2,095 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
Access Policy Design for Cognitive Secondary Users under a Primary Type-I HARQ Process
In this paper, an underlay cognitive radio network that consists of an
arbitrary number of secondary users (SU) is considered, in which the primary
user (PU) employs Type-I Hybrid Automatic Repeat Request (HARQ). Exploiting the
redundancy in PU retransmissions, each SU receiver applies forward interference
cancelation to remove a successfully decoded PU message in the subsequent PU
retransmissions. The knowledge of the PU message state at the SU receivers and
the ACK/NACK message from the PU receiver are sent back to the transmitters.
With this approach and using a Constrained Markov Decision Process (CMDP) model
and Constrained Multi-agent MDP (CMMDP), centralized and decentralized optimum
access policies for SUs are proposed to maximize their average sum throughput
under a PU throughput constraint. In the decentralized case, the channel access
decision of each SU is unknown to the other SU. Numerical results demonstrate
the benefits of the proposed policies in terms of sum throughput of SUs. The
results also reveal that the centralized access policy design outperforms the
decentralized design especially when the PU can tolerate a low average long
term throughput. Finally, the difficulties in decentralized access policy
design with partial state information are discussed
Spectral Efficiency of Multi-User Adaptive Cognitive Radio Networks
In this correspondence, the comprehensive problem of joint power, rate, and
subcarrier allocation have been investigated for enhancing the spectral
efficiency of multi-user orthogonal frequency-division multiple access (OFDMA)
cognitive radio (CR) networks subject to satisfying total average transmission
power and aggregate interference constraints. We propose novel optimal radio
resource allocation (RRA) algorithms under different scenarios with
deterministic and probabilistic interference violation limits based on a
perfect and imperfect availability of cross-link channel state information
(CSI). In particular, we propose a probabilistic approach to mitigate the total
imposed interference on the primary service under imperfect cross-link CSI. A
closed-form mathematical formulation of the cumulative density function (cdf)
for the received signal-to-interference-plus-noise ratio (SINR) is formulated
to evaluate the resultant average spectral efficiency (ASE). Dual decomposition
is utilized to obtain sub-optimal solutions for the non-convex optimization
problems. Through simulation results, we investigate the achievable performance
and the impact of parameters uncertainty on the overall system performance.
Furthermore, we present that the developed RRA algorithms can considerably
improve the cognitive performance whilst abide the imposed power constraints.
In particular, the performance under imperfect cross-link CSI knowledge for the
proposed `probabilistic case' is compared to the conventional scenarios to show
the potential gain in employing this scheme
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