2,105 research outputs found

    Modeling the relationship between network operators and venue owners in public Wi-Fi deployment using non-cooperative game theory

    Get PDF
    Wireless data demands keep rising at a fast rate. In 2016, Cisco measured a global mobile data traffic volume of 7.2 Exabytes per month and projected a growth to 49 Exabytes per month in 2021. Wi-Fi plays an important role in this as well. Up to 60% of the total mobile traffic was off-loaded via Wi-Fi (and femtocells) in 2016. This is further expected to increase to 63% in 2021. In this publication, we look into the roll-out of public Wi-Fi networks, public meaning in a public or semi-public place (pubs, restaurants, sport stadiums, etc.). More concretely we look into the collaboration between two parties, a technical party and a venue owner, for the roll-out of a new Wi-Fi network. The technical party is interested in reducing load on its mobile network and generating additional direct revenues, while the venue owner wants to improve the attractiveness of the venue and consequentially generate additional indirect revenues. Three Wi-Fi pricing models are considered: entirely free, slow access with ads or fast access via paid access (freemium), and paid access only (premium). The technical party prefers a premium model with high direct revenues, the venue owner a free/freemium model which is attractive to its customers, meaning both parties have conflicting interests. This conflict has been modeled using non-cooperative game theory incorporating detailed cost and revenue models for all three Wi-Fi pricing models. The initial outcome of the game is a premium Wi-Fi network, which is not the optimal solution from an outsider's perspective as a freemium network yields highest total payoffs. By introducing an additional compensation scheme which corresponds with negotiation in real life, the outcome of the game is steered toward a freemium solution

    Performance analysis of feedback-free collision resolution NDMA protocol

    Get PDF
    To support communications of a large number of deployed devices while guaranteeing limited signaling load, low energy consumption, and high reliability, future cellular systems require efficient random access protocols. However, how to address the collision resolution at the receiver is still the main bottleneck of these protocols. The network-assisted diversity multiple access (NDMA) protocol solves the issue and attains the highest potential throughput at the cost of keeping devices active to acquire feedback and repeating transmissions until successful decoding. In contrast, another potential approach is the feedback-free NDMA (FF-NDMA) protocol, in which devices do repeat packets in a pre-defined number of consecutive time slots without waiting for feedback associated with repetitions. Here, we investigate the FF-NDMA protocol from a cellular network perspective in order to elucidate under what circumstances this scheme is more energy efficient than NDMA. We characterize analytically the FF-NDMA protocol along with the multipacket reception model and a finite Markov chain. Analytic expressions for throughput, delay, capture probability, energy, and energy efficiency are derived. Then, clues for system design are established according to the different trade-offs studied. Simulation results show that FF-NDMA is more energy efficient than classical NDMA and HARQ-NDMA at low signal-to-noise ratio (SNR) and at medium SNR when the load increases.Peer ReviewedPostprint (published version

    Impact of EU duty cycle and transmission power limitations for sub-GHz LPWAN SRDs : an overview and future challenges

    Get PDF
    Long-range sub-GHz technologies such as LoRaWAN, SigFox, IEEE 802.15.4, and DASH7 are increasingly popular for academic research and daily life applications. However, especially in the European Union (EU), the use of their corresponding frequency bands are tightly regulated, since they must confirm to the short-range device (SRD) regulations. Regulations and standards for SRDs exist on various levels, from global to national, but are often a source of confusion. Not only are multiple institutes responsible for drafting legislation and regulations, depending on the type of document can these rules be informational or mandatory. Regulations also vary from region to region; for example, regulations in the United States of America (USA) rely on electrical field strength and harmonic strength, while EU regulations are based on duty cycle and maximum transmission power. A common misconception is the presence of a common 1% duty cycle, while in fact the duty cycle is frequency band-specific and can be loosened under certain circumstances. This paper clarifies the various regulations for the European region, the parties involved in drafting and enforcing regulation, and the impact on recent technologies such as SigFox, LoRaWAN, and DASH7. Furthermore, an overview is given of potential mitigation approaches to cope with the duty cycle constraints, as well as future research directions

    Improving Energy Efficiency Through Multimode Transmission in the Downlink MIMO Systems

    Get PDF
    Adaptively adjusting system parameters including bandwidth, transmit power and mode to maximize the "Bits per-Joule" energy efficiency (BPJ-EE) in the downlink MIMO systems with imperfect channel state information at the transmitter (CSIT) is considered in this paper. By mode we refer to choice of transmission schemes i.e. singular value decomposition (SVD) or block diagonalization (BD), active transmit/receive antenna number and active user number. We derive optimal bandwidth and transmit power for each dedicated mode at first. During the derivation, accurate capacity estimation strategies are proposed to cope with the imperfect CSIT caused capacity prediction problem. Then, an ergodic capacity based mode switching strategy is proposed to further improve the BPJ-EE, which provides insights on the preferred mode under given scenarios. Mode switching compromises different power parts, exploits the tradeoff between the multiplexing gain and the imperfect CSIT caused inter-user interference, improves the BPJ-EE significantly.Comment: 19 pages, 10 figures, EURASIP Journal on Wireless Communications and Networking; EURASIP Journal on Wireless Communications and Networking (2011) 2011:20

