Channel Side Information Research Articles

Power allocation algorithm for capacity maximization in 5G MIMO systems

The multiple input - multiple output (MIMO) systems have sparked a lot of research interest because of their ability to increase the system capacity without using much bandwidth. The High-Speed Train (HST) communication system's capacity was calculated by assuming that the SNR is unaffected by variations in attenuation and channel losses. Here, considering this optimization problem, a power allocation algorithm using the Water-Filling technique is proposed for the MIMO system with known Channel Side Information (CSI) at the transmitter and receiver. The presented algorithm may be efficiently optimized for improving channel capacity to achieve spectral efficiency (SE).

Duality Between Source Coding With Quantum Side Information and Classical-Quantum Channel Coding

In this paper, we establish an interesting duality between two different quantum information-processing tasks, namely, classical source coding with quantum side information, and channel coding over classical-quantum channels. The duality relates the optimal error exponents of these two tasks, generalizing the classical results of Ahlswede and Dueck [ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Trans. Inf. Theory</i> , 28(3):430–443, 1982]. We establish duality both at the operational level and at the level of the entropic quantities characterizing these exponents. For the latter, the duality is given by an exact relation, whereas for the former, duality manifests itself in the following sense: an optimal coding strategy for one task can be used to construct an optimal coding strategy for the other task. Along the way, we derive a bound on the error exponent for classical-quantum channel coding with constant composition codes which might be of independent interest. Finally, we consider the task of variable-length classical compression with quantum side information, and a duality relation between this task and classical-quantum channel coding can also be established correspondingly. Furthermore, we study the strong converse of this task, and show that the strong converse property does not hold even in the i.i.d. scenario.

Power Management Strategies in Energy-Harvesting Wireless Sensor Networks

Power management strategies are extremely important in Wireless Sensor Networks (WSNs). The objective is to make the nodes operate as long as possible. In the same context, in this article, our aim is to provide the optimal transmission power to maximize the network lifetime using the Orthogonal Multiple Access Channel (OMAC) in Harvesting System (HS). We consider that the nodes have direct communication with a Fusion Center (FC) with causal Channel Side Information (CSI) at the sender and receiver.We begin the analysis by considering a single transmitter node powered by a rechargeable battery with limited capacity energy. Afterward, we generalize the analysis with M transmitter nodes. In both cases, the transmitters are able to harvest energy from nature.Eventually, we show the viability of our approach in simulations results.

Neural Network-Based Transceiver Design for VLC System over ISI Channel

In this letter, we construct the neural network (NN)-based transceiver to compensate for the varying inter-symbol-interference (ISI) effect in visible light communication (VLC) systems. For processing variable-length sequences, the convolution neural network (CNN) is utilized, and then the residual network structure is further leveraged at the receiver part to enhance the performance. To cope with varying ISI, the pilot sequence, instead of channel side information (CSI) obtained by an additional module, is integrated into the framework to recover the data sequence directly. Simulation results show that the symbol error rate (SER) performance of the proposed NN-based transceiver can outperform separately designed transceiver schemes and approach the ideal perfect CSI (PCSI) case with a few pilot symbols or even no pilot.

Classical state masking over a quantum channel

Transmission of classical information over a quantum state-dependent channel is considered, when the encoder can measure channel side information (CSI) and is required to mask information on the quantum channel state from the decoder. In this quantum setting, it is essential to conceal the CSI measurement as well. A regularized formula is derived for the masking equivocation region, and a full characterization is established for a class of measurement channels.

