Blockchain for Transparent Data Management Toward 6G

The wealth of user data acts as a fuel for network intelligence toward the sixth generation wireless networks(6G).Due to data heterogeneity and dynamics,decentralized data management(DM)is desirable for achieving transparent data operations across network domains,and blockchain can be a promising solution.However,the increasing data volume and stringent data privacy-preservation requirements in 6G bring significantly technical challenge to balance transparency,efficiency,and privacy requirements in decentralized blockchain-based DM.In this paper,we investigate blockchain solutions to address the challenge.First,we explore the consensus protocols and scalability mechanisms in blockchains and discuss the roles of DM stakeholders in blockchain architectures.Second,we investigate the authentication and authorization requirements for DM stakeholders.Third,we categorize DM privacy requirements and study blockchain-based mechanisms for collaborative data processing.Subsequently,we present research issues and potential solutions for blockchain-based DM toward 6G from these three perspectives.Finally,we conclude this paper and discuss future research directions.

An Adaptive Grouping Scheme in Ultra-Dense IEEE 802.11ax Network Using Buffer State Report Based Two-Stage Mechanism

IEEE 802.11ax,which is an emerging WLAN standard,aims at providing highly efficient communication in ultra-dense wireless networks.However,due to a large number of stations(STAs)in the ultra-dense device deployment scenarios,the potentially high packet collision rate significantly decreases the communication efficiency of WLAN.In this paper,we propose an adaptive STA grouping scheme to overcome this dense network challenge in IEEE 802.11ax by using Buffer State Report(BSR)based Two-stage Mechanism(BTM).In order to achieve the optimal efficiency of BSR delivery,we analyze the functional relationship between STA number in group and Resource Unit(RU)efficiency.Based on this analysis results,an adaptive STA grouping algorithm with variable group size is proposed to achieve efficient grouping in BTM.The numerical results demonstrate that the proposed adaptive BTM grouping algorithm significantly improves the BSR delivery efficiency and the throughput of overall system and each STA in the ultra-dense wireless network.

Filtered-OFDM——Enabling Flexible Waveformfor the 5G Cellular Networks

To deal with the expected diversification on traffic types, the fundamental waveform of the upcoming5 G standard must be sufficiently flexible. In the 4th generation wireless networks(e.g., cellular LTE and Wi- Fi802.11ac), orthogonal frequency division multiplexing(OFDM) has been widely adopted to combat frequency selectivity and thus improve the spectrum efficiency. Holding various advantages such as backward compatibility with LTE, ease of hardware implementation, time-localized low-latency transmission and straightforward combination with multi-antenna transmission, OFDM will remain as an important waveform candidate for 5G. However,OFDM alone appears to be insufficient in terms of the requirements faced by 5G waveform, such as high flexibility to accommodate different waveform numerologies for an efficient support of diversified traffic types and channel characteristics. In this work, we present a new waveform format, named as filtered-OFDM(f-OFDM) and illustrate its potential and benefits for serving as the underlying waveform of 5G.

Optical wavelength meter with machine learning enhanced precision

A photonic implementation of a wavelength meter typically applies an interferometer to measure the frequencydependent phase shift provided by an optical delay line.This work shows that the information to be retrieved is encoded by a vector restricted to a circular cone within a 3D Cartesian object space.The measured data belong to the image of the object space under a linear orthogonal map.Component impairments result in broken orthogonal symmetry,but the mapping remains linear.The circular cone is retained as the object space,which suggests that the conventional conic section fitting for the wavelength meter application is a premature reduction of the object space from R^(3)to R^(2).The inverse map,constructed by a learning algorithm,compensates impairments such as source intensity fluctuation and errors in delay time,coupler transmission,and photoreceiver sensitivity while being robust to noise.The simple algorithm does not require initial estimates for all parameters except for a broad bracket of the delay;further,weak nonlinearity introduced by uncertain delay can be corrected by a robust golden search algorithm.The phase-retrieval process is invariant to source power and its fluctuation.Simulations demonstrate that,to the extent that the ten parameters of the interferometer model capture all significant impairments,a precision limited only by the level of random noise is attainable.Applied to measured data collected from a fabricated Si_(3)N_(4)wavelength meter,greater than an order of magnitude improvement in precision compared with the conventional method is achieved.

Cubic Regularization Methods with Second-Order Complexity Guarantee Based on a New Subproblem Reformulation

The cubic regularization(CR)algorithm has attracted a lot of attentions in the literature in recent years.We propose a new reformulation of the cubic regularization subproblem.The reformulation is an unconstrained convex problem that requires computing the minimum eigenvalue of the Hessian.Then,based on this reformulation,we derive a variant of the(non-adaptive)CR provided a known Lipschitz constant for the Hessian and a variant of adaptive regularization with cubics(ARC).We show that the iteration complexity of our variants matches the best-known bounds for unconstrained minimization algorithms using first-and second-order information.Moreover,we show that the operation complexity of both of our variants also matches the state-of-the-art bounds in the literature.Numerical experiments on test problems from CUTEst collection show that the ARC based on our new subproblem reformulation is comparable to the existing algorithms.

On building machine learning pipelines for Android malware detection:a procedural survey of practices,challenges and opportunities

As the smartphone market leader,Android has been a prominent target for malware attacks.The number of malicious applications(apps)identified for it has increased continually over the past decade,creating an immense challenge for all parties involved.For market holders and researchers,in particular,the large number of samples has made manual malware detection unfeasible,leading to an influx of research that investigate Machine Learning(ML)approaches to automate this process.However,while some of the proposed approaches achieve high performance,rapidly evolving Android malware has made them unable to maintain their accuracy over time.This has created a need in the community to conduct further research,and build more flexible ML pipelines.Doing so,however,is currently hindered by a lack of systematic overview of the existing literature,to learn from and improve upon the existing solutions.Existing survey papers often focus only on parts of the ML process(e.g,data collection or model deployment),while omitting other important stages,such as model evaluation and explanation.n this paper,we address this problem with a review of 42 highly-cited papers,spanning a decade of research(from 2011 to 2021).We introduce a novel procedural taxonomy of the published literature,covering how they have used ML algorithms,what features they have engineered,which dimensionality reduction techniques they have employed,what datasets they have employed for training,and what their evaluation and explanation strategies are.Drawing from this taxonomy,we also identify gaps in knowledge and provide ideas for improvement and future work.

Mode-conversion-based silicon photonic modulator loaded by a combination of lateral and interleaved p-n junctions

We experimentally demonstrate a silicon photonic modulator that can be loaded with a combination of lateral and interleaved p-n junctions to enhance its phase modulation.We use an asymmetric Bragg grating to introduce mode conversion in the active area,allowing the modulator to operate in reflection without introducing additional on-chip loss.With a compact footprint(phase shifter length of 290μm),the modulator demonstrates a modulation speed up to 45 Gb/s with a bit error rate below the 7% forward-error-correction(FEC)threshold(up to 55 Gb/s with 20% FEC),and a low power consumption of 226 fJ/bit.