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The zero-error capacity of a channel is defined as the maximum rate at which it is possible to transmit information with zero probability of error. In this paper, we settle all previously unsolved cases for the zero-error capacity of binary channels with one memory. [ABSTRACT FROM AUTHOR]

We present a simple method to calculate the marginal capacity credit of energy limited resources with increased penetration. Energy limited resources are defined as any resource with limited hours of dispatch across a day, month, or year. This includes emission limited resources, time-limited demand response, and diurnal energy storage such as batteries and pumped storage. This paper focuses on 4 h energy limited resources with daily dispatch. The method modifies well-established effective load carrying capability methodology by optimally allocating limited capacity to the highest loss of load hours. Subsequent energy limited resources are iteratively stacked upon the prior solution, creating a marginal capacity credit curve with increased penetration of energy limited resources. Two systems are tested using this method. The initial capacity credit depends on system characteristics such as load and renewable penetration. Regardless, the capacity credit for energy limited resources declines steeply with penetration. [ABSTRACT FROM AUTHOR]

This paper investigates the performance analysis of dual selection (i.e., combining user selection and transmit antenna selection) with maximal ratio combining (MRC) over nonidentical imperfect channel estimation in a multiple-input multiple-output system. This paper derives the exact and approximate cumulative density function and probability density function (PDF) for the dual selection with MRC over nonidentical imperfect channel estimation. Using the distribution, the exact and approximate closed-form expressions of the outage probability are derived with quantification of diversity order. Further, this paper derived the exact ergodic capacity and approximate outage capacity of the the dual selection via Gaussian and Gamma approximations. The numerical results show that the capacity of the dual selection with MRC is enhanced by the number of receive antennas as well as the number of user terminals and sum of channel correlation coefficients, while the order of the receive spatial diversity for outage probability is eliminated by the nonidentical channel estimation error. [ABSTRACT FROM AUTHOR]

Motivated by the idea of “transmitting energy and information simultaneously,” we investigate, in this paper, the impact of constraints on the amount of energy that individual symbols carry, i.e., minimum amplitude constraints. We consider a Gaussian mixture noise channel with both minimum and peak amplitude constraints. First, we prove that the capacity-achieving input has a discrete distribution with a finite number of probability mass points. Then, we further investigate the number and positions of the probability mass points for the capacity-achieving input. Specifically, when the interference is constant and known at both the transmitter and the receiver, it can be totally eliminated so that the channel operates like an AWGN channel. In this case, we give a theorem to determine whether the optimal input is binary. For more general cases, such as non-binary inputs and non-constant interference, we investigate optimal inputs and capacities via numerical computations. [ABSTRACT FROM AUTHOR]

In 2010, Silva et al. studied certain classes of finite-field matrix channels in order to model random linear network coding where exactly $t$ random errors are introduced. In this paper, we consider a generalization of these matrix channels where the number of errors is not required to be constant, indeed the number of errors may follow any distribution. We show that a capacity-achieving input distribution can always be taken to have a very restricted form (the distribution should be uniform given the rank of the input matrix). This result complements, and is inspired by a paper of Nobrega et al., which establishes a similar result for a class of matrix channels that model network coding with link erasures. Our result shows that the capacity of our channels can be expressed as maximization over probability distributions on the set of possible ranks of input matrices: a set of linear rather than exponential size. [ABSTRACT FROM AUTHOR]

This paper investigates the fundamental issues of mode selection and spectrum partition in cellular networks with in-band device-to-device (D2D) communication from the physical-layer security (PLS) perspective. We consider a mode selection scheme allowing each D2D pair to probabilistically switch between the underlay and overlay modes, and also a spectrum partition scheme where the system spectrum is orthogonally partitioned between cellular and overlay D2D communications. We first develop a general theoretical framework to model the secrecy outage and secrecy capacity performance of cellular users as well as the outage and capacity performance of D2D pairs. Optimization problems are also solved to identify the optimal mode selection and spectrum partition for secrecy capacity maximization and secrecy outage probability minimization. A case study is then provided to demonstrate the application of our theoretical framework for performance modeling and optimization, and also to illustrate the impacts of mode selection and spectrum partition on the PLS performances of in-band D2D communications. [ABSTRACT FROM AUTHOR]

In a recent work, S. Nitinawarat obtained a lower bound and an upper bound for the minimum list size in list decoding for an arbitrarily varying multiple access channel (AVMAC), for which the interior of the capacity region of deterministic list codes is nonempty. In the same paper, he proved that for a binary AVMAC, the minimum list size is finite, if and only if the interior of the capacity region of random correlated codes is nonempty. The goal of this paper is to close the gap between the two bounds for the minimum list size. We find a necessary and sufficient condition for the list codes for an AVMAC to have a capacity region with a nonempty interior in terms of bipartite graphs. Therefore, we determine the minimum list size. Moreover, we prove that for any AVMAC, the minimum list size is finite, if and only if the interior of the capacity region of random correlated codes is nonempty. [ABSTRACT FROM PUBLISHER]

In this paper, we consider a cognitive radio (CR) network that consists of multiple secondary users (SUs), each of which is equipped with an energy harvesting capability. We consider the opportunistic use of the idle spectrum, unused by primary users (PUs), through a mechanism of two-step opportunistic spectrum access for the SUs, consisting of random channel sensing followed by random channel access. Unlike the majority of previous studies, which have considered CR networks with energy harvesting under a single user setting, the goal of this paper is to investigate the joint impact of sensing probability, access probability, and energy queue capacity on the maximum achievable throughput in a multiuser CR network incorporating energy harvesting. For two extreme cases, those where the energy queue capacity is either infinite or extremely small, we show that the maximum achievable throughput is not affected by the channel access probability if the channel sensing probability is chosen appropriately and the optimal sensing probability is derived as a function of network parameters such as the energy arrival rate, channel availability probability, and number of contending SUs. [ABSTRACT FROM AUTHOR]

