6,308 research outputs found
Low-complexity iterative frequency domain decision feedback equalization
Single-carrier transmission with frequency domain equalization (SC-FDE) offers a viable design alternative to the classic orthogonal frequency division multiplexing technique. However, SC-FDE using a linear equalizer may suffer from serious performance deterioration for transmission over severely frequency-selective fading channels. An effective method of solving this problem is to introduce non-linear decision feedback equalization (DFE) to SC-FDE. In this contribution, a low complexity iterative decision feedback equalizer operating in the frequency domain of single-carrier systems is proposed. Based on the minimum mean square error criterion, a simplified parameter estimation method is introduced to calculate the coefficients of the feed-forward and feedback filters, which significantly reduces the implementation complexity of the equalizer. Simulation results show that the performance of the proposed simplified design is similar to the traditional iterative block DFE under various multipath fading channels but it imposes a much lower complexity than the latter
Cooperative Feedback for Multi-Antenna Cognitive Radio Networks
Cognitive beamforming (CB) is a multi-antenna technique for efficient
spectrum sharing between primary users (PUs) and secondary users (SUs) in a
cognitive radio network. Specifically, a multi-antenna SU transmitter applies
CB to suppress the interference to the PU receivers as well as enhance the
corresponding SU-link performance. In this paper, for a
multiple-input-single-output (MISO) SU channel coexisting with a
single-input-single-output (SISO) PU channel, we propose a new and practical
paradigm for designing CB based on the finite-rate cooperative feedback from
the PU receiver to the SU transmitter. Specifically, the PU receiver
communicates to the SU transmitter the quantized SU-to-PU channel direction
information (CDI) for computing the SU transmit beamformer, and the
interference power control (IPC) signal that regulates the SU transmission
power according to the tolerable interference margin at the PU receiver. Two CB
algorithms based on cooperative feedback are proposed: one restricts the SU
transmit beamformer to be orthogonal to the quantized SU-to-PU channel
direction and the other relaxes such a constraint. In addition, cooperative
feedforward of the SU CDI from the SU transmitter to the PU receiver is
exploited to allow more efficient cooperative feedback. The outage
probabilities of the SU link for different CB and cooperative
feedback/feedforward algorithms are analyzed, from which the optimal
bit-allocation tradeoff between the CDI and IPC feedback is characterized.Comment: 26 pages; to appear in IEEE Trans. Signal Processin
Recurrent Neural Networks For Accurate RSSI Indoor Localization
This paper proposes recurrent neuron networks (RNNs) for a fingerprinting
indoor localization using WiFi. Instead of locating user's position one at a
time as in the cases of conventional algorithms, our RNN solution aims at
trajectory positioning and takes into account the relation among the received
signal strength indicator (RSSI) measurements in a trajectory. Furthermore, a
weighted average filter is proposed for both input RSSI data and sequential
output locations to enhance the accuracy among the temporal fluctuations of
RSSI. The results using different types of RNN including vanilla RNN, long
short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM
(BiLSTM) are presented. On-site experiments demonstrate that the proposed
structure achieves an average localization error of m with of the
errors under m, which outperforms the conventional KNN algorithms and
probabilistic algorithms by approximately under the same test
environment.Comment: Received signal strength indicator (RSSI), WiFi indoor localization,
recurrent neuron network (RNN), long shortterm memory (LSTM),
fingerprint-based localizatio
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