On the Capacity of Training-Based Transmissions with Input Peak Power Constraints

The capacity of the channel defined by the stochastic nonlinear Schr\"odinger equation, which includes the effects of the Kerr nonlinearity and amplified spontaneous emission noise, is considered in the case of zero dispersion. In the absence of dispersion, this channel behaves as a collection of parallel per-sample channels. The conditional probability density function of the nonlinear per-sample channels is derived using both a sum-product and a Fokker-Planck differential equation approach. It is shown that, for a fixed noise power, the per-sample capacity grows unboundedly with input signal. The channel can be partitioned into amplitude and phase subchannels, and it is shown that the contribution to the total capacity of the phase channel declines for large input powers. It is found that a two-dimensional distribution with a half-Gaussian profile on the amplitude and uniform phase provides a lower bound for the zero-dispersion optical fiber channel, which is simple and asymptotically capacity-achieving at high signal-to-noise ratios (SNRs). A lower bound on the capacity is also derived in the medium-SNR region. The exact capacity subject to peak and average power constraints is numerically quantified using dense multiple ring modulation formats. The differential model underlying the zero-dispersion channel is reduced to an algebraic model, which is more tractable for digital communication studies, and in particular it provides a relation between the zero-dispersion optical channel and a $2 \times 2$ multiple-input multiple-output Rician fading channel. It appears that the structure of the capacity-achieving input distribution resembles that of the Rician fading channel, i.e., it is discrete in amplitude with a finite number of mass points, while continuous and uniform in phase.

In this paper, we address the problem of finding the worst jamming distribution in terms of channel capacity for the securely precoded OFDM (SP-OFDM) system, so as to evaluate the performance of SP-OFDM under destructive hostile jamming. We consider a practical communication scenario, where the transmitting symbols are uniformly distributed over a discrete and finite alphabet, and the jamming interference is subject to both average and peak power constraints. Using tools in functional analysis and complex analysis, first, we show the existence and uniqueness of the worst jamming distribution; then we further prove that the worst jamming distribution is discrete in amplitude with a finite number of mass points. Numerical examples are provided under different scenarios to demonstrate our theoretical results.

This paper proposes a method to maximize the rate of reliable communication for polar codes operating on channels with memory. The channel is learned implicitly from data by optimizing a neural polar decoder (NPD). This approach enables simultaneous optimization of the code rate over the input distribution and the design of a practical coding scheme within the framework of polar codes. The proposed approach applies to scenarios where the channel model is unknown and treated as a black-box that produces output samples from input samples. We use NPDs to estimate the mutual information (MI) between the channel inputs and outputs, and optimize a parametric model of the input distribution. The methodology involves a two-phase process: a training phase and an inference phase. In the training phase, two steps are repeated iteratively. The first step optimizes the NPD to estimate the MI of the channel inputs and outputs. The second step improves the input distribution parameters by maximizing the MI estimate obtained with the NPD. In the inference phase, the optimized model is used to construct polar codes. This approach uses the Honda-Yamamoto (HY) scheme, which implements polar codes with optimized input distributions, together with list decoding. Experimental results on memoryless and finite state channels (FSCs) demonstrate the effectiveness of this approach, particularly in cases where the channel’s capacity-achieving input distribution is non-uniform. For these cases, significant improvements in MI and bit error rates (BERs) are shown over those achieved by uniform and independent and identically distributed (i.i.d.) input distributions, validating our method for block lengths up to 1024. This data-driven approach can be utilized in real-world communication systems, bridging theoretical capacity estimation and practical coding performance.

The capacity limits of fiber-optic communication systems in the nonlinear regime are not yet well understood. In this paper, we study the capacity of amplitude modulated first-order soliton transmission, defined as the maximum of the so-called time-scaled mutual information. Such definition allows us to directly incorporate the dependence of soliton pulse width to its amplitude into capacity formulation. The commonly used memoryless channel model based on noncentral chi-squared distribution is initially considered. Applying a variance normalizing transform, this channel is approximated by a unit-variance additive white Gaussian noise (AWGN) model. Based on a numerical capacity analysis of the approximated AWGN channel, a general form of capacity-approaching input distributions is determined. These optimal distributions are discrete comprising a mass point at zero (off symbol) and a finite number of mass points almost uniformly distributed away from zero. Using this general form of input distributions, a novel closed-form approximation of the capacity is determined showing a good match to numerical results. Finally, mismatch capacity bounds are developed based on split-step simulations of the nonlinear Schrdinger equation considering both single soliton and soliton sequence transmissions. This relaxes the initial assumption of memoryless channel to show the impact of both inter-soliton interaction and Gordon–Haus effects. Our results show that the inter-soliton interaction effect becomes increasingly significant at higher soliton amplitudes and would be the dominant impairment compared to the timing jitter induced by the Gordon–Haus effect.

