Abstract
Two key challenges in underlay dynamic spectrum access (DSA) are how to establish an interference limit from the primary network (PN) and how cognitive radios (CRs) in the secondary network (SN) become aware of the interference they create on the PN, especially when there is no exchange of information between the two networks. These challenges are addressed in this paper by presenting a fully autonomous and distributed underlay DSA scheme where each CR operates based on predicting its transmission effect on the PN. The scheme is based on a cognitive engine with an artificial neural network that predicts, without exchanging information between the networks, the full adaptive modulation and channel coding configuration for the primary link that is received with highest power by a transmitting CR. By managing the effect of the SN on the PN, the presented technique maintains the relative average throughput change in the PN within a prescribed maximum value, while also finding transmit settings for the CRs that result in throughput as large as allowed by the PN interference limit. Simulation results show that the ability of the cognitive engine in estimating the effect of a CR transmission on the full adaptive modulation and coding (AMC) mode leads to a very fine resolution underlay transmit power control. This ability also provides higher transmission opportunities for the CRs, compared to a scheme that can only estimate the modulation scheme used at the PN link.
Highlights
The cognitive radio (CR) paradigm is seen as a key solution to the radio spectrum scarcity problem stemming from the inefficiency of the established static spectrum allocation policy, [1], and the growing connectivity needs from wireless applications
The goal of the proposed underlay dynamic spectrum access (DSA) mechanism at the secondary network (SN) is to find the transmit power at the SUs that results in an equivalent interference, denoted as interference limit I0, that is as large as possible while the relative average throughput change in the primary network (PN) remains below a limit that we will denote as ε
In this paper we have presented a fully autonomous and distributed underlay DSA technique that is based on a Nonlinear Autoregressive with eXogenous input (NARX) neural network cognitive engine
Summary
The cognitive radio (CR) paradigm is seen as a key solution to the radio spectrum scarcity problem stemming from the inefficiency of the established static spectrum allocation policy, [1], and the growing connectivity needs from wireless applications. The main contribution of this paper resides in presenting an underlay DSA technique to infer, without tapping into feedback or control channels from another network, the experienced throughput and the modulation order and the channel coding rate used in the transmission of the other network (the primary network here), and to leverage this inference in the realization of a fully autonomous and distributed underlay DSA scheme This ability to estimate the effective throughput enables a finer control knob for a more accurate power allocation at the SN with less harmful effect on PN transmissions as compared to techniques that rely only on the estimation of modulation order (from applying signal processing on the transmission waveform).
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