Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Based Joint Detection and Decoding of Non-Orthogonal Multiple Access Systems

In recent years, many studies have applied deep learning techniques to the field of communication. Many of them have achieved reduced complexity or improved performance. In this paper, we apply deep learning techniques to joint multiuser detection and decoding of non-orthogonal multiple access system. We construct the neural network detector and decoder based on message passing algorithm (MPA) and belief propagation (BP) algorithm, respectively. And the decoder is cascaded with the detector to form a larger neural network. To achieve this, we assign weights to the edges of the respective factor graphs and then use deep learning approaches to train these weights to get better performance. Compared with the conventional method (i.e. MPA for multiuser detection and BP for decoding), the proposed method improves the joint detection and decoding performance, while the computational complexity is not greatly increased.
Attacks on Machine Learning: Lurking Danger for Accountability

It is well-known that there is no safety without security. That being said, a sound investigation of security breaches on Machine Learning (ML) is a prerequisite for any safety concerns. Since attacks on ML systems and their impact on the security goals threaten the safety of an ML system, we discuss the impact attacks have on the ML models’ security goals, which are rarely considered in published scientific papers. The contribution of this paper is a non-exhaustive list of published attacks on ML models and a categorization of attacks according to their phase (training, after-training) and their impact on security goals. Based on our categorization we show that not all security goals have yet been considered in the literature, either because they were ignored or there are no publications on attacks targeting those goals specifically, and that some are difficult to assess, such as accountability. This is probably due to some ML models being a black box.
Spatial Multiplexing and Antenna Selection in Relay-Assisted Transmissions with Heterogeneous Nodes

This paper considers network information flow in a multiple-input multiple-output (MIMO) wireless network with four users where (i) two primary users equipped with multiple antennas exchange independent messages via a half-duplex relay and (ii) two secondary users with single antenna transmit to and receive data from the relay. This asymmetric traffic flow problem is a specialized version of a multi-pair two-way relay channel where, in the multiple access (MAC) phase, four users send all messages to the relay simultaneously, and, in the broadcast (BC) phase, the relay transmits messages to users also on a single MIMO channel access. When spatially multiplexing two- and one-way data transmissions via a cooperating relay implementing decode-and- forward (DF) processing, the objective is to work with a minimized number of antennas at all nodes in the system. Specifically, this paper presents a design for precoding and nulling vectors in the proposed signal alignment (SA) scheme with zero- forcing (ZF) decoding. In this design, there is a single redundant antenna at the relay during the MAC phase, which is exploited to provide the diversity advantage. The optimum subset of receiving antennas at the relay is selected by a Max-Min criterion with respect to the post- processing SNR. Simulation results show an improvement in terms of BER when optimization is deployed during the antenna selection process.
Asymptotic Analysis of Regularized Zero-Forcing in Double Scattering Channels

This paper studies the sum-rate performance of regularized zero-forcing (RZF) precoding in a multi-user multiple-input single-output (MISO) system, where the channel between the base station (BS) and each user is modeled by the double scattering channel model. This non-Gaussian channel accounts for both the spatial correlation in the antenna arrays and the structure of scattering in the propagation environment. The user population is divided into $G$ groups, where the users in the same group experience similar propagation conditions and are characterized by common correlation matrices. Under this setting, we derive deterministic approximations of the signal-to-interference-plus-noise ratio (SINR) and the sum-rate with RZF precoding, which are almost surely tight in the large system limit. Simulation results confirm the close match provided by the asymptotic analysis for moderate system dimensions.
Sound Zones As An Optimal Filtering Problem

The design of so-called sound zones is a fairly old idea which has recently gained some research attention. The idea is to control a loudspeaker array to reproduce a desired sound field in certain regions. In this paper, we show how the sound zone control problem can be solved using techniques from speech enhancement. Specifically, we first describe in detail the recently introduced variable span linear filtering (VSLF) framework which unifies the popular optimal filtering and subspace-based approaches to speech enhancement. We then show how the sound zone control problem can be solved using the VSLF framework and how a number of well-known sound zone control methods can be viewed as special cases of this solution. We also discuss in detail the differences between the speech enhancement problem and the sound zone control problem and argue that the VSLF framework is actually even better suited for controlling sound zones than for enhancing speech.
Stackelberg Game-Based Energy Efficient Power Allocation for Heterogeneous NOMA Networks

