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This paper considers a general stochastic resource allocation problem that arises widely in wireless networks, cognitive radio, networks, smart-grid communications, and cross-layer design. The problem formulation involves expectations with respect to a collection of random variables with unknown distributions, representing exogenous quantities such as channel gain, user density, or spectrum occupancy. We consider the constant step-size stochastic dual subgradient descent (SDSD) method that has been widely used for online resource allocation in networks. The problem is solved in dual domain, which results in a primal resource allocation subproblem at each time instant. The goal here is to characterize the non-asymptotic behavior of such stochastic resource allocations in an almost sure sense. It is well known that with a step size of \epsilon , SDSD converges to an \mathcal {O}(\epsilon) -sized neighborhood of the optimum. In practice, however, there exists a trade-off between the rate of convergence and the choice of \epsilon $ . This paper establishes a convergence rate result for the SDSD algorithm that precisely characterizes this trade-off. Toward this end, a novel stochastic bound on the gap between the objective function and the optimum is developed. The asymptotic behavior of the stochastic term is characterized in an almost sure sense, thereby generalizing the existing results for the stochastic subgradient methods. For the stochastic resource allocation problem at hand, the result explicates the rate with which the allocated resources become near-optimal. As an application, the power and user-allocation problem in device-to-device networks is formulated and solved using the SDSD algorithm. Further intuition on the rate results is obtained from the verification of the regularity conditions and accompanying simulation results. [ABSTRACT FROM PUBLISHER]

Maximizing the information per the expected unit energy cost in biological neurons is vital in understanding the functionality of our nervous system and improving the bio-inspired nano-networks. Hence, toward a better comprehension of neuronal information processing and communication from an information-energy standpoint, this paper presents the following novel results on the capacity-achieving distribution: 1) For the first time, the paper presents the probability mass function of the number of spikes observed in a given time window where inter-spike intervals follow the Inverse Gaussian distribution. To extend this result, the first and second moments of the underlying probability mass function will be determined. 2) The paper proves that the relationship between the underlying probability mass function and the marginal distribution of the intensity parameter is one-to-one when the parameter is a random variable. 3) Proof that a unique capacity-achieving distribution for the stimulus intensity exists in a discrete form with a finite number of mass points that varies with the energy budget. Then, the paper presents an algorithm that numerically computes a trade-off between the maximum transmit bits and the coding-duration energy expenditure. Subsequently, the optimal multiplier along with the optimal distribution and the corresponding capacity will be obtained. [ABSTRACT FROM AUTHOR]

A generalized framework of spectrum sensing disruption for a power-limited adversary is proposed in this paper. In the literature, a conventional sensing attack typically assumes that the adversary has perfect knowledge of the spectral usage status. The framework in this paper considers a more general case where there are uncertainties in the estimates at the adversary. These uncertainties are modeled utilizing the probability of detection and the probability of false alarm. Then, the sum of the conditional probabilities of false detection at the secondary within the spectral range of interest, conditioned on the adversary’s estimated spectrum usage status, is maximized. It is shown that the optimal sensing attack, given perfect estimation is a special case of the proposed framework. When the adversary has perfect spectrum usage information, this framework reduces to a previously demonstrated optimal sensing disruption. When the adversary has imperfect information on the spectral status, the proposed framework is significantly more robust than conventional sensing attacks. Further, when the adversary’s power budget increases, it asymptotically approaches the sensing disruption performance upper bound. [ABSTRACT FROM AUTHOR]

Visible light communications (VLC) has been studied thoroughly in recent years as an alternative or complementary technology to radio frequency communications. The reliability of VLC channels highly depends on the availability and alignment of line of sight links. In this paper, we study the effect of random receiver orientation for mobile users over VLC downlink channels, which affects the existence and quality of line of sight links. Based on the statistics of the receiver location and relative orientation with respect to the transmitter LED, we develop an analytical framework to characterize the statistical distribution of VLC downlink channels, which is then utilized to obtain the outage probability and the bit error rate. Our analysis is generalized for arbitrary distributions of relative orientation and location for a single transmitter, and extended to multiple transmitter case for some certain scenarios. Extensive Monte Carlo simulations show a perfect match between the analytical and the simulation data in terms of both the statistical channel distribution and the resulting bit error rate. Our results also characterize the channel attenuation due to random receiver orientation and location for various scenarios of interest. [ABSTRACT FROM AUTHOR]

Iterative decoding is an efficient error-correction tool based on the exchange of extrinsic probabilities between the constituent decoders. In this paper, the properties of the mutual information between the extrinsic LLR at the output of two constituent decoders are analyzed with application to turbo and LDPC codes. This is a bridge between information-theoretic analysis and practical implementations. It is proved here that the mutual information between extrinsics is a lower bound of the mutual information between each extrinsic and the transmitted message. In addition, an efficient online evaluation is provided in the paper with accuracy validated through numerical experiments. As an application, the mutual information between extrinsics is used for designing efficient stopping criterion and error detection rules at the decoder side. Two online methods for the estimation of optimal scaling factor to be applied to the extrinsic LLR are also derived. In contrast with most references, an analytical expression is obtained that does not require estimation of the actual transmitted bits. All results in the paper are derived for Gaussian distributed LLR with independent mean and variance. [ABSTRACT FROM PUBLISHER]

This paper analyzes the reliability and throughput of the telegram-splitting concept in the presence of inter-system and intra-system interference. Using telegram splitting, the data of one telegram is split into multiple sub-packets, which are then transmitted with temporal spacing. As a result, collisions with other telegrams destroy only some sub-packets that can be recovered using forward error correction. Consequently, the reliability of the data transmission in case of inter- and intra-system interference—a significant problem in license-exempt bands—is highly improved. In this paper, probabilities for the resulting telegram error rate of random media access systems using telegram splitting are derived. Furthermore, the achievable throughput is analyzed. The theoretical considerations in addition to simulation results prove the significant gain of the telegram-splitting concept for telemetry systems. [ABSTRACT FROM PUBLISHER]