    Authenticated secret key generation in delay-constrained wireless systems

    Get PDF
    With the emergence of 5G low-latency applications, such as haptics and V2X, low-complexity and low-latency security mechanisms are needed. Promising lightweight mechanisms include physical unclonable functions (PUF) and secret key generation (SKG) at the physical layer, as considered in this paper. In this framework, we propose (i) a zero round trip time (0-RTT) resumption authentication protocol combining PUF and SKG processes, (ii) a novel authenticated encryption (AE) using SKG, and (iii) pipelining of the AE SKG and the encrypted data transfer in order to reduce latency. Implementing the pipelining at PHY, we investigate a parallel SKG approach for multi-carrier systems, where a subset of the subcarriers are used for SKG and the rest for data transmission. The optimal solution to this PHY resource allocation problem is identified under security, power, and delay constraints, by formulating the subcarrier scheduling as a subset-sum 0−1 knapsack optimization. A heuristic algorithm of linear complexity is proposed and shown to incur negligible loss with respect to the optimal dynamic programming solution. All of the proposed mechanisms have the potential to pave the way for a new breed of latency aware security protocols

    Quasi-optics-inspired low-profile endfire antenna element

    Get PDF
    In this paper, the design, operational principle and experimental validation of an endfire antenna element that is inspired by the energy-focusing characteristics of graded-index optical fibre are presented. The antenna operates with a bandwidth of 1.5 GHz centred around 14 GHz. It has a gain of around 5.5 dBi along the band of interest and a good pattern stability over the band. The antenna derives its unique nature from the arrangement of the arc-shaped parasitics, that couple onto the antenna´s driver dipole. An electromagnetic refractive index retrieval mechanism is used to guide the placement of the parasitics to enhance the gain. In addition to the design principle, a parametric study of the main parameters and their influence on the antenna behaviour is presented. The antenna is a potential candidate for use in multi-user massive MIMO antenna arrays for 5G communications where space is premium and in antenna array applications where a low-profile antenna element with a high gain is a necessity.This work was supported by the 5G wireless project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 641985

    Data assessment and prioritization in mobile networks for real-time prediction of spatial information using machine learning

    Get PDF
    A new framework of data assessment and prioritization for real-time prediction of spatial information is presented. The real-time prediction of spatial information is promising for next-generation mobile networks. Recent developments in machine learning technology have enabled prediction of spatial information, which will be quite useful for smart mobility services including navigation, driving assistance, and self-driving. Other key enablers for forming spatial information are image sensors in mobile devices like smartphones and tablets and in vehicles such as cars and drones and real-time cognitive computing like automatic number/license plate recognition systems and object recognition systems. However, since image data collected by mobile devices and vehicles need to be delivered to the server in real time to extract input data for real-time prediction, the uplink transmission speed of mobile networks is a major impediment. This paper proposes a framework of data assessment and prioritization that reduces the uplink traffic volume while maintaining the prediction accuracy of spatial information. In our framework, machine learning is used to estimate the importance of each data element and to predict spatial information under the limitation of available data. A numerical evaluation using an actual vehicle mobility dataset demonstrated the validity of the proposed framework. Two extension schemes in our framework, which use the ensemble of importance scores obtained from multiple feature selection methods, are also presented to improve its robustness against various machine learning and feature selection methods. We discuss the performance of those schemes through numerical evaluation

    Experimental analysis and evaluation of wide residual networks based agricultural disease identification in smart agriculture system

    Get PDF
    Specialised pest and disease control in the agricultural crops industry have been a high-priority issue. Due to great cost-effectiveness and efficient automation, computer vision (CV)–based automatic pest or disease identification techniques are widely utilised in the smart agricultural systems. As rapid development of artificial intelligence, in the field of computer vision–based agricultural pest identification, an increasing number of scholars have begun to move their attentions from traditional machine learning models to deep learning techniques. However, so far, deep learning techniques still have been suffering from many problems such as limited data samples, cost-effectiveness of network structure, and high image quality requirements. These issues greatly limit the potential utilisation of deep-learning techniques into smart agricultural systems. This paper aims at investigating utilization of one new deep-learning model WRN (wide residual networks) into CV-based automatic disease identification problem. We first built up a large-scale agricultural disease images dataset containing over 36,000 pieces of diseases, which includes typical types of disease in tomato, potato, grape, corn and apple. Then, we analysed and evaluated wide residual networks algorithm using the Tesla K80 graphics processor (GPU) in the TensorFlow deep-learning framework. A set of comprehensive experimental protocols have been designed in comparing with GoogLeNet Inception V4 regarding several benchmarks. The experimental results indicate that (1) under WRN architecture, Softmax loss function gives a faster convergence and improved accuracy than GoogLeNet inception V4 network. (2) While WRN shows a good effect for identification of agricultural diseases, its effectiveness has a strong need on the number of training samples of dataset like at least 36 k images in our experiment. (3) The overall performance is better than 800 sheets. The disease identification results show that the WRN model can be applied to the identification of agricultural diseases

    Channel estimation for massive MIMO TDD systems assuming pilot contamination and flat fading

    Get PDF
    Channel estimation is crucial for massive massive multiple-input multiple-output (MIMO) systems to scale up multi-user (MU) MIMO, providing great improvement in spectral and energy efficiency. This paper presents a simple and practical channel estimator for multi-cell MU massive MIMO time division duplex (TDD) systems with pilot contamination in flat Rayleigh fading channels, i.e., the gains of the channels follow the Rayleigh distribution. We also assume uncorrelated antennas. The proposed estimator addresses performance under moderate to strong pilot contamination without previous knowledge of the cross-cell large-scale channel coefficients. This estimator performs asymptotically as well as the minimum mean square error (MMSE) estimator with respect to the number of antennas. An approximate analytical mean square error (MSE) expression is also derived for the proposed estimator
    corecore