Target Rate Constraint Research Articles

Game Theoretic Approach for a Service Function Chain Routing in NFV With Coupled Constraints

In this brief, the service function chain (SFC) routing in network function virtualization (NFV) with server capacity limitation and user minimum target rate constraint is investigated simultaneously. After relaxing the constraints by the addition of extra cost terms, an extended NFV game incorporating the coupled constraints is formulated. By using semi-tensor product of matrices, we prove that the formulated game is a potential game which possesses at least one Nash equilibrium. We then present the equilibrium point algorithm for solving the SFC routes problem. The numerical simulation of two cases with constraints and without constraints illustrates the influence of coupled limitations on the SFC routing.

Multi-Objective Optimization of Quality in VVC Rate Control for Low-Delay Video Coding.

In this paper, a multi-objective optimization of quality based coding tree unit (CTU) level rate control method, named MORC, for low-delay video coding of Versatile Video Coding (VVC) is proposed. Unlike the traditional rate control algorithms, objectives of minimizing average distortion and minimizing quality fluctuation are introduced and considered simultaneously to obtain the optimal solution. Weighting method is then used to convert the multi-objective optimization problem into a simplistic single-objective problem. Accordingly, given the target rate constraint, rate allocation is formulated as a convex optimization problem to achieve better trade-off among three multiple objectives: minimizing average video distortion, meeting rate constraint and minimizing video quality fluctuation. Based on the D- λ model, a two-stage method is further proposed to solve the optimization problem. Experiments have been conducted on the test model of VVC, which is VTM 3.0. Compared with the state-of-the-art rate control algorithms, the proposed method can obtain the Pareto optimal solution, which dominates the solution of other methods and is able to achieve better trade-off among multiple objectives.

A physical layer security scheme for full-duplex communication systems with residual self-interference and non-eavesdropping CSI

We discuss the physical layer security scheme in the Full-Duplex (FD) MIMO point-to-point two-way communication system with residual self-interference, in which legitimate nodes send confidential information and null space Artificial Noise (AN) while receiving information. Because the Channel State Information (CSI) of the eavesdropper is unavailable, we optimize the covariance matrices of the information signal as well as the allocation of the antenna for transmitting and receiving to minimize the signal power consumption under the target rate constraint. As a result, the power of AN is maximized within the limit of total power, so the interception capability of the eavesdropper is suppressed as much as possible. Since self-interference cannot be completely eliminated, the optimization process of one legitimate node depends on the optimization result of the other. By substituting self-interference power in the secrecy rate formula with its average value, the joint optimization process at the two nodes is transformed into two separate and solvable optimization processes. Then, the Water-Filling Algorithm (WFA) and bisection algorithm are used to get the optimal covariance matrices of the signal. Furthermore, we derive the theoretical lower bound of ergodic achievable secrecy rate under rayleigh channels to evaluate the performance of the scheme. The simulation results show that the theoretical derivation is correct, and the actual achievable rate is very close to the target rate, which means that the approximation in the optimization is feasible. The results also show that secrecy transmission can be realized because a considerable secrecy rate can be achieved.

Optimal Lagrange multipliers for dependent rate allocation in video coding

In a typical video rate allocation problem, the objective is to optimally distribute a source rate budget among a set of (in)dependently coded data units to minimize the total distortion. Conventional Lagrangian approaches convert the lone rate constraint to a linear rate penalty scaled by a multiplier in the objective, resulting in a simpler unconstrained formulation. However, the search for the “optimal” multiplier – one that results in a distortion-minimizing solution among all Lagrangian solutions that satisfy the original rate constraint – remains an elusive open problem in the general setting. To address this problem, we are the first in the literature to construct a computation-efficient search strategy to identify this optimal multiplier numerically in the general dependent coding scenario. Specifically, we first formulate a general rate allocation problem where each data unit can be dependently coded at different quantization parameters (QP) using a previous unit as predictor, or left uncoded at the encoder and subsequently interpolated at the decoder using neighboring coded units. After converting the original rate-constrained problem to the unconstrained Lagrangian counterpart, we design an efficient dynamic programming (DP) algorithm that finds the optimal Lagrangian solution for a fixed multiplier. In extensive monoview and multiview video coding experiments, we show that for fixed target rate constraints, our algorithm is able to find the optimal multipliers in a distortion minimum sense among all Lagrangian solutions. Moreover, we show that our simple solution is able to compete with complex rate control (RC) solutions used in video compression standards such as HEVC and 3D-HEVC, which outlines the importance of the proper choice of the Lagrangian multipliers.