The capacity of the AWGN broadcast channel is achieved by superposition coding, but the superposition of individual coded modulations expands the modulation alphabet and distorts its configuration. Hierarchical modulations, which appear in the literature mostly in the context of unequal error protection, can approach only a few isolated points on the boundary of the broadcast capacity region. This paper studies multilevel coding (MLC) for constellation-constrained broadcast-coded modulation. The conditions under which multilevel codes can achieve the constellation-constrained capacity of the AWGN broadcast channel are derived. We propose a pragmatic multilevel design technique with near-constellation-constrained-capacity performance where the coupling of the superposition inner and outer codes is localized to each bit-level. It is shown that this can be further relaxed to a code coupling on only one bit-level, with a little or no penalty under natural labeling. The rate allocation problem between the bit levels of the two users is studied and a pragmatic method is proposed, again with near-capacity performance. In further pursuit of lower complexity, a hybrid MLC-bit-interleaved-coded modulation is proposed, whose performance is shown to be very close to the boundary of the constellation-constrained capacity region. Simulation results show that good point-to-point LDPC codes produce excellent performance in the proposed coded modulation framework. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) assisted edge caching has been emerged as a promising solution to alleviate network congestion, which can provide users with their desired contents with reduced latency. For achieving effective UAV-assisted edge caching, how to jointly design the trajectory and caching strategy is critical, which, however, is not straightforward due to the heterogeneous and uncertain demand in the network. In this paper, aiming at maximizing the reduced delay brought by the UAV-assisted caching, we propose a proactive joint strategy on trajectory and caching for the UAV, where the demand uncertainty is particularly studied. Specifically, by regarding the demand on each content as a random variable, we formulate the strategy design as a risk-averse stochastic optimization problem to make the network performance guaranteed under certain confidence level. Different from most existing works assuming the perfect distributional information is available to deal with the uncertainty, we develop a data-driven approach based on the first and second order statistics to achieve a distributionally robust (DR) solution, which can make the strategy trustworthy with guaranteed network performance even though the specific distributional information is unknown. Simulation results have demonstrated the effectiveness of the proposed DR strategy. [ABSTRACT FROM AUTHOR]

Inverse, or negative binomial, sampling is often used when the observation of interest occurs extremely infrequently. As this is the case in bit error rate (BER) simulations, especially in high signal-to-noise ratio cases, negative binomial sampling can be advantageously employed in a computationally economic fashion to compare bit error rates between different systems. When the results of two negative binomial sampling tests are compared, point estimates and interval estimates quantify the performance relationship between the results of the tests. This paper derives a new, optimal, logarithmically symmetric confidence interval estimator for the ratio of BER estimates derived from two negative binomial tests. In addition, a three-sided hypothesis test with a single significance level is derived to quantify the confidence of the relationship between the two systems. Low-BER approximations for the confidence interval and decision thresholds are derived based on the F-distribution. The approximation is shown to work with BERs as high as 10^-2. An example inspired by bit interleaved coded modulation shows how the technique can be used to reduce simulation time by an order of magnitude and facilitate straightforward interpretation and comparison between different systems. Negative binomial sampling is recommended for comparison experiments where BER is the key metric. [ABSTRACT FROM PUBLISHER]

It is challenging to quantify the left tail behavior of the cumulative distribution function of a sum of lognormal random variables, especially when they are correlated and non-identically distributed. The mathematical intractability hampers the analysis on diversity receptions over lognormal fading channels. In this paper, a novel asymptotic technique is exploited to study non-identically distributed dual-branch lognormal fading channels with correlation. Closed-form asymptotic outage probability expressions are derived for maximum-ratio combining (MRC), equal-gain combining (EGC), and selection combining (SC). Three counterintuitive facts are revealed: 1) one of the two channels contributes no asymptotic performance gain to the diversity reception systems under certain conditions, implying that a link can be discarded without causing asymptotic performance loss; 2) SC outperforms EGC under certain conditions; and 3) the outage probability of SC is arbitrarily close to that of MRC under certain conditions. The new result reveals insights into the long-standing problem of asymptotic analysis for correlated lognormal fading channels, paving the way for analysis on more general channel models. This paper also provides an efficient approach to evaluate the non-identically distributed lognormal fading channels at a high signal-to-noise ratio. Practically, the analytical results prevent some redundant units that contribute no performance gain. [ABSTRACT FROM AUTHOR]

Multicasting can reduce network traffic in multiuser systems by serving multiple users simultaneously. The benefits of multicasting basically come from the overlapping and synchronism of user requests. Pushing and caching are techniques that prestore content items in buffers closer to users based on the prediction of user requests, thereby providing a promising approach to eliminating the asynchronism of user requests and creating multicasting opportunities. This paper studies a multiuser wireless communication system, in which the buffers for caching are deployed in the user terminals. Based on the request delay information (RDI) which describes when the users request content items in a deterministic or statistical way, a joint pushing and caching (JPC) method is presented to schedule the content items pushed by the base station and cached in the user buffers. In this paper, multicasting JPC systems work in two modes, without or with the feedback of RDI. Furthermore, RDI is classified into three forms, namely, noncausal, statistical, and causal. Static and dynamic JPC policies are, respectively, proposed for the two work modes. In addition, the effective throughput resulting from JPC under the three forms of RDI is demonstrated via theoretical analysis and simulation. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a tractable semi-analytical approach for the network performance analysis of uplink (UL) cellular networks, which is based on a deterministic network analysis using a Gaussian approximation (DNA-GA). The key contribution of this paper is to investigate the UL signal-to-interference ratio (SIR) performance using the DNA-GA analysis. In particular, the SIR is modeled as a ratio of two random variables (RVs), representing the signal power and the aggregate interference power, respectively. The signal power is further characterized by a product of two RVs, i.e., a lognormal RV and an RV with an arbitrary distribution. The former RV comes from a common assumption of lognormal shadow fading, and the latter one takes the rest of random factors into account, such as random user positions, arbitrary types of multi-path fading, and so on. The aggregate interference power is approximated by an RV with a power lognormal distribution. The proposed DNA-GA analysis has several desirable features: 1) it naturally considers lognormal shadow fading; 2) it can treat arbitrary shape and/or size of cell coverage areas; 3) it can handle non-uniform user distributions; 4) it can cope with any type of multi-path fading; and 5) it can be applied to multi-antenna base stations. These features make the DNA-GA analysis very useful for the network performance analysis of the 5th generation systems with general cell deployment and user distribution. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of designing good non-binary LDPC codes for Slepian-Wolf coding. The design method is based on Density Evolution which gives the asymptotic error probability of the decoder for given code degree distributions. Density Evolution was originally introduced for channel coding under the assumption that the channel is symmetric. In Slepian-Wolf coding, the correlation channel is not necessarily symmetric and the source distribution has to be taken into account. In this paper, we express the non-binary Density Evolution recursion for Slepian-Wolf coding. From Density Evolution, we then perform code degree distribution optimization using an optimization algorithm called differential evolution. Both asymptotic performance evaluation and finite-length simulations show the gain at considering optimized degree distributions for SW coding. [ABSTRACT FROM AUTHOR]