This paper presents the optimal power control policies maximizing the effective capacity achieved with arbitrary input distributions subject to an average power constraint and quality of service (QoS) requirements. The analysis leads to simplified expressions for the optimal power control strategies in the low power regime and two limiting cases, i.e., extremely stringent QoS constraints and vanishing QoS constraints. In the low power regime, the energy efficiency (EE) performance with the constant-power scheme is also determined by characterizing both the minimum energy per bit and wideband slope for arbitrary input signaling and general fading distributions. Subsequently, the results are specialized to Nakagami- $m$ and Rician fading channels. Also, tradeoff between the effective capacity and EE is studied by determining the optimal power control scheme that maximizes the effective capacity subject to constraints on the minimum required EE and average transmission power. Circuit power consumption is explicitly considered in the EE formulation. Through numerical results, the performance comparison between constant-power and optimal power control schemes for different signal constellations and Gaussian signals is carried out. The impact of QoS constraints, input distributions, fading severity, and average transmit power level on the proposed power control schemes, maximum achievable effective capacity and EE is evaluated. [ABSTRACT FROM AUTHOR]

A great deal of previous work has studied methods to determine system reliability. That work has defined system reliability as the probability that the production output meets a predetermined demand for a network with many workstations, each of which has random capacity determined by a discrete probability distribution. It is noteworthy that the archival work presents numerous examples, wherein entities are discrete and indivisible, while the analysis is based upon continuous flow, much like a fluid, through the network. The inconsistency, inherent in mixing discrete-entity examples with continuous-flow analysis, can result in erroneous conclusions about the system reliability. The current paper presents a rigorous discrete-analysis analytical approach, which is called the Song rule (Stochastic “Output $\geq$ demand” Networks and their Generation). Based on all examples studied in this paper, the absolute difference ratios between the previous incorrect (as published) and the correct probabilities (from the Song rule) are all greater than 23%. The proposed Song rule analysis is verified to be correct using discrete-event simulation. In addition to providing rigorous analysis for the network problems under consideration, the Song rule is a useful tool for assessing the validity of any future proposed approach for other stochastic reliability problems. [ABSTRACT FROM PUBLISHER]

Electric vehicles (EVs) have received considerable attention in dealing with severe environmental and energy crises. The capacity planning of public charging stations has been a major factor in facilitating the wide market penetration of EVs. In this paper, we present an optimization model for charging station capacity planning to maximize the fuzzy quality of service (FQoS) considering queuing behavior, blocking reliability, and multiple charging options classified by battery technical specifications. The uncertainty of the EV arrival and service time are taken into account and described as fuzzy numbers characterized by triangular membership functions. Meanwhile, an $\alpha$ -cuts-based algorithm is proposed to defuzzify the FQoS. Finally, the numerical results illustrate that a more robust plan can be obtained by accounting for FQoS. The contribution of the proposed model allows decision-makers and operators to plan the capacity of charging stations with fuzzy EV arrival rate and service rate and provide a better service for customers with different charging options. [ABSTRACT FROM AUTHOR]

In this paper, secure communication aided by a multiantenna cooperative jammer is investigated. Under very practical but adverse assumptions, i.e., no instantaneous jammer–eavesdropper channel state information (CSI) and imperfect instantaneous jammer–receiver CSI, the residual interference from the jammer to the legitimate receiver appears, even with spatial beamforming. Then, this paper analyzes the impact of imperfect CSI on the function of cooperative jamming and derives closed-form expressions of multiple secrecy performance metrics, including ergodic secrecy rate, secrecy outage capacity, and interception probability. Interestingly, it is found that two antennas at the jammer is always optimal. Furthermore, some insights are obtained through asymptotic analysis, e.g., the secrecy performance is saturated as the source transmit power increases, the ergodic secrecy rate is a linear function of CSI feedback bits in the region of high transmit power at the source/jammer, and interception probability is independent of transmit power at the source and is a decreasing function of transmit power at the jammer. Finally, theoretical claims are validated by simulation results. [ABSTRACT FROM PUBLISHER]

In this paper, we introduce a new channel model termed as the $q$ -ary multi-bit channel. This channel models a memory device, where $q$ -ary symbols ($q=2^{s}$) are stored in the form of current/voltage levels. The symbols are read in a measurement process, which provides a symbol bit in each measurement step, starting from the most significant bit. An error event occurs when not all the symbol bits are known. To deal with such error events, we use GF($q$) low-density parity-check (LDPC) codes and analyze their decoding performance. We start with iterative-decoding threshold analysis and derive optimal edge-label distributions for maximizing the decoding threshold. We later move to a finite-length iterative-decoding analysis and propose an edge-labeling algorithm for the improved decoding performance. We then provide a finite-length maximum-likelihood decoding analysis for both the standard non-binary random ensemble and LDPC ensembles. Finally, we demonstrate by simulations that the proposed edge-labeling algorithm improves the finite-length decoding performance by orders of magnitude. [ABSTRACT FROM AUTHOR]

This paper proposes a new method to evaluate the reliability of a wind farm considering the correlation between wind turbine reliability and wind speed. With increasing integration of wind generation into the grid, the reliability and stability of the grid are increasingly impacted. Although there are obvious benefits from wind generation, the stochastic nature of wind speed causes several operational challenges. Recent research has shown that wind speeds also impact the failure rates of the turbines, thereby compounding the effect of wind speed variation. The objective of this paper is to evaluate the impact of wind speed on the reliability of a wind farm considering the correlation between wind speed and wind turbine failure rate. The method proposed in this paper is implemented using discrete convolution. The effectiveness of the proposed method is proved by comparing reliability indexes of the modified IEEE RTS-79 system with and without considering the impacts of the negative correlation between wind turbine reliability and wind speed. [ABSTRACT FROM AUTHOR]

Process industries operate in commodity markets with low product variety, little product differentiation, and highly capital-intensive manufacturing. Because prices are volatile and demand is uncertain, manufacturing capabilities become the main strategic competitive lever. The purpose of this paper is to evaluate the interplay between market conditions, capital expenditure, manufacturing flexibility, and production capacity to effectively analyze the consequences of manufacturing investments in both the short and the long terms. In order to achieve this, we developed an econometric model linking market conditions and operational decisions. We tested the model on a sample of 480 firms from the mining sector and 1053 firms from the oil and gas sector. The results show that firms in process industries follow the market with their investments, which impacts their operations in the short term as well as their capacity growth in the long term. Additionally, from our findings, we propose a firm value driver model that managers could consider when deciding on capital expenditures. Finally, our results indicate that the managers of financially healthy companies should resist the stock market's short-term pressure to reduce fixed costs and instead play the long game. [ABSTRACT FROM AUTHOR]

