Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Minimax MSE Transceiver and Power Splitting Design for Multiuser MIMO SWIPT: An LMI Approach

This paper proposes a robust joint transceiver and power splitting (PS) design according to a minimax mean-squar-error (MSE) scheme for multiuser multi- input multi-output (MU-MIMO)simultaneous wireless information and power transfer (SWIPT) system. The proposed scheme considers channel uncertainty as a bound on the spectral matrix norm of each estimated error. Since the proposed robust minimax MSE design problem jointly optimizes the precoder, the equalizers and the PS factors, the proposed design problem is non-convex and non-deterministic polynomial-time hard (NP-hard). Thus, we transform the original optimization problem into an iteratively linear matrix inequalities (LMIs)- constrained optimization problem, which can be solved efficiently by the convex optimization toolbox. According to the simulation results of the MU-MIMO PS SWIPT wireless system with the benchmark of robust precoder, the proposed method can validate the theoretical analysis to achieve robust transceiver design effectively.
Scrambled Coded Multiple Access, Variations and Improvements

A family of new non-orthogonal multiple access techniques named Scrambled Coded Multiple Access (SCMA) is described. It allows different users of a communications system to efficiently share the same bandwidth by using specific scramblers to separate user bursts. SCMA technology is designed for satellite communications with extremely small antenna terminals for potential applications such as asset tracking, sensor networking, and smart grid; and also for latency reduction. The latest variation, Asynchronous Scrambled Coded Multiple Access (A-SCMA) on the other hand, not only removes the need for timeslot synchronization and precise power control, but also offers an unprecedented system throughput of more than 4 bits/symbol at a modest per user signal to noise ratio (SNR).
Blind Deconvolution: Convex Geometry and Noise Robustness

Blind deconvolution problems arise in many areas in science and engineering such as communications and astronomy. For this reason, this problem has been a subject of intense study for many decades. Recently, motivated by the success of randomization in compressed sensing and low-rank matrix recovery, a new viewpoint has been introduced. Namely one assumes that the convolved signals are contained in known subspaces, which possess a certain degree of randomness. Such a scenario appears, for example, in wireless communications. Here the idea is to randomly embed the signal into a higher dimensional space before transmission through an unknown channel. The resulting redundancy can then be used for recovery. The first approach for this subspace model, proposed by Ahmed, Recht, and Romberg, was to lift the problem into the space of matrix representation and use the nuclear norm as a regularizer. Their recovery guarantees also apply for noisy measurements, but the error bounds involve seemingly suboptimal dimensional scaling factors. In this paper we will introduce a new geometric analysis based on the conic singular value of the descent cone, which explains these factors. Furthermore, we show that for $\mathcal {O}(1)$ noise-levels, these factors can be avoided and one can obtain near-optimal error bounds.
On Policy Based RAN Slicing for Emerging 5G TDD Networks

This paper introduces the concept of soft Policy based Slicing in the emerging 5G Time Division Duplex (TDD) networks, considering varying traffic conditions and asymmetric application usage patterns. The proposed approach relies on a flexible service-oriented slicing considering diverse applications with heterogeneous Quality of Service (QoS) demands in terms of throughput, latency, etc. It introduces a separate and customized Uplink&#x002F;Downlink (UL&#x002F;DL) pattern per slice taking into account the desired Service Level Agreement (SLA), which is mapped on to a QoS policy for facilitating slicing of the radio access resources. Such a QoS policy allows the flexible allocation of Resource Blocks (RBs) considering the entire spectrum for individual services, while a dynamic resource variation additionally allocates temporarily unused RBs to particular slices in need. The end-to-end system aspects considering the 3rd Generation Partnership Project (3GPP) network orchestration and management architecture are also discussed and an exhaustive simulation campaign is conducted to validate our proposals against the current state- of-the-art.
Low-Complexity Fingerprint Matching for Real-Time Indoor Localization Systems