This paper considers an unlicensed multiple-access channel (MAC) that coexists with a licensed point-to-point user, following the underlay cognitive radio paradigm. We assume that every transceiver except the secondary base station has one antenna and that the primary user (PU) is protected by a minimum rate constraint. In contrast to the conventional assumption of proper Gaussian signaling, we allow the secondary users to transmit improper Gaussian signals, which are correlated with their complex conjugate. When the secondary base station performs zero-forcing, we show that improper signaling is optimal if the sum of the interference channel gains (in an equivalent canonical model) is above a certain threshold. Additionally, we derive an efficient algorithm to compute the transmission parameters that attain the rate region boundary for this scenario. The proposed algorithm exploits a single-user representation of the secondary MAC along with new results on the optimality of improper signaling in the single-user case when the PU is corrupted by an improper noise. [ABSTRACT FROM AUTHOR]

Enabled by new storage mediums, Computation-in-Memory is a novel architecture that has shown great potential in reducing the burden of massive data processing by bypassing the communication and memory access bottleneck. Suggested by Cassuto and Crammer, allowing for ultra-fast Hamming distance computations to be performed in resistive memory with low-level conductance measurements has the potential to drastically speed up many modern machine learning algorithms. Meanwhile, Hamming distance Computation-in-Memory remains a challenging task as a result of the non-negligible device variability in practical resistive memory. In this paper, build upon the work of Cassuto and Crammer, we study memristor variability due to two distinct sources: resistance variation, and the non-deterministic write process. First, we introduce a technique for estimating the Hamming distance under resistance variation alone. Then, we propose error-detection and error-correction schemes to deal with non-ideal write process. We then combine these results to concurrently address both sources of memristor variabilities. In order to preserve the low latency property of Computation-in-Memory, all of our approaches rely on only a single vector-level conductance measurement. We use so-called inversion coding as a key ingredient in our solutions and we prove the optimality of this code given the restrictions on bit-accessible information. Finally, we demonstrate the efficacy of our approaches on the k-nearest neighbors classifier. [ABSTRACT FROM AUTHOR]

This paper derives the asymptotic distribution of the normalized $k$ -th maximum order statistics of a sequence of non-central chi-square random variables with non-identical non-centrality parameters. We demonstrate the utility of these results in characterizing the signal-to-noise ratio in three different applications in wireless communication systems where the statistics of the $k$ -th maximum channel power over Rician fading links are of interest. Furthermore, we derive simple expressions for the asymptotic outage probability, average throughput, achievable throughput, and average bit error probability. The proposed results are validated via extensive Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

This paper considers a cell-free massive multiple-input multiple-output network (cfm-MIMO) with a massive number of access points (APs) distributed across an area to deliver information to multiple users. Based on only local channel state information, conjugate beamforming is used under both proper and improper Gaussian signalings. To accomplish the mission of cfm-MIMO in providing fair service to all users, the problem of power allocation to maximize the geometric mean (GM) of users’ rates (GM-rate) is considered. A new scalable algorithm, which iterates linear-complex closed-form expressions and thus is practical regardless of the scale of the network, is developed for its solution. The problem of quality-of-service (QoS) aware network energy-efficiency is also addressed via maximizing the ratio of the GM-rate and the total power consumption, which is also addressed by iterating linear-complex closed-form expressions. Intensive simulations are provided to demonstrate the ability of the GM-rate based optimization to achieve multiple targets such as a uniform QoS, a good sum rate, and a fair power allocation to the APs. [ABSTRACT FROM AUTHOR]

We provide nonasymptotic upper and lower bounds on the sum-rate capacity of Rayleigh block-fading multiple-access channels for the set up where a priori channel state information is not available. The upper bound relies on a dual formula for channel capacity and on the assumption that the users can cooperate perfectly. The lower bound is derived assuming a noncooperative scenario where each user employs unitary space-time modulation (independently from the other users). Numerical results show that the gap between the upper and the lower bound is small already at moderate SNR values. This suggests that the sum-rate capacity gains obtainable through user cooperation are minimal for the scenarios considered in the paper. [ABSTRACT FROM PUBLISHER]

In this paper, fast fading Gaussian multiuser channels are considered. If the channel state information (CSI) is perfectly known to the transmitter, capacities have been derived for many cases in which the channels satisfy certain information-theoretic orders such as degradedness, or strong/very strong interference. We study the case when only the statistics of the CSI are known at the transmitter, which is an open problem in general. The main contributions of this paper are the following: First, we derive a sufficient condition to construct equivalent (having the same capacity region as the original channel) degraded Gaussian broadcast channels, which is based on the marginal distributions of the channel gains. To achieve this goal, we leverage three schemes: coupling, maximal coupling, and copulas, in addition to the same marginal property. The underlying idea of all schemes is to obtain an equivalent channel by changing the joint distribution in such a way that it satisfies a certain information-theoretic order while ensuring that the marginal distributions of the channels to different users are not changed. The construction of this equivalent multiuser channel allows us to directly apply existing capacity results. We then further derive the capacity regions of Gaussian interference channels with strong and very strong interferences, and also the secrecy capacity of Gaussian wiretap channels, while in all cases, the transmitters know only the statistics of the channels. Several practical examples such as Rayleigh fading and Nakagami- ${m}$ fading illustrate the applicability of the derived results. [ABSTRACT FROM AUTHOR]