For an assembly manufacturing system, its capacity planning problem and production control problem are usually considered separately. However, production capacity cannot exert its maximum productivity without employing optimal production control; and optimal production control may not satisfy the demand without sufficient production capacity. Therefore, it is important to jointly optimize capacity planning and production control. First, the mathematical formulation of integrated production control and capacity planning problem for an assembly manufacturing system with multiple upstream production lines and one downstream assembly line is proposed in this paper. Second, we develop a method to obtain the marginal probability distribution of the inventory level in the assembly system, which can be employed to calculate the average production cost under an approximate optimal production control policy. Since optimal control policy is subject to production capacity (i.e., machine number), we employ the integer programming method to optimize the machine number (i.e., production capacity), which consequently influences the control policy. Numerical experiments are conducted to verify the correctness of the method of calculating steady-state probability distribution, and the effectiveness of the algorithm of integrated optimization of production control and production capacity. [ABSTRACT FROM AUTHOR]

High penetrations of distributed energy resources (DERs) are driving the transformation of traditional distribution networks into transactive energy distribution systems (TEDS). TEDS depart from conventional distribution system constructs to 1) enable a local distribution operator to maximize social welfare, 2) enable peer-to-peer energy transactions, 3) extract maximum participation and benefits from DERs, 4) usher in competition in the distribution sector to supply electricity via DERs, and 5) hold the potential for a lower asset cost solution, greater customer choice, and higher reliability. The conventional distribution system planning is inadequate for this purpose. Following the new TEDS paradigm, an incremental capacity auction (ICA) is proposed in this paper considering: 1) bids for power capacity from all energy sources (DERs and transmission), 2) bids for network asset upgrades from equipment vendors, and 3) bids from new loads. A holistic auction settlement procedure is developed for the proposed ICA mechanism that can concurrently determine the optimal power capacity to be procured and network asset upgrading, while maximizing social welfare. Further, locational marginal price for power capacity demand is estimated as a by-product of the ICA solution. Benefits are demonstrated via case studies and include increased participation of DERs to supply energy, maximizing the social welfare, and enabling assets deferral considering the available local resources. [ABSTRACT FROM AUTHOR]

The most commonly used setting in the coded caching literature consists of the following four elements: (i) homogeneous file sizes, (ii) homogeneous cache sizes, (iii) user-independent homogeneous file popularity (i.e., all users share the same file preference), and (iv) worst-case rate analysis. While recent results have relaxed some of these assumptions, deeper understanding of the full heterogeneity setting is still much needed since traditional caching schemes place little assumptions on file/cache sizes and almost always allow each user to have his/her own file preference through individualized file request prediction. Taking a microscopic approach, this paper characterizes the exact capacity of the smallest 2-user/2-file ($N=K=2$) problem but under the most general setting that simultaneously allows for (i) heterogeneous files sizes, (ii) heterogeneous cache sizes, (iii) user-dependent file popularity, and (iv) average-rate analysis. Solving completely the case of $N=K=2$ could shed further insights on the performance and complexity of optimal coded caching with full heterogeneity for arbitrary $N$ and $K$. [ABSTRACT FROM AUTHOR]

Unmanned-aerial-vehicle (UAV)-based communications are expected to play an important role in the future generation wireless communication networks. It is a promising solution to enhance the wireless connectivity of devices without infrastructure. As compared to the terrestrial communications, UAVs offer various advantages such as line-of-sight (LoS) connectivity, dynamic deployment and flexible reconfiguration. Further, free space optics (FSO) communication is considered as a possible solution to enable UAV-based communication links due to its cost effective and high bandwidth nature. However, despite offering large bandwidth, UAV-based FSO links are marred by atmospheric path loss, atmospheric turbulence, non-zero boresight pointing errors, and angle-of-arrival (AoA) fluctuations. In this paper, the performance of a hovering UAV-based FSO communication system is investigated. The closed-form expressions for outage probability, average symbol error rate (SER), and ergodic capacity are derived taking into account both heterodyne (HD) and direct detection (DD) techniques for ground-to-UAV (G2U), UAV-to-UAV (U2U), and UAV-to-ground (U2G) links over the generalized Málaga distribution. Moreover, the asymptotic expressions for the above performance metrics are also derived to get the diversity gain of the system. [ABSTRACT FROM AUTHOR]

In this paper, we consider the transmission of confidential message through single-input–multiple-output (SIMO) identically and independent generalized-$K$ fading channels in the presence of an eavesdropper. We derive the analytical expressions for the probability of strictly positive secrecy capacity (SPSC), secure outage probability (SOP), and average secrecy capacity (ASC) of SIMO systems. Numerical results are presented and verified via Monte Carlo simulation. [ABSTRACT FROM PUBLISHER]

We study communication systems over the state-dependent channels in the presence of a malicious state-aware jamming adversary. The channel has a memoryless state with an underlying distribution. The adversary introduces a jamming signal into the channel. The message and the entire state sequence are known non-causally to both the encoder and the adversary. This state-aware adversary may choose an arbitrary jamming vector depending on the message and the state vector. Taking an arbitrarily varying channel (AVC) approach, we consider two setups, namely, the discrete memoryless Gel’fand–Pinsker AVC and the additive white Gaussian dirty paper (DP) AVC. We determine the randomized coding capacity of both the AVCs under a maximum probability of error criterion. Similar to other randomized coding setups, we show that the capacity is the same even under the average probability of error criterion. Though the adversary can choose an arbitrary vector jamming strategy, we prove that the adversary cannot affect the rate any worse than when it employs a memoryless strategy, which depends only on the instantaneous state. Thus, the AVC capacity characterization is given in terms of the capacity of the worst memoryless channels with state, induced by the adversary employing such memoryless jamming strategies. For the DP-AVC, it is further shown that among memoryless jamming strategies, none impact the communication more than a memoryless Gaussian jamming strategy which completely disregards the knowledge of the state. Thus, the capacity of the DP-AVC equals that of a standard additive white Gaussian noise (AWGN) channel with two independent sources of AWGN, i.e., the channel noise and the jamming noise. [ABSTRACT FROM PUBLISHER]