Fast Water-Filling Method for Sum-Power Minimization in OFDMA Networks

In this letter, we investigate the problem of sum-power minimization for the users in orthogonal frequency division multiple access (OFDMA) networks subject to the target rate constraint. To address this problem, two fast water-filling (WF) methods are developed under two scenarios: without and with considering the peak-power constraint on the subchannels, which are referred to as FWF and FWF-PP, respectively. We show that our proposed methods converge to the optimal solution, while the computational complexity is reduced compared to the existing WF methods. Our results are also confirmed via simulations.

Energy Efficiency of Downlink Transmission Strategies for Cloud Radio Access Networks

This paper studies the energy efficiency of the cloud radio access network (C-RAN), specifically focusing on two fundamental and different downlink transmission strategies, namely the data-sharing strategy and the compression strategy. In the data-sharing strategy, the backhaul links connecting the central processor (CP) and the base-stations (BSs) are used to carry user messages—each user’s messages are sent to multiple BSs; the BSs locally form the beamforming vectors then cooperatively transmit the messages to the user. In the compression strategy, the user messages are precoded centrally at the CP, which forwards a compressed version of the analog beamformed signals to the BSs for cooperative transmission. This paper compares the energy efficiencies of the two strategies by formulating an optimization problem of minimizing the total network power consumption subject to user target rate constraints, where the total network power includes the BS transmission power, BS activation power, and load-dependent backhaul power. To tackle the discrete and nonconvex nature of the optimization problems, we utilize the techniques of reweighted $\ell_1$ minimization and successive convex approximation to devise provably convergent algorithms. Our main finding is that both the optimized data-sharing and compression strategies in C-RAN achieve much higher energy efficiency as compared to the nonoptimized coordinated multipoint transmission, but their comparative effectiveness in energy saving depends on the user target rate. At low user target rate, data-sharing consumes less total power than compression; however, as the user target rate increases, the backhaul power consumption for data-sharing increases significantly leading to better energy efficiency of compression at the high user rate regime.

Discrete Cosine Transformer Based Visual Secret Sharing With Chaos Visual Cryptography

With the increasing threats on multimedia information sharing, degradation of security is one of the most important concerns. Visual Cryptography is an emerging field for improving the security level by encoding the secret video using encryption scheme and performs computational free decoding process. The secret sharing through visual cryptographic techniques for multiple stack users is not sufficient in providing high percentage result on video sharing domain. Video sharing did not develop source key properties for individual frames and as a result the quality estimation strategy on video frame was not achieved. Moreover, the target rate constraint for video quality under the encryption-decryption was not achieved. This encryption-decryption target key rate declines on arranging multiple sequences of frames from multiple stack users. To improve the quality of visual secret video sharing scheme, a Discrete Cosine Transform Chaos Cryptographic (DCTCC) mechanism is proposed in this paper. The main objective of DCTCC is to enhance the security level of different level video frame using the encryption key properties. Initially, sender takes the video of differing frame length and compresses the video on image using the Discrete Cosine Transformer. The Discrete Cosine Transformer based visual secret sharing of video in the image takes the luminance values and provides high lossless compression. Second, the embedded video frame is encrypted to perform the secret sharing (i.e., visual cryptographic) using the chaos method. The Chaos method uses the enhanced RSA key properties to improve the video quality estimation strategy. Chaos method in DCTCC works effectively with non-linear dynamic set of video frames from users and also with the discrete time dynamical video cryptographic system. On the decryption side, the chaos method is applied in the reverse procedural order to fetch the video quality of video frames from original image. Experiment is conducted on factors such as mean square error rate, and secret video sharing efficacy level and encryption time.

