rewards. Simulation results demonstrate that the proposed online learning outperforms conventional codebook-based beamforming schemes using the same number of feedback information. It is further shown that joint beam tracking and beam pattern adaptation provides a significant SNR gain compared to the beam tracking only schemes, especially as the user mobility increases.

We consider the uplink of a cellular network wherein each base-station (BS) simultaneously communicates with multiple users and is equipped with a large number of antenna elements that are driven by a limited number of RF chains. Each RF chain (on each BS) houses an analog-to-digital converter (ADC) whose bit resolution can be configured. We seek to jointly optimize user transmit powers and ADC bit resolutions in order to maximize the network spectral efficiency, subject to power budget constraints at each user and BS. This joint optimization becomes intractable if we insist on exactly modeling the non-linear quantization operation performed at each ADC. On the other hand, simplistic approximations made for tractability need not be meaningful. We propose a methodology based on constrained worst-case quantization noise formulation, along with another one that assumes quantization noise covariance to be diagonal. In each case, using effective mathematical re-formulations we can express our problem in a form well-suited for alternating optimization, in which each sub-problem can be optimally solved. Using a detailed performance analysis, we demonstrate that the optimized transmit powers and bit resolutions yield very significant improvements in achievable spectral efficiency, at a reduced sum power consumption and an affordable complexity.

We explore the feasibility of Multiple-Input-Multiple-Output (MIMO) communication through vibrations over human skin. Using off-the-shelf motors and piezo transducers as vibration transmitters and receivers, respectively, we build a 2x2 MIMO testbed to collect and analyze vibration signals from real subjects. Our analysis reveals that there exist multiple independent vibration channels between a pair of transmitter and receiver, confirming the feasibility of MIMO. Unfortunately, the slow ramping of mechanical motors and rapidly changing skin channels make it impractical for conventional channel sounding based channel state information (CSI) acquisition, which is critical for achieving MIMO capacity gains. To solve this problem, we propose Skin-MIMO, a deep learning based CSI acquisition technique to accurately predict CSI entirely based on inertial sensor (accelerometer and gyroscope) measurements at the transmitter, thus obviating the need for channel sounding. Based on experimental vibration data, we show that Skin-MIMO can improve MIMO capacity by 2.3X compared to Single-Input-Single-Output (SISO) or open-loop MIMO, which do not have access to CSI. A surprising finding is that gyroscope, which measures the angular velocity, is found to be superior in predicting skin vibration than accelerometer, which measures linear acceleration and used widely in previous research for vibration communications over solid objects.

In modern online social networks, each user is typically able to provide a value to indicate how trustworthy their direct friends are. Inferring such a value of social trust between any pair of nodes in online social networks is useful in a wide variety of applications, such as online marketing and recommendation systems. However, it is challenging to accurately and efficiently evaluate social trust between a pair of users in online social networks. Existing works either designed handcrafted rules that rely on specialized domain knowledge, or required a significant amount of computation resources, which affected their scalability.

In recent years, graph convolutional neural networks (GCNs) have been shown to be powerful in learning on graph data. Their advantages provide great potential to trust evaluation as social trust can be represented as graph data. In this paper, we propose {\em Guardian}, a new end-to-end framework that learns latent factors in social trust with GCNs. {\em Guardian} is designed to incorporate social network structures and trust relationships to estimate social trust between any two users. Extensive experimental results demonstrated that {\em Guardian} can speedup trust evaluation by up to \(2,827\times\) with comparable accuracy, as compared to the state-of-the-art in the literature.

in this paper we aim to offer the joint inference of truth/rumor and their sources. Our insights is that a joint inference can enhance the mutual performance on both sides. To this end, we propose a framework named SourceCR, which alternates between two modules, i.e., credibility-reliability training for truth/rumor inference and division-querying for source detection in a joint, iterative manner. To elaborate, the former module performs simultaneous estimation of the claim credibility and user reliability based on users' opinions, which takes the source reliability outputted from the latter module as the initial input. The latter module divides the network into a truth subnetwork and a rumor one via the claim credibility, and then applies querying to users selected with their reliability estimation returned by the former module in each divided subnetwork for source inference within a theoretically guaranteed budget. The proposed SourceCR is provably convergent, and algorithmic implementable with reasonable computational complexity. We empirically validate the effectiveness of the proposed framework in both synthetic and real datasets, where the joint inference leads to an up to 35% accuracy of credibility gain and 29% source detection rate gain compared with the separate counterparts.