Properties of Noncommutative Rényi and Augustin Information

R\'enyi and Augustin information are generalizations of mutual information defined via the R\'enyi divergence, playing a significant role in evaluating the performance of information processing tasks by virtue of its connection to the error exponent analysis. In quantum information theory, there are three generalizations of the classical R\'enyi divergence -- the Petz's, sandwiched, and log-Euclidean versions, that possess meaningful operational interpretation. However, the associated quantum R\'enyi and Augustin information are much less explored compared with their classical counterpart, and lacking crucial properties hinders applications of these quantities to error exponent analysis in the quantum regime. The goal of this paper is to analyze fundamental properties of the R\'enyi and Augustin information from a noncommutative measure-theoretic perspective. Firstly, we prove the uniform equicontinuity for all three quantum versions of R\'enyi and Augustin information, and it hence yields the joint continuity of these quantities in order and prior input distributions. Secondly, we establish the concavity of the scaled R\'enyi and Augustin information in the region of $s\in(-1,0)$ for both Petz's and the sandwiched versions. This completes the open questions raised by Holevo [IEEE Trans.~Inf.~Theory, 46(6):2256--2261, 2000], and Mosonyi and Ogawa [Commun.~Math.~Phys., 355(1):373--426, 2017]. For the applications, we show that the strong converse exponent in classical-quantum channel coding satisfies a minimax identity, which means that the strong converse exponent can be attained by the best constant composition code. The established concavity is further employed to prove an entropic duality between classical data compression with quantum side information and classical-quantum channel coding, and a Fenchel duality in joint source-channel coding with quantum side information.

Communication Over Quantum Channels With Parameter Estimation

Communication over a random-parameter quantum channel when the decoder is required to reconstruct the parameter sequence is considered. We study scenarios that include either strictly-causal, causal, or non-causal channel side information (CSI) available at the encoder, and also when CSI is not available. This model can be viewed as a form of quantum metrology, and as the quantum counterpart of the classical rate-and-state channel with state estimation at the decoder. Regularized formulas for the capacity-distortion regions are derived. In the special case of measurement channels, single-letter characterizations are derived for the strictly-causal and causal settings. Furthermore, in the more general case of entanglement-breaking channels, a single-letter characterization is derived when CSI is not available. As a consequence, we obtain regularized formulas for the capacity of random-parameter quantum channels with CSI, generalizing previous results by Boche <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2016, on classical-quantum channels. Bosonic dirty paper coding is introduced as a consequence, where we demonstrate that the optimal coefficient is not necessarily that of minimum mean-square error estimation as in the classical setting.

Deterministic Identification Over Channels With Power Constraints

The deterministic identification (DI) capacity is developed in multiple settings of channels with power constraints. A full characterization is established for the DI capacity of the discrete memoryless channel (DMC) with and without input constraints. Originally, Ahlswede and Dueck established the identification capacity with local randomness at the encoder, resulting in a double exponential number of messages in the block length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> . In the deterministic setup, the number of messages scales exponentially, as in Shannon’s transmission paradigm, but the achievable identification rates are higher. An explicit proof was not provided for the deterministic setting. In this paper, a detailed proof is presented for the DMC. Furthermore, Gaussian channels with fast and slow fading are considered, when channel side information is available at the decoder. A new phenomenon is observed as we establish that the number of messages scales as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{n\log (n)R}$ </tex-math></inline-formula> by deriving lower and upper bounds on the DI capacity on this scale. Consequently, the DI capacity of the Gaussian channel is infinite in the exponential scale and zero in the double exponential scale, regardless of the channel noise.

Performance analysis of 5G Downlink Cooperative NOMA network with multi-antenna relay

In this paper, a 5G Downlink Cooperative amplify-and-forward (AF) NOMA communication system with multi-antenna relay is investigated. By exploring the advantage of multiple antenna, the best antenna selecting and switching scheme at relay is considered. Then the analytical expressions for outage probability and ergodic sum rate are derived, and the asymptotic result for outage probability is also obtained to give easy evaluation. Based on the results, the effects of the important system parameters, such as antenna number, the outdated channel side information(CSI), power allocation, are analyzed. From the asymptotic formula, we find that the system can get the diversity order N, which is the antenna number at the relay, when perfect CSI can be obtained. And the diversity order decreases to one when outdated CSI appears, regardless of multi-antenna. Computer simulations are also done to validate the correctness of theoretical analysis.

