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Register a new userSense-and-Predict: Harnessing Spatial Interference Correlation for Cognitive Radio Networks
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Added by
Jeemin Kim
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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Cognitive radio (CR) is a key enabler realizing future networks to achieve higher spectral efficiency by allowing spectrum sharing between different wireless networks. It is important to explore whether spectrum access opportunities are available, but conventional CR based on transmitter (TX) sensing cannot be used to this end because the paired receiver (RX) may experience different levels of interference, according to the extent of their separation, blockages, and beam directions. To address this problem, this paper proposes a novel form of medium access control (MAC) termed sense-and-predict (SaP), whereby each secondary TX predicts the interference level at the RX based on the sensed interference at the TX; this can be quantified in terms of a spatial interference correlation between the two locations. Using stochastic geometry, the spatial interference correlation can be expressed in the form of a conditional coverage probability, such that the signal-to-interference ratio (SIR) at the RX is no less than a predetermined threshold given the sensed interference at the TX, defined as an opportunistic probability (OP). The secondary TX randomly accesses the spectrum depending on OP. We optimize the SaP framework to maximize the area spectral efficiencies (ASEs) of secondary networks while guaranteeing the service quality of the primary networks. Testbed experiments using USRP and MATLAB simulations show that SaP affords higher ASEs compared with CR without prediction.
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Interference between nodes directly limits the capacity of mobile ad hoc networks. This paper focuses on spatial interference cancelation with perfect channel state information (CSI), and analyzes the corresponding network capacity. Specifically, by using multiple antennas, zero-forcing beamforming is applied at each receiver for canceling the strongest interferers. Given spatial interference cancelation, the network transmission capacity is analyzed in this paper, which is defined as the maximum transmitting node density under constraints on outage and the signal-to-interference-noise ratio. Assuming the Poisson distribution for the locations of network nodes and spatially i.i.d. Rayleigh fading channels, mathematical tools from stochastic geometry are applied for deriving scaling laws for transmission capacity. Specifically, for small target outage probability, transmission capacity is proved to increase following a power law, where the exponent is the inverse of the size of antenna array or larger depending on the pass loss exponent. As shown by simulations, spatial interference cancelation increases transmission capacity by an order of magnitude or more even if only one extra antenna is added to each node.
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We study the high-power asymptotic behavior of the sum-rate capacity of multi-user interference networks with an equal number of transmitters and receivers. We assume that each transmitter is cognizant of the message it wishes to convey to its corresponding receiver and also of the messages that a subset of the other transmitters wish to send. The receivers are assumed not to be able to cooperate in any way so that they must base their decision on the signal they receive only. We focus on the networks pre-log, which is defined as the limiting ratio of the sum-rate capacity to half the logarithm of the transmitted power. We present both upper and lower bounds on the networks pre-log. The lower bounds are based on a linear partial-cancellation scheme which entails linearly transforming Gaussian codebooks so as to eliminate the interference in a subset of the receivers. Inter alias, the bounds give a complete characterization of the networks and side-information settings that result in a full pre-log, i.e., in a pre-log that is equal to the number of transmitters (and receivers) as well as a complete characterization of networks whose pre-log is equal to the full pre-log minus one. They also fully characterize networks where the full pre-log can only be achieved if each transmitter knows the messages of all users, i.e., when the side-information is full.
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