2,669 research outputs found
Cognitive Access Policies under a Primary ARQ process via Forward-Backward Interference Cancellation
This paper introduces a novel technique for access by a cognitive Secondary
User (SU) using best-effort transmission to a spectrum with an incumbent
Primary User (PU), which uses Type-I Hybrid ARQ. The technique leverages the
primary ARQ protocol to perform Interference Cancellation (IC) at the SU
receiver (SUrx). Two IC mechanisms that work in concert are introduced: Forward
IC, where SUrx, after decoding the PU message, cancels its interference in the
(possible) following PU retransmissions of the same message, to improve the SU
throughput; Backward IC, where SUrx performs IC on previous SU transmissions,
whose decoding failed due to severe PU interference. Secondary access policies
are designed that determine the secondary access probability in each state of
the network so as to maximize the average long-term SU throughput by
opportunistically leveraging IC, while causing bounded average long-term PU
throughput degradation and SU power expenditure. It is proved that the optimal
policy prescribes that the SU prioritizes its access in the states where SUrx
knows the PU message, thus enabling IC. An algorithm is provided to optimally
allocate additional secondary access opportunities in the states where the PU
message is unknown. Numerical results are shown to assess the throughput gain
provided by the proposed techniques.Comment: 16 pages, 11 figures, 2 table
Maximizing System Throughput Using Cooperative Sensing in Multi-Channel Cognitive Radio Networks
In Cognitive Radio Networks (CRNs), unlicensed users are allowed to access
the licensed spectrum when it is not currently being used by primary users
(PUs). In this paper, we study the throughput maximization problem for a
multi-channel CRN where each SU can only sense a limited number of channels. We
show that this problem is strongly NP-hard, and propose an approximation
algorithm with a factor at least where is a system
parameter reflecting the sensing capability of SUs across channels and their
sensing budgets. This performance guarantee is achieved by exploiting a nice
structural property of the objective function and constructing a particular
matching. Our numerical results demonstrate the advantage of our algorithm
compared with both a random and a greedy sensing assignment algorithm
RF-Powered Cognitive Radio Networks: Technical Challenges and Limitations
The increasing demand for spectral and energy efficient communication
networks has spurred a great interest in energy harvesting (EH) cognitive radio
networks (CRNs). Such a revolutionary technology represents a paradigm shift in
the development of wireless networks, as it can simultaneously enable the
efficient use of the available spectrum and the exploitation of radio frequency
(RF) energy in order to reduce the reliance on traditional energy sources. This
is mainly triggered by the recent advancements in microelectronics that puts
forward RF energy harvesting as a plausible technique in the near future. On
the other hand, it is suggested that the operation of a network relying on
harvested energy needs to be redesigned to allow the network to reliably
function in the long term. To this end, the aim of this survey paper is to
provide a comprehensive overview of the recent development and the challenges
regarding the operation of CRNs powered by RF energy. In addition, the
potential open issues that might be considered for the future research are also
discussed in this paper.Comment: 8 pages, 2 figures, 1 table, Accepted in IEEE Communications Magazin
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