Most existing random access schemes for machine-type communications (MTC) simply adopt a uniform preamble selection distribution, irrespective of the underlying device activity distributions. Hence, they may yield unsatisfactory access efficiency. In this paper, we model device activities for MTC as multiple Bernoulli random variables following an arbitrary multivariate Bernoulli distribution which can reflect both dependent and independent device activities. Then, we optimize preamble selection and access barring for random access in MTC according to the underlying joint device activity distribution. Specifically, we investigate three cases of the joint device activity distribution, i.e., the cases of perfect, imperfect, and unknown joint device activity distributions, and formulate the average, worst-case average, and sample average throughput maximization problems, respectively. The problems in the three cases are challenging nonconvex problems. In the case of perfect joint device activity distribution, we develop an iterative algorithm and a low-complexity iterative algorithm to obtain stationary points of the original problem and an approximate problem, respectively. In the case of imperfect joint device activity distribution, we develop an iterative algorithm and a low-complexity iterative algorithm to obtain a Karush-Kuhn-Tucker (KKT) point of an equivalent problem and a stationary point of an approximate problem, respectively. Finally, in the case of unknown joint device activity distribution, we develop an iterative algorithm to obtain a stationary point. The proposed solutions are widely applicable and outperform existing solutions for dependent and independent device activities. [ABSTRACT FROM AUTHOR]

In this paper, we consider a decode-and-forward-based wireless multihop network with a single source node, a single destination node, and $N$ intermediate nodes. To increase the spectral efficiency and energy efficiency of the system, we propose a cooperative multihop communication protocol with spatial reuse, in which interference is treated as noise or can be canceled. The performance of a spatial-reused space–time-coded cooperative multihop network is analyzed over Rayleigh fading channels. In particular, the exact closed-form expression for the outage probability at the $n$th receiving node is derived when there are multiple interference sources over non-i.i.d. Rayleigh fading channels. Furthermore, the outage probability expressions are derived when nodes are equipped with more than one antenna. In addition, to reduce the effect of interference on multihop transmission, we propose a simple power control scheme that is only dependent on the statistical knowledge of channels. In the second approach for managing the interference, linear interference cancelation schemes are employed for both noncooperative and cooperative spatial-reused multihop transmissions. Finally, the analytic results were confirmed by simulations. Simulation results show that the spatial-reused multihop transmission outperforms the interference-free multihop transmission in terms of energy efficiency in low- and medium-signal-to-noise scenarios. [ABSTRACT FROM PUBLISHER]

In this paper we analyze design aspects of a single-carrier digital communications receiver in the presence of impulsive noise. We use the additive white symmetric $\alpha$-stable noise (\AWS\alpha\SN) to model the channel noise. By introducing passband sampling, efficient constellations and suitable baseband detectors, we show that the uncoded error performance of the conventional (linear) receiver can be enhanced given the real and imaginary components of the transmitted symbol are decoded separately. The performance may be improved further by sacrificing the linearity of the system. Various non-linear estimation and joint-detection schemes are discussed and their error performance analyzed. It is shown that if the receiver bandwidth is large enough, impulsive noise may be effectively countered in a single-carrier communications system. [ABSTRACT FROM AUTHOR]

The meta distribution of the signal-to-interference-ratio (SIR) is an important performance indicator for wireless networks because, for ergodic point processes, it describes the fraction of scheduled links that achieve certain reliability, conditionally on the point process. In this paper, we calculate the moments of the meta distribution in vehicular ad hoc networks (VANETs) along high-speed motorways. Due to the high speeds, the drivers maintain large safety distances, and the Poisson point process (PPP) becomes a poor deployment model. Because of that, we model the distribution of inter-vehicle distance equal to the sum of a constant hardcore distance and an exponentially distributed random variable. We design a novel discretization model for the locations of vehicles which can be used to approximate well the meta distribution of the SIR due to the hardcore process. We validate the model against synthetic motorway traces. On the other hand, the PPP overestimates significantly the coefficient-of-variation of the meta distribution due to the hardcore process, and its predictions fail. In addition, we show that the calculation of the meta distribution becomes especially meaningful in the upper tail of the SIR distribution. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of error-free communication over the i.i.d. duplication channel which acts on a transmitted sequence $x_{1} \cdots x_{n}$ by inserting a random number of copies of each symbol $x_{i}$ next to the original symbol. The random variables representing the numbers of inserted copies at each position $i$ are independent and take values in $\{0, 1, \ldots, r\}$ , where $r$ is a fixed parameter. A more general model in which blocks of $\ell $ consecutive symbols are being duplicated, and which is inspired by DNA-based data storage systems wherein the stored molecules are subject to tandem-duplication mutations, is also analyzed. A construction of optimal codes correcting all patterns of errors of this type is described, and the zero-error capacity of the duplication channel—the largest rate at which information can be transmitted through it in an error-free manner—is determined for each $\ell $ and $r$. [ABSTRACT FROM AUTHOR]

Cellular wireless systems rely on frame-based transmissions. The frame design is conventionally based on heuristics, consisting of a frame header and a data part. The frame header contains control information that provides pointers to the messages within the data part. In this paper, we revisit the principles of frame design and show the impact of the new design in scenarios that feature short data packets, which are central to various 5G and Internet of Things applications. We treat framing for downlink transmission in an AWGN broadcast channel with $K$ users, where the sizes of the messages to the users are random variables. Using approximations from finite blocklength information theory, we establish a framework in which a message to a given user is not necessarily encoded as a single packet, but may be grouped with messages to other users and benefit from the improved efficiency of longer codes. This requires changes in the way control information is sent, and it requires that the users need to spend power decoding other messages, thereby increasing the average power consumption. We show that the common heuristic design is only one point on a curve that represents the tradeoff between latency and power consumption. [ABSTRACT FROM PUBLISHER]