In this paper, an approximate closed-form probability density function expression for the sum of lognormal-Rician turbulence channels with Rayleigh pointing errors is developed. The results of Kolmogorov-Smirnov goodness-of-fit statistical tests show that the proposed approximation is highly accurate across a wide range of channel conditions. Also, the analysis of approximation error is presented in detail, and it indicates that a more efficient approximation can be achieved for larger coherence parameter $r$ and smaller variance $\sigma _{z}^{2}$. To reveal the importance of proposed approximation, the closed-form expressions for the ergodic capacity, outage probability, and bit-error rate are derived in terms of Meijer’s G-function. The performance of multiple-input multiple-output (MIMO) free-space optical (FSO) systems with equal gain combining (EGC) diversity technique are analyzed in detail under different scenarios, including the number of transmit and receive apertures, turbulence channels, and presence of pointing errors. It is observed that MIMO technology can offer a significant improvement in FSO performance when compared with the single-input single-output (SISO) systems. The ergodic capacity and BER performance at high signal-to-noise ratio are also obtained to provide further insights. Numerical results demonstrate the accuracy of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents the first systematic study of the zero-error capacity regions of noisy networks. First, we consider two simple such networks, each consisting of a stationary memoryless multiple access channel with two binary inputs and one discrete output. There are two users in each network. Each of the two users transmits a message through the network, and the sink(s) of the network can decode both messages with zero error. A graph is used to represent the distinguishability of the inputs of the channel, and a graph set is used to represent the distinguishability of the inputs of the network. We show that for two networks represented by the same graph set, their zero-error capacity regions are the same. We list all the possible graph sets for the two networks and determine the zero-error capacity regions for some of these graph sets. Based on this result, we explore a relation between graph theory and set theory, and then redefine the cancellative pair of families of subsets. We further extend the problem formulation to a general network called the parallel network, which may consist of more than one channel with multiple inputs and multiple outputs. [ABSTRACT FROM AUTHOR]

New nonasymptotic random coding theorems (with error probability $\epsilon $ and finite block length $n$ ) based on Gallager parity check ensemble and general parity check ensembles are derived in this paper. The resulting nonasymptotic achievability bounds, when combined with nonasymptotic equipartition properties developed in this paper, can be easily computed. Analytically, these nonasymptotic achievability bounds are shown to be asymptotically tight up to the second order of the coding rate as $n$ goes to infinity with either constant or subexponentially decreasing $\epsilon $ in the case of Gallager parity check ensemble, and to imply that low density parity check (LDPC) codes be capacity-achieving in the case of LDPC ensembles. Numerically, they are also compared favorably, for finite $n$ and $\epsilon $ of practical interest, with existing nonasymptotic achievability bounds in the literature. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven distributionally robust chance constrained real-time dispatch (DRCC-RTD) considering renewable generation forecasting errors. The proposed DRCC-RTD model minimizes the expected quadratic cost function and guarantees that the two-sided chance constraints are satisfied for any distribution in the ambiguity set. The Wasserstein-distance-based ambiguity set, which is a family of distributions centered at an empirical distribution, is employed to hedge against data perturbations. By applying the reformulation linearization technique (RLT) to relax the quadratic constraints of the worst-case costs and constructing linear reformulations of the DRCCs, the proposed DRCC-RTD model is cast into a deterministic linear programming (LP) problem, which can be solved efficiently by off-the-shelf solvers. Case studies are carried out on a 6-bus system and the IEEE 118-bus system to validate the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

The wireless industry is moving towards developing smart cellular architectures that dynamically adjust the use of the network elements according to the service demand, and automating their operations in order to minimize both capital expenditure (CAPEX) and operation expenditure (OPEX). This involves developing efficient and unsupervised radio access network (RAN) planning, which has a direct impact on the system performance and CAPEX. This intelligent cellular planning aims at providing the base stations (BSs) configurations (e.g., coverage, user associations and antenna radiation pattern) that minimize the number of deployed BSs and meet the requirements in terms of coverage and capacity. The cellular planning optimization problem has been shown to be complex and non-scalable. Moreover, most of the existing cellular planning techniques result in an over or under provisioning architecture. Motivated by the above, we propose in this paper a novel and efficient unsupervised planning process. We make use of statistical machine learning (SML) to solve the problem at hand. The core idea of SML is that the planning parameters are treated as random variables. The parameters that maximize the corresponding joint probability distribution, conditioned on observations of users’ positions, are learned or inferred using Gibbs sampling theory and Bayes’ theory. To apply this theory to the planning problem, we make significant efforts to properly formulate the problem to be able to incorporate the constraints into the inference process and extract the planning parameters from the inferred model. Through several numerical examples, we compare the performance of the proposed approach to clustering-based and optimization-based existing planning approaches, and demonstrate the efficacy of our approach. We also demonstrate how our approach can leverage existing cellular infrastructures into the new design. [ABSTRACT FROM AUTHOR]

With the soaring wireless traffic for Internet of Things (IoT), spectrum shortage becomes an extremely serious problem, leading to the paradigm shift in spectrum usage from an exclusive mode to a sharing mode. However, how to guarantee the quality of service (QoS) when using the shared spectrum is not straightforward due to its uncertain availability. In this paper, from a session-based view, we propose a metric to evaluate how much data can be delivered via a shared band during a session period, named probabilistic link capacity (PLC), which offers us an effective way to guarantee the QoS statistically. Different from most existing works where the distributional information is assumed exactly known, we develop a distributionally robust (DR) data-driven approach to estimate the value of the PLC based on the first and second order statistics. Two cases are considered that the statistics are exact or uncertain with estimation errors. For each case, to calculate the DR-PLC, we formulate it into a semidefinite programming problem based on the worst-case of conditional-value-at-risk. With the proposed metric, we further design a service-based spectrum-aware data transmission scheme, which allows us to efficiently use different kinds of spectrums to satisfy the diverse IoT service requirements. [ABSTRACT FROM AUTHOR]

