Distributed systems operate under storage access and download service uncertainty. We consider two access models. In one, a user can access each storage node with a fixed probability, and in the other, a user can access any fixed-size subset of nodes. We consider two download service models. In the first (small file) model, the time to transmit file data is negligible compared to the overall average download time. In the second (large file) model, the download time scales with the amount of downloaded data. The performance metric is the system’s service rate. For a fixed redundancy level, the systems’ service rate depends on the allocation of coded chunks over the storage nodes. Since finding the general optimal allocation is prohibitively hard, we consider quasi-uniform allocations, where coded content is equally spread among a subset of nodes. The question we address asks what the size of this subset (spreading) should be. We show that concentrating the coded content to a minimum-size subset is universally optimal for the small file model. However, for the large file model, the optimal spreading depends on the system parameters. These conclusions hold for both access models.

In an Internet of Things (IoT) network, heterogeneous users with different priorities and service requirements will co-exist. This makes scheduling access to the shared communication medium a major challenge. To tackle this challenge, we consider the application of irregular repetition slotted ALOHA (IRSA), one of the best-performing random access protocols for homogeneous networks, for a heterogeneous multiclass IoT network. To this end, centralized and distributed implementations of the IRSA for multiclass IoT networks is proposed. Then, we focus on finding the network performance boundaries by studying the set of feasible throughput values for each class achieved via IRSA, called the capacity region. In addition to identifying the capacity region, the average and maximum delay of the users' packet delivery for both centralized and distributed IRSA are investigated. Our throughput and delay analysis reveals that the performance of distributed IRSA achieves that for the centralized implementation as the number of users increases. Further, we use our capacity region analysis to find the optimal IRSA strategy that maximizes the weighted sum-throughput of the network.

The ever increasing demands for mobile network access have resulted in a significant increase in bandwidth usage. By improving the system spectral efficiency, multi-way relay networks (MWRNs) provide promising approaches to address this challenge. In this paper, we propose a novel linear beamforming design, namely partial zero-forcing (PZF), for MWRNs with a multiple-input-multiple-output relay. Compared to zero-forcing (ZF), PZF relaxes the constraints on the relay beamforming matrix such that only partial user-interference, instead of all, is canceled at the relay. The users eliminate the remaining interferences through self-interference and successive interference cancellation. A sum-rate maximization problem is formulated and solved to exploit the extra degrees-of-freedom resulted from PZF. Simulation results show that the proposed PZF relay beamforming design achieves significantly higher network sum-rates than the existing linear beamforming designs.

Distributed storage systems (DSSs) provide a scalable solution for reliably storing massive amounts of data coming from various sources. Heterogeneity of these data sources often means different data classes (types) exist in a DSS, each needing a different level of quality of service (QoS). As a result, efficient data storage and retrieval processes that satisfy various QoS requirements are needed. This paper studies storage allocation, meaning how the data of different classes is spread over storage nodes, for a multi-class DSS. More specifically, assuming a probabilistic access to the storage nodes, we aim at maximizing the weighted sum of the probability of successful data recovery of data classes, when for each class a minimum QoS (probability of successful recovery) is guaranteed. Solving this optimization problem for a general setup is intractable. Thus, we find the optimal storage allocation when the data of each class is spread minimally over the nodes, i.e. minimal spreading allocation (MSA). Then, by comparing the performance of the optimal MSA with the performance upper bound, we show that the optimal MSA is indeed the optimal storage allocation in many practical cases. Numerical examples are also presented for better illustration of the results.

In a multiway relay channel (MWRC), pairwise transmission strategy can be used to reduce the computational complexity at the relay and the users without sacrificing the data rate significantly. The performance of such pairwise strategies, however, is affected by the way that the users are paired to transmit. In this paper, we study the effect of pairing on the common rate and sum rate of an MWRC with functional-decode-forward (FDF) relaying strategy where users experience asymmetric channel conditions. To this end, we first develop a graphical model for an MWRC with pairwise transmission strategy. Using this model, we then find the maximum achievable common rate and sum rate as well as the user pairings that achieve these rates. This marks the ultimate performance of FDF relaying in an MWRC setup. Further, we show that the rate enhancement achieved through the optimal user pairing becomes less pronounced at higher signal to noise ratios. Using computer simulations, the performance of the optimal pairing is compared with those of other proposed pairings in the literature.