The detection of a weak signal in additive Cauchy noise is of great importance in many applications. A locally optimum detector (LOD) exists for such a scenario; however, it is non-linear in nature. In general, implementation of non-linear detectors is difficult in practice, and linear detectors with good properties, such as high asymptotic relative efficiency (ARE) with respect to the LOD, are often desirable. In this paper, we propose a linear detector for a weak signal in additive Cauchy noise. The proposed test statistic is a linear combination of order statistics. For the special case of a constant signal in additive Cauchy noise, we prove the asymptotic normality of the trimmed linear detector, and show that the ARE of the trimmed linear detector with respect to the LOD is unity. Extensive simulation results are provided to demonstrate that the loss in the performance of the linear detector is very small compared with the non-linear LOD. We also discuss the hardware complexities of the LOD and the linear detector, and demonstrate the advantages of the linear detector over the LOD, in terms of hardware implementation. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of designing asymptotically optimal improper constellations with a given circularity coefficient (correlation coefficient between the constellation and its complex conjugate). The designed constellations are optimal in the sense that, at high signal-to-noise-ratio (SNR) and for a large number of symbols, yield the lowest probability of error under an average power constraint for additive white Gaussian noise channels. As the number of symbols grows, the optimal constellation is the intersection of the hexagonal lattice with an ellipse whose eccentricity determines the circularity coefficient. Based on this asymptotic result, we propose an algorithm to design finite improper constellations. The proposed constellations provide significant SNR gains with respect to previous improper designs, which were generated through a widely linear transformation of a standard $M$ -ary quadrature amplitude modulation constellation. As an application example, we study the use of these improper constellations by a secondary user in an underlay cognitive radio network. [ABSTRACT FROM AUTHOR]

In this paper, the sum rate of opportunistic interference alignment (OIA) is analyzed in multiple-input-multiple-output interfering broadcast channels. The alignment metric upon which users are scheduled is based on the chordal distance between certain interfering subspaces at each receiver, and the closed-form expressions for the rates of the scheduled users are derived. Furthermore, we show that for a system in which each user has $N$ receive antennas and the $j$ th base station transmits $d_{j}$ data streams, where $\sum _{j=1}^{I}d_{j}={N}+1$ and ${N}\ge 2$ , the rate for each user can be approximated by the mean of a Gumbel random variable. Further analysis reveals that if the number of users in cell $i$ scales as $\rho ^{\alpha }$ , where $\rho $ is the normalized transmit power and $\alpha \in [{0,1}]$ , then cell $i$ can achieve $\alpha d_{i}$ degrees of freedom. The simulation results confirm the validity of the theoretical analysis and the accuracy of the approximation. Thus, the sum rate analysis provided herein is an effective performance evaluation method for multi-cell OIA. [ABSTRACT FROM AUTHOR]

In ultra-dense networks (UDNs), the dense deployment of base stations (BSs) is facing challenges due to the pronounced unbalanced traffic loads, severe inter-cell interference, and uncertain traffic demands. In this paper, we tame traffic uncertainty for the joint optimization of BS activation and user association in UDNs to mitigate interference and balance traffic loads among BSs. Specifically, we address the traffic uncertainty by using chance constraint programming with the known first- and second-order statistics of the uncertain traffic. We formulate the joint BS activation and user association problem as a mixed integer non-linear programming problem, which is then decomposed into a set of user association sub-problems by modeling the BS states (active or idle) as a Markov chain. We solve the user association sub-problem at each BS state by transforming it into a convex problem over the positive orthant. In particular, at each BS state, the candidate serving BSs that lead to the optimal load balancing performance are identified for each user and parts of the user’s traffic are offloaded to the identified BSs. Based on the obtained solutions, we propose a distributed near-optimal BS activation and user association scheme. Numerical results demonstrate that our proposed scheme is more robust to traffic uncertainty and provides better load-balancing performance than the existing schemes. [ABSTRACT FROM AUTHOR]

An information source generates independent and identically distributed status update messages from an observed random phenomenon which takes $n$ distinct values based on a given probability mass function (PMF). These update packets are encoded at the transmitter node to be sent to a receiver node which wants to track the observed random variable with as little age as possible. The transmitter node implements a selective $k$ encoding policy such that rather than encoding all possible $n$ realizations, the transmitter node encodes the most probable $k$ realizations. We consider three different policies regarding the remaining $n-k$ less probable realizations: highest $k$ selective encoding which disregards whenever a realization from the remaining $n-k$ values occurs; randomized selective encoding which encodes and sends the remaining $n-k$ realizations with a certain probability to further inform the receiver node at the expense of longer codewords for the selected $k$ realizations; and highest $k$ selective encoding with an empty symbol which sends a designated empty symbol when one of the remaining $n-k$ realizations occurs. For all of these three encoding schemes, we find the average age and determine the age-optimal real codeword lengths, including the codeword length for the empty symbol in the case of the latter scheme, such that the average age at the receiver node is minimized. Through numerical evaluations for arbitrary PMFs, we show that these selective encoding policies result in a lower average age than encoding every realization, and find the corresponding age-optimal $k$ values. Since we focus on real-valued codeword lengths in this paper, the resulting age value obtained in each case studied here serves as a lower bound to what can be attained by integer-valued codeword lengths in that case. [ABSTRACT FROM AUTHOR]

This paper considers noisy Wyner–Ziv coding (WZC), in which a remote noisy source is compressed with side information at the decoder only. The decoder side information is not limited to being Gaussian, thus this noisy WZC problem cannot be transformed into the conventional one tailored for noiseless sources. A new coding structure, named the residual-quantization (RQ) based noisy WZC, is proposed to solve the problem. This scheme explicitly constructs the theoretical auxiliary random variable to facilitate optimal reconstruction of the noisy source. In this two-stage encoder, the noisy source is quantized twice and the quantization error (residue) of the first stage is the input of the second stage. By sending only the quantization index of the second stage to the decoder, the corresponding code rate can theoretically approach the noisy WZC bound. Moreover, the RQ-based noisy WZC is implemented using graph-based codes. The main challenge is that it is necessary to design a codebook that is simultaneously good for source and channel coding for the first stage quantization, since this quantization code also acts as a channel code at the decoder. This problem is solved by constructing a low-density parity check (LDPC) code with edge degrees optimized for channel coding, and enhancing its performance for source coding by using a modified reinforced belief-propagation quantization algorithm. Simulation results show that the noisy Wyner-Ziv bounds can be practically approached by our implementation. In addition, the proposed implementation offers more flexibility in the code rates compared with the existing practical designs, making it more suitable for emerging applications such as fronthaul compression. [ABSTRACT FROM AUTHOR]

