24 research outputs found
Enhanced Pilot-Based Spectrum Sensing Algorithm
In this paper, we develop an enhanced pilot-based spectrum sensing algorithm
for cognitive radio. Unlike conventional pilot-based detectors which merely
detect the presence of pilot signals, the proposed detector also utilizes the
presence of the signal that carries the actual information. We analytically
compare the performance of the proposed detector with the conventional one, and
we show that the detection performance is significantly improved.Comment: 4 pages, 2 figures; published in Proc. IEEE Biennial Symps. on
Commun. (QBSC'14), June 201
Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks
Cognitive radio has been widely considered as one of the prominent solutions
to tackle the spectrum scarcity. While the majority of existing research has
focused on single-band cognitive radio, multiband cognitive radio represents
great promises towards implementing efficient cognitive networks compared to
single-based networks. Multiband cognitive radio networks (MB-CRNs) are
expected to significantly enhance the network's throughput and provide better
channel maintenance by reducing handoff frequency. Nevertheless, the wideband
front-end and the multiband spectrum access impose a number of challenges yet
to overcome. This paper provides an in-depth analysis on the recent
advancements in multiband spectrum sensing techniques, their limitations, and
possible future directions to improve them. We study cooperative communications
for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also
investigate several limits and tradeoffs of various design parameters for
MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that
differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE
Journal, Special Issue on Future Radio Spectrum Access, March 201
Spectrum Sharing for Massive Access in Ultra-Narrowband IoT Systems
Ultra-narrowband (UNB) communications has become a signature feature for many
emerging low-power wide-area (LPWA) networks. Specifically, using extremely
narrowband signals helps the network connect more Internet-of-things (IoT)
devices within a given band. It also improves robustness to interference,
extending the coverage of the network. In this paper, we study the coexistence
capability of UNB networks and their scalability to enable massive access. To
this end, we develop a stochastic geometry framework to analyze and model UNB
networks on a large scale. The framework captures the unique characteristics of
UNB communications, including the asynchronous time-frequency access, signal
repetition, and the absence of base station (BS) association. Closed-form
expressions of the transmission success probability and network connection
density are presented for several UNB protocols. We further discuss multiband
access for UNB networks, proposing a low-complexity protocol. Our analysis
reveals several insights on the geographical diversity achieved when devices do
not connect to a single BS, the optimal number of signal repetitions, and how
to utilize multiple bands without increasing the complexity of BSs. Simulation
results are provided to validate the analysis, and they show that UNB
communications enables a single BS to connect thousands of devices even when
the spectrum is shared with other networks.Comment: This paper is accepted for publication in the IEEE Journal on
Selected Areas in Communications. arXiv admin note: text overlap with
arXiv:1811.1109