Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Two-Stage Beamforming with Power Allocation in Multicell Massive MIMO

We consider a multicellular system in the downlink where at each base station (BS), massive multiple-input multiple-output (MIMO) is utilized via large antenna array. However, big numbers of antennas and users result in a huge channel state information (CSI) overhead and big computational complexity. To cope with these challenges, we propose a two-stage beamforming with power allocation using the signal to leakage and noise ratio (SLNR) as a performance metric since it is known to decouple the optimization problems compared to conventional design methods, allowing independent, distributed processing at each BS. As a first contribution, we derive a deterministic equivalent of the SLNR in the distributed two-stage beamforming context using random matrix theory which provides very accurate approximations in closed-form. The second contribution consists of beamforming design and power allocation. More precisely, in the first stage of the proposed beamforming, an outer beamformer is designed using the deterministic equivalent of the SLNR which requires only statistical CSI and produces an effective system of lower dimension. In the second stage, an inner beamformer applies regularized zero forcing on the low-dimensional effective channel to combat the interference. Additionally, to improve the user fairness, power allocation which maximizes the minimum effective SLNR is proposed and shown to be a convex problem. Simulation results confirm that both the proposed beamforming as well as the combination of the proposed beamforming with power allocation reduce the system dimensionality without degradation of system performance in terms of throughput and user fairness.
Nonrigid Registration Techniques and Evaluation for Augmented Reality in Robotic Assisted Minimally Invasive Surgery

Author(s): Ho, Nelson | Advisor(s): Kastner, Ryan | Abstract: Augmented Reality (AR) presents many new research opportunities in the medical field to improve physician training, provide new forms of patient rehabilitation, and advance patient specific treatment, among many other possibilities. This work focuses on the application of AR to robotic assisted Minimally Invasive Surgery (MIS) and its challenges. Robotic MIS improves patient care and recovery time because of reduced trauma to the patient during the procedured, but the loss of sensory feedback from the surgical site resulting from the indirection of using a robot presents a real challenge to the surgeon. AR makes it possible to replace some of the lost sensory information by overlaying pre-operative imaging in the AR field onto the surgical scene in vivo. This will enable the surgeon to see the structures under the surface of the organ, which are visible in penetrative imaging but invisible to the surgeon's eye. AR also enables an opportunity to present additional information to the surgeon depending on what is currently in scene. To perform this overlay, the captured surgical scene needs to be segmented, and the results registered to the pre-operative imaging. There are existing non-rigid registration algorithms developed for other applications that may be used. However, the algorithms are slow, and there exists no way of determining the accuracy of such methods on a surgical application. Accurate datasets for the evaluation of such applications are difficult to obtain. This work addresses each of these issues by providing methods for creating a clinically relevant dataset and an evaluation framework for algorithms run on this data. We show how existing AR algorithms can be run on our dataset, and how our evaluation framework can be used to comprehend those results.
Toward Efficient and Secure Deep Packet Inspection for Outsourced Middlebox

With the increasing network traffic volume in the big data era, enterprises have paid significant attentions on outsourcing middlebox services to the public cloud. While offering appealing benefits, including network resource scalability and management cost reduction, it also raises severe privacy and security issues, such as the exposure of packet payload and middlebox rules. Since the traffic is redirected to cloud server, the exposure of packet payload and middlebox rule becomes inevitable. Simply encrypting the traffic can mitigate this problem at the cost of sacrificing data utility, which poses great challenges on deep packet inspection. In this paper, an efficient and secure Deep Packet Inspection (DPI) scheme is proposed based on non-collusion two cloud servers to enable data utility, while protecting the packet payload and middlebox rules. We leverage encrypted Matryoshka filter and T-set to process DPI. Since both the middlebox rule and packet payload are encrypted, cloud server cannot breach the confidentiality of them. We also build a secure hash chain to prevent the leakage of token order information. Extensive experiments demonstrate that proposed scheme performances better in terms of packet processing, rule preparation and rule matching.
Virtual Core Network Resource Allocation in 5G Systems using Three-Sided Matching

Network Function Virtualization (NFV) is one of the key drivers of 5G systems, which involves the virtualization of the Evolved Packet Core (EPC) and the 5G Core. This entails deploying virtual instances of core network functions in Cloud Networks (CNs), resulting in a virtual EPC (vEPC)/5G Core network. NFV resource allocation is a popular research topic in 5G systems, and a distributed solution based on the interdependencies between all the important entities is very crucial. Accordingly, in this paper, we propose a three-sided matching based framework for virtual resource allocation in next-generation networks. We utilize the Restricted Three-sided Matching with Size and Cyclic preference (R-TMSC) problem to model the relationships between Tracking Areas (TAs) (Base Stations (BSs) organized together in groups), CNs and Virtual Network Function (VNF) instances in a vEPC/5G Core network. The simulation results clearly demonstrate the superior performance of the proposed framework in terms of the data rates provided by the CNs and user satisfaction, compared to a centralized random allocation approach.
SDN-Ti: A General Solution Based on SDN to Attacker Traceback and Identification in IPv6 Networks