This paper analyzes the high-order moments of linearly modulated single-carrier as well as orthogonal frequency-division multiplexing (OFDM) signals assuming that the modulated symbols that form resulting continuous signal waveform are statistically independent. For a single-carrier case with the ideal low-pass filter used for pulse shaping, closed-form expressions of the moments with up to the eighth-order are derived. The proposed approach is then extended to Gaussianity assessment of OFDM signals; we show how the moments of OFDM signals can approach to those of Gaussian distribution as the number of subcarriers increases for a given modulation constraint on each subcarrier. Also reported is the peculiar nonstationary behavior of the fourth-order moments in the case of BPSK modulated OFDM signals. [ABSTRACT FROM AUTHOR]

While cooperative spectrum sensing improves sensing reliability when secondary users (SUs) have independent measurements, these gains are limited in the presence of correlated or malicious SUs. In this paper, we propose algorithms to improve spectrum sensing performance when the system contains both honest and malicious SUs all of whom may be experiencing correlated fading channels. We show that, when the SUs’ reports at each time slot are independent and identically distributed, it is impossible to distinguish between malicious collaboration, i.e., collusion, and correlations caused by the environment. Thus, the optimal test statistic for cooperative spectrum sensing depends on the correlations in the SU reports but not on the source of these correlations. We propose two algorithms: one to identify SUs whose reports are correlated and another to infer the spectrum occupancy by using the learned structure of correlations. Groups of SUs whose reports are correlated are identified by learning the structure of the underlying Bayesian network model. This structure is implemented as a factor graph to infer the spectrum occupancy using a loopy belief propagation algorithm. We derive an upper bound of the error probability of the proposed structure learning algorithm and prove convergence of our loopy belief propagation algorithm. [ABSTRACT FROM AUTHOR]

Assuming a network of infinite extent, several researchers have analyzed small-cell networks using a Poisson point process (PPP) location model, leading to simple analytic expressions. The general assumption has been that these results apply to finite-area networks as well. However, do the results of infinite-area networks apply to finite-area networks? In this paper, we answer this question by obtaining an accurate approximation for the achievable signal-to-interference-plus-noise ratio (SINR) and user capacity in the downlink of a finite-area network with a fixed number of access points (APs). The APs are uniformly distributed within the area of interest. Our analysis shows that, crucially, the results of infinite-area networks are very different from those for finite-area networks of low-to-medium AP density. Comprehensive simulations are used to illustrate the accuracy of our analysis. For practical values of signal transmit powers and AP densities, the analytic expressions capture the behavior of the system well. As an added benefit, the formulations developed here can be used in parametric studies for network design. Here, the analysis is used to obtain the required number of APs to guarantee a desired target capacity in a finite-area network. [ABSTRACT FROM AUTHOR]

To enhance the multiplexing gain of two-receiver Multiple-Input-Single-Output Broadcast Channel with imperfect channel state information at the transmitter (CSIT), a class of Rate-Splitting (RS) approaches has been proposed recently, which divides one receiver's message into a common and a private part, and superposes the common message on top of Zero-Forcing precoded private messages. In this paper, with quantized CSIT, we study the ergodic sum rate of two schemes, namely RS-S and RS-ST, where the common message(s) are transmitted via a space and space-time design, respectively. Firstly, we upper-bound the sum rate loss incurred by each scheme relative to Zero-Forcing Beamforming (ZFBF) with perfect CSIT. Secondly, we show that, to maintain a constant sum rate loss, RS-S scheme enables a feedback overhead reduction over ZFBF with quantized CSIT. Such reduction scales logarithmically with the constant rate loss at high Signal-to-Noise-Ratio (SNR). We also find that, compared to RS-S scheme, RS-ST scheme offers a further feedback overhead reduction that scales with the discrepancy between the feedback overhead employed by the two receivers when there are alternating receiver-specific feedback qualities. Finally, simulation results show that both schemes offer a significant SNR gain over conventional single-user/multiuser mode switching when the feedback overhead is fixed. [ABSTRACT FROM AUTHOR]

This paper derives the distribution of the maximum end-to-end (e2e) signal to noise ratio (SNR) in an opportunistic relay selection based cooperative relaying network having a large number of non-identical relay links. The source is simultaneous wireless information and power transfer enabled, and the relays are capable of both time splitting (TS) and power splitting (PS) based energy harvesting (EH). Contrary to the majority of literature in communication, which uses extreme value theory (EVT) to derive the statistics of extremes of sequences of independent and identically distributed random variables (RVs), we demonstrate how EVT can be used to derive the maximum statistics of sequences of independent and non-identically distributed normalised SNR and hence the distribution of the maximum SNR. Using these results, we derive simple expressions for evaluating the outage capacity, and achievable throughput at the destination. Finally, we present the utility of the results for deciding the optimum TS and PS factors of the hybrid EH relays that maximise outage capacity, and achievable throughput at the destination. Furthermore, we establish the stochastic ordering of the e2e SNR, which in turn allows the characterisation of the variations in the e2e performance with respect to the variations in different system parameters. [ABSTRACT FROM AUTHOR]

In this paper, we investigate multi user chirp spread spectrum with noncoherent detection as a continuation of our work on coherent detection. We derive the analytical bit error ratio (BER) expression for binary chirp spread spectrum (BCSS) in the presence of multiple access interference (MAI) caused by correlation with other user signals because of either asynchronism or Doppler shifts, or both, and validate with simulations. To achieve this we analyze the signal cross correlations, and compare traditional linear chirps with our recently-proposed nonlinear chirps introduced and with other nonlinear chirps from the literature. In doing so we illustrate the superior performance of our new nonlinear chirp designs in these practical conditions, for the noncoherent counterpart. [ABSTRACT FROM AUTHOR]