In this paper, we propose a distributed cluster formation (CF) and resource allocation (RA) framework for non-ideal non-orthogonal multiple access (NOMA) schemes in heterogeneous networks. The imperfection of the underlying NOMA scheme is due to the receiver sensitivity and interference residue from non-ideal successive interference cancellation (SIC), which is generally characterized by a fractional error factor (FEF). Our analytical findings first show that several factors have a significant impact on the achievable NOMA gain. Then, we investigate fundamental limits on NOMA cluster size as a function of FEF levels, cluster bandwidth, and quality of service (QoS) demands of user equipments (UEs). Thereafter, a clustering algorithm is developed by taking feasible cluster size and channel gain disparity of UEs into account. Finally, we develop a distributed $\alpha $ -fair RA framework where $\alpha $ governs the tradeoff between maximum throughput and proportional fairness objectives. Based on the derived closed-form optimal power levels, the proposed distributed solution iteratively updates bandwidths, clusters, and UEs’ transmission powers. Numerical results demonstrate that proposed solutions deliver a higher spectral and energy efficiency than traditionally adopted basic NOMA cluster size of two. We also show that an imperfect NOMA cannot always provide better performance than orthogonal multiple access under certain conditions. Finally, our numerical investigations reveal that NOMA gain is maximized under downlink/uplink decoupled (DUDe) UE association. [ABSTRACT FROM AUTHOR]

This paper investigates the channel capacity of coherent reception with antenna diversity in Rayleigh fading using asymmetric constellations, such as nonbinary two-sided amplitude-shift keying (ASK) and quadrature amplitude modulation (QAM) with ≥ 8 levels. Due to the asymmetries, the capacity achieving input or a priori probability distributions deviate from uniformity. To perform the investigation, an analytical expression for the mutual information, in terms of an integral over a single variable, between the input (transmitted symbol) and the output (received signal vector comprising signals at receive antennas), is derived. For two-sided ASK, 8-QAM with cross constellation, and 16-QAM, analytical closed form expressions for the high and low signal-to-noise (SNR) approximations, respectively, of the capacity achieving input probability distribution, the capacity, and the relative gain in mutual information (between using the uniform and the capacity achieving input probability distributions) are obtained. Numerical results show that at low SNRs, the use of the capacity achieving nonuniform input probability distribution offers significant advantage over the use of the uniform input probability distribution, while at high SNRs, this advantage is significant only for configurations with one receive antenna. Furthermore, the high and low SNR approximations are shown to be reasonably accurate. [ABSTRACT FROM AUTHOR]

Planning distribution systems without considering the operation status of multiple distributed generation (DG) units could result in constraining the network, lowering the utilization of its assets and minimizing the total DG capacity that can be accommodated. In this paper, the impact of multiple DG configurations on the potential of active network management (ANM) schemes is firstly investigated. Secondly, the paper proposes a multi-configuration multi-period optimal power flow (OPF)-based technique (MMOPF) for assessing the maximum DG capacity under ANM schemes considering 1) variability of demand and generation profiles (multi-period scenarios), and 2) different operational status of DG units (multi-configurations). The results show that the availability of DGs at certain locations could critically impact the amount of DG capacity at other locations. If DGs are properly allocated and sized at certain locations up to the optimal limits, even with a “fit-and-forget” approach, the total connected DG capacity can be maximized, with minimum utilization of ANM schemes. However, exceeding these optimal limits may lead to minimizing the total DG penetration in the long term, impacting the system reliability due to the operational status of multiple DG units, and consequently, imposing more investments on ANM schemes to increase the amount of connected DG capacity. [ABSTRACT FROM AUTHOR]

It was previously shown by Hashimoto that Forney’s optimal erasure decoder can be significantly simplified, in the sense that a simplified decoder achieves the same random coding bounds, for the ensemble of independent and identically distributed codewords. In this paper, the analysis of simplified decoders is refined and generalized in several aspects. First, tighter random coding bounds for simplified decoders are derived, which equal the exact exponential behavior of the fixed composition ensemble average. Second, the exponential bounds are valid both in the erasure mode and in the list mode. Third, the analysis pertains to a rather general class of simplified decoders, including the case of mismatch in the threshold function of the decoder. Fourth, expurgated exponents, which are larger than the random coding exponents at low rates, are shown to be achievable using a significantly simpler decoder than Forney’s optimal decoder. It is shown numerically that, from the aspect of exact random coding exponents, a decoder in the spirit of Hashimoto’s is as good as Forney’s in the erasure mode, as well as in the list mode. [ABSTRACT FROM PUBLISHER]

The screening curve method (SCM) is an intuitive and fast model that estimates the least-cost generation mix for generation planning purposes. It calculates an economically adapted generation mix for a target load duration curve within a few seconds, but lacks many detailed considerations of the generation system. In order to improve SCM, many developments have been made in recent years. However, one of the biggest limitations had always been that the SCM assumed all capacity to be new with no existing capacity. This drawback made SCM less useful compared to other generation planning models. In this paper, we develop a direct way to model existing capacity in SCM. The proposed method is studied in this paper and is illustrated with an example. Finally, an ERCOT year 2030 case is simulated. [ABSTRACT FROM AUTHOR]

This paper proposes mathematical models for sequential coordination of transmission expansion planning with strategic generation investments. The proactive and reactive coordinations are modeled and studied. The interaction between transmission company (Transco) and strategic generation companies (Gencos) is modeled using the sequential-move game. This is while the interaction between the strategic Gencos is modeled as a simultaneous-move game. In the proactive coordination, the Transco expands its future transmission capacities taking into account the strategic investments by Gencos. In the reactive coordination, strategic Gencos move first and expand their future generation capacities and then Transco expands the transmission capacity. The proactive coordination is modeled as a mixed-integer bilevel linear program (MIBLP) and the reactive coordination is modeled as a mixed-integer linear program (MILP). The MIBLP has binary variables in both upper and lower levels. The Moore–Bard algorithm is parallelized and used to solve the MIBLP. The mathematical models and the parallelized Moore–Bard algorithm are tested on 3-bus and 6-bus example systems and the modified IEEE-RTS96. Also, the IEEE 118-bus test system is studied using a heuristic version of the Moore–Bard algorithm. [ABSTRACT FROM AUTHOR]