Due to their robust performance in complex non- line-of-sight environments, fingerprinting-based approaches have long been favored for indoor localization. However, many state-of-the-art schemes rely on a very dense fingerprint map. A larger fingerprint database means a higher achievable localization accuracy, at the same time it means a larger number of required fingerprint comparisons and longer computation delays in the system. This paper presents a novel low-effort approach, that uses a restructured fingerprint database to precisely calculate the location of a user, while comparing the data measured by the user to only a subset of database entries. Several novel approximations of the algorithm are also presented, that trade-off computation complexity and localization accuracy. For comparison, the complexities of a number of state-of-the-art fingerprinting schemes are derived. The effectiveness of the proposed approach is demonstrated through simulation. The presented results show that if the allowed number of fingerprint comparisons is set, the proposed approach and its approximations produce up to 34% lower localization errors than the traditional approach applied to a reduced database.
Learning in Power Distribution Grids under Correlated Injections

Identifying the operational lines and estimating their impedances are critical problems in distribution grids with applications in fault localization, power flow optimization and others. This paper proposes an exact topology and impedance learning algorithm with low complexity that is able to solve problems using only voltage and injection measurements from the terminal nodes in the grid. The crucial benefit of this approach compared to existing works is that it does not require independence of nodal injections. That is, the proposed algorithm is able to recover the topology and impedances even when injections at the terminal nodes are correlated. In addition, its sample complexity for the accurate recovery is described under the multivariate Gaussian assumption of terminal nodes injections. The performance of our learning algorithm is demonstrated through numerical simulations on both synthetic grids and MATPOWER test grid with linearized and non-linear power flow samples.
Successive Self-Interference Cancellation in a Low-Complexity WCP-OFDM Radar Receiver

In this paper, we consider a multicarrier waveform to perform simultaneously data transmission and radar sensing. On the radar receiver side, a state-of-the-art symbol-based algorithm generates a range-Doppler map affected by a self-interference phenomenon, potentially leading to target masking issues. Herein, we propose a successive interference cancellation procedure to enhance the radar performance while keeping a low-complexity implementation. We show that a very low reconstruction error is obtained in various scenarios. We also investigate the robustness of the proposed algorithm since it is subject to error propagation.
User Experience-Enhanced and Energy-Efficient Task Scheduling on Heterogeneous Multi-Core Mobile Systems

Heterogeneous Multi-Core Mobile Systems has been widely used to improve performance. However, it faces with the challenge of tradeoff between energy saving and user experience. ARM big. LITTLE architecture, a heterogeneous computing architecture, is a power-optimization technology. In most big. LITTLE devices, however, it still cannot achieve excellent user experience and higher energy saving. In this paper, we propose an improved task scheduling (UCES-GTS) by introducing the concept of user-centric task on big. LITTLE mobile device. In order to enhance user experience, the response time of user-centric tasks is shortened with reducing slack time of them properly. We then present a detailed algorithm to compute appropriate frequency and allocate the CPU resources to each task. The experimental evaluation results show that our improved global task scheduling model can achieve 17 % and 8 % energy saving average compared with the clustered switching scheduling and the original global task scheduling respectively. And the response time of user-centric tasks can decrease 27 % average, which means excellent user experience.
Performance Analysis and Caching Design in Fog Radio Access Networks

In this paper, the edge caching problem in fog radio access networks (F-RAN) is investigated. Considering that fog access points (F-APs) can offer cooperation gain by jointly transmitting the same file or content diversity gain by concurrently transmitting the coded subfiles, we propose to use both joint transmission (JT) strategy and parallel transmission (PT) strategy to serve users. Using stochastic geometry, we first derive the successful transmission probability (STP) with different transmission strategies, and further derive the fractional offloaded traffic (FOT). Finally, the optimal caching design is obtained by maximizing the STP and FOT. Simulation results show that our proposed caching design achieves a significant performance gain in comparison with the baselines.
Partitioning and Bucketing Techniques to Speed up Query Processing in Spark-SQL