Power Management Strategies in Energy-Harvesting Wireless Sensor Networks

Power management strategies are extremely important in Wireless Sensor Networks ( WSNs ). The objective is to make the nodes operate as long as possible. In the same context, in this article, our aim is to provide the optimal transmission power to maximize the network lifetime using the Orthogonal Multiple Access Channel ( OMAC ) in Harvesting System (HS). We consider that the nodes have direct communication with a Fusion Center ( FC ) with causal Channel Side Information ( CSI ) at the sender and receiver. We begin the analysis by considering a single transmitter node powered by a rechargeable battery with limited capacity energy. Afterward, we generalize the analysis with M transmitter nodes. In both cases, the transmitters are able to harvest energy from nature. Eventually, we show the viability of our approach in simulations results.

Quantum Channel State Masking

Communication over a quantum channel that depends on a quantum state is considered when the encoder has channel side information (CSI) and is required to mask information on the quantum channel state from the decoder. A full characterization is established for the entanglement-assisted masking equivocation region with a maximally correlated channel state, and a regularized formula is given for the quantum capacity-leakage function without assistance. For Hadamard channels without assistance, we derive single-letter inner and outer bounds, which coincide in the standard case of a channel that does not depend on a state.

Uplink Channel Estimation With Hardware Imperfections, Antenna Position Error and Channel Correlation for Nested and Linear Array Antennas

In this treatise, we have proposed an uplink channel estimation algorithm for nested and linear array antennas with hardware imperfections, antenna position error, and channel correlation in a multi-cell, multi-user wireless system with a one-ring scattering channel model. Hardware impairments and antenna position errors assume special importance in modern wireless system due to their severe impacts on system performance. With the increase of radio frequency (RF) in the modern wireless system and the subsequent usage of a large number of antennas for beamforming and meeting ever-increasing data rates, those errors are much relevant today. We have considered a sparse nested array (NA) and uniform linear array (ULA) for array geometry at the base station (BS). In this work, closed-form expression and bounds on the mean square error (MSE) of the linear minimum mean square error (LMMSE) channel estimate are derived. We have also analyzed the spectral efficiency (SE) of a simplified system with ULA and NA at the BS, given the model with hardware distortion and antenna position error. Two cases for (a) perfect channel side information (CSI) and (b) estimated channel are evaluated and compared in terms of their SEs, while the closed-form expression for SE with ULA at the BS considering perfect CSI is derived and verified. Numerical simulations are conducted to verify the accuracies of our derived results.

Statistical Learning Based Joint Antenna Selection and User Scheduling for Single-Cell Massive MIMO Systems

Large number of antennas and radio frequency (RF) chains at the base stations (BSs) lead to high energy consumption in massive MIMO systems. Thus, how to improve the energy efficiency (EE) with a computationally efficient approach is a significant challenge in the design of massive MIMO systems. With this motivation, a learning-based stochastic gradient descent algorithm is proposed in this article to obtain the optimal joint uplink and downlink EE with joint antenna selection and user scheduling in single-cell massive MIMO systems. Using Jensen's inequality and the characteristics of wireless channels, a lower bound on the system throughput is obtained. Subsequently, incorporating the power consumption model, the corresponding lower bound on the EE of the system is identified. Finally, learning-based stochastic gradient descent method is used to solve the joint antenna selection and user scheduling problem, which is a combinatorial optimization problem. Rare event simulation is embedded in the learning-based stochastic gradient descent method to generate samples with very small probabilities. In the analysis, both perfect and imperfect channel side information (CSI) at the BS are considered. Minimum mean-square error (MMSE) channel estimation is employed in the study of the imperfect CSI case. In addition, the effect of a constraint on the number of available RF chains in massive MIMO system is investigated considering both perfect and imperfect CSI at the BS.