Reed–Muller (RM) codes encode an m -variate polynomial of degree at most r by evaluating it on all points in \0,1\^m . We denote this code by RM(r,m) . The minimum distance of RM(r,m) is 2^{m-r} and so it cannot correct more than half that number of errors in the worst case. For random errors one may hope for a better result. In this paper we give an efficient algorithm (in the block length n=2^{m} ) for decoding random errors in RM codes far beyond the minimum distance. Specifically, for low-rate codes (of degree r=o(\sqrt m) ), we can correct a random set of (1/2-o(1))n errors with high probability. For high rate codes (of degree m-r for r=o(\sqrt {m/\log m}) ), we can correct roughly m^{r/2}$ errors. More generally, for any integer $r$ , our algorithm can correct any error pattern in $RM(m-(2r+2),m)$ , for which the same erasure pattern can be corrected in $RM(m-(r+1),m)$ . The results above are obtained by applying recent results of Abbe, Shpilka, and Wigderson (STOC, 2015) and Kudekar et al. (STOC, 2016) regarding the ability of RM codes to correct random erasures. The algorithm is based on solving a carefully defined set of linear equations and thus it is significantly different than other algorithms for decoding RM codes that are based on the recursive structure of the code. It can be seen as a more explicit proof of a result of Abbe et al. that shows a reduction from correcting erasures to correcting errors, and it also bares some similarities with the error-locating pair method of Pellikaan, Duursma, and Kötter that generalizes the Berlekamp–Welch algorithm for decoding Reed–Solomon codes. [ABSTRACT FROM PUBLISHER]

In this paper, we discuss the coding schemes and error probabilities in information transmission over additive Gaussian noise channels with feedback, where the Gaussian noise processes are stationary but not necessarily white. In the case of the white Gaussian channel it is known that the minimum error probability, under the average power constraint, decreases faster than the exponential of any order. Recently Gallager and Nakiboğlu (2010) proposed a coding scheme for the white Gaussian channel and successfully showed the multiple-exponential decay of the error probability for all rates below capacity. This paper aims to prove that, without any special assumptions on the noise of the stationary Gaussian channel, the minimum error probability decreases multiple-exponentially fast. In general, no explicit formulas are known for the capacity of the stationary Gaussian channel. In this paper, we introduce a lower bound C^* on the capacity C. Then we prove that the minimum error probability decreases multiple-exponentially fast for all rates below C^*, but not for all rates below C. In the process of proving, the scheme proposed by Gallager and Nakiboğlu proves itself to be quite useful, even though the Gaussian channel is not white. [ABSTRACT FROM AUTHOR]

The input-constrained binary erasure channel (BEC) with strictly causal feedback is studied. The channel input sequence must satisfy the $(0,k)$ -runlength limited (RLL) constraint, i.e., no more than $k$ consecutive ‘0’s are allowed. The feedback capacity of this channel is derived for all $k\geq 1$ , and is given by $C^{\mathrm {fb}}_{(0,k)}(\varepsilon) = \max \frac {\overline {\varepsilon }H_{2}(\delta _{0})+\sum _{i=1}^{k-1}\left ({\overline {\varepsilon }^{i+1}H_{2}(\delta _{i})\prod _{m=0}^{i-1}\delta _{m}}\right)}{1+\sum _{i=0}^{k-1}\left ({\overline {\varepsilon }^{i+1} \prod _{m=0}^{i}\delta _{m}}\right)}$ , where $\varepsilon $ is the erasure probability, $\overline {\varepsilon }=1-\varepsilon $ and $H_{2}(\cdot)$ is the binary entropy function. The maximization is only over $\delta _{k-1}$ , while the parameters $\delta _{i}$ for $i\leq k-2$ are straightforward functions of $\delta _{k-1}$. The lower bound is obtained by constructing a simple coding for all $k\geq 1$. It is shown that the feedback capacity can be achieved using zero-error, variable length coding. For the converse, an upper bound on the non-causal setting, where the erasure is available to the encoder just prior to the transmission, is derived. This upper bound coincides with the lower bound and concludes the search for both the feedback capacity and the non-causal capacity. As a result, non-causal knowledge of the erasures at the encoder does not increase the feedback capacity for the $(0,k)$ -RLL input-constrained BEC. This property does not hold in general: the $(2,\infty)$ -RLL input-constrained BEC, where every ‘1’ is followed by at least two ‘0’s, is used to show that the feedback capacity can be strictly smaller than the non-causal capacity. [ABSTRACT FROM AUTHOR]

This paper explores the relationship between two ideas in the network information theory: edge removal and strong converses. Edge removal properties state that if an edge of small capacity is removed from a network, the capacity region does not change too much. Strong converses state that, for rates outside the capacity region, the probability of error converges to 1 as the blocklength goes to infinity. Various notions of edge removal and strong converse are defined, depending on how edge capacity and error probability scale with blocklength, and relations between them are proved. Each class of strong converse implies a specific class of edge removal. The opposite directions are proved for deterministic networks. Furthermore, a technique based on a novel, causal version of the blowing-up lemma is used to prove that for discrete memoryless networks, the weak edge removal property—that the capacity region changes continuously as the capacity of an edge vanishes—is equivalent to the exponentially strong converse—that outside the capacity region, the probability of error goes to 1 exponentially fast. This result is used to prove exponentially strong converses for several examples, including the discrete two-user interference channel with strong interference, with only a small variation from traditional weak converse proofs. [ABSTRACT FROM AUTHOR]

Power grid reliability impacts could be significant when a large amount of variable wind generation is integrated with the electric power system. The widely used deterministic reliability assessment method is invalid when modeling the intermittency of wind energy sources. The energy-based probabilistic reliability assessment models are required in system reliability impact assessment in order to consider the stochastic characteristic of wind resources. This paper investigates different stochastic characteristics in wind energy integration, including resource availability, generation facility outage, and transmission availability. A probabilistic framework of reliability modeling for renewable resource integration such as wind energy conversion system is proposed in this paper. Using the proposed reliability models and framework, the cost of wind energy integration with the power grid for maintaining system adequacy and reliability can be evaluated realistically. The IEEE Reliability Test System is utilized to demonstrate the developed models and methods. The methodology can be used for modeling the reliability of industrial and commercial facilities being serviced by wind farms and transmission lines. [ABSTRACT FROM AUTHOR]

