6,475 research outputs found
Spectral and Energy Efficiency in Cognitive Radio Systems with Unslotted Primary Users and Sensing Uncertainty
This paper studies energy efficiency (EE) and average throughput maximization
for cognitive radio systems in the presence of unslotted primary users. It is
assumed that primary user activity follows an ON-OFF alternating renewal
process. Secondary users first sense the channel possibly with errors in the
form of miss detections and false alarms, and then start the data transmission
only if no primary user activity is detected. The secondary user transmission
is subject to constraints on collision duration ratio, which is defined as the
ratio of average collision duration to transmission duration. In this setting,
the optimal power control policy which maximizes the EE of the secondary users
or maximizes the average throughput while satisfying a minimum required EE
under average/peak transmit power and average interference power constraints
are derived. Subsequently, low-complexity algorithms for jointly determining
the optimal power level and frame duration are proposed. The impact of
probabilities of detection and false alarm, transmit and interference power
constraints on the EE, average throughput of the secondary users, optimal
transmission power, and the collisions with primary user transmissions are
evaluated. In addition, some important properties of the collision duration
ratio are investigated. The tradeoff between the EE and average throughput
under imperfect sensing decisions and different primary user traffic are
further analyzed.Comment: This paper is accepted for publication in IEEE Transactions on
Communication
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
Sensing-Throughput Tradeoff for Interweave Cognitive Radio System: A Deployment-Centric Viewpoint
Secondary access to the licensed spectrum is viable only if interference is
avoided at the primary system. In this regard, different paradigms have been
conceptualized in the existing literature. Of these, Interweave Systems (ISs)
that employ spectrum sensing have been widely investigated. Baseline models
investigated in the literature characterize the performance of IS in terms of a
sensing-throughput tradeoff, however, this characterization assumes the
knowledge of the involved channels at the secondary transmitter, which is
unavailable in practice. Motivated by this fact, we establish a novel approach
that incorporates channel estimation in the system model, and consequently
investigate the impact of imperfect channel estimation on the performance of
the IS. More particularly, the variation induced in the detection probability
affects the detector's performance at the secondary transmitter, which may
result in severe interference at the primary users. In this view, we propose to
employ average and outage constraints on the detection probability, in order to
capture the performance of the IS. Our analysis reveals that with an
appropriate choice of the estimation time determined by the proposed model, the
degradation in performance of the IS can be effectively controlled, and
subsequently the achievable secondary throughput can be significantly enhanced.Comment: 13 pages, 10 figures, Accepted to be published in IEEE Transactions
on Wireless Communication
Algorithms for 5G physical layer
There is a great activity in the research community towards the investigations of the various aspects of 5G at different protocol layers and parts of the network. Among all, physical
layer design plays a very important role to satisfy high demands in terms of data rates, latency, reliability and number of connected devices for 5G deployment. This thesis addresses
he latest developments in the physical layer algorithms regarding the channel coding, signal detection, frame synchronization and multiple access technique in the light of 5G use
cases. These developments are governed by the requirements of the different use case scenarios that are envisioned to be the driving force in 5G.
All chapters from chapter 2 to 5 are developed around the need of physical layer algorithms dedicated to 5G use cases. In brief, this thesis focuses on design, analysis, simulation and
he advancement of physical layer aspects such as 1. Reliability based decoding of short length Linear Block Codes (LBCs) with very good properties in terms of minimum hamming
istance for very small latency requiring applications. In this context, we enlarge the grid of possible candidates by considering, in particular, short length LBCs (especially extended CH codes) with soft-decision decoding; 2. Efficient synchronization of preamble/postamble in a short bursty frame using modified Massey correlator; 3. Detection of Primary User
activity using semiblind spectrum sensing algorithms and analysis of such algorithms under practical imperfections; 4. Design of optimal spreading matrix for a Low Density Spreading (LDS) technique in the context of non-orthogonal multiple access. In such spreading matrix, small number of elements in a spreading sequences are non zero allowing each user to
spread its data over small number of chips (tones), thus simplifying the decoding procedure using Message Passing Algorithm (MPA)
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 sensing algorithms and software-defined radio implementation for cognitive radio system
The scarcity of spectral resources in wireless communications, due to a fixed frequency allocation policy, is a strong limitation to the increasing demand for higher data rates. However, measurements showed that a large part of frequency channels are underutilized or almost unoccupied. The cognitive radio paradigm arises as a tempting solution to the spectral congestion problem. A cognitive radio must be able to identify transmission opportunities in unused channels and to avoid generating harmful interference with the licensed primary users. Its key enabling technology is the spectrum sensing unit, whose ultimate goal consists in providing an indication whether a primary transmission is taking place in the observed channel. Such indication is determined as the result of a binary hypothesis testing experiment wherein null hypothesis (alternate hypothesis) corresponds to the absence (presence) of the primary signal. The first parts of this thesis describes the spectrum sensing problem and presents some of the best performing detection techniques. Energy Detection and multi-antenna Eigenvalue-Based Detection algorithms are considered. Important aspects are taken into account, like the impact of noise estimation or the effect of primary user traffic. The performance of each detector is assessed in terms of false alarm probability and detection probability. In most experimental research, cognitive radio techniques are deployed in software-defined radio systems, radio transceivers that allow operating parameters (like modulation type, bandwidth, output power, etc.) to be set or altered by software.In the second part of the thesis, we introduce the software-defined radio concept. Then, we focus on the implementation of Energy Detection and Eigenvalue-Based Detection algorithms: first, the used software platform, GNU Radio, is described, secondly, the implementation of a parallel energy detector and a multi-antenna eigenbased detector is illustrated and details on the used methodologies are given. Finally, we present the deployed experimental cognitive testbeds and the used radio peripherals. The obtained algorithmic results along with the software-defined radio implementation may offer a set of tools able to create a realistic cognitive radio system with real-time spectrum sensing capabilities
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