In this paper, the capacity and spectral efficiency of peak power limited Rician block-fading channels when neither the receiver nor the transmitter knows the fading coefficients is studied. The capacity-achieving input amplitude distribution of the average power limited memoryless unknown Rayleigh fading channel is discrete with a finite number of mass points is proved. The spectral-efficiency and the bit energy tradeoff in the low power regime is studied.

This paper proposes a modified bat algorithm for optimal resource allocation scheme in closed access femtocell networks. Bat algorithm is a new nature-inspired metaheuristic algorithm which is inspired by the behaviour of bats echolocation. The main objective of the proposed algorithm is to select the best combination of resource blocks for femto users so as to minimize the co- and cross-tier interference in macrocell-femtocell network. To deal with a discrete solution in resource allocation problem, the original bat algorithm updating rule is modified using nearest integer method. The simulation results show that the proposed algorithm improves the throughput of the femtocell networks compared to that of random allocation scheme.

Communication over the noncoherent additive white Gaussian noise (AWGN) channel is considered, where the transmitted signal undergoes a phase rotation, unknown to the transmitter and the receiver. The effects of phase dynamics are explicitly taken into account by considering a block-independent model for the phase process: the unknown phase is constant for a block of N complex symbols and independent from block to block. In the first part of the paper, the capacity-achieving input distribution is characterized. In particular, it is shown that the maximizing density has circular symmetry, is discrete in amplitude with infinite number of mass points, and always has a mass point at zero. Furthermore, asymptotic expressions and bounds for the capacity are derived. Based on these results, the capacity is evaluated through numerical optimizations for unconstrained and modulation-constrained input distributions. In the second part of this paper, inspired by the capacity results, two classes of coding and modulation schemes are proposed for fast and moderate phase dynamics. In the case of fast phase dynamics (i.e., small N), optimized modulation alphabets are designed having exponential complexity with N at the demodulator. In the case of moderate phase dynamics (i.e., moderate values of N), specially designed modulation alphabets are utilized that have linear complexity with N. These alphabets are used together with optimized irregular low-density parity-check (LDPC) codes. Simulation results show that these codes can achieve close-to-capacity performance with moderate complexity, and outperform the best known codes so far.

Several information-theoretic studies on channels with output quantization have identified the capacity-achieving input distributions for different fading channels with 1-bit in-phase and quadrature (I/Q) output quantization. However, an exact characterization of the capacity-achieving input distribution for channels with multi-bit phase quantization has not been provided. In this paper, we consider four different channel models with multi-bit phase quantization at the output and identify the optimal input distribution for each channel model. We first consider a complex Gaussian channel with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula> -bit phase-quantized output and prove that the capacity-achieving distribution is a rotated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{b}$ </tex-math></inline-formula> -phase shift keying (PSK). The analysis is then extended to multiple fading scenarios. We show that the optimality of rotated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{b}$ </tex-math></inline-formula> -PSK continues to hold under noncoherent fast fading Rician channels with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula> -bit phase quantization when line-of-sight (LoS) is present. When channel state information (CSI) is available at the receiver, we identify <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\frac {2\pi }{2^{b}}$ </tex-math></inline-formula> -symmetry and constant amplitude as the necessary and sufficient conditions for the ergodic capacity-achieving input distribution; which a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{b}$ </tex-math></inline-formula> -PSK satisfies. Finally, an optimum power control scheme is presented which achieves ergodic capacity when CSI is also available at the transmitter.

Rhino poaching is a persistent problem in Southern Africa which may lead to the extinction of the white and black rhinoceros. Recent studies have shown that the Stackelberg model can be an effective tool for security games through optimal resource allocation strategies. Few of these studies focus their attention on wildlife crime or, more generally, green security games in which frequent attacks occur. Furthermore, there does not appear to be a standard way to evaluate the uncertainty and performance of such models. In this work, a simulation platform that emulates realistic movement of rhinos, rangers and poachers in the park, is presented which can ultimately be used for evaluation of game theory models. The game theory model implemented in the simulation platform is evaluated using the uncertainty representation and evaluation framework (URREF) ontology.

In order to assist the decision-making of shared resource optimal allocation by multiple manufacturing tasks for modern distributed manufacturing systems, this article proposes a new approach based on non-cooperative game theory with complete information. Firstly, the manufacturing resources are treated as shared and valuable alternatives, and manufacturing tasks as participants of resource competition. Then the strategy set for resource allocation and the utility of each participant under a certain strategy profile are represented based on a quantitative evaluation. Subsequently, a multi-dimension game model is constructed by using the quantified elements of strategy as dimensions. The game is assumed as static and with complete information, and the pure or mixed Nash equilibrium solution is adopted as optimal resource allocation strategy. The goal of the solution is to maximize the individual profit of each manufacturing task. The desired equilibrium will meet the requirements for resource to the greatest degree to make each manufacturing process achieve optimal production arrangement, and simultaneously the resource utilization efficiency will be improved. The practicability of the approach is verified through a practical example in the last section.