To reduce the computational burden of capacity expansion models, power system operations are commonly accounted for in these models using representative time periods of the planning horizon such as hours, days, or weeks. However, the validity of these time-period aggregation approaches to determine the capacity expansion plan of future power systems is arguable, as they fail to capture properly the mid-terms dynamics of renewable power generation and to model accurately the operation of electricity storage. In this paper, we propose a new time-period clustering method that overcomes the aforementioned drawbacks by maintaining the chronology of the input time series throughout the whole planning horizon. Thus, the proposed method can correctly assess the economic value of combining renewable power generation with interday storage devices. Numerical results from a test case based on the European electricity network show that our method provides more efficient capacity expansion plans than existing methods while requiring similar computational needs. [ABSTRACT FROM AUTHOR]

As a promising non-orthogonal multiple access scheme, sparse code multiple access (SCMA) technology has attracted much attention. Because inter-user interference is present in code domain and multi-user iterative detection is required, user capacity and outage probability analysis for uplink SCMA systems are challenging and have not been presented in the literature. In this paper, the capacity region for uplink SCMA systems is analyzed, based on which the common and individual outage probability regions are calculated. Optimizing the outage probability within the outage probability region can be casted as an Lagrangian duality problem and solved by an iterative descent algorithm, which however imposes high complexity since the expectation operation is required in each iteration. To reduce the computational complexity of solving this Lagrangian duality problem, an adaptive algorithm is developed, which is capable of providing the optimal outage probability and adaptively updating it. Furthermore, a power allocation policy is naturally obtained to achieve the optimized outage probability in the outage probability region. [ABSTRACT FROM AUTHOR]

In typical wireless heterogeneous networks (HetNets), users are clustered around known hotspots, e.g., shopping centers or schools, but such a non-uniform distribution of nodes is difficult to analyze. This paper explicitly models this scenario, with macro base stations (MBSs) modeled by a homogeneous Poisson point process (PPP), and millimeter-wave small base stations (mmWave SBSs) and users clustered around the hotspot centers, forming two Poisson cluster processes (PCPs), respectively. Fractional frequency reuse (FFR) and coordinated multi-point transmission (CoMP) are assumed since they help to limit the co-tier interference and enhance the coverage and capacity of the network. We present a distance-based approach for grouping macro user equipments (MUEs) from the cell center (CC) and cell edge (CE) regions for FFR analysis. We first derive some distance distributions, including joint distance distribution from the typical user to the cooperative open-access mmWave SBS and distance distribution from the typical user to the non-cooperative open-access mmWave SBS. We obtain expressions for various performance metrics, including association probability, signal to interference-plus-noise ratio (SINR) coverage probability, and ergodic capacity, under these conditions. Due to the complexity of the exact expressions, we derive novel approximations, using Alzer's lemma, to obtain the lower bounds on coverage and ergodic capacity, which are shown to be accurate through Monte Carlo simulation. Simulation results analyze the effect of different parameters on the network performance to give some guidance for the design of future networks. Numerical optimization of a key parameter, in terms of association probability, coverage probability, and ergodic capacity, is enabled by our analysis. [ABSTRACT FROM AUTHOR]

Soft open points (SOPs) are power electronic devices which provide interconnection between two feeders in place of normally open points in electricity distribution networks. SOPs can continuously control active power flow between feeders and inject reactive power controllably at both nodes, which can be used to provide substantial capacity support to the system. This paper provides a reliability-based method to quantify the capacity value (the additional load which can be accommodated without reducing reliability) of SOPs using the Effective Load Carrying Capability method within a Monte Carlo Simulation (MCS). Optimization of post-fault active/reactive power injections by SOPs to minimize energy not supplied is formulated (directly in matrix form) as a second-order cone programming problem. This results in very low computational times which enables embedding of the optimization problem within the MCS. The proposed methodology is applied to a modified real-world distribution network considering three different SOP sizes (5 SOPs totalling 2.5, 5, 10 MVA) across three redundancy levels (N-1, N-0.75, N-0.5), and on an unbalanced network with distributed generation. Results demonstrate capacity values ranging from 2.4–12.84 MVA. When operating under a relaxed redundancy level, the capacity value of a given SOP capacity can more than double relative to N-1. [ABSTRACT FROM AUTHOR]

In this paper, the average achievable rate and error probability of a reconfigurable intelligent surface (RIS) aided systems is investigated for the finite blocklength (FBL) regime. The performance loss due to the presence of phase errors arising from limited quantization levels as well as hardware impairments at the RIS elements is also discussed. First, the composite channel containing the direct path plus the product of reflected channels through the RIS is characterized. Then, the distribution of the received signal-to-noise ratio (SNR) is matched to a Gamma random variable whose parameters depend on the total number of RIS elements, phase errors and the channels’ path loss. Next, by considering the FBL regime, the achievable rate expression and error probability are identified and the corresponding average rate and average error probability are elaborated based on the proposed SNR distribution. Furthermore, the impact of the presence of phase error due to either limited quantization levels or hardware impairments on the average rate and error probability is discussed. The numerical results show that Monte Carlo simulations conform to matched Gamma distribution to received SNR for sufficiently large number of RIS elements. In addition, the system reliability indicated by the tightness of the SNR distribution increases when RIS is leveraged particularly when only the reflected channel exists. This highlights the advantages of RIS-aided communications for ultra-reliable and low-latency systems. The difference between Shannon capacity and achievable rate in FBL regime is also discussed. Additionally, the required number of RIS elements to achieve a desired error probability in the FBL regime will be significantly reduced when the phase shifts are performed without error. [ABSTRACT FROM AUTHOR]