Horizontal partitioning is an optimization technique applied to improve query processing time in distributed data warehouses. Scanning a large number of HDFS data blocks, to respond to the ad-hoc or OLAP queries, is a heavy operation. We can skip loading unnecessary data blocks if we partition or index some tables by the appropriate predicate attributes. However, the way of selecting the candidate's attributes remains a challenging task to handle. In this paper, we propose a technique based on frequent itemset mining, to Partition, Bucket and Sort the Tables (PBSTs) of a big data warehouse with the more frequent predicate attributes in the queries. We take into account the density of the attributes of the tables, data skew, and the physical characteristics of the cluster nodes. To evaluate our approach, we conducted some experiments in a cluster of 15 slave nodes. Experimental results show that with our method, we improve the query response time by 50 % over existing skipping techniques.
LightFD: A Lightweight Flow Detection Mechanism for Traffic Grooming in Optical Wireless DCNs

Wireless data centers (DCs) are enablers of re-configurable data center network (DCN) topologies by augmenting the cabling complexity and inflexibility of traditional wired DCs. In this paper, we propose an optical traffic grooming (TG) for mice flows (MFs) and elephant flows (EFs) in a wireless DCN which is interconnected with free-space optical (FSO) links operating on wavelength division multiplexing (WDM). Since handling the bandwidth-hungry EFs along with delay-sensitive MFs over the same network resources have undesirable consequences, proposed TG policy treat MFs and EFs separately. MFs&#x002F;EFs destined to the same rack are groomed into larger rack-to-rack MF&#x002F;EF flows over dedicated lightpaths whose routes and capacities are jointly determined taking the load balancing into account. Performance evaluations of proposed TG policy show a significant throughput improvement thanks to bandwidth efficient utilization of the wireless links. Therefore, proposed TG requires expeditious flow detection mechanisms which can immediately classify EFs with very high accuracy. Since these demands cannot be met by existing sampling and port- mirroring based solutions, we propose a lightweight and fast in-network flow detection (LightFD) mechanism. LightFD is designed as a module on the Virtual-Switch&#x002F;Hypervisor, which detects EFs based on acknowledgment sequence number of flow packets. Emulation results show that LightFD can provide up to 110 times faster detection speeds than sampling-based methods with \%100 detection accuracy. We also demonstrate that the EF detection speed has a considerable impact on achievable EF throughput.
Joint Scheduling and Power Adaptation in NOMA-Based Fog-Radio Access Networks

Non-Orthogonal Multiple Access (NOMA) is a promising technology for 5G that enables each resource unit to simultaneously serve multiple users. This work evaluates the potential benefit of joint scheduling and power adaptation in NOMA- based downlink in Fog-Radio Access Networks (FRAN). We consider the downlink of a FRAN, where the Fog Access Points (FAPs) are connected to central cloud baseband units (BBUs) through capacity-constrained fronthaul links. The FAPs adopt a two-user NOMA scheme, within each resource block (RB), to serve a common set of users. The paper formulates an optimization problem which maximizes a network-wide {rate-based utility} function subject to fronthaul- capacity constraints, so as to determine both the user- to-FAP assignment and the power levels of the users served by each FAP. The main contribution of the paper is solving this mixed-integer non-convex optimization problem using a two- step centralized-distributed approach, which is aligned with FRAN operation that {aims to} %relies on partially shifting the network control to the FAPs so as to overcome delays due to fronthaul rate constraints. The assignment step is first solved at the centralized BBU pool by reformulating the problem such that the Hungarian algorithm is applicable. The power {adaptation} is then solved at every FAP using a barrier method. Simulation results show that the proposed NOMA-based algorithm outperforms conventional Orthogonal Multiple Access (OMA) algorithms, even with stringent fronthaul limitations. The proposed algorithm further shows an appreciable {performance trade-off} between the rate and fairness metrics.
Energy-Efficient Hybrid D-A Precoders Design for mm-Wave Systems