Security-oriented error-detecting codes are used to detect fault injection attacks on cryptographic devices. These codes are usually designed for uniformly distributed codewords, i.e., for codes that have maximal entropy. In practice, the codewords are not uniformly distributed; thus, their entropy is smaller and their efficiency in detecting attacks degrades. This paper analyzes the relation between the entropy of a code and its worst error masking probability. Based on this relation, a method for determining the rate and structure of a code that provides the required error masking probability is presented. [ABSTRACT FROM AUTHOR]

The channel capacity for the multiple-input multiple-output (MIMO) relay channel is still an open problem. In view of this gap, upper and lower bounds were found by Wang et al., 2005. This paper presents an extensive study of mutual information and outage probability of Rayleigh fading MIMO relay channels based on the eigenvalues distribution for the sum of complex Wishart matrices. Interestingly, for certain scenarios, upper and lower bounds are coincident providing the real channel capacity. To compute outage probability, a Gaussian approximation for mutual information has been used. Furthermore, it was proposed an equivalent distribution for the sum of Wishart matrices, which proves to be excellent in all scenarios. Closed-form expressions for probability density function and outage probability have been found for the general case of any number of antennas and any SNR values. Analytical expressions have been validated by means of Monte Carlo simulations. [ABSTRACT FROM PUBLISHER]

This paper initially presents the channel modeling for full duplex (FDX) modulating retro-reflector (MRR) free-space optical (FSO) systems under weak-to-strong turbulence conditions. The uplink channel in the MRR FSO communication is a conventional FSO channel that is typically modeled by a Gamma–Gamma ($\Gamma \Gamma $) distribution. The downlink channel is jointly affected by the turbulence-induced fading in two opposite passes as well as the reflection effect of the MRR, and its normalized channel fading can be described as the product of two correlated $\Gamma \Gamma $ random variables (RVs). The channel model of the downlink is approximated by a single $\alpha $ - $\mu $ distribution via moment matching method. Then, we propose an effective signal modulation and detection scheme to achieve the FDX communication. The bit error rates of the MRR FSO systems are calculated by the analytical formulas that are derived based on the channel models of the two links, and also evaluated by the Monte-Carlo simulations to validate the derived formulas. The effect of system configurations and propagation conditions on the system performance is investigated, and the optimal designs to make a trade-off between the uplink and downlink performance are discussed. [ABSTRACT FROM AUTHOR]

Information inequalities can be used to derive the fundamental limits of information systems. Many information inequalities and problem-specific constraints are linear equalities or inequalities of joint entropies, and thus, outer bounding the fundamental limits can be viewed as and in principle computed through linear programming. However, for many practical engineering problems, the resultant linear program (LP) is very large, rendering such a computational approach almost completely inapplicable in practice. It was shown recently that symmetry can be used to effectively reduce the scale of the LP; however, the precise amount of reduction was not well understood. In this paper, we provide a method to pinpoint this reduction by counting the number of orbits induced by the symmetry on the set of the LP variables and the LP constraints, respectively. The Pólya counting theorem is a powerful tool for such counting task, which requires identifying the cycle index function. We propose a generic three-layer decomposition of the group structures for the quantities in typical information systems to facilitate such a calculation. Three problems are studied using this approach: extremal pairwise cyclically symmetric entropy inequalities, the regenerating code problem, and the caching problem, for which explicit formulas are provided for the cycle indices of the induced permutations. [ABSTRACT FROM AUTHOR]

The Beckmann distribution has a wide range of applications in radio-frequency communications, free-space optical (FSO) communications, and underwater wireless optical communications (UWOC). However, the cumulative distribution function (cdf) of the Beckmann random variable (RV) does not have a closed-form expression, which makes it challenging to derive analytical solutions for the outage probability of systems involving Beckmann RVs. In this paper, we study the generalized Beckmann distribution, which includes the Beckmann, Rayleigh, Rician, Nakagami- $m$ , Hoyt, $\kappa $ - $\mu $ , $\eta $ - $\mu $ , single-sided Gaussian, and the Beaulieu-Xie distributions as special cases. Three approaches are proposed to estimate the cdf of the generalized Beckmann distribution, including closed-form upper and lower cdf bounds, single-fold integration based on the closed-form characteristic function, and a left-tail cdf approximation. We compare the three approaches in terms of the ranges of applications and the computation time complexity. Based on the new cdf estimation techniques, one can efficiently evaluate the outage probabilities of pointing-error-limited FSO systems, UWOC systems, and maximum-ratio combining over arbitrarily correlated generalized Beckmann channels. [ABSTRACT FROM PUBLISHER]

Asymptotic analysis plays an important role in the performance evaluation and technology design of large multiple-input multiple-output (MIMO) systems. This paper investigates an asymptotic property of large MIMO systems, called distance hardening. It involves distance metrics defined in terms of many factors, such as the channel matrix and the difference matrix of two arbitrary transmitted codewords. The asymptotic property is revealed by the use of the Chernoff bounding method and expressed in an analogous way to the well-known channel hardening. More specifically, we first derive Chernoff bounds on the tail probabilities of distance metrics, in which we are interested. Then, we formulate the asymptotic property based on a generalized definition of the hardening phenomena by referring to a formal definition of the channel hardening. The distance hardening is shown to have implications for space-time code design, MIMO detection, and millimeter wave MIMO systems. Numerical simulations are conducted to verify our theoretical development. [ABSTRACT FROM AUTHOR]

Inter-cell interference (ICI) is a significant cause of errors in flash memories. In single-level cell (SLC) flash memory, ICI arises when 1 0 1 patterns are programmed either in the horizontal or vertical directions. Since data pages are written sequentially in horizontal wordlines, one can mitigate the effects of horizontal ICI by applying conventional constrained codes that forbid the 1 0 1 pattern. This approach does not address the problem of vertical ICI, however. In this paper, a row-by-row coding technique that eliminates vertical 1 0 1 patterns while preserving the sequential wordline programming order is presented. This scheme, though efficient, necessarily suffers a rate loss of almost 20%. We therefore propose another coding scheme, combining a weak constraint on vertical 1 0 1 patterns with a systematic error-correcting code, that can mitigate vertical ICI errors while achieving a higher overall coding rate, provided that the vertical ICI error probability is sufficiently small. Some extensions for multi-level cell (MLC) flash memory are discussed as well. [ABSTRACT FROM PUBLISHER]