In this paper, we consider the transmission of confidential information over a $\kappa $ – \mu $ fading channel in the presence of an eavesdropper who also experiences \kappa $ – \mu ) for independent and non-identically distributed channel coefficients without parameter constraints. We also provide a closed-form expression for the probability of SPSC when the \mu $ parameter is assumed to take positive integer values. Monte-Carlo simulations are performed to verify the derived results. The versatility of the \kappa $ – \mu for a large number of other fading scenarios, such as Rayleigh, Rice (Nakagami- n$ ), Nakagami- m$ , One-Sided Gaussian, and mixtures of these common fading models. In addition, due to the duality of the analysis of secrecy capacity and co-channel interference (CCI), the results presented here will have immediate applicability in the analysis of outage probability in wireless systems affected by CCI and background noise (BN). To demonstrate the efficacy of the novel formulations proposed here, we use the derived equations to provide a useful insight into the probability of SPSC and SOP for a range of emerging wireless applications, such as cellular device-to-device, peer-to-peer, vehicle-to-vehicle, and body centric communications using data obtained from real channel measurements. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for N-1 contingency constrained transmission capacity expansion planning (TCEP), which is formulated as a mixed-integer programming (MIP) problem. In relatively well-designed power systems, a single outage of a majority of lines will not usually cause overload on other lines in most loading conditions. Thus they will not affect the feasible region and the optimal answer of the TCEP optimization problem, and can be safely removed from contingency analysis if we can identify them. A contingency identification index is developed to detect these lines and create variable contingency lists (VCL) for different network loading conditions. In our proposed method, we use results of a relaxed version of the original problem as a lower bound answer in the first step, and integrate contingencies into TCEP in the next steps to solve this optimization problem faster while still satisfying N-1 criterion. For solving TCEP with contingencies, two options are offered, i.e., option A that uses an updated system as its base case (original existing network together with selected lines by the relaxed problem) and option B that uses the original existing network as its base case (without results of the relaxed problem). Option A is faster than option B because it usually should select fewer new lines compared to B, but cannot guarantee optimality. Option B provides the optimal answer while taking more computational time. An ERCOT case study is used to show capabilities of the proposed method for solving large scale problems, and the numerical result demonstrates this method is much faster than the integrated MIP method that directly incorporates all contingencies. [ABSTRACT FROM AUTHOR]

This paper studies the second-order asymptotics of the Gaussian multiple-access channel with degraded message sets. For a fixed average error probability $ \varepsilon \in (0,1)$ and an arbitrary point on the boundary of the capacity region, we characterize the speed of convergence of rate pairs that converge to that boundary point for codes that have asymptotic error probability no larger than $ \varepsilon $ . As a stepping stone to this local notion of the second-order asymptotics, we study a global notion, and establish relationships between the two. We provide a numerical example to illustrate how the angle of approach to a boundary point affects the second-order coding rate. This is the first conclusive characterization of the second-order asymptotics of a network information theory problem in which the capacity region is not a polygon. [ABSTRACT FROM PUBLISHER]

We consider the use of the well-known dual capacity bounding technique for deriving upper bounds on the capacity of indecomposable finite-state channels (FSCs) with finite input and output alphabets. In this technique, capacity upper bounds are obtained by choosing suitable test distributions on the sequence of channel outputs. We propose test distributions that arise from certain graphical structures called Q-graphs. As we show in this paper, the advantage of this choice of test distribution is that, for the important sub-classes of unifilar and input-driven FSCs, the resulting upper bounds can be formulated as a dynamic programming (DP) problem, which makes the bounds tractable. We illustrate this for several examples of FSCs, where we are able to solve the associated DP problems explicitly to obtain capacity upper bounds that either match or beat the best previously reported bounds. For instance, for the classical trapdoor channel, we improve the best known upper bound of 0.661 (due to Lutz (2014)) to 0.584, shrinking the gap to the best known lower bound of 0.572, all bounds being in units of bits per channel use. [ABSTRACT FROM AUTHOR]

To fully unleash the potentials of the cognitive radio (CR) paradigm, new challenges must be addressed. Specifically, as regards the network layer, the problem of the route priority, i.e., the problem of prioritizing the routes for the CR packet transmission, is crucial, since the communication opportunities provided by a route are deeply affected by the primary-user (PU) activity. Furthermore, whenever the CR network layer exploits proactively acquired information on the PU activity, update packets need to be exchanged among the CR users, inducing so a route overhead independently of the adopted routing protocol. Hence, in this paper, we analytically derive the optimal route priority rule, i.e., the route priority rule maximizing the achievable capacity, by jointly accounting for the PU activity and the route overhead. To this aim, at first, we formulate the optimal route priority problem, and we prove that its computational complexity through exhaustive search is exponential. Then, we provide the closed-form expressions of the achievable capacity. Stemming from these expressions, we derive the optimal route priority, and we design a computational-efficient search algorithm. All the theoretical results are derived by adopting two routing strategies and two PU activity models. [ABSTRACT FROM PUBLISHER]

As intermittent energy resources become more significant in power production, traditional capacity planning may be insufficient to ensure reliable system operation. A system planner must ensure that flexibility solutions are available to respond to large and uncertain ramping events. These solutions may be operational, such as improved unit commitment and dispatch, curtailment of renewables, or demand response; procurement based, such as new fast ramping resources or batteries; or involve market reform. This paper outlines a new methodology for modeling the economic tradeoffs in implementing flexibility solutions for integrating renewables. The proposed model includes both a stochastic treatment of system states to account for a wide range of operating conditions and an adapted production simulation methodology that weighs the cost of reliability and subhourly flexibility violations against the cost of the operational flexibility solutions available to mitigate them. The model's functionality is demonstrated with a case study of California at a 50% RPS in 2030. The model tests the value of 1088 MW of generic flexible units, relative to the same capacity of must-run resources, finding an expected annual value of \$347\pm 42 million/yr. Potential applications of the model for resource planning and procurement are also discussed. [ABSTRACT FROM PUBLISHER]

In network cooperation strategies, nodes work together with the aim of increasing transmission rates or reliability. This paper demonstrates that enabling cooperation between the transmitters of a two-user multiple access channel via a cooperation facilitator that has access to both messages results in a network whose maximal- and average-error capacity regions are the same; this benefit ensues even when the information received by each transmitter is negligible. From this result, it follows that if a multiple access channel with no transmitter cooperation has different maximal- and average-error sum-capacities, then the maximal-error sum-capacity of the network consisting of this channel and a cooperation facilitator is not continuous with respect to the output edge capacities of the facilitator. Thus, there exist networks where adding negligible rate yields a non-negligible benefit. [ABSTRACT FROM AUTHOR]