3,194 research outputs found
A General Framework for Transmission with Transceiver Distortion and Some Applications
A general theoretical framework is presented for analyzing information
transmission over Gaussian channels with memoryless transceiver distortion,
which encompasses various nonlinear distortion models including transmit-side
clipping, receive-side analog-to-digital conversion, and others. The framework
is based on the so-called generalized mutual information (GMI), and the
analysis in particular benefits from the setup of Gaussian codebook ensemble
and nearest-neighbor decoding, for which it is established that the GMI takes a
general form analogous to the channel capacity of undistorted Gaussian
channels, with a reduced "effective" signal-to-noise ratio (SNR) that depends
on the nominal SNR and the distortion model. When applied to specific
distortion models, an array of results of engineering relevance is obtained.
For channels with transmit-side distortion only, it is shown that a
conventional approach, which treats the distorted signal as the sum of the
original signal part and a uncorrelated distortion part, achieves the GMI. For
channels with output quantization, closed-form expressions are obtained for the
effective SNR and the GMI, and related optimization problems are formulated and
solved for quantizer design. Finally, super-Nyquist sampling is analyzed within
the general framework, and it is shown that sampling beyond the Nyquist rate
increases the GMI for all SNR. For example, with a binary symmetric output
quantization, information rates exceeding one bit per channel use are
achievable by sampling the output at four times the Nyquist rate.Comment: 32 pages (including 4 figures, 5 tables, and auxiliary materials);
submitted to IEEE Transactions on Communication
Millimeter Wave Cellular Networks: A MAC Layer Perspective
The millimeter wave (mmWave) frequency band is seen as a key enabler of
multi-gigabit wireless access in future cellular networks. In order to overcome
the propagation challenges, mmWave systems use a large number of antenna
elements both at the base station and at the user equipment, which lead to high
directivity gains, fully-directional communications, and possible noise-limited
operations. The fundamental differences between mmWave networks and traditional
ones challenge the classical design constraints, objectives, and available
degrees of freedom. This paper addresses the implications that highly
directional communication has on the design of an efficient medium access
control (MAC) layer. The paper discusses key MAC layer issues, such as
synchronization, random access, handover, channelization, interference
management, scheduling, and association. The paper provides an integrated view
on MAC layer issues for cellular networks, identifies new challenges and
tradeoffs, and provides novel insights and solution approaches.Comment: 21 pages, 9 figures, 2 tables, to appear in IEEE Transactions on
Communication
Revisiting Lightweight Encryption for IoT Applications: Error Performance and Throughput in Wireless Fading Channels with and without Coding
© 2013 IEEE. Employing heavy conventional encryption algorithms in communications suffers from added overhead and processing time delay; and in wireless communications, in particular, suffers from severe performance deterioration (avalanche effect) due to fading. Consequently, a tremendous reduction in data throughput and increase in complexity and time delay may occur especially when information traverse resource-limited devices as in Internet-of-Things (IoT) applications. To overcome these drawbacks, efficient lightweight encryption algorithms have been recently proposed in literature. One of those, that is of particular interest, requires using conventional encryption only for the first block of data in a given frame being transmitted. All the information in the remaining blocks is transmitted securely without the need for using heavy conventional encryption. Unlike the conventional encryption algorithms, this particular algorithm achieves lower overhead/complexity and higher data throughput. Assuming the additive white Gaussian noise (AWGN) channel, the performance of the lightweight encryption algorithm under study had been evaluated in literature in terms of throughput under the assumption that the first block, that undergoes conventional encryption, is free of error, which is practically unfeasible. In this paper, we consider the AWGN channel with Rayleigh fading and assume that the signal experiences a certain channel bit error probability and investigate the performance of the lightweight encryption algorithm under study in terms of bit error probability and throughput. We derive analytical expressions for these performance metrics considering modulated signals with and without coding. In addition, we propose an extension to the lightweight encryption algorithm under study by further enhancing its security level without significantly affecting the overhead size and processing time. Via numerical results we show the superiority of the lightweight encryption algorithm under study over the conventional encryption algorithms (like the AES) and the lightweight encryption algorithms proposed in literature in terms of error and throughput performance
Revisiting the RBLE design based on Matlab simulation
As a key low-power communication technique, backscatter communication has
received significant attention since the rising of the Internet of Things
(IoT). We revisit the state-of-the-art backscatter system, RBLE [1]. It solves
several key reliability issues of backscatter system including unreliable
two-step modulation, productive-data dependency, and lack of interference
countermeasures. We implement a Matlab simulation version of this. It uses the
reverse whiten technique to generate a single tone signal, operates direct
frequency on it and calculates the bit error rate (BER) to evaluate. We give
the spectrograms of the middle waveform results, compare the influence of
different modulation methods and analyze the cause of high BER. In the end, we
discuss the future prospects of the applications using RBLE.Comment: 6 pages, 14 figure
Secure Communication for Spatially Sparse Millimeter-Wave Massive MIMO Channels via Hybrid Precoding
In this paper, we investigate secure communication over sparse millimeter-wave (mm-Wave) massive multiple-input multiple-output (MIMO) channels by exploiting the spatial sparsity of legitimate user's channel. We propose a secure communication scheme in which information data is precoded onto dominant angle components of the sparse channel through a limited number of radio-frequency (RF) chains, while artificial noise (AN) is broadcast over the remaining nondominant angles interfering only with the eavesdropper with a high probability. It is shown that the channel sparsity plays a fundamental role analogous to secret keys in achieving secure communication. Hence, by defining two statistical measures of the channel sparsity, we analytically characterize its impact on secrecy rate. In particular, a substantial improvement on secrecy rate can be obtained by the proposed scheme due to the uncertainty, i.e., 'entropy', introduced by the channel sparsity which is unknown to the eavesdropper. It is revealed that sparsity in the power domain can always contribute to the secrecy rate. In contrast, in the angle domain, there exists an optimal level of sparsity that maximizes the secrecy rate. The effectiveness of the proposed scheme and derived results are verified by numerical simulations
SCAN: Semantic Communication with Adaptive Channel Feedback
In existing semantic communication systems for image transmission, some
images are generally reconstructed with considerably low quality. As a result,
the reliable transmission of each image cannot be guaranteed, bringing
significant uncertainty to semantic communication systems. To address this
issue, we propose a novel performance metric to characterize the reliability of
semantic communication systems termed semantic distortion outage probability
(SDOP), which is defined as the probability of the instantaneous distortion
larger than a given target threshold. Then, since the images with lower
reconstruction quality are generally less robust and need to be allocated with
more communication resources, we propose a novel framework of Semantic
Communication with Adaptive chaNnel feedback (SCAN). It can reduce SDOP by
adaptively adjusting the overhead of channel feedback for images with different
reconstruction qualities, thereby enhancing transmission reliability. To
realize SCAN, we first develop a deep learning-enabled semantic communication
system for multiple-input multiple-output (MIMO) channels (DeepSC-MIMO) by
leveraging the channel state information (CSI) and noise variance in the model
design. We then develop a performance evaluator to predict the reconstruction
quality of each image at the transmitter by distilling knowledge from
DeepSC-MIMO. In this way, images with lower predicted reconstruction quality
will be allocated with a longer CSI codeword to guarantee the reconstruction
quality. We perform extensive experiments to demonstrate that the proposed
scheme can significantly improve the reliability of image transmission while
greatly reducing the feedback overhead
- …