15,873 research outputs found
Performance of Cognitive Radio Systems with Imperfect Radio Environment Map Information
In this paper we describe the effect of imperfections in the radio
environment map (REM) information on the performance of cognitive radio (CR)
systems. Via simulations we explore the relationship between the required
precision of the REM and various channel/system properties. For example, the
degree of spatial correlation in the shadow fading is a key factor as is the
interference constraint employed by the primary user. Based on the CR
interferers obtained from the simulations, we characterize the temporal
behavior of such systems by computing the level crossing rates (LCRs) of the
cumulative interference represented by these CRs. This evaluates the effect of
short term fluctuations above acceptable interference levels due to the fast
fading. We derive analytical formulae for the LCRs in Rayleigh and Rician fast
fading conditions. The analytical results are verified by Monte Carlo
simulations.Comment: presented at IEEE AusCTW 2009. Journal versions are under
preparation. This posting is the same as the original one. Only author's list
is updated that was unfortunately not correctly mentioned in the first
versio
Transmit without regrets: Online optimization in MIMO-OFDM cognitive radio systems
In this paper, we examine cognitive radio systems that evolve dynamically
over time due to changing user and environmental conditions. To combine the
advantages of orthogonal frequency division multiplexing (OFDM) and
multiple-input, multiple-output (MIMO) technologies, we consider a MIMO-OFDM
cognitive radio network where wireless users with multiple antennas communicate
over several non-interfering frequency bands. As the network's primary users
(PUs) come and go in the system, the communication environment changes
constantly (and, in many cases, randomly). Accordingly, the network's
unlicensed, secondary users (SUs) must adapt their transmit profiles "on the
fly" in order to maximize their data rate in a rapidly evolving environment
over which they have no control. In this dynamic setting, static solution
concepts (such as Nash equilibrium) are no longer relevant, so we focus on
dynamic transmit policies that lead to no regret: specifically, we consider
policies that perform at least as well as (and typically outperform) even the
best fixed transmit profile in hindsight. Drawing on the method of matrix
exponential learning and online mirror descent techniques, we derive a
no-regret transmit policy for the system's SUs which relies only on local
channel state information (CSI). Using this method, the system's SUs are able
to track their individually evolving optimum transmit profiles remarkably well,
even under rapidly (and randomly) changing conditions. Importantly, the
proposed augmented exponential learning (AXL) policy leads to no regret even if
the SUs' channel measurements are subject to arbitrarily large observation
errors (the imperfect CSI case), thus ensuring the method's robustness in the
presence of uncertainties.Comment: 25 pages, 3 figures, to appear in the IEEE Journal on Selected Areas
in Communication
A Distributed Approach to Interference Alignment in OFDM-based Two-tiered Networks
In this contribution, we consider a two-tiered network and focus on the
coexistence between the two tiers at physical layer. We target our efforts on a
long term evolution advanced (LTE-A) orthogonal frequency division multiple
access (OFDMA) macro-cell sharing the spectrum with a randomly deployed second
tier of small-cells. In such networks, high levels of co-channel interference
between the macro and small base stations (MBS/SBS) may largely limit the
potential spectral efficiency gains provided by the frequency reuse 1. To
address this issue, we propose a novel cognitive interference alignment based
scheme to protect the macro-cell from the cross-tier interference, while
mitigating the co-tier interference in the second tier. Remarkably, only local
channel state information (CSI) and autonomous operations are required in the
second tier, resulting in a completely self-organizing approach for the SBSs.
The optimal precoder that maximizes the spectral efficiency of the link between
each SBS and its served user equipment is found by means of a distributed
one-shot strategy. Numerical findings reveal non-negligible spectral efficiency
enhancements with respect to traditional time division multiple access
approaches at any signal to noise (SNR) regime. Additionally, the proposed
technique exhibits significant robustness to channel estimation errors,
achieving remarkable results for the imperfect CSI case and yielding consistent
performance enhancements to the network.Comment: 15 pages, 10 figures, accepted and to appear in IEEE Transactions on
Vehicular Technology Special Section: Self-Organizing Radio Networks, 2013.
Authors' final version. Copyright transferred to IEE
Compressed Sensing based Dynamic PSD Map Construction in Cognitive Radio Networks
In the context of spectrum sensing in cognitive radio networks, collaborative spectrum sensing has been proposed as a way to overcome multipath and shadowing, and hence increasing the reliability of the sensing. Due to the high amount of information to be transmitted, a dynamic compressive sensing approach is proposed to map the PSD estimate to a sparse domain which is then transmitted to the fusion center. In this regard, CRs send a compressed version of their estimated PSD to the fusion center, whose job is to reconstruct the PSD estimates of the CRs, fuse them, and make a global decision on the availability of the spectrum in space and frequency domains at a given time. The proposed compressive sensing based method considers the dynamic nature of the PSD map, and uses this dynamicity in order to decrease the amount of data needed to be transmitted between CR sensors’ and the fusion center. By using the proposed method, an acceptable PSD map for cognitive radio purposes can be achieved by only 20 % of full data transmission between sensors and master node. Also, simulation results show the robustness of the proposed method against the channel variations, diverse compression ratios and processing times in comparison with static methods
Resource Allocation for Downlink Multi-Cell OFDMA Cognitive Radio Network Using Hungarian Method
This paper considers the problem of resource allocation for downlink part of an OFDM-based multi-cell cognitive radio network which consists of multiple secondary transmitters and receivers communicating simultaneously in the presence of multiple primary users. We present a new framework to maximize the total data throughput of secondary users by means of subchannel assignment, while ensuring interference leakage to PUs is below a threshold. In this framework, we first formulate the resource allocation problem as a nonlinear and non-convex optimization problem. Then we represent the problem as a maximum weighted matching in a bipartite graph and propose an iterative algorithm based on Hungarian method to solve it. The present contribution develops an efficient subchannel allocation algorithm that assigns subchannels to the secondary users without the perfect knowledge of fading channel gain between cognitive radio transmitter and primary receivers. The performance of the proposed subcarrier allocation algorithm is compared with a blind subchannel allocation as well as another scheme with the perfect knowledge of channel-state information. Simulation results reveal that a significant performance advantage can still be realized, even if the optimization at the secondary network is based on imperfect network information
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