اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدCross-layer Band Selection and Routing Design for Diverse Band-aware DSA Networks
118
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
As several new spectrum bands are opening up for shared use, a new paradigm of textit{Diverse Band-aware Dynamic Spectrum Access} (d-DSA) has emerged. d-DSA equips a secondary device with software defined radios (SDRs) and utilize whitespaces (or idle channels) in textit{multiple bands}, including but not limited to TV, LTE, Citizen Broadband Radio Service (CBRS), unlicensed ISM. In this paper, we propose a decentralized, online multi-agent reinforcement learning based cross-layer BAnd selection and Routing Design (BARD) for such d-DSA networks. BARD not only harnesses whitespaces in multiple spectrum bands, but also accounts for unique electro-magnetic characteristics of those bands to maximize the desired quality of service (QoS) requirements of heterogeneous message packets; while also ensuring no harmful interference to the primary users in the utilized band. Our extensive experiments demonstrate that BARD outperforms the baseline dDSAaR algorithm in terms of message delivery ratio, however, at a relatively higher network latency, for varying number of primary and secondary users. Furthermore, BARD greatly outperforms its single-band DSA variants in terms of both the metrics in all considered scenarios.
قيم البحث
اقرأ أيضاً
A cross-layer cognitive radio system is designed to support unicast and multicast traffic with integration of dynamic spectrum access (DSA), backpressure algorithm, and network coding for multi-hop networking. The full protocol stack that operates wi
th distributed coordination and local information exchange is implemented with software-defined radios (SDRs) and assessed in a realistic test and evaluation (T&E) system based on a network emulation testbed. Without a common control channel, each SDR performs neighborhood discovery, spectrum sensing and channel estimation, and executes a distributed extension of backpressure algorithm that optimizes the spectrum utility (that represents link rates and traffic congestion) with joint DSA and routing. The backpressure algorithm is extended to support multicast traffic with network coding deployed over virtual queues (for multicast destinations). In addition to full rank decoding at destinations, rank deficient decoding is also considered to reduce the delay. Cognitive network functionalities are programmed with GNU Radio and Python modules are developed for different layers. USRP radios are used as RF front ends. A wireless network T&E system is presented to execute emulation tests, where radios communicate with each other through a wireless network emulator that controls physical channels according to path loss, fading, and topology effects. Emulation tests are presented for different topologies to evaluate the throughput, backlog and energy consumption. Results verify the SDR implementation and the joint effect of DSA, backpressure routing and network coding under realistic channel and radio hardware effects.
Localization in long-range Internet of Things networks is a challenging task, mainly due to the long distances and low bandwidth used. Moreover, the cost, power, and size limitations restrict the integration of a GPS receiver in each device. In this
work, we introduce a novel received signal strength indicator (RSSI) based localization solution for ultra narrow band (UNB) long-range IoT networks such as Sigfox. The essence of our approach is to leverage the existence of a few GPS-enabled sensors (GSNs) in the network to split the wide coverage into classes, enabling RSSI based fingerprinting of other sensors (SNs). By using machine learning algorithms at the network backed-end, the proposed approach does not impose extra power, payload, or hardware requirements. To comprehensively validate the performance of the proposed method, a measurement-based dataset that has been collected in the city of Antwerp is used. We show that a location classification accuracy of 80% is achieved by virtually splitting a city with a radius of 2.5 km into seven classes. Moreover, separating classes, by increasing the spacing between them, brings the classification accuracy up-to 92% based on our measurements. Furthermore, when the density of GSN nodes is high enough to enable device-to-device communication, using multilateration, we improve the probability of localizing SNs with an error lower than 20 m by 40% in our measurement scenario.
A novel and compact dual band planar antenna for 2.4/5.2/5.8-GHz wireless local area network(WLAN) applications is proposed and studied in this paper. The antenna comprises of a T-shaped and a F-shaped element to generate two resonant modes for dual
band operation. The two elements can independently control the operating frequencies of the two excited resonant modes. The T-element which is fed directly by a 50 $Omega$ microstrip line generates a frequency band at around 5.2 GHz and the antenna parameters can be adjusted to generate a frequency band at 5.8 GHz as well, thus covering the two higher bands of WLAN systems individually. By couple-feeding the F-element through the T-element, a frequency band can be generated at 2.4 GHz to cover the lower band of WLAN system. Hence, the two elements together are very compact with a total area of only 11$times$6.5 mm$^{2}$. A thorough parametric study of key dimensions in the design has been performed and the results obtained have been used to present a generalized design approach. Plots of the return loss and radiation pattern have been given and discussed in detail to show that the design is a very promising candidate for WLAN applications.
The underwater acoustic channel is one of the most challenging communication channels. Due to periodical tidal and daily climatic variation, underwater noise is periodically fluctuating, which result in the periodical changing of acoustic channel qua
lity in long-term. Also, time-variant channel quality leads to routing failure. Routing protocols with acoustic channel estimation, namely underwater channel-aware routing protocols are recently proposed to maintain the routing performance. However, channel estimation algorithms for these routing protocols are mostly linear and rarely consider periodicity of acoustic channels. In this paper, we introduce acoustic channel estimation based on nearest neighbor regression for underwater acoustic networks. We extend nearest neighbor regression for SNR (Signal-to-Noise Ratio) time series prediction, providing an outstanding prediction accuracy for intricately periodical and fluctuating received SNR time series. Moreover, we propose a quick search algorithm and use statistical storage compression to optimize the time and space complexity of the algorithm. In contrast with linear methods, this algorithm significantly improves channel prediction accuracy (over three times at most) on both simulation and sea trial data sets. With this channel estimation method, we then propose a Depth-Based Channel-Aware Routing protocol (DBCAR). Taking advantage of depth-greedy forwarding and channel-aware reliable communication, DBCAR has an outstanding network performance on packet delivery ratio, average energy consumption and average transmission delay which is validated through extensive simulations.
MPTCP is a new transport protocol that enables mobile devices to use multiple physical paths simultaneously through several network interfaces, such as WiFi and Cellular. However, wireless path capacities change frequently in the mobile environments,
causing challenges for path selection. For example, WiFi associated paths often become poor as devices walk away, since WiFi has intermittent connectivity caused by the short signal coverage and stochastic interference. MPTCPs native decision based on hysteretic TCP-layer estimation will miss the real switching point of wireless quality, which may cumulate packets on the broken path and causes serious packets reinjection. Through analyzing a unique dataset in the wild, we quantitatively study the impact of MAC-layer factors on the aggregated performance of MPTCP. We then propose a decision tree approach for cross-layer path selection that decides which path to carry the incoming packets dynamically according to the prior learned schemes. A prototype of the path selection system named SmartPS, which proactively probes the wireless environments, is realized and deployed in Linux and Android. Evaluation results demonstrate that our SmartPS can efficiently utilize the faster path, with goodput improvements of up to 29%.
الأسئلة المقترحة
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
