Journal of Information and Intelligence最新文献

In view of the difficulty of obtaining downlink channel state information, partial reciprocity based channel covariance matrix (CCM) reconstruction has attracted a lot of attention in frequency division duplex (FDD) multi-antenna systems. Taking both the impact of CCM reconstruction on system performance and design complexity, we investigate an adaptive CCM reconstruction in this paper. Specifically, to effectively evaluate the validity of the reciprocity, we firstly analyze the characteristics of the partial reciprocity and define a reciprocity evaluation criterion. Then, we propose a partial antenna based angular power spectrum (APS) estimating algorithm to further reduce the complexity of the CCM reconstruction. Finally, simulation results demonstrate the superiority of our proposed schemes.

Deep learning based channel state information (CSI) fingerprint indoor localization schemes need to collect massive labeled data samples for training, and the parameters of the deep neural network are used as the fingerprints. However, the indoor environment may change, and the previously constructed fingerprint may not be valid for the changed environment. In order to adapt to the changed environment, it requires to recollect massive amount of labeled data samples and perform the training again, which is labor-intensive and time-consuming. In order to overcome this drawback, in this paper, we propose one novel domain adversarial neural network (DANN) based CSI Fingerprint Indoor Localization (D-Fi) scheme, which only needs the unlabeled data samples from the changed environment to update the fingerprint to adapt to the changed environment. Specifically, the previous environment and changed environment are treated as the source domain and the target domain, respectively. The DANN consists of the classification path and the domain-adversarial path, which share the same feature extractor. In the offline phase, the labeled CSI samples are collected as source domain samples to train the neural network of the classification path, while in the online phase, for the changed environment, only the unlabeled CSI samples are collected as target domain samples to train the neural network of the domain-adversarial path to update parameters of the feature extractor. In this case, the feature extractor extracts the common features from both the source domain samples corresponding to the previous environment and the target domain samples corresponding to the changed environment. Experiment results show that for the changed localization environment, the proposed D-Fi scheme significantly outperforms the existing convolutional neural network (CNN) based scheme.

In this paper, based on the block Markov superposition transmission (BMST) technique, we present a new class of coupled low-density parity-check (LDPC) codes for the transport block (TB)-based transmission to improve the error-correcting performance. For encoding, the previous LDPC codewords corresponding to a TB (at prior time slot) are interleaved and superimposed onto the current LDPC codewords, resulting in the transmitted codewords. For decoding, the sliding window decoding algorithm with sum-product or min-sum implementations can be employed, inheriting a relatively low-latency decoding. A distinguished advantage of the proposed coded transmission over spatially coupled LDPC (SC-LDPC) codes is that the encoder/decoder of the proposed codes can be designed by reusing the encoder/decoder architecture of component block LDPC codes. To analyze the waterfall performance of BMST-LDPC code ensembles, we present the protograph-based EXIT chart analysis, which can efficiently predict the error-correcting performance in waterfall region. To analyze the error-floor performance of BMST-LDPC codes, we employ the genie-aided (GA) lower bound, which can efficiently predict the error-correcting performance in error-floor region. For ease of implementation, the BMST-LDPC codes are constructed by taking the (2, 4)-raptor-like LDPC codes or the 5G LDPC codes as the basic components. The numerical results reveal that the proposed codes can have capacity-approaching performance, exhibiting a gap of 0.007 ​dB away from the corresponding Shannon limit. They also reveal that, by using the proposed BMST construction, the error-correcting performance of the original 5G block LDPC codes can be significantly improved, achieving coding gains up to one dB over the AWGN channels and two dB over the fast fading channels.