In an online social network, users exhibit personal information to enjoy social interaction. The social network provider (SNP) exploits users' information for revenue generation through targeted advertising. The SNP can present ads to proper users efficiently. Therefore, an advertiser is more willing to pay for targeted advertising. However, the over-exploitation of users' information would invade users' privacy, which would negatively impact users' social activeness. Motivated by this, we study the optimal privacy policy of the SNP with targeted advertising business. We characterize the privacy policy in terms of the fraction of users' information that the provider should exploit, and formulate the interactions among users, advertiser, and SNP as a three-stage Stackelberg game. By carefully leveraging supermodularity property, we reveal from the equilibrium analysis that higher information exploitation will discourage users from exhibiting information, lowering the overall amount of exploited information and harming advertising revenue. We further characterize the optimal privacy policy based on the connection between users' information levels and privacy policy. Numerical results reveal some useful insights that the optimal policy can well balance the users' trade-off between social benefit and privacy loss, and enable the provider to earn more advertising revenue than the cases with poor privacy protection.

A widely adopted approach to guarantee high-quality services on on-demand service platforms is to introduce a reputation system, where good reputation workers will receive a bonus for providing high-quality services. In this paper, we propose a general reputation framework motivated by various practical examples. Our model captures the evolution of a reputation system, jointly considering worker's strategic behaviors and imperfect customer reviews that are usually studied separately before. We characterize the stationary equilibrium of the market, in particular, the existence and uniqueness of a non-trivial equilibrium that ensures high-quality services. Furthermore, we propose an efficient subsidization mechanism that helps induce high-quality services on the platform, and show the market convergence to the high service quality equilibrium under such a mechanism.

As the Wi-Fi technology goes through its sixth generation (Wi-Fi 6), there is a growing consensus on the need to support security and coordination functions at the Physical (PHY) layer, beyond traditional functions such as frame detection and rate adaptation. In contrast to the costly approach of extending the PHY-layer header to support new functions (e.g., Target Wake Time field in 802.11ax), we propose to turn the frame preamble into a user-defined data field while maintaining its primary functions. Specifically, in this paper, we develop a scheme called extensible preamble modulation (eP-Mod) for the MIMO-based 802.11ac protocol. eP-Mod can embed up to 20 user-defined bits into the 802.11ac preamble in 1 × 2 or 2 × 1 MIMO transmission modes. It allows legacy (eP-Mod-unaware) devices to continue to process the received preamble as normal by guaranteeing that our proposed preamble waveforms satisfy the structural properties of a standardized preamble. The proposed scheme enables several promising PHY-layer services, such as PHY-layer encryption and channel/device authentication, PHYlayer signaling, etc. Through numerical analysis, extensive simulations, and hardware experiments, we validate the practicality and reliability of eP-Mod.

Modulation recognition (modrec) is an essential functional component of future wireless networks with critical applications in DSA. While predominantly studied in SISO systems, practical modrec for MIMO communications requires more research. Existing MIMO modrec requires that the number of sensor antennas be equal or double that at the transmitter. This poses a prohibitive sensor cost and severely hampers progress DSA with advanced higher-order MIMO.

We design a MIMO modrec framework that enables efficient and cost-effective modulation classification for under-determined settings characterized by fewer sensor antennas than those used for transmission. We exploit the inherent multi-scale self-similarity of MIMO modulation IQ constellations, which persists in under-determined settings. Our framework called SYMMeTRy (Self-similaritY for MIMO ModulaTion Recognition) designs domain-aware classification features with high discriminative potential by summarizing regularities of symbol co-location in the MIMO constellation. To this end, we summarize the fractal geometry of observed samples to extract discriminative features for supervised MIMO modrec. We evaluate SYMMeTRy in a realistic simulation and in a small-scale MIMO testbed. We demonstrate that it maintains high and consistent performance across various noise regimes, channel fading conditions and with increasing MIMO transmitter complexity. Our efforts highlight SYMMeTRy's high potential to enable efficient and practical MIMO modrec.

We consider online downlink precoding design for multiple-input multiple-output (MIMO) wireless network virtualization (WNV) in a fading environment with imperfect channel state information (CSI). In our WNV framework, a base station (BS) owned by an infrastructure provider (InP) is shared by several service providers (SPs) who are oblivious to each other. The SPs design their virtual MIMO transmission demands to serve their own users, while the InP designs the actual downlink precoding to meet the service demands from the SPs. Therefore, the impact of imperfect CSI is two-fold, on both the InP and the SPs. We aim to minimize the long-term time-averaged expected precoding deviation, considering both long-term and short-term transmit power limits. We propose a new online MIMO WNV algorithm to provide a semi-closed-form precoding solution based only on the current imperfect CSI. We derive a performance bound for our proposed algorithm and show that it is within an \(O(\delta)\) gap from the optimum over any given time horizon, where \(\delta\) is a normalized measure of CSI inaccuracy. Extensive simulation results with two popular precoding techniques validate the performance of our proposed algorithm under typical urban micro-cell Long-Term Evolution network settings.