Network attacks have become a growing threat to the current Internet. For the enhancement of network security and accountability, it is urgent to find the origin and identity of the adversary who misbehaves in the network. Some studies focus on embedding users' identities into IPv6 addresses, but such design cannot support the Stateless Address Autoconfiguration (SLAAC) protocol which is widely deployed nowadays. In this paper, we propose SDN-Ti, a general solution to traceback and identification for attackers in IPv6 networks based on Software Defined Network (SDN). In our proposal, the SDN switch performs a translation between the source IPv6 address of the packet and its trusted ID-encoded address generated by the SDN controller. The network administrator can effectively identify the attacker by parsing the malicious packets when the attack incident happens. Our solution not only avoids the heavy storage overhead and time synchronism problems, but also supports multiple IPv6 address assignment scenarios. What's more, SDN-Ti does not require any modification on the end device, hence can be easily deployed. We implement SDN-Ti prototype and evaluate it in a real IPv6 testbed. Experiment results show that our solution only brings very little extra performance cost, and it shows considerable performance in terms of latency, CPU consumption and packet loss compared to the normal forwarding method. The results indicate that SDN-Ti is feasible to be deployed in practice with a large number of users.
Combating Unknown Eavesdroppers by using Multipath Wireless Receptions

Unlike many existing studies on physical layer security which use multi-antenna techniques to improve the transmission security, this paper exploits the multipath wireless channel to achieve secure transmission. Specifically, we consider a time-reversal transmission system where the signal waveform is designed as the conjugated time reversed counterpart of the wireless multipath channel. Thus, the wireless channel acts as a matched filter that can boost the signal power at the intended receiver. To further improve the transmission security, we also inject a time-domain artificial noise which causes no interference to the intended receiver's signal detection. As for the eavesdroppers, we assume that their specific number and locations are unknown and use the homogenous Poisson point process (HPPP) to model the distribution of them. First, we study the average achievable rate of the intended user and a given eavesdropper, respectively. Then, based on the HPPP model, we can obtain the secrecy outage probability under a given density of eavesdropper. With a given requirement on the secrecy outage probability, we can finally obtain the optimal energy allocation parameter and the corresponding maximum secure transmission rate. Numerical results are presented to show that the proposed scheme can guarantee the transmission security under various system parameters.
Variational Bayesian Channel Estimation for Wideband Multiuser mmWave Systems

In this paper, a frequency-distributed variational Bayesian (F-DVB) channel estimation algorithm is proposed for wideband multiuser millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems, where hybrid precoding architectures are adopted and frequency selective fading channels are assumed. First, a distributed compressed sensing-based method is employed by leveraging the joint sparsity of different subcarriers in the frequency domain, reducing the required pilot overhead significantly. Next, a hierarchical channel model, which adopts an identify-and-reject strategy to deal with hardware impairments, is designed to enhance the robustness of the proposed algorithm. Finally, the channel information is estimated by a modified variational Bayesian method, which improves the channel estimation accuracy dramatically. Simulation results verify that the proposed algorithm outperforms the state-of-the-art channel estimation strategies at low SNR and pilot overhead.
Scalable completion of nonnegative matrices with the separable structure

Matrix completion is to recover missing/unobserved values of a data matrix from very limited observations. Due to widely potential applications, it has received growing interests in fields from machine learning, data mining, to collaborative filtering and computer vision. To ensure the successful recovery of missing values, most existing matrix completion algorithms utilise the low-rank assumption, i.e., the fully observed data matrix has a low rank, or equivalently the columns of the matrix can be linearly represented by a few numbers of basis vectors. Although such low-rank assumption applies generally in practice, real-world data can process much richer structural information. In this paper, we present a new model for matrix completion, motivated by the separability assumption of nonnegative matrices from the recent literature of matrix factorisations: there exists a set of columns of the matrix such that the resting columns can be represented by their convex combinations. Given the separability property, which holds reasonably for many applications, our model provides a more accurate matrix completion than the low-rank based algorithms. Further, we derives a scalable algorithm to solve our matrix completion model, which utilises a randomised method to select the basis columns under the separability assumption and a coordinate gradient based method to automatically deal with the structural constraints in optimisation. Compared to the state-of-the-art algorithms, the proposed matrix completion model achieves competitive results on both synthetic and real datasets.
Accuracy, speed and error tolerance of short DNA sequence aligners

