Statistical CSIT Aided User Scheduling for Broadcast MU-MISO System

Abstract: Recent studies show that the statistical channel state information (SCSI) helps to largely increase the capacity of communication systems when the instantaneous perfect CSI (IPCSI) is unavailable. In this paper, we consider multi-user multipleinput- single-output (MU-MISO) broadcast channels where the transmitter has the knowledge of SCSI. The major issue concerned in our work is to improve the average group-rate of the whole system by scheduling users over different time slots. With SCSI at the transmitter side, we are able to precode signals and hence compute the theoretical achievable group-rate of arbitrary user groups. Based on the group-rates, we propose tier-2 Munkres user scheduling algorithm (T2-MUSA) which leads to higher average group-rate than existing algorithms with generally better fairness. The optimality of the proposed algorithm in energy-fair user scheduling space is proved and we derive a lower bound of a special case to verify the validity of our simulations. In addition, many conventional user scheduling algorithms maintain queue stability by solving a weighted sum-rate (WSR) problem, using queue lengths to represent weight coefficients. Inspired by T2-MUSA we propose a QoS-based Munkres user scheduling algorithm (QB-MUSA) aimed at stabilizing queue lengths and maximizing throughput. In results, we show that QB-MUSA exhibits higher throughput than the conventional weighted sumrate (WSR) based algorithm

Road Side Unit-Assisted Learning-Based Partial Task Offloading for Vehicular Edge Computing System

The rapid development of vehicular networks creates diverse ultra-low latency constrained and computation-intensive applications, which bring challenges to both communication and computation capabilities of the vehicles and their transmission. By offloading tasks to the edge servers or vehicles in the neighbourhood, vehicular edge computing (VEC) provides a cost-efficient solution to this problem. However, the channel state information and network structure in the vehicular network varies fast because of the inherent mobility of vehicle nodes, which brings an extra challenge to task offloading. To address this challenge, we formulate the task offloading in vehicular network as a multi-armed bandit (MAB) problem and propose a novel road side unit (RSU)-assisted learning-based partial task offloading (RALPTO) algorithm. The algorithm enables vehicle nodes to learn the delay performance of the service provider while offloading tasks. Specifically, the RSU could assist the learning process by sharing the learning information among vehicle nodes, which improves the adaptability of the algorithm to the time-varying networks. Simulation results demonstrate that the proposed algorithm achieves lower delay and better learning performance compared with the benchmark algorithms

Forgery detection using chaotic watermarking in image key areas

U radu proučavamo algoritam za nanošenje vodenog žiga na komprimiranim slikama u primjeni protiv falsifikata na novčanicama. Najprije se proučava osnovni algoritam za nanošenje vodenog žiga na osnovu informacije o rubu slike. Šifrirani vodeni žig se pretvara u binarne vrijednosti i ugrađuje u rubove, a u međuvremenu se originalni oblik ruba štiti od vidljivog uništenja. Zatim se uz pomoć flip-invariantnih SIFT karakteristika lokalizira ključno područje sadržaja poput brojki i slova na slici. Treće, primjenjuju se Tent mapa i hash funkcija u daljnjoj zaštiti tajnog vodenog žiga. Svojstvo Tent mape osiguralo je osjetljivost na promjene u početnoj vrijednosti. Stoga možemo bolje zaštititi i prikriti originalni vodeni žig. Izvršenje operacija na binarno kodiranoj slici zasniva se na kodiranim nizovima dobivenim iz kaotične mape. Odabrane su razne operacije kako bi se generirao robustni prikriveni vodeni žig. Konačno smo verificirali naš algoritam u otkrivanju falsifikata. Istraživala se i osjetljivost prema tajnim ključnim vrijednostima. Predloženi je sustav osjetljiv na ključne vrijednosti pa učinkovito štiti vodeni žig od napada. Trošak izračuna se također mjerio u praktičnoj primjeni kaotičkog vodenog žiga. Eksperimentalni rezultati pokazuju da je predložena metoda pouzdano učinkovita.In this paper we study watermarking algorithm for compressed images in the application of antiforgery in financial bills. First, the basic watermarking algorithm based on image edge information is studied. The encrypted watermark is converted into binary values and embedded into the edges, meanwhile the original edge shape is preserved from noticeable destruction. Second, the flip-invariant SIFT features are used to localize the key content area like digits and letters in the image. Third, Tent map and a hash function is used to further protect the secrete watermark. The property of Tent map ensured the sensitivity towards changes in the initial value. Therefore we can better protect and encrypt the original watermark. The operations performing on the binary coded image are based on the encryption sequences generated from chaotic map. Different operations are chosen to generate robust encrypted watermark. Finally, we verify our algorithm in antiforgery detection. The sensitivity towards secrete key values is further investigated. The proposed system is sensitive to the key values, hence it effectively protects the watermark from attack. The computational cost is also measured for practical application of the chaotic watermarking. Experimental results show that the proposed method is reliably efficient