Federated learning is a very promising machine learning paradigm where a large number of clients cooperatively train a global model using their respective local data. In this paper, we consider the application of federated learning in wireless networks featuring uplink multiuser multiple-input and multiple-output (MU-MIMO), and aim at optimizing the communication efficiency during the aggregation of client-side updates by exploiting the inherent superposition of radio frequency (RF) signals. We propose a novel approach named Physical-Layer Arithmetic (PhyArith), where the clients encode their local updates into aligned digital sequences which are converted into RF signals for sending to the server simultaneously, and the server directly recovers the exact summation of these updates as required from the superposed RF signal by employing a customized sum-product algorithm. PhyArith is compatible with commodity devices due to the use of full digital operation in both the client-side encoding and the server-side decoding processes, and can also be integrated with other updates compression based acceleration techniques. Simulation results show that PhyArith further improves the communication efficiency by $1.5$ to $3$ times for training LeNet-5, compared with solutions only applying updates compression.

Millimeter-wave (mmW) spectrum is a major candidate to support the high data rates of 5G systems. However, due to directionality of mmW communication systems, misalignments between the transmit and receive beams occur frequently, making link maintenance particularly challenging and motivating the need for fast and efficient beam tracking. In this paper, we propose a multi-armed bandit framework, called MAMBA, for beam tracking in mmW systems. We develop a reinforcement learning algorithm, called adaptive Thompson sampling (ATS), that MAMBA embodies for the selection of appropriate beams and transmission rates along these beams. ATS uses prior beam-quality information collected through the initial access and updates it whenever an ACK/NAK feedback is obtained from the user. The beam and the rate to be used during next downlink transmission are then selected based on the updated posterior distributions. Due to its model-free nature, ATS can accurately estimate the best beam/rate pair, without making assumptions regarding the temporal channel and/or user mobility. We conduct extensive experiments over the 28 GHz band using a 4 x 8 phased-array antenna to validate the efficiency of ATS, and show that it improves the link throughput by up to 182%, compared to the beam management scheme proposed for 5G.

As 5G deployments start to roll-out, indoor solutions are increasingly pressed towards delivering a similar user experience. Wi-Fi is the predominant technology of choice indoors and major vendors started addressing this need by incorporating the mmWave band to their products. In the near future, mmWave devices are expected to become pervasive, opening up new business opportunities to exploit their unique properties.

In this paper, we present a novel PASsive Intrusion Detection system, namely PASID, leveraging on already deployed indoor mmWave communication systems. PASID is a software module that runs in off-the-shelf mmWave devices. It automatically models indoor environments in a passive manner by exploiting regular beamforming alignment procedures and detects intruders with a high accuracy. We model this problem analytically and show that for dynamic environments machine learning techniques are a cost-efficient solution to avoid false positives. PASID has been implemented in commercial off-the-shelf devices and deployed in an office environment for validation purposes. Our results show its intruder detection effectiveness (~ 99% accuracy) and localization potential (~ 2 meters range) together with its negligible energy increase cost (~ 2%).

Hybrid beamforming (HB) architecture has been widely recognized as the most promising solution to mmWave MIMO systems. A major practical challenge for HB is to obtain a solution in \(\sim\)1 ms -- an extremely stringent time requirement considering the complexities involved in HB. In this paper, we present the design and implementation of Turbo-HB -- a novel beamforming design under the HB architecture that can obtain the beamforming matrices in about 1 ms. The key ideas in our design include (i) reducing the complexity of SVD techniques by exploiting the limited number of channel paths at mmWave frequencies, and (ii) achieving large-scale parallel computation. To validate our design, we implement Turbo-HB on an off-the-shelf Nvidia GPU and conduct extensive experiments. We show that Turbo-HB can meet \(\sim\)1 ms timing requirement while delivering competitive throughput performance compared to state-of-the-art algorithms.

Multi-user transmission in 60 GHz Wi-Fi can achieve data rates up to 100 Gb/sec by multiplexing multiple user data streams. However, a fundamental limit in the approach is that each RF chain is limited to supporting one stream or one user. To overcome this limit, we propose \(\textit{\(\textbf{SI}\)ngle RF chain \(\textbf{M}\)ulti-user \(\textbf{B}\)e\(\textbf{A}\)mforming (SIMBA)}\), a novel framework for multi-stream multi-user downlink transmission via a single RF chain. We build on single beamformed transmission via overlayed constellations to multiplex multiple users' modulated symbols such that grouped users at different locations can share the same transmit beam from the AP. For this, we introduce user grouping and beam selection policies that span tradeoffs in data rate, training, and computation overhead. We implement a programmable WLAN testbed using software-defined radios and commercial 60-GHz transceivers and collect over-the-air measurements using phased array antennas and horn antennas with varying beamwidth. We find that in comparison to single user transmissions, \(\textit{SIMBA}\) achieves \(2\times\) improvement in aggregate rate and two-fold delay reduction for simultaneous transmission to four users.