Storage allocation affects important performance measures of distributed storage systems. Most previous studies on the storage allocation consider its effect separately either on the success of the data recovery or on the service rate (time) where it is assumed that no access failure happens in the system. In this paper, we go one step further and incorporate the access model and the success of data recovery into the service rate analysis. In particular, we focus on quasi-uniform storage allocation and provide a service rate analysis for both fixed-size and probabilistic access models at the nodes. Using this analysis, we then show that for the case of exponential waiting time distribution at individuals storage nodes, minimal spreading allocation results in the highest system service rate for both access models. This means that for a given storage budget, replication provides a better service rate than a coded storage solution.

To protect data communication from eavesdropper nodes, different techniques have been developed to improve the physical‐layer security (PLS) of communications systems. Destination‐based cooperative signalling (DBCS) is one of such techniques where the destination sends an intended artificial noise to the untrusted listeners helping to protect the source message from being captured reliably at eavesdroppers. In this study, the authors investigate the application of DBCS to improve the PLS, and as a consequence the secrecy performance of a two‐hop amplify‐and‐forward cooperative system with an untrusted relay. To get the best performance out of DBCS, the transmit power of the source's signal as well as the artificial noise should be carefully adjusted. To address this, they have introduced the optimal power allocation to maximise the secrecy rate of the system under a sum‐power constraint at the network nodes. For a system with large‐scale antenna arrays at the base station, then then find the closed‐form solution for the secrecy outage probability and the ergodic secrecy rate of the optimised system for both uplink and downlink. The presented simulation results validate the authors’ theoretical analysis and reveal that the proposed DBCS with optimal power allocation significantly improves the secrecy performance of the system.

Sustaining high data rates over an indoor broadband power line communication (PLC) link is a challenging task due to the significant attenuation of high frequency signals transmitted over power cables. Repeaters are often used to alleviate the problem of signal attenuation, however, this comes at a loss of multiplexing gain. On the other hand, modern relaying concepts, as used in wireless systems, are not as effective in PLC setups due to the absence of diversity improvement. In this study, the authors address this issue and propose a new approach to improve the multiplexing gain of relay‐aided PLC systems. The authors consider a multi‐user indoor PLC scenario where all users want to share their data. For this setup, the authors then advocate the application of amplify‐and‐forward (AF) multi‐way relaying (MWR). Considering the practical constraints of an indoor PLC system, the authors study the achievable data rates of AF MWR, and compare them to those achievable with direct (non‐cooperative) transmission and conventional relaying schemes. The simulations demonstrate that depending on the network topology, MWR can result in significant performance improvement. The authors further discuss how their rate analysis can be exploited to decrease the energy consumption in the system.

Multi-way relaying is a cooperative scheme for communication scenarios where several users want to share their data with each other. While the capacity of multi-way relay channels (MWRCs) is still unknown to completely understand their potentials, the achievable data rates of MWRCs have been studied for specific setups. In this work, we aim to get a better understanding of the achievable data rates of an amplify-and-forward (AF) MWRC. To this end, we first present an achievable upper bound for the common data rate of an AF MWRC and discuss how users' power allocation affects the common rate. While full power transmission at the users assures reaching the maximum achievable common rate in one-way relaying, we show that it is not the case for MWRCs. Thus, we formulate an optimization problem to find the optimal users' power allocation to gain the maximum common rate. Finding an optimal solution for this optimization problem is quite complex since the number of constraints grows exponentially with the number of users. We then focus on a relaxed version of the rate optimization problem and propose a suboptimal algorithm to solve it. Our simulation results show that the proposed algorithm can achieve rates higher than the ones achieved through full power transmission with noticeably lower transmit power at the users.

