A cluster-based cooperative communication system and method is provided for mitigating inter-cell interference effectively. A cluster-based cooperative communication method of a mobile station includes receiving allocation of a resource of a weighted subband mapped to one of preset cooperative base station clusters, the selected cooperative base station cluster having a group including a serving base station of the mobile station and dominant interfering base stations found by channel estimation, and communicating with the serving base station in cooperation with the dominant interfering base stations.

Cluster-based cooperative communication system and method

With the development of Internet of Medical Things, massive healthcare sensor data (HSD) are transmitted in the Internet, which faces various security problems. Healthcare data are sensitive and important for patients. Automatic classification of HSD has significant value for protecting the privacy of patients. Recently, the edge computing-based federated learning has brought new opportunities and challenges. It is difficult to develop a lightweight HSD classification system for edge computing. In particular, the classification system should consider the dynamic characteristics of HSD, e.g., the change of data distributions and the appearance of initially unknown classes. To solve these problems, the paper proposes a scalable and transferable classification system, called SCALT. It is a one-classifier-per-class system based on federated learning. It comprises a one-dimensional convolution-based network for feature extraction, and an individual mini-classifier for each class. It is easy to be scaled when new class appears since only a mini-classifier will be trained. The feature extractor is updated only when it is transferred to a new task. SCALT has a parameter protection mechanism, which can avoid catastrophic forgetting in sequential HSD classification tasks. We conduct comprehensive experiments to evaluate SCALT on three different physiological signal datasets: Electrocardiogram, Electroencephalogram and Photoplethysmograph. The accuracies on the three datasets are 98.65%, 91.10% and 89.93% respectively, which are higher than the compared state-of-the-art works. At last, an application of applying SCALT to protect the privacy of patients is presented. 

A Scalable and Transferable Federated Learning System for Classifying Healthcare Sensor Data

In a wireless network, a basestation transmits data to mobiles at time-varying, mobile-dependent rates due to the ever changing nature of the communication channels. In this article, we consider a wireless system in which the channel conditions and data arrival processes are governed by an adversary. We first consider a single server and a set of users. At each time step t, the server can only transmit data to one user. If user i is chosen, the transmission rate is ri(t). We say that the system is (w, ϵ)-admissible if in any window of w time steps the adversary can schedule the users so that the total data arriving to each user is at most 1−ϵ times the total service it receives.Our objective is to design online scheduling algorithms to ensure stability in an admissible system. We first show, somewhat surprisingly, that the admissibility condition alone does not guarantee the existence of a stable online algorithm, even in a subcritical system (i.e., ϵ > 0). For example, if the nonzero rates in an infinite rate set can be arbitrarily small, then a subcritical system can be unstable for any deterministic online algorithm.On a positive note, we present a tracking algorithm that attempts to mimic the behavior of the adversary. This algorithm ensures stability for all (w, ϵ)-admissible systems that are not excluded by our instability results. As a special case, if the rate set is finite, then the tracking algorithm is stable even for a critical system (i.e., ϵ = 0). Moreover, the queue sizes are independent of ϵ. For subcritical systems, we also show that a simpler max weight algorithm is stable as long as the user rates are bounded away from zero.The offline version of our problem resembles the problem of scheduling unrelated machines and can be modeled by an integer program. We present a rounding algorithm for its linear relaxation and prove that the rounding technique cannot be substantially improved.

Scheduling over a time-varing user-dependent channel with applications to high speed wireless data

With the continuous increase in avenues of personal data generation, privacy protection has become a hot research topic resulting in various proposed mechanisms to address this social issue. The main technical solutions for guaranteeing a user’s privacy are encryption, pseudonymization, anonymization, differential privacy (DP), and obfuscation. Despite the success of other solutions, anonymization has been widely used in commercial settings for privacy preservation because of its algorithmic simplicity and low computing overhead. It facilitates unconstrained analysis of published data that DP and the other latest techniques cannot offer, and it is a mainstream solution for responsible data science. In this paper, we present a comprehensive analysis of clustering-based anonymization mechanisms (CAMs) that have been recently proposed to preserve both privacy and utility in data publishing. We systematically categorize the existing CAMs based on heterogeneous types of data (tables, graphs, matrixes, etc.), and we present an up-to-date, extensive review of existing CAMs and the metrics used for their evaluation. We discuss the superiority and effectiveness of CAMs over traditional anonymization mechanisms. We highlight the significance of CAMs in different computing paradigms, such as social networks, the internet of things, cloud computing, AI, and location-based systems with regard to privacy preservation. Furthermore, we present various proposed representative CAMs that compromise individual privacy, rather than safeguarding it. Finally, we discuss the technical challenges of applying CAMs, and we suggest promising opportunities for future research. To the best of our knowledge, this is the first work to systematically cover current CAMs involving different data types and computing paradigms. 

Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

The link quality reporting method includes measuring link quality for received radio blocks, and if there is a modulation scheme with a largest number of radio blocks, reporting link quality for the modulation scheme, and if there is a plurality of schemes with the same number of radio blocks, reporting link quality for a modulation scheme selected using a predetermined method. Performance deterioration can be minimized in a link adaptation process.

Method of requesting and reporting link quality in an egprs2 system

Study QoS-aware Fog Computing for Disease Diagnosis and Prognosis

In channel adaptive OFDM A systems, it is important to feed back channel information in a fast, accurate and efficient way to improve the system performance. However, base station requires a large amount of feedback information of channel state information (CSI) in the adaptive channel. In order to reduce the overload of CSI feedback, we propose a feedback scheme which only selects the users scheduled in the next time slot to feed back the channel information according to user mobility. We use adaptive modulation to apply bit rate adaptation depending on user channel condition, where the scheduling time is fixed. By fixing the packet size, scheduling time and feedback period can be fit to different time slots according to user transmission rate. We demonstrate by computer simulation that the proposed scheme can reduce the overload of user feedback with the minimum suppression of the throughput degradation.

Reduction of Feedback Overload by Exploiting Adaptive Channel in OFDMA systems

In this paper, we propose a packet scheduling algorithm for cellular relay networks by considering relay selection, variation of channel quality, and packet delay. In the networks, mobile users are equipped with not only cellular but also user relaying radio interfaces, where base station exploits adaptive high speed downlink channel. Our proposed algorithm selects a user with good cellular channel condition as a relay station for other users with bad cellular channel condition but can get access to relay link with good quality. This can achieve flexible packet scheduling by adjusting transmission rates of cellular link. Packets are scheduled for transmission depending on scheduling indexes which are calculated based on user's achieved transmission rate, packet utility, and proportional fairness of their throughput. The performance results obtained by using computer simulation show that the proposed scheduling algorithm is able to achieve high network capacity, low packet loss, and good fairness in terms of received throughput of mobile users.

Packet scheduling for cellular relay networks by considering relay selection, channel quality, and packet utility

In this paper, we propose a resource allocation scheme that limits allocating sub-channels to other cells based on the amount of interference estimated by beacon signals from adjacent cellspsila BSs (base station) to users to reduce the co-channel interference and re-allocates sub-channels to cell-edge users in multi-cell orthogonal frequency division multiplexing access (OFDMA) system. The proposed scheme can improve the outage probability that users donpsilat achieve the required rate. The computer simulation shows that the proposed scheme can provide better system utilization and outage probability performance compared with the conventional scheme.

Resource allocation scheme for multi-cell OFDMA considering the interference to cell-edge users

We propose a packet scheduling algorithm to improve the throughput performance of non-real time (NRT) traffic while satisfying quality of service (QoS) of real-time (RT) traffic in orthogonal frequency division multiple access (OFDMA) system compared to the conventional algorithm In order to support both RT and NRT packets adaptively in the varying traffic situation, the proposed algorithm adjusts the length of marginal scheduling time interval (MSTI) for the time-utility function (TUF), which is related to the urgency of packets in time at a base station In the proposed algorithm, if both the number of NRT traffic packets in buffers for each user become zero, the lengths of MSTI for voice traffic and video traffic are adjusted to the maximum available delays of voice and video traffic Computer simulation results show that the proposed algorithm is able to provide less packet loss rate for RT traffic and better throughput performance for NRT traffic

Packet Scheduling Algorithm with Modified Time-Utility Function to Improve NRT Throughput in OFDMA System

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