Energy-Efficient Resource Allocation for Industrial Cyber-Physical IoT Systems in 5G Era

Cyber-physical Internet of things system (CPIoTS), as an evolution of Internet of things (IoT), plays a significant role in industrial area to support the interoperability and interaction of various machines (e.g. sensors, actuators, and controllers) by providing seamless connectivity with low bandwidth requirement. The fifth generation (5G) is a key enabling technology to revolutionize the future of industrial CPIoTS. In this paper, a communication framework based on 5G is presented to support the deployment of CPIoTS with a central controller. Based on this framework, multiple sensors and actuators can exchange information with the central controller in full-duplex mode. To accommodate the signal data in the available channel band, a resource allocation problem is formulated as a mixed integer non-convex programming problem, aiming to maximize the sum energy efficiency of CPIoTS. By introducing the transformation, we decompose the resource allocation problem into power allocation and channel allocation. Moreover, we consider an energy-efficient power allocation algorithm based on game theory and Dinkelbach's algorithm. Finally, to reduce the computational complexity, the channel allocation is modeled as a 3-dimensional matching problem, and solved by iterative Hungarian method with virtual devices (IHM-VD). A comparison is completed with well-known existing algorithms to demonstrate the performance of the proposed one. Simulation results confirm the efficiency of the proposed model, which significantly outperforms other benchmark algorithms in terms of meeting the energy efficiency and the QoS requirements

Fabrication of astaxanthin-loaded electrospun nanofiber-based mucoadhesive patches with water‐insoluble backing for the treatment of oral premalignant lesions

Oral premalignant lesions (OPL) are one of the most common oral diseases, affecting the quality of life and even leading to oral cancer. Current treatments commonly use steroids/retinoids in mouthwashes, films, or ointments. However, conventional drugs/formulations have significant side effects/limitations. Herein, astaxanthin-loaded polycaprolactone (PCL)/gelatin (GT) nanofiber-based mucoadhesive patches (PGA) with the water‐insoluble PCL nanofiber backing (PCL/PGA) are developed via electrospinning for the management of OPL. The saliva-insoluble PCL backing could greatly prevent drug loss after application in the oral cavity. The prepared PCL/PGA patches exhibit a suitable astaxanthin release rate for achieving high local drug concentration, which permeated into buccal mucosa. In addition, the developed thin patches display excellent wet tissue adhesion and great air permeability due to their high porosity. Notably, the in vivo experiment shows that the bioactive mucoadhesive patches significantly promote the recovery of OPL by suppressing the expression of Ki67 and cyclooxygenase-2 (COX-2), comparable to clinical tretinoin cream formulation. Also, the patches did not induce any side effects (i.e., hair loss and oral ulcers) compared to clinical tretinoin cream formulation. The results demonstrate that this novel electrospun mucoadhesive bilayer patch holds great potential for the treatment of OPL

Joint UL/DL Resource Allocation for UAV-Aided Full-Duplex NOMA Communications

This paper proposes an unmanned aerial vehicle (UAV)-aided full-duplex non-orthogonal multiple access (FD-NOMA) method to improve spectrum efficiency. Here, UAV is utilized to partially relay uplink data and achieve channel differentiation. Successive interference cancellation algorithm is used to eliminate the interference from different directions in FD-NOMA systems. Firstly, a joint optimization problem is formulated for the uplink and downlink resource allocation of transceivers and UAV relay. The receiver determination is performed using an access-priority method. Based on the results of the receiver determination, the initial power of ground users (GUs), UAV, and base station is calculated. According to the minimum sum of the uplink transmission power, the Hungarian algorithm is utilized to pair the users. Secondly, the subchannels are assigned to the paired GUs and the UAV by a message-passing algorithm. Finally, the transmission power of the GUs and the UAV is jointly fine-tuned using the proposed access control methods. Simulation results confirm that the proposed method achieves higher performance than state-of-the-art orthogonal frequency division multiple-access method in terms of spectrum efficiency, energy efficiency, and access ratio of the ground users

Optimal Resource Allocation for NOMA-TDMA Scheme with α-Fairness in Industrial Internet of Things

In this paper, a joint non-orthogonal multiple access and time division multiple access (NOMA-TDMA) scheme is proposed in Industrial Internet of Things (IIoT), which allowed multiple sensors to transmit in the same time-frequency resource block using NOMA. The user scheduling, time slot allocation, and power control are jointly optimized in order to maximize the system α -fair utility under transmit power constraint and minimum rate constraint. The optimization problem is nonconvex because of the fractional objective function and the nonconvex constraints. To deal with the original problem, we firstly convert the objective function in the optimization problem into a difference of two convex functions (D.C.) form, and then propose a NOMA-TDMA-DC algorithm to exploit the global optimum. Numerical results show that the NOMA-TDMA scheme significantly outperforms the traditional orthogonal multiple access scheme in terms of both spectral efficiency and user fairness