In a multi-input multi-output (MIMO) interference network, the maximum achievable signal-to-interference-plus-noise ratio (SINR) determines the level of quality of service (QoS) that can be supported by each stream. In this paper, we study the problem of maximizing the minimum SINR of independent parallel data streams across all users in a MIMO interference network that employs interference alignment (IA) in the presence of channel state information (CSI) uncertainty. To this end, we propose a low-complexity robust SINR fairness algorithm based on the power allocation across data streams. Building on the conventional IA design, we provide a closed-form solution for the power allocation problem. It is observed that the proposed power allocation (PA)-based robust design outperforms the conventional robust design, when the channel changes drastically and the CSI uncertainty cannot be reliably bounded. Furthermore, the PA-based robust design provides significant performance gain over the benchmark Max-SINR algorithm.

In this paper, we study the effect of users' transmission ordering on the common rate and sum rate of pairwise multiway relay channels (MWRCs) with functional-decode-forward strategy. To this end, we first develop a graphical model for the data transmission in a pairwise MWRC. Using this model, we then find the optimal orderings that achieve the maximum common rate and sum rate of the system, respectively. The achieved maximum common/sum rate is also found. Moreover, we show that the performance gap between optimal orderings and a random ordering vanishes when SNR increases. Computer simulations are presented for better illustration of the results.

Power line communication (PLC) provides a reliable and inexpensive solution to enable high data rate services for indoor environments. In this work, we consider an indoor PLC network where several devices (or users) want to exchange data. To prevent severe signal attenuation and thus performance degradation over relatively long PLC links, we consider the use of a relay at a central location to assist the users in their data communication. For this scenario, we suggest the application of amplify-and-forward (AF) multi-way relaying (MWR) based on physical-layer network coding at the relay. To evaluate the performance of this scheme, its achievable data rate is studied with practical assumptions applicable to PLC systems. Our study shows that the employed AF MWR improves the system performance and increases the achievable common data rate at the users.

Amplify-and-forward (AF) and demodulate-and-forward (DMF) can be effectively used in memoryless two-way relay channels (TWRCs) to enhance the spectral efficiency. In this paper, we study AF and DMF, in terms of their achievable data exchange rate, for a memoryless TWRC. Our results show that unlike memoryless one-way relaying, increasing users' SNR benefits AF more than DMF. Another interesting observation is that while with DMF a higher data rate is provided for the user whose channel condition is better, with AF the situation is the reverse. That is, the user with worse channel condition can receive at a higher data rate. Further, we find that for a TWRC with asymmetric users' channels, AF can take advantage of power back-off at the users, without degrading the data rate, to improve the energy efficiency while power back-off is not beneficial for DMF.

We consider an erasure multi-way relay channel (EMWRC) in which several users share their data through a relay over erasure links. Assuming no feedback channel between the users and the relay, we first identify the challenges for designing a data sharing scheme over an EMWRC. Then, to overcome these challenges, we propose practical low-latency and low-complexity data sharing schemes based on fountain coding. Later, we introduce the notion of end-to-end erasure rate (EEER) and analytically derive it for the proposed schemes. EEER is then used to calculate the achievable rate and transmission overhead of the proposed schemes. Using EEER and computer simulations, the achievable rates and transmission overhead of our proposed schemes are compared with the ones of one-way relaying. This comparison implies that when the number of users and the channel erasure rates are not large, our proposed schemes outperform one-way relaying. We also find an upper bound on the achievable rates of EMWRC and observe that depending on the number of users and channel erasure rates, our proposed solutions can perform very close to this bound.

Multi-way relaying is a promising approach to enhance the spectral efficiency in multi-user communication systems. Several relaying strategies have been proposed recently to be used in multi-user communication systems. In this paper, we analyze the gap between the achievable rate of some of these relaying techniques and the capacity of the Gaussian multiway relay channels (GMWRCs). To this end, for a symmetric GMWRC with K users, we prove that lattice-based relaying guarantees a gap less than 1/2(K-1) bit from the capacity upper bound. Also, we show that decode-and-forward and amplify-and-forward relaying may have a larger capacity gap than 1/2(K-1) bit depending on the relay and users' SNR. Then, we find the SNR regions where these two techniques also ensure a 1/2(K-1)-bit gap.

In a system employing physical-layer network coding, the superimposed constellation at the relay may have ambiguity where one received signal is associated with more than one possible combination of users' data. We find the necessary and sufficient condition on a user bit mapping which removes this ambiguity for phase shift keying modulation. Further, we introduce the concept of semi-Gray mapping which improves the system BER performance and achievable rate. The necessary and sufficient condition of having a semi-Gray mapping is also found. Interestingly, the widely used binary reflected Gray mapping does not satisfy the semi-Gray mapping condition resulting in a poorer performance compared to our proposed semi-Gray scheme.