Leakage-Aware Energy-Efficient Beamforming for Heterogeneous Multicell Multiuser Systems

Energy-efficient communications has attracted much interest in the research of 5G cellular systems. In this paper, we study energy-efficient coordinated beamforming design for heterogeneous multicell multiuser downlink systems. The considered problem is formulated as maximizing the weighted sum per-cell energy efficiencies (WSPEEMax) subject to predefined per-user target rate demands, maximum leakage interference power constraints, and per-BS transmit power constraints. This formulation is more general than the conventional EE optimization problem and provides a unified way to consider the EE of heterogeneous networks. However, it is hard to tackle due to the weighted sum-of-ratios form of the objective function and the non-convex nature of per-user target rate constraints. To address it, we propose to first transform the original problem into a polynomial form optimization by introducing some auxiliary variables and then further reveal their equivalence in finding the solution. Then, an efficient block coordinate ascent optimization algorithm is developed to solve the equivalent problem by exploiting the concave nature of the considered problem with respect to each optimization variable. To further improve the network EE, we also develop an energy-efficient transmission method for each small-cell network. Finally, extensive numerical results are provided to verify the effectiveness of the proposed schemes and show that both the EE and spectral efficiency (SE) of heterogeneous network can be significantly improved by energy-efficient coordinated multiple-input multiple-output (MIMO) transmission.

Distributed near lossless compression algorithm for hyperspectral images

This paper presents a new near lossless compression algorithm for hyperspectral images based on distributed source coding. The algorithm is performed on blocks that have the same location and size in each band. Because the importance varies from block to block along the spectral orientation, an adaptive rate allocation algorithm that weights the energy of each block under the target rate constraints is introduced. A simple linear prediction model is employed to construct the side information of each block for Slepian–Wolf coding. The relationship between the quantized step size and the allocated rate of each block is determined under the condition of correct reconstruction with the side information at the Slepian–Wolf decoder. Slepian–Wolf coding is performed on the quantized version of each block. Experimental results show that the performance of the proposed algorithm is competitive with that of state-of-the-art compression algorithms, making it appropriate for on-board compression.

Near lossless compression of hyperspectral images based on distributed source coding

Effective compression technique of on-board hyperspectral images has been an active topic in the field of hyperspectral remote sensintg. In order to solve the effective compression of on-board hyperspectral images, a new distributed near lossless compression algorithm based on multilevel coset codes is proposed. Due to the diverse importance of each band, a new adaptive rate allocation algorithm is proposed, which allocates rational rate for each band according to the size of weight factor defined for hyperspectral images subject to the target rate constraints. Multiband prediction is introduced for Slepian-Wolf lossless coding and an optimal quantization algorithm is presented under the correct reconstruction of Slepian-Wolf decoder, which minimizes the distortion of reconstructed hyperspectral images under the target rate. Then Slepian-Wolf encoder exploits the correlation of the quantized values to generate the final bit streams. Experimental results show that the proposed algorithm has both higher compression efficiency and lower encoder complexity than several existing classical algorithms.

Bit Loading Algorithms for Cooperative OFDM Systems

We investigate the resource allocation problem for an OFDM cooperative network with a single source-destination pair and multiple relays. Assuming knowledge of the instantaneous channel gains for all links in the entire network, we propose several bit and power allocation schemes aiming at minimizing the total transmission power under a target rate constraint. First, an optimal and efficient bit loading algorithm is proposed when the relay node uses the same subchannel to relay the information transmitted by the source node. To further improve the performance gain, subchannel permutation, in which the subchannels are reallocated at relay nodes, is considered. An optimal subchannel permutation algorithm is first proposed and then an efficient suboptimal algorithm is considered to achieve a better complexity-performance tradeoff. A distributed bit loading algorithm is also proposed for ad hoc networks. Simulation results show that significant performance gains can be achieved by the proposed bit loading algorithms, especially when subchannel permutation is employed.