Centralized vs Decentralized Targeted Brute-Force Attacks: Guessing With Side-Information

According to recent empirical studies, a majority of users have the same, or very similar, passwords across multiple password-secured online services. This practice can have disastrous consequences, as one password being compromised puts all the other accounts at much higher risk. Generally, an adversary may use any side-information he/she possesses about the user, be it demographic information, password reuse on a previously compromised account, or any other relevant information to devise a better brute-force strategy (so called targeted attack). In this work, we consider a distributed brute-force attack scenario in which $m$ adversaries, each observing some side information, attempt breaching a password secured system. We compare two strategies: an uncoordinated attack in which the adversaries query the system based on their own side-information until they find the correct password, and a fully coordinated attack in which the adversaries pool their side-information and query the system together. For passwords X of length $n$ , generated independently and identically from a distribution $P_{X}$ , we establish an asymptotic closed-form expression for the uncoordinated and coordinated strategies when the side-information $\mathrm {Y}_{(m)}$ are generated independently from passing X through a memoryless channel $P_{Y|X}$ , as the length of the password $n$ goes to infinity. We illustrate our results for binary symmetric channels and binary erasure channels, two families of side-information channels which model password reuse. We demonstrate that two coordinated agents perform asymptotically better than any finite number of uncoordinated agents for these channels, meaning that sharing side-information is very valuable in distributed attacks.

Power Control for Wireless VBR Video Streaming: From Optimization to Reinforcement Learning

In this paper, we investigate the problem of power control for streaming variable bit rate (VBR) videos over wireless links. A system model involving a transmitter (e.g., a base station) that sends VBR video data to a receiver (e.g., a mobile user) equipped with a playout buffer is adopted, as used in dynamic adaptive streaming video applications. In this setting, we analyze power control policies considering the following two objectives: 1) the minimization of the transmit power consumption, and 2) the minimization of the transmission completion time of the communication session. In order to play the video without interruptions, the power control policy should also satisfy the requirement that the VBR video data is delivered to the mobile user without causing playout buffer underflow or overflows. A directional water-filling algorithm, which provides a simple and concise interpretation of the necessary optimality conditions, is identified as the optimal offline policy. Following this, two online policies are proposed for power control based on channel side information (CSI) prediction within a short time window. Dynamic programming is employed to implement the optimal offline and the initial online power control policies that minimize the transmit power consumption in the communication session. Subsequently, reinforcement learning (RL) based approach is employed for the second online power control policy. Via simulation results, we show that the optimal offline power control policy that minimizes the overall power consumption leads to substantial energy savings compared to the strategy of minimizing the time duration of video streaming. We also demonstrate that the RL algorithm performs better than the dynamic programming based online grouped water-filling (GWF) strategy unless the channel is highly correlated.

Physical Layer Security for RF Satellite Channels in the Finite-Length Regime

Secure communications are becoming increasingly relevant in the development of space technology. Well-established cryptographic technology is already in place and is expected to continue to be so. On the other hand, information theoretical security emerges as a post-quantum versatile candidate to complement overall security strength. In order to prove such potential, performance analysis methods are needed that consider realistic legitimate and eavesdropper system assumptions and non-asymptotic coding lengths. In this paper, we propose the design of secure radio frequency (RF) satellite links with realistic system assumptions. Our contribution is three-fold. First, we propose a wiretap channel model for the finite-length regime. The model includes a stochastic wiretap encoding method using existing practical linear error correcting codes and hash codes. Secrecy is provided with privacy amplification, for which the finite-length secrecy metric is given that upper bounds semantic secrecy. Second, we derive a novel RF (broadcast) satellite wiretap channel model that parameterizes the stochastic degraded channel around the legitimate channel, a necessary condition to enable secure communication. Finally, we show the design of a secure satellite physical layer and finite-length performance evaluation. In doing so, we define as sacrifice rate the fixed fraction of the overall coding rate budget for reliability that needs to be allocated to secrecy. Our methodology does not make use of channel side information of the eavesdropper, it only makes assumptions on Eve’s noise power. We illustrate our proposed design method with numerical results using practical error correcting codes in current standards of satellite communication.