In this paper, we study an amplify-and-forward OFDM-based wireless relay network and provide an exact analysis of the system achievable average throughput and probability of outage. It is assumed that the users transmits their data over a Rayleigh fading channel. Further, to increase the system throughput and use the channel more efficiently, adaptive modulation is applied on each subcarrier and the relaying strategy is selective OFDMA. Our analysis shows that how the system performance enhances by using selective OFDMA relaying scheme in terms of both average throughput and probability of outage. Simulation results verifies the accuracy of our proposed analysis.

Considering event-driven clustered wireless sensor networks, a probabilistic approach for analyzing the network lifetime is presented when events occur randomly over the network field. To this end, we first model the packet transmission rate of the sensors, using the theory of coverage processes and Voronoi tessellation. Then, the probability of achieving a given lifetime by individual sensors is found. This probability is then used to study the cluster lifetime. In fact, we find an accurate approximation for the probability of achieving a desired lifetime by a cluster. Our proposed analysis includes the effect of packet generation model, random deployment of sensors, dynamic cluster head assignment, data compression, and energy consumption model at the sensors. The analysis is presented for event-driven networks, but it comprises time-driven networks as a special case. Computer simulations are used to verify the results of our analysis.

Universal decentralized estimation (UDES) is one of the most well-known decentralized estimation techniques proposed for wireless sensor networks (WSNs). UDES and other decentralized estimation algorithms are proposed in view of the limited available energy at the sensors. In this work, we study energy efficiency of UDES for randomly deployed WSNs via finding the distribution and expected value of the number of bits that a sensor sends to the fusion center. Then, we find the probability of achieving a desired lifetime by the sensor. Our analytical results are verified through computer simulation.

Multiple-input multiple-output (MIMO) channels have been studied from various aspects including the average of the mutual information between the transmitter and receiver (ergodic capacity) when the channel gains are known to the receiver only. A common approach for capacity analysis is to find the moment generating function (MGF) of the mutual information and by direct differentiation, the mean of the mutual information (capacity) is calculated. Recently, character expansions of groups have been used for integration over unitary matrices to obtain the joint eigenvalue distribution of the correlated Wishart matrix i.e. HH* where H is the zero mean full correlated complex Gaussian random MIMO channel matrix. In this paper, we show that the previous attempt for capacity analysis of full correlated MIMO channels is correct for square channel matrices only. We modify the approach from square matrices to rectangular matrices to obtain the correct joint eigenvalue distribution of the correlated Wishart matrix. The result can be used to obtain the MGF and the capacity of full correlated MIMO channels.

In event-driven wireless sensor networks, the network lifetime has a random nature due to the randomness of data reporting. The lifetime is even more nondeterministic when sensors are also deployed randomly. The lifetime of such a network is influenced by node deployment, initial energy of sensors, packet generation model and the number of sensors. This work quantifies the effect of these parameters on the lifetime of randomly deployed event-driven networks. First, the lifetime of individual sensors are studied. Then, an analytical expression is obtained for the complementary cumulative density function of the network lifetime. Such an analysis can be used for choosing the network parameter and efficiently optimizing the network lifetime. The results of this work are obtained for both multi- hop and single-hop wireless sensor networks and are verified with computer simulation. The approaches of this paper are shown to be applicable to more general cases.

Clustering sensors is considered as an efficient way for increasing the lifetime of a wireless sensor network. One of the main advantages of these networks, leading to a longer lifetime, is the ability of data aggregation by the cluster head (CH). Assuming a fixed-shape for the cluster, the probability of achieving a desired lifetime by the cluster has been derived when the nodes are randomly deployed within the cluster. Having the clusters lifetime, the network lifetime can be consequently determined. In addition, it is shown that clustered networks are not necessarily better than the non-clustered networks in terms of the energy consumption and consequently the network lifetime. To this end, a condition related to the data aggregation capability of CH is found indicating when the clustered networks outperform non-clustered ones.