Multi-core processor is widely used as the running platform for safety-critical real-time systems such as spacecraft, and various types of real-time tasks are dynamically added at runtime. In order to improve the utilization of multi-core processors and ensure the real-time performance of the system, it is necessary to adopt a reasonable real-time task allocation method, but the existing methods are only for single-core processors or the performance is too low to be applicable. Aiming at the task allocation problem when mixed real-time tasks are dynamically added, we propose a heuristic mixed real-time task allocation algorithm of virtual utilization VU-WF (Virtual Utilization Worst Fit) in multi-core processor. First, a 4-tuple task model is established to describe the fixed-point task and the sporadic task in a unified manner. Then, a VDS (Virtual Deferral Server) for serving execution requests of fixed-point task is constructed and a schedulability test of the mixed task set is derived. Finally, combined with the analysis of VDS's capacity, VU-WF is proposed, which selects cores in ascending order of virtual utilization for the schedulability test. Experiments show that the overall performance of VU-WF is better than available algorithms, not only has a good schedulable ratio and load balancing but also has the lowest runtime overhead. In a 4-core processor, compared with available algorithms of the same schedulability ratio, the load balancing is improved by 73.9%, and the runtime overhead is reduced by 38.3%. In addition, we also develop a visual multi-core mixed task scheduling simulator RT-MCSS (open source) to facilitate the design and verification of multi-core scheduling for users. As the high performance, VU-WF can be widely used in resource-constrained and safety-critical real-time systems, such as spacecraft, self-driving cars, industrial robots, etc.

Locator/identifier separation paradigm (LISP) is an emerging Internet architecture evolution trend that decouples the identifier and location of an entity attached to the Internet. Due to its flexibility, LISP has seen its application in various fields such as mobile edge computing, and V2X networks. However, LISP relies on a DNS-like mapping system to associate identifiers and locations before connection establishment. Such a procedure incurs an extra latency overhead and thus hinders the adoption of LISP in delay-sensitive use cases. In this paper, we propose a novel RNN-based mapping prediction scheme to boost the performance of the LISP mapping resolution, by modeling the mapping procedure as a time series prediction problem. The key idea is to predict the mapping data regarding services to be utilized by users in edge networks administered by xTRs and proactively cache the mapping information within xTRs in advance. We compare our approach with several baseline methods, and the experiment results show a 30.02% performance gain in LISP cache hit ratio and 55.6% delay reduction compared with the case without mapping prediction scheme. This work preliminarily proves the potential of the approach in promoting low-latency LISP-based use cases.

The sharding technique enables blockchain to process transactions in parallel by dividing blockchain nodes into small groups, each of which handles a subset of all transactions. One of the issues with blockchain sharding is generating a large number of cross-shard transactions that need to be checked on the output shard as well as the destination shard. Our analysis suggests that the processing efficiency of cross-shard transactions is consistent with the barrel effect, i.e., that efficiency is more dependent on slower processing shard. Most of the existing studies focus on how to deal with cross-shard transactions, but neglecting the fact that the relative independence between sharding results in different incentive costs between sharding. We perform a sharding analysis on 100,000 real transactions data on Ethereum, and the results show that there is a large difference in gas prices between different shards indeed. In this paper, we propose an Adaptive Weight Incentive (AWI) for Blockchain Sharding, which uses adaptive weight in place of traditional incentive, to address the problem of differing incentive costs for each shard. Take Ethereum as an example, AWI-BS computes the weight of a transaction as a function of a combination of the underlying gas price, the latency of the transaction, and the urgency of the transaction. Then the node chooses which transaction to pack based on the AWI-BS. Lastly, we also perform an in-depth analysis of AWI-BS's security and effectiveness. The evaluation indicates that AWI-BS outperforms the other alternatives in terms of transaction confirmation latency, transaction hit rate, and system throughput.

In this paper, we introduce a new concept, namely ε-arithmetics, for real vectors of any fixed dimension. The basic idea is to use vectors of rational values (called rational vectors) to approximate vectors of real values of the same dimension within ε range. For rational vectors of a fixed dimension m, they can form a field that is an mth order extension Q(α) of the rational field Q where α has its minimal polynomial of degree m over Q. Then, the arithmetics, such as addition, subtraction, multiplication, and division, of real vectors can be defined by using that of their approximated rational vectors within ε range. We also define complex conjugate of a real vector and then inner product and convolutions of two real vectors and two real vector sequences (signals) of finite length. With these newly defined concepts for real vectors, linear processing, such as linear filtering, ARMA modeling, and least squares fitting, can be implemented to real vector-valued signals with real vector-valued coefficients, which will broaden the existing linear processing to scalar-valued signals.