Recently, it was shown that non-orthogonal multiple access (NOMA) became a hot topic due to its capacity for ameliorating spectral efficiency. In this paper, power allocation in heterogeneous NOMA networks with multiple users are formulated as a Stackelberg game. The competition between the leaders and followers is considered as the energy efficiency (EE) maximization between the small base stations (SBSs) and macro base stations (MBSs). We propose an algorithm to obtain optimal power allocation in MBSs layer and SBSs layer, respectively. Then, Stackelberg iteration is used among MBSs and SBSs to reach the equilibrium point during the game. Simulation results demonstrate the effectiveness of proposed algorithms.
Aggregate Preamble Sequence Design for Massive Machine-Type Communications in 5G Networks

Massive machine-type communications (mMTC) is a major use case in the fifth generation (5G) wireless networks. mMTC aims at supporting a large number of Internet of Things (IoT) connections within a coverage area. The current random access procedure in the Long Term Evolution (LTE) networks may not be able to handle a large number of simultaneous connection requests due to the limited number of random access preambles. Hence, it is essential to modify the random access procedure to support mMTC. In this paper, we propose a new preamble sequence design in which two Zadoff-Chu preamble sequences are aggregated together. This design enables us to have a larger set of random access preambles consisting of all combinations of pairing two Zadoff-Chu preamble sequences. Moreover, we consider a subset of all combinations that satisfy a certain maximum peak-to-average-power-ratio (PAPR) threshold criterion to reduce the energy consumption of the IoT devices. The proposed design requires only minor changes in the conventional transmitter and receiver design for generating and decoding the aggregated preamble sequences, respectively. Results show that the proposed design reduces the probability of preamble collision to less than 10^{-4}, which is lower than LTE. Furthermore, it outperforms other collision avoidance techniques such as access class barring (ACB) in terms of a lower average total service time. The modified receiver detects the aggregated preambles successfully and avoids detecting false preambles. Both the probabilities of misdetection and false alarm are less than 10^{-3} when the signal-to-noise ratio (SNR) is larger than -7 dB.
How Does the Workload Look Like in Production Cloud? Analysis and Clustering of Workloads on Alibaba Cluster Trace

Cloud computing technology is widely used in today's datacenters due to the benefits such as high scalability, on-demand services and low cost. An in-depth understanding of the characteristics of workloads running in production cloud environments is very important for improving the resource management efficiency. In this paper, we make a detailed analysis with visualization techniques and clustering methods on the trace dataset released by Alibaba which contains 11089 online services and 12951 batch jobs running on 1313 machines. Our methodology for clustering workloads contains: i) Select effective feature vectors as the dimensions of clustering; ii) Identify the cluster boundaries of each dimension using K-Means algorithm; iii) Classify jobs by combining the feature vectors which uses the results from previous step; iv) Analyze the characteristics of workload groups at runtime. Our analysis reveals several insights which previous work has not found on Alibaba cluster trace. For batch jobs: a) Average CPU cores of all batch jobs show bimodal-distribution obviously. b) At a random sampling time, more than 50 % machines only run one group of jobs with a short duration, medium CPU cores and small memory utilization, the remaining machines run mixed groups of jobs. For online instances: a) The resource usage (CPU, Memory, and Disk) of most online instances is low; b) There are up to six groups running on the same machine according to our clustering method at a random sampling time.
Fronthaul Capacity Requirement Minimization via Physical Layer Caching in Cloud-RAN

The performance of cloud radio access networks (Cloud-RAN) is constrained by the limited fronthaul capacity. Physical layer caching is an effective technique to reduce the requirement on fronthaul capacity in peak time, which depends on the cache-hit ratio and the wireless transmission performance. Considering a dynamic points selection (DPS) based coordinated multi-point (CoMP) scheme to enhance the wireless transmission performance, this paper proposes an optimal probabilistic caching scheme to minimize the fronthaul capacity requirement in cloud-RAN. First of all, given the content caching probabilities, the average outage probability of cache-based services is derived. Based on the derivation, the relationship between the wireless transmission performance and the fronthaul capacity requirement is established. Then, an optimization problem is formulated to find an optimal probabilistic caching scheme to minimize the fronthaul capacity requirement, which can be solved using the interior point method. Simulation results show that using CoMP transmissions and optimizing the caching probabilities, the proposed scheme can reduce the fronthaul capacity requirement efficiently. Compared to the existing scheme which does not consider the effect of the wireless transmission performance on the fronthaul capacity requirement, the fronthaul capacity requirement can be reduced by 36\%. Moreover, the proposed scheme can also improve the cache service probability.
Data Pipeline Selection and Optimization

Data pipelines are known to influence machine learning performances. In this paper, we formulate the data pipeline hyperparameter optimization problem as a standard optimization problem that can be solved by (meta)optimizer. We apply Sequential Model-Based Optimization techniques to demonstrate how it can automatically select and tune preprocessing operators to improve baseline score with a restricted budget. For NLP preprocessing operators, we found that some optimal configurations are optimal for several different algorithms. It suggests that there exist algorithm-independent optimal parameter configurations for some datasets.
Wireless Technologies with Standard Interference Properties