Hybrid digital to analog precoding is widely used in millimeter wave system to reduce power consumption. The radio frequency (RF) chains consume maximum transmitted power, therefore, in this paper optimal number of RF chains are selected to achieve the desired energy efficiency (EE). The novelty of the proposed method is in two folds. First, the optimal number of RF chains are selected by the proposed bisection algorithm. Second, the optimal analog precoder is designed by power iteration algorithm, followed by the digital precoder which is designed by the known least squares method. The effect of the resolution of the phase shifters used in the analog precoder on EE is investigated. Simulation results show that the proposed algorithms outperform the existing algorithms in terms of the EE and the achievable capacity. It also showed that the hybrid precoding with an optimal number of quantized bits of resolution attains the best EE performance.
Resource Management for Improving Performance of IEEE 802.15.4-Based Home Automated Systems

Recently, renewed interest has been given to improving energy efficiency of automated home systems for which Zigbee (over IEEE 802.15.4) is a potential technology. The IEEE 802.15. 4 standard supports both contention-free and contention-based services. Contention-free services can be provided via guaranteed time slots (GTS) in beacon mode. However, directly applying GTS scheduling in IEEE 802.15. 4 for resource management is not efficient in improving network performance. First, the improvement of GTS scheduling requires adding more bits and fields to IEEE 802.15. 4 frames or more layers to Zigbee network stacks, this results in incompatibility problems with deployed products. Second, the network coordinator fails to assess the exact bandwidth demand from sensors for optimal resource management; resulting in the waste of network resources. This paper therefore proposes a resource management approach for improving the performance of IEEE 802.15. 4-based automated home systems. The solution includes four extensions of the IEEE 802.15. 4 standard. First, sensors specify data size they have to transmit instead of number of GTSs in their GTS command frames. Second, the coordinator handles two separate queues: a GTS allocation queue and a GTS deallocation one. Third, the coordinator runs the optimal GTS scheduling algorithm and fourth, the sensor operates according to the optimal power- saving algorithm. Extensive simulations show that the proposed approach significantly improves network performance in terms of power efficiency, bandwidth utilization and throughput while guaranteeing delay requirements for home automation applications.
On the Feasibility of Hybrid Interference Alignment in 60 GHz Millimeter-Wave Dense Networks

To address the challenge of poor link budget and co-channel interference (CCI) mitigation in 60 GHz millimeter-wave (mmWave) dense networks, we investigate the feasibility of employing hybrid interference alignment (IA) method to approach the capacity, where the analog precoder/combiner is utilized to harvest the largest array gain for improving the received signal strength on each communicating link, subsequently a low-dimensional digital IA processing is further performed to eliminate the CCI. This paper establishes a feasible condition on the required minimum number of RF chains to achieve hybrid IA, i.e., the number of RF chains at the transmitter (TX) and receiver (RX) only needs to be twice the number of data streams. In addition, a joint max-SINR and min-MSE iterative algorithm based on channel reciprocity is proposed. Furthermore, a detailed analysis of the proposed algorithm on the large scale system is also provided. The simulation shows that hybrid IA design outperforms the conventional digital and hybrid schemes under the conditions of different SNR and different numbers of users or antennas in 60 GHz mmWave system.
Mask R-CNN Based Object Detection for Intelligent Wireless Power Transfer

Resonant Beam Charging (RBC) is a promising multi-Watt and multi-meter wireless power transfer method with safety, mobility and simultaneously-charging capability. However, RBC system operation relies on information availability including power receiver location, class label and the receiver number. Since smartphone is the most widely-used mobile device, we propose a Mask R-CNN based smartphone detection model in the RBC system. Experiments illustrate that our model reduces the smartphone scanning time to one third. Thus, this machine learning detection approach provides an intelligent way to improve the user experience in wireless power transfer for mobile and Internet of Things (IoT) devices.
Evaluation of Self-Calibration Techniques for NR Massive MIMO Systems