Aligning short DNA sequence reads to the genome is an early step in the processing of many types of genomics data, and impacts on the fidelity of downstream results. In this work, the accuracy, speed and tolerance to errors are evaluated in read of varied length for six commonly used mapping tools; BWA aln, BWA mem, Bowtie2, Soap2, Subread and STAR. The accuracy evaluation using Illumina-like simulated reads showed that accuracy varies by read length, but overall BWA aln was most accurate, followed by BWA mem and Bowtie2. BWA mem was most accurate with Ion Torrent-like read sets. STAR was at least 5 fold faster than Bowtie2 or BWA mem. BWA mem tolerated the highest density of mismatches and indels compared to other mappers. These data provide important accuracy and speed benchmarks for commonly used mapping software.
Enhancing the AR Experience with Machine Learning Services

In this paper, we present and evaluate a web service that offers cloud-based machine learning services to improve Augmented Reality applications on mobile and web clients with special regards to tracking quality and registration of complex scenes that require an application-specific coordinate frame. Specifically, our service aims at reducing camera drift that still occurs in modern AR frameworks as well as helps with the initial camera alignment in a known scene by estimating the absolute camera pose using a configurable context-based image segmentation in combination with an adaptive image classification. We demonstrate real-world applications that utilize our web service and evaluate the performance and accuracy of the underlying image segmentation and the camera pose estimation. We also discuss the initial configuration along with the semi-automatic process of generating training data, and the training of the machine learning models for the corresponding tasks.
Mobility-Aware Service Function Chaining in 5G Wireless Networks with Mobile Edge Computing

The incorporation of Virtual Network Function (VNF)/Service Function Chaining (SFC) and Mobile Edge Computing (MEC) allows 5G networks to deliver a variety of services and applications in a more flexible manner. However, without inter-MEC handoffs for the VNFs of the corresponding SFC, user mobility may affect user satisfaction of the service due to unacceptably long service delay. To tackle this problem, one has to determine, upon a handoff, which VNFs of the SFC to be migrated, to which MEC server(s), and with how much resource of each MEC server to be allocated to the user so that the service delay constraint of the SFC will not be violated. This problem is referred to as the mobile SFC embedding problem, which is NP-Hard, and is formulated via integer programming to minimize service interruption for mobile users. We then propose an on-line algorithm called Follow-Me Chain, which includes two key components: SFC placement and SFC migration, to solve this problem. The simulation results show that the proposed Follow-Me Chain outperforms existing solutions in terms of user satisfaction under the same number of users accommodated in the system, demonstrating that FMC is indeed an efficient and effective solution for mobile SFC embedding problem in 5G networks with MEC.
3D Point Cloud Compression: A Survey

In recent years, 3D point clouds have enjoyed a great popularity for representing both static and dynamic 3D objects. When compared to 3D meshes, they offer the advantage of providing a simpler, denser and more close-to-reality representation. However, point clouds always carry a huge amount of data. For a typical example of a point cloud with 0.7 million points per 3D frame at 30 fps, the point cloud raw video needs a bandwidth around 500MB/s. Thus, efficient compression methods are mandatory for ensuring the storage/transmission of such data, which include both geometry and attribute information. In the last years, the issue of 3D point cloud compression (3D-PCC) has emerged as a new field of research. In addition, an ISO/MPEG standardization process on 3D-PCC is currently on-going. In this paper, a comprehensive overview of the 3D-PCC state-of-the-art methods is proposed. Different families of approaches are identified, described in details and summarized, including 1D traversal compression, 2D-oriented techniques, which take leverage of existing 2D image/video compression technologies and finally purely 3D approaches, based on a direct analysis of the 3D data.
Resource Allocation for URLLC in 5G Mission-Critical IoT Networks

Ultra-reliable and low-latency communication (URLLC) is one of three pillar applications that should be supported by the fifth generation (5G) communications. The research on this topic is still in its infancy due to the difficulties in guaranteeing extremely high reliability (say 10<sup>−9</sup>) and low latency (say 1 ms) simultaneously. The achievable data rate under the short packet transmission is a complicated function of the transmission power, the blocklength and the decoding error probability. In this paper, we consider resource allocation problem in a factory automation scenario, where the central controller aims for transmitting different packets to two devices (e.g., a robot and an actuator). Two transmission schemes are considered: orthogonal multiple access (OMA) and relay-assisted transmission. We aim to jointly optimize the blocklength and power allocation to minimize the error probability of the actuator subject to reliability requirement of the robot as well as the latency constraints. We develop low-complexity algorithms to address the optimization problems for each transmission scheme. Simulation results demonstrate that the relay-assisted transmission significantly outperforms the OMA scheme.
Generalized Beamspace Modulation using Multiplexing for mmWave MIMO