Optimal resource distribution between protection and redundancy considering the time and uncertainties of attacks

In the current Internet, large flows co-exist with small flows and often consume a significant proportion of the network bandwidth. This imposes a challenge on network resource management. Various hybrid switching architectures have been proposed in recent years, aiming to address this challenge and improve energy efficiency and resource utilization. However, there is still a need for means for the design and dimensioning of resources in multiple switching planes and understanding the system behavior under different traffic patterns. In this paper, we propose a framework that we call the blocking-loss curve (BLOC) to address the resource allocation problem in hybrid switching systems. BLOC can capture the behavior of hybrid switching systems under different resource allocation solutions considering packet loss and request blocking. This framework also allows us to identify the feasible region for resource allocation, which can satisfy both the packet loss and request blocking requirements. An optimal resource allocation strategy may be found based on this feasible region. We also study the application of BLOC to a specific hybrid switching system, where packet switching comprises TCP flows with active queue management, and circuit switching is single granular, e.g., lightpath switching. Numerical results demonstrate how the flow characteristics and system parameters may affect the feasible region and the optimal allocation of resources.

Enhanced mobile broadband (eMBB) is a major scenario in the prospective fifth generation (5G) wireless communications. Achieving high user experienced data rate (UEDR) is one of vital goals of eMBB, which needs to consume massive bandwidth. Considering the limited radio frequency (RF) spectrum resource has got heavily congested, we explore the multi-packet reception (MPR) aided visible light communications (VLCs) system to support eMBB services in this paper. MPR can efficiently alleviate collisions for VLC system and enhance UEDR. However, the implementation of MPR in random access system is challenging because of dynamic and unpredictable transmissions of eMBB terminals. For fully utilizing MPR capability and promoting UEDR maximally, we design a two-period frame structure including the access request period (ARP) and the data transmission period (DTP). UEDR emphasizes not only the data rate measured in physical layer but also the network operation behaviour experienced by users which connects closely with quality of service (QoS). In this paper, effective capacity (EC) is employed to depict UEDRs for eMBB terminals for the first time, which provides an efficient link model to evaluate data rates with consideration of statistical delay QoS. Then, the aggregate UEDR is derived and defined as a system metric, which reflects the transmission features of both two periods. We formulate the hybrid access algorithm for eMBB terminals with heterogeneous QoS requirements as an aggregate UEDR maximization problem and find the optimum access probability (for ARP) and optimal resource allocation strategy (for DTP) via the particle swarm optimization-initialization intervention (PSO-II) algorithm.

The multi-user MIMO space time scheduling problem is the prime focus of the paper. Optimizing the link level performance of the MIMO system does not always imply achieving scheduling level optimization. Therefore,design optimization across the link layer and the scheduling layer is very important to exploit fully the temporal and spatial dimensions of the communication channel. We address the design of the optimal space time scheduler for a multi-user MIMO system based on an information theoretical approach. We assume a partial feedback channel, namely a scaler rate-feedback channel, is available to the transmitter, but the full channel matrix is unknown to the transmitter. With the partial feedback, we find that the optimal resource allocation strategy is water-filling in both the temporal domain and spatial domain. The optimal scheduler should allocate all the power to at most n/sub R/ users at any particular instant. The optimal scheduler performance depends on the total number of users (K), the number of receive antennas at the base station (n/sub R/), and the number of transmit antennas at the mobile (n/sub T/). The scheduling gain increases with n/sub R/ due to the distributed MIMO configuration formed between mobile users and base station. With a single power feedback channel, the scheduling gain reduces as n/sub T/ increases, illustrating that transmit diversity hurts the scheduling performance in the case of scalar feedback. Finally, scheduling performance improves as K increases due to the multi-user selection diversity.

In current networks, large flows often co-exist with small flows and consume a significant proportion of the network bandwidth. This imposes a challenge on network resource management. Various hybrid switching architectures have been proposed in recent years aiming to address this challenge and improve efficiency and resource utilization. However, there is still a need for means for design and dimensioning of resources among multiple switching planes. In this work, we first argue that the resource allocation in hybrid switching systems is fact the interaction between three components: resource allocation, traffic partitioning and performance (or cost) constraints. We then introduce a framework called Blocking-LOss Curve (BLOC) to address the resource allocation problem in hybrid switching systems. BLOC can capture the behavior of hybrid switching systems under different resource allocation strategies. This framework also allows us to identify the feasible region for resource allocation, within which performance metrics like the packet loss and request blocking requirements can be satisfied. An optimal resource allocation strategy may be found based on the feasible region. We also discuss the application of BLOC in hybrid switching Data Center networks.