The Massive MIMO principle is becoming a standard component of the next generation wireless communication systems such as 5G. Beamforming and beam-sweeping algorithms have to be inherently supported. In this paper, we give details on a Massive MIMO SDR, which was designed for testing of new radio standards. Furthermore, we highlight a novel self-calibration concept based on Hadamard sequences transmitted over a Wilkinson divider network. The results show that for a fully calibrated system, we can achieve a precision for the phase error of less than 2°, showing that the chosen system can be used for testing of novel NR MIMO algorithms.
Communication Scheduling Optimization for Distributed Deep Learning Systems

Deep learning is an increasingly important technique that can solve complex problems. Due to the growth of data and model complexity, large-scale deep learning has became an important issue. Distributed deep learning is an efficient way to address these complexity issues in training a huge model. However, in a distributed environment network bandwidth becomes a performance bottleneck for deep learning. We propose various optimizations in reducing network usage by scheduling network request events properly, so as to reduce the total training time. These scheduling optimization only requires software innovation and without the need to upgrade physical network bandwidth, thus is economically competitive. The experiments indicate that our scheduler achieves up to 25 % speedup over traditional schedulers.
Confidential Information Ensurance through Physical Layer Security in Device-to-Device Communication

This paper inquires the achievement of secret key generation (SKG) in device-to-device (D2D) communications with the aid of relay. The confidential information between D2D users is taken under the consideration of physical layer secret key generation scheme with the help of colluding or non-colluding relay node. The selected relay conforms to help in the generation of secret keys to keep the information confidential from eavesdropping. In order to ensure the information confidential between D2D users, we explicate a mechanism for selecting relay node based on two basic social phenomena for the selection of relay node. The non-colluding relay selection is considered under the scenario of social trust, while colluding relay selection is based on social reciprocity. Furthermore, we utilize coalition game theory for the selection of optimal relay node in order to improve secret key generation rate (SKGR). Particularly, to attain more eminent SKGR within channel coherence time, the coalition game approach is determined to select an optimal node for relaying by D2D users. On the basis of relay selection, social phenomena, and coalition game theory, we propose an algorithm for achieving higher SKGR. The generated keys are not only protected from eavesdropper but also from the selected (colluding or non-colluding) relay. The performance of our proposed scheme validates and guarantees information confidentiality in D2D communications.
WiLock: Exploiting Wireless Signals for Device-Free Continuous Authentication

Mobile devices use time-based de-authentication to secure themselves against un-authorized users, and usually lock themselves after a pre- defined time of inactivity, known as "lock-out time". The technique is effective, however lack of an adaptive de-authentication mechanism may limit the usability and security: if the lock-out time is too short, the usability suffers from requirement of frequent and possibly unnecessary user authentications, and if too long, the security suffers from an increased window of opportunity for lunch-time attacks. In this paper, we propose WiLock; a WiFi-assisted proximity-based device locking technique, to complement the traditional authentication and de-authentication mechanism on the mobile devices in simplistic but highly common scenarios, to avoid unnecessary de- authentication of the user and to secure devices against lunch-time attacks. We introduce the concept of "Personal Space" (PS) as a safe zone around the device in which the solo presence of the user is considered safe. This approach adopts analyzing wireless signals received at the device to sense human presence in device proximity and make security- aware decisions on locking the device. Physical proximity of the device, along with the presence and relative locations of human objects in it as an authentication factor has been studied and is shown to be effective for enhancing security and usability of mobile devices. After benchmarking different classifiers, we adopt a k-NN based learning method to classify collected information into lock and unlock classes. Our evaluation through extensive experiments on real collected data using off-the-shelf WiFi equipments confirms our scheme's performance and shows an average detection accuracy of 92.62% and 78.73% for stationary and moving objects respectively in the experiment's environment and setting.