Spatial multiplexing (SMX) multiple-input multiple-output (MIMO) over the best beamspace was considered as the best solution for millimeter wave (mm ave) communications regarding spectral efficiency (SE), referred as the best beamspace selection (BBS) solution. The equivalent MIMO water-filling (F-MIMO) channel capacity was treated as an unsurpassed SE upper bound. Recently, researchers have proposed various schemes trying to approach the benchmark and the performance bound. In this paper, we challenge the benchmark and the corresponding bound by proposing a better transmission scheme that achieves higher SE, namely the Generalized Beamspace Modulation using Multiplexing (GBMM). Inspired by the concept of spatial modulation, we not only use the selected beamspace to transmit information but also use the selection operation to carry information. e prove that GBMM is superior to BBS in terms of SE and can break through the well known upper bound. That is, GBMM renews the upper bound of the system SE. e investigate SE-oriented precoder activation probability optimization, fully-digital precoder design and hybrid precoder design for GBMM. Comparisons with the benchmark (i.e., F-MIMO channel capacity) are made under different system configurations to show the superiority of GBMM.
Feedback Reduction for Beyond 5G Cellular Systems

Cell-less system is a promising technology of the next generation wireless communications where a group of basestations intelligently recognizes user's communication environments and cooperatively serves the user. In order to maximize the gain obtained by the basestation cooperation, acquisition of accurate downlink channel state information (CSI) at the basestation is crucial. While this task is relatively easy for the time division duplexing (TDD) systems due to the channel reciprocity, it is not easy for the frequency division duplexing (FDD) systems due to the CSI feedback overhead. This issue is even more pronounced in the cell-less systems since the user needs to estimate and feed back the downlink CSIs of multiple basestations. In this paper, we propose a multi-path selection based feedback reduction technique for the FDD-based cell-less systems. Key ingredient of the proposed technique is that the spatial domain channel can be represented by a small number of multi-path components (angle of departure (AoD) and path gain). By exploiting the property that the AoDs are quite similar in the uplink and downlink channels, we only feed back the path gain information (PGI) to the basestations. Furthermore, by choosing a few paths maximizing the sum-rate and only feed back the PGI of the chosen paths, we can further reduce the feedback overhead substantially. From the simulations on realistic scenarios, we demonstrate that the proposed selective PGI feedback scheme is very effective in achieving the feedback overhead reduction compare to the conventional scheme relying on the CSI feedback.
Full-Duplex and C-RAN Based Multi-Cell Non-Orthogonal Multiple Access Over 5G Wireless Networks

In this paper, we propose the full-duplex and cloud radio access network (C-RAN) based multi-cell non-orthogonal multiple access schemes over 5G mobile wireless networks. To cope with the severe intra-cell and inter-cell interferences as well as perform the centralized optimization, we adopt the C-RAN architecture, where the baseband processing and resource management are conducted at a central unit (CU). With the goal of maximizing the weighted sum achievable rate, we formulate the sum rate maximization power allocation problem as a non-convex problem. Thanks to the hidden monotonicity structure of the considered problem, the optimal power allocation algorithm is developed by the monotonic optimization method. Besides, we propose another suboptimal algorithm by employing successive convex approximation method to obtain the close-to-optimal solution with a significantly reduced computational complexity. Extensive simulations are conducted to verify the effectiveness of our proposed power allocation schemes, and confirm the superiority of our proposed C-RAN architecture.
Spatial Modulation Aided Cooperative NOMA: Implementation and Achievable Rate Analysis

In this paper, spatial modulation (SM) aided cooperative non-orthogonal multiple access (NOMA) is studied and its spectral efficiency is analyzed. Specifically, we extend the SM-aided NOMA with bit allocation (BA) proposed recently to cooperative NOMA system, and the resulted scheme is named SM-C-NOMA-BA. Both half-duplex cooperation and full-duplex cooperation are considered. In broadcast phase of the proposed half-duplex SM-C-NOMA-BA, information bits intended for strong and weak users are taken as amplitude-phase modulation (APM) bits and active antenna index bits, respectively, which are used to form the SM transmit signal at base station (BS). In cooperative phase, strong user decodes both APM bits and index bits and forwards index bits to weak user, and weak user decodes the index bits after receiving the signals from both BS and strong user in the two phases. In the full-duplex SM-C-NOMA-BA, strong user receives the BS signal and forwards the information of weak user simultaneously. Furthermore, the achievable rates with finite alphabet inputs are analyzed. Finally, numerical results are provided to demonstrate the performance of the proposed scheme.