The standard interference (SI) framework denotes a set of mathematical properties claimed capable of describing interference in numerous wireless technologies. By showing that interference behaves "standard", so the idea, known algorithms for power control and interference management can be reapplied to novel technologies. Even further, interference across physical (PHY) layers can be managed with a single technique based on SI, as long as all involved technologies behave standard. Although some examples for technologies with standard interference are well-known, e.g., single-channel communication and beamforming, a systematic search for systems with standard properties has not been done in literature. In this work, we present proofs for the standard behavior of interference in numerous wireless technologies. We show that apart from common single-input single-output (SISO) links, also links with transmit- and receive beamforming, successive interference cancellation (SIC), amplify and forward (AF) relaying and fullduplex (FD) communication exhibit standard properties. Further, the standard property can be claimed for links with probabilistic channels and&#x002F;or transmissions, such as ALOHA based networks. Finally, we investigate multi-channel interference. By leveraging the newly proven result that standard properties can directly be deduced from the associated rate function, we find that interference in multi-channel and multi-input multi-output (MIMO) systems, too, exhibit the standard properties. The variety of SI systems motivates that although abstract, standard interference has the potential to create a generalized interference management framework, capable of incorporating most existing communication types.
Efficient and Privacy-Preserving Roadmap Data Update for Autonomous Vehicles

Autonomous vehicles (AV) need an access to a big amount of data from online roadmap servers and local sensors to make real-time control decision for safety. Considering the roadmap data may change in cases of road maintenance, new&#x002F;broken road signs, accidents, traffic jams, AV should keep its local roadmap data updated with the online roadmap servers. In this paper, we propose an efficient and privacy-preserving roadmap data update scheme for AV, which achieves the privacy preservation of route information while minimizing the update overhead. We define a new type of privacy sensitivity of a road segment based on its three properties, hotspot sensitivity, proximity sensitivity, and route sensitivity. We then define a new type of $k$-anonymity protection on road segment, i.e., to protect a target segment with sensitivity $l$, the anonymity segment set must include at least $k-1$ segments with sensitivity $\ge l$. In addition, we consider for a more sensitive segment, $k$ needs to be larger to protect the segment. Finally, we compare the number of segments from a privacy-preserving request with the number of segments that need to be updated, and choose the minimum number to keep the communication overhead small while the wanted privacy is achieved. Based on the roadmap data from Google Map, we evaluated our scheme for roadmap data update and route conditions and showed that a tradeoff between well-preserved route privacy and the minimized communication overhead.
From Insight to Impact: Building a Sustainable Edge Computing Platform for Smart Homes

There is a growing trend for engaging edge computing to help smart homes to improve the living comfort of residents. However, the rapidly rising interest in such deployments does not normally integrate with energy management schemes, which is a central issue that smart homes have to face. In this paper, we propose a unified energy management framework for enabling a sustainable edge computing paradigm while meeting the needs of home energy management and smart home applications. This framework aims to enable the full use of renewable energy while reducing electricity bills for households. A prototype system was implemented by using low-cost and easy-to-get hardware. The experiment results demonstrated that renewable energy is fully capable of supporting the reliable running of edge computing devices and electricity bills could be cut by up to 86% when our proposed framework was employed.
Parallelizing Recursive Backtracking Based Subgraph Matching on a Single Machine

We propose PSM, an algorithmic framework to parallelize a common class of subgraph matching algorithms, which are based on recursive backtracking. Specifically, we abstract the matching process as a tree search in the state space and different matching algorithms as different orders in the search. Subsequently, we parallelize such subgraph matching by dividing up the state space search tree and exploring it in parallel. Different from traditional approaches that parallelize the search by each individual state, we dynamically split the state tree into search regions each of which consist of a subtree. We further optimize PSM for load balance and communication efficiency. As case studies, we have parallelized three representative recursive backtracking based subgraph matching algorithms in PSM and studied their performance in comparison with their sequential counterparts. Our results show that the PSM -style parallel algorithms achieved a speedup of 15X-19X on the in-memory execution time on a twenty-core machine.
Socially Aware Task Selection Game for Users in Mobile Crowdsensing