A novel broadband metasurface (MTS) antenna array with high front-to-back ratio (FBR) is proposed for 28 GHz millimeter-wave applications. With slot pairs loaded on patch cells, an aperture-coupled slotted-mushroom MTS antenna is designed to obtain broadband radiation characteristics with a compact size. To suppress the backward radiation of this antenna, the printed ridge gap waveguide (PRGW) technology with a perfect magnetic conductor (PMC) shielding made of mushroom unit-cells underneath the microstrip feeding line is applied. On this basis, a 4 × 4 MTS antenna array with the PRGW feed network is developed. Simulated results show that the FBR can be highly improved by over 16 dB within the entire bandwidth. To validate the design, a prototype of the proposed antenna is fabricated. Measured results show that an FBR greater than 28 dB can be obtained over a 24% impedance bandwidth (from 24.9 GHz to 31.7 GHz) with the reflection coefficient less than −10 dB. The measured antenna gain ranges from 17 dBi to 19.2 dBi and the corresponding measured aperture efficiencies are 35% and 45.6%. The measured results also suggest that the proposed MTS antenna possesses −35 dB cross-polarization level and stable radiation patterns. In addition, the proposed antenna remains a very low profile of 1.7 mm (0.17λ₀ at 28 GHz). All the achieved features indicate that the proposed MTS antenna is an important candidate for B5G and 6G wireless communication.

In this paper, we present a soil methane emissions suppression approach using swarms of unmanned aerial vehicles (UAVs), by spreading biochar mulch on top of the detected methane emissions area/source. Soil microorganisms can produce methane and release it into the atmosphere causing climate change such as global warming. However, people lack methods to manage soil methane emissions, especially quantification of methane emissions from the soil. Current measurement and suppression of methane methods are often limited due to the maintenance, installation, and calibration requirements of these sensing systems. To overcome these drawbacks, we present a new method called FADE-MAS2D (Fractional Advection Diffusion Mobile Actuator and Sensor) in which swarming UAVs are applied as optimal coverage control actuators to various methane release scenarios (from single to multi-source disturbances) utilizing an anomalous diffusion model with different time, and space fractional orders subject to wind fields. This strategy is based on the premise that methane diffusion can be modeled as an anomalous diffusion equation, and swarming UAVs can be applied to tackle the optimal coverage control issue. To simulate methane diffusion under the wind, we utilize the fractional calculus to solve the anomalous diffusion equation and define wind force with the drag equation. In addition, we integrated emissions control, UAV control efforts, and UAV location error in our cost function. Finally, we evaluated our approach using simulation experiments with methane diffusion and multiple methane emission sources in the time and space domain, respectively. The results show that when α ​= ​0.8 and β ​= ​1.8, the shape and emissions of methane perform well. Furthermore, our approach resulted in great control performance with multiple methane emission sources and different wind velocities and directions.

Satellite communications and reconfigurable intelligent surface (RIS) are considered as two promising technologies that can significantly improve the coverage and energy efficiency of future wireless communication networks. The satellite communications security is often threatened due to its broadcasting nature. To enhance the physical layer security (PLS) of satellite communications with channel similarity, an aerial RIS-aided dual full-duplex (DFD-ARIS) cooperative jamming method is presented in this paper. Specifically, unlike the existing works that relied on channel difference, DFD-ARIS utilizes the channel similarity against the eavesdroppers with the help of ARIS. In addition, the power allocation is further studied in conjunction with the phase design of RIS to minimize the total power under the constraints of data rate, satellite power limitation and secrecy rate. Then, the closed-form solutions are achieved. Simulation results show that the performance of the proposed scheme is superior to the traditional method.