New binary particle swarm optimization on dummy sequence insertion method for nonlinear reduction in optical direct-detection orthogonal frequency division multiplexing system

In the paper, a novel new binary particle swarm optimization method based on dummy sequence insertion is proposed and experimentally demonstrated in the IM-DD optical orthogonal frequency division multiplexing (OOFDM) system. This technique can mitigate nonlinearity of OOFDM system without any channel side information. Experimental results demonstrate that compared to the original scheme, the improvement in the receiver sensitivity by the proposed scheme is 1.9 dB and 3.2 dB with launch powers of 2 dBm and 8 dBm, respectively, at the BER of FEC 3.8 × 10−3 after transmission over 100-km standard single-mode fiber. At a complementary cumulative distribution function of 10−4, the PAPR of OFDM signal can be reduced about 2.8 dB by using the proposed scheme, while the receiver-side hardware is the same as the origin.

Buffer-Aided Two-Hop Secure Communications With Power Control and Link Selection

This paper investigates the link selection policy for secure communications over a buffer-aided two-hop communication link. It is assumed that a source wishes to send information to a destination with the aid of a trusted half-duplex relay node compromised by an eavesdropper, and that there is no direct link between the source and the destination. The buffer-aided relay forwards the information to the destination by employing the randomize-and-forward scheme. Both the source and the relay transmissions are assumed to be vulnerable to the eavesdropper. The perfect channel side information of the network is assumed to be available at the source and the relay. Initially, conventional relaying protocols where equal partition of the time for the reception and transmission of the relay is considered. Then, the optimal time fraction with the optimal power control policy that maximizes the secrecy throughput is identified. Moreover, the optimal joint link selection and power control policy that maximizes the secrecy throughput is derived. Subsequently, a low complexity and asymptotically optimal link selection with ON/OFF power control is proposed. Numerical results demonstrate that the proposed link selection policies with power control can significantly improve the achievable secrecy throughput of the buffer-aided two-hop communication system.

Multiple access multicarrier continuous-variable quantum key distribution

One of the most important practical realizations of the fundamentals of quantum mechanics is continuous-variable quantum key distribution (CVQKD). Here we propose the adaptive multicarrier quadrature division–multiuser quadrature allocation (AMQD–MQA) multiple access technique for continuous-variable quantum key distribution. The MQA scheme is based on the AMQD modulation, which granulates the inputs of the users into Gaussian subcarrier continuous-variables (CVs). In an AMQD–MQA multiple access scenario, the simultaneous reliable transmission of the users is handled by the dynamic allocation of the Gaussian subcarrier CVs. We propose two different settings of AMQD–MQA for multiple input-multiple output communication. We introduce a rate-selection strategy that tunes the modulation variances and allocates adaptively the quadratures of the users over the sub-channels. We also prove the rate formulas if only partial channel side information is available for the users of the sub-channel conditions. We show a technique for the compensation of a nonideal Gaussian input modulation, which allows the users to overwhelm the modulation imperfections to reach optimal capacity-achieving communication over the Gaussian sub-channels. We investigate the diversity amplification of the sub-channel transmittance coefficients and reveal that a strong diversity can be exploited by opportunistic Gaussian modulation.

Joint channel mitigation and side information estimation for GFDM systems in indoor environments

Generalized frequency division multiplexing (GFDM) is the foremost contender for physical layer of 5G communication. The flexible nature of GFDM allows multiple symbols in time domain to superimpose, thereby creating high peak to average power ratio (PAPR). The idea of selective mapping (SLM) is to use a sequence of phase rotation vectors for generating alternatives using original GFDM signal. Among them, signal with lowest PAPR is selected for transmission. This procedure requires mandatory side information (SI) estimation or transmission which in turn decreases data reliability or efficiency. To address these issues, authors present a modified approach of pilot-assisted GFDM SLM system without need of SI transmission and to enable joint PAPR reduction and data recovery. In the proposed approach, we utilize a common modulating phase to modulate all subcarriers in a subsymbol assuming that each subsymbol consists of at least two pilots. This creates an inherent SI cancellation mechanism using the pilots which are employed for channel estimation. For practical validation of proposed concepts, we used software-defined radio (SDR) experimental setup employing universal software radio peripheral USRP) 2953R as hardware and Labview as software. Experimental results show a significant reduction in out-of-band spectral leakage without disturbing the estimated channel response.