Mobile Crowdsensing (MCS) has become an emerging paradigm to solve complex urban sensing problems by utilizing the ubiquitous sensing capacities of the crowd. One critical issue in MCS is to efficiently allocate tasks to users. We focus on addressing the task allocation problem in a distributed manner, where each user individually and freely makes his decision to undertake tasks. Existing distributed schemes simply consider users behave completely selfishly, which leads to inefficient solutions and damages the overall benefit of all users. Different from existing schemes, in this paper we integrate the social relationship into users' decision making and build a socially aware utility model for each user, which consists of both user's own utility and the weighted sum of his social neighbors' utilities. Based on this, we formulate a novel Socially Aware Task Selection (SATS) game for users in MCS. We theoretically prove the existence of pure Nash equilibrium in the SATS game with the help of a potential game framework. We further propose a distributed user selection algorithm to actually achieve the pure Nash equilibrium. Extensive simulations based on both real and synthetic social relationship graph datasets demonstrate that our approach can achieve more efficient solutions which improve users' overall benefit compared with existing schemes.
Dewing in Fog: Incentive-Aware Micro Computing Cluster Formation for Fog Computing

Fog computing is envisioned as a promising approach for supporting emerging mission-critical applications on capacity and battery constrained mobile devices. By harvesting and collaborating a massive crowd of devices in close proximity for computation and communication resource sharing, it enables significant potentials in low-latency and energy-efficient mobile task execution. It is readily acknowledged, however, that without an efficient incentive mechanism that stimulates resources sharing among devices, the benefits of fog computing cannot be fully realized. Leveraging coalitional game theory, this work presents an efficient incentive mechanism to incentivize mutually-beneficial resource cooperation among the devices for collaborative task execution. Specially, to prevent the over-exploiting and free-riding behaviors that harm resource-rich device's willingness to collaborate, the proposed mechanism groups the devices into multiple micro computing clusters (MC-C). Within each MCC, devices can exchange mutually beneficial actions by helping to compute or transmit tasks, making all of them better off. The solution of the MCC formation is devised by a network-assisted mechanism, which is further proven to admit nice properties such as top coalition and core solution.
Combining Code-Domain and Power-Domain NOMA for Supporting Higher Number of Users

Non-orthogonal multiple access (NOMA) is one of the key technologies being evaluated for the fifth generation (5G) wireless communications. In this paper, a novel NOMA mechanism combining both the code-domain and power-domain techniques is proposed to potentially support a higher number of users. In particular, considering the code-domain NOMA method of sparse code multiple access (SCMA) as the baseline, new low data rate (LDR) users are added on top of it using power-domain NOMA. The optimization problem involving resource and power allocation in such a system is solved such that the overall achievable sum-rate is maximized. Simulation results indicate that the proposed mechanism not only supports higher number of users, but also demonstrates a higher achievable sum-rate than the original SCMA-based system.
Permissioned Blockchain-Based Distributed Software-Defined Industrial Internet of Things

Recently, rapid advances in information and communications technologies (ICTs) have improved the performance of software-defined industrial Internet of things (SDIIoT). Due to a variety of data, flows and smart devices in SDIIoT, a distributed SDN control plane is necessary. However, how to achieve consensus among controllers efficiently and safely is an intractable problem. In this paper, we use a permissioned blockchain approach to reach consensus in distributed SDIIoT. Since lots of data needs to be synchronized, and the throughput of traditional Byzantine fault tolerance protocol used in permissioned blockchain is limited by many aspects, i.e., the trust features of nodes in blockchain, the trust features of controllers in distributed SDIIoT, and the computing capacity of blockchain, we jointly consider these aspects to improve the throughput of permissioned blockchain. Accordingly, we formulate view change, access selection, and computing resources allocation as a joint optimization problem. This joint problem is highly dimensional, and is hard to be solved by traditional methods. Therefore, we propose a dueling deep Q-learning approach to address the problem. Simulation results show the effectiveness of our proposed scheme.
Decoding Behavioral Accuracy in an Attention Task Using Brain fMRI Data

In this paper, we investigate whether we can distinguish that a subject is making a correct or incorrect behavioral response by analyzing the fMRI data of localized brain regions, obtained from a feature-based attention experiment. For each subject, we first construct the feature vectors for each region of interest (including V1, MT or IPS1) from the fMRI signals. Second, we project the feature vectors onto a lower dimensional subspace using Linear Discriminant Analysis (LDA), where the difference between two classes (correct vs. incorrect response) is maximized. Finally, we apply the Bayesian classifier to the projected data, and find that the classification accuracies corresponding to V1, MT and IPS1 are $87.2\%$, $90.8\%$ and $81.7\%$, respectively, when all the trials are considered. Our analysis indicates that: when people make correct or incorrect responses, significant difference exists in the fMRI signals, especially in V1 and MT regions, and the difference can be effectively captured by the LDA-Bayesian classifier. We also prove that: when the original data are normally distributed, LDA, which aims to maximize the difference between different classes, is equivalent to the optimal Maximum Likelihood (ML) based classification method.