It is of central interest in information theory to determine whether a given vector in the entropy space is an almost entropic vector. This problem can be answered if all the information inequalities are known, but this is an extremely challenging problem. On the other hand, we can establish that a given vector is an entropy vector if we can show the existence of distribution such that the corresponding entropy vector is the same as the given vector. However, there is no known algorithm to solve this problem. Only for the simplest case of binary entropy vectors, an algorithm is known to solve this problem. In this paper, we present a recursive algorithm to determine whether a given vector is a quasi-uniform entropy vector and, if it is, to return a consistent quasi-uniform distribution. We also present two applications of the recursive procedure: (i) to generate all quasi-uniform distributions motivated by the problem of finding the smallest quasi-uniform distribution such that its entropy vector violates the well known Ingleton inequality and (ii) to obtain an entropy vector (not necessarily quasi-uniform) near to a target vector in the entropy space for random variables with given alphabet size. [ABSTRACT FROM AUTHOR]

In the fields of information theory and network coding, a complete characterization of the almost entropic region is a fundamental but inherently difficult open problem. This paper focuses on the characterization of this region via explicit inner bounds, optimization of functions over this region, and network code construction. One approach to study the entropic region is to put a constraint on the alphabets for involved random variables, enabling us to focus on a specific set of joint distributions. We present the notion of alphabet constrained entropic set and describe its properties, namely, its a closed and path-connected set. Motivated by the properties, a random local search algorithm is designed to find an entropic vector and associated distribution near to a given target vector and extended to optimize functions of joint entropies. We present significantly improved inner bound compared to known inner bounds for the almost entropic region involving four random variables using a refinement of our algorithm. Finally, another application of the algorithm is shown to construct network codes and index codes for a given rate vector in the capacity region. This approach to constructing network codes is novel and has an entirely different flavor than classical coding theoretic construction. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the two-way communication between two users assisted by a reconfigurable intelligent surface (RIS). The scheme that two users communicate simultaneously over Rayleigh fading channels is considered. The channels between the two users and RIS can either be reciprocal or non-reciprocal. For reciprocal channels, we determine the optimal phases at the RIS to maximize the signal-to-interference-plus-noise ratio (SINR). We then derive exact closed-form expressions for the outage probability and spectral efficiency for single-element RIS. By capitalizing the insights obtained from the single-element analysis, we introduce a gamma approximation to model the product of Rayleigh random variables which is useful for the evaluation of the performance metrics in multiple-element RIS. Asymptotic analysis shows that the outage decreases at $\left ({\log (\rho)/\rho }\right)^{L}$ rate where $L$ is the number of elements, whereas the spectral efficiency increases at $\log (\rho)$ rate at large average SINR $\rho $. For non-reciprocal channels, the minimum user SINR is targeted to be maximized. For single-element RIS, closed-form solution is derived whereas for multiple-element RIS the problem turns out to be non-convex. The latter one is solved through semidefinite programming relaxation and a proposed greedy-iterative method, which can achieve higher performance and lower computational complexity, respectively. [ABSTRACT FROM AUTHOR]

This paper presents a novel systematic methodology to obtain new simple and tight approximations, lower bounds, and upper bounds for the Gaussian $Q$ -function, and functions thereof, in the form of a weighted sum of exponential functions. They are based on minimizing the maximum absolute or relative error, resulting in globally uniform error functions with equalized extrema. In particular, we construct sets of equations that describe the behaviour of the targeted error functions and solve them numerically in order to find the optimized sets of coefficients for the sum of exponentials. This also allows for establishing a trade-off between absolute and relative error by controlling weights assigned to the error functions’ extrema. We further extend the proposed procedure to derive approximations and bounds for any polynomial of the $Q$ -function, which in turn allows approximating and bounding many functions of the $Q$ -function that meet the Taylor series conditions, and consider the integer powers of the $Q$ -function as a special case. In the numerical results, other known approximations of the same and different forms as well as those obtained directly from quadrature rules are compared with the proposed approximations and bounds to demonstrate that they achieve increasingly better accuracy in terms of the global error, thus requiring significantly lower number of sum terms to achieve the same level of accuracy than any reference approach of the same form. [ABSTRACT FROM AUTHOR]

This paper provides a complete characterization of the boundary of an achievable rate region, called the Pareto boundary, of the single-antenna Z interference channel (Z-IC), when interference is treated as noise and users transmit complex Gaussian signals that are allowed to be improper. By considering the augmented complex formulation, we derive a necessary and sufficient condition for improper signaling to be optimal. This condition is stated as a threshold on the interference channel coefficient, which is a function of the interfered user rate and which allows insightful interpretations into the behavior of the achievable rates in terms of the circularity coefficient (i.e., degree of impropriety). Furthermore, the optimal circularity coefficient is provided in closed form. The simplicity of the obtained characterization permits interesting insights into when and how improper signaling outperforms proper signaling in the single-antenna Z-IC. We also provide an in-depth discussion on the optimal strategies and the properties of the Pareto boundary. [ABSTRACT FROM AUTHOR]

Freshness of status update packets is essential for enabling services where a destination needs the most recent measurements of various sensors. In this paper, we study the information freshness of single-server multi-source queueing models under a first-come first-served (FCFS) serving policy. In the considered model, each source independently generates status update packets according to a Poisson process. The information freshness of the status updates of each source is evaluated by the average age of information (AoI). We derive an exact expression for the average AoI for the case with exponentially distributed service time, i.e., for a multi-source M/M/1 queueing model. Moreover, we derive three approximate expressions for the average AoI for a multi-source M/G/1 queueing model having a general service time distribution. Simulation results are provided to validate the derived exact average AoI expression, to assess the tightness of the proposed approximations, and to demonstrate the AoI behavior for different system parameters. [ABSTRACT FROM AUTHOR]