Unmanned Aerial Vehicle Base Stations (UAV-BSs) are effective to support mobile wireless systems in situations where an unusually high density of users require enhanced coverage and capacity such as in large sport events or music festivals. However, the joint deployment problem of UAV-BSs is NP-hard, and the optimization methods used to solve such a class of problems are often too slow for (quasi-)real-time applications when the density of users and the number of UAV-BS are high. This inefficiency creates a need for more adaptable methods to solve the UAV-BS positioning problem. This paper proposes a solution by transforming the UAV-BSs’ placement problem into a decision-making process using the Analytic Hierarchy Process (AHP) method. Our solution considers that all UAV-BSs move along scanning points with a predetermined path, each with a minimal distance from one another. The proposed algorithm, named UAV-AHP, efficiently determines the UAV-BSs’ positions, thereby improving the network performance based on (quasi-) real-time acquisition of user signals at scanning points. For the high-density scenarios under investigation, our numerical results demonstrate that UAV-AHP outperforms commonly used heuristics that sub-optimally solve NP-hard problems, namely, cuckoo search (CS), particle swarm optimization (PSO), and a genetic algorithm (NSGA-II). The proposed method for UAV-BS deployment requires considerably lower running times to find satisfactory solutions than CS, PSO, and NSGA-II.

Automating the task of fault detection and diagnosis is essential for reducing the operational and maintenance costs of power electronic converters. Hence, this paper introduces an online optimization methodology for digital twins to diagnose multiple faults in grid connected inverters. In addition to enhancing accuracy, we channelize our efforts towards reducing computational complexity in parameter configuration under a limited set of training data. To address cybersecurity vulnerabilities during the training phase of our digital twin, we performdata sanitizationwith the aid of a quantitative association rule mining technique. For classification performance assessment, various fault cases in a virtual synchronous generator are considered to demonstrate the efficacy of our approach. Our research outcomes reveal increased accuracy and fidelity levels achieved by our digital-twin design, paving the way for reliable and trustworthy decision-making under anomalous conditions.

In this work, we investigate the secrecy performance of a wireless network that enables an aerial intelligent reflecting surface-unmanned aerial vehicle for non-orthogonal multiple access (NOMA) transmissions in the presence of a friendly jammer. For a practical NOMA-based system with imperfect successive interference cancellation, we develop a theoretical framework for the secrecy outage probability (SOP) and the strictly positive secrecy capacity. However, the derived expressions are complex and therefore, do not provide important insights into the system design. In this regard, explicit and simplified formulas of the asymptotic SOP are derived under high signal-to-noise ratio conditions for scenarios with and without jamming. Through extensive simulations, we verify the correctness of the proposed theoretical framework. Notably, numerical results reveal the superior performance of the jamming-enabled system over its non-jamming counterpart and demonstrate the efficacy of the proposed scheme.

User-centric (UC) distributed massive multiple-input multiple-output (D-mMIMO), commonly called cell-free mMIMO, is an essential technology for ensuring more uniform coverage and higher spectral and energy efficiencies in next-generation communication systems. This paper investigates an alternative effective channel estimation method to address the issue of the lower channel hardening degree experienced by UC D-mMIMO systems. Specifically, this paper proposes a hybrid approach for effective channel estimation, allowing users to estimate their channels either through downlink pilot-based training, by using a blind algorithm, or by relying on statistical channel state information (CSI). The hybrid estimation method is compared with the case where each method is applied individually, as well as with the ideal case of perfect CSI. The analysis is conducted using various system parameter settings, such as the number of antennas and users, and it accounts for the presence of pilot contamination. Simulation results reveal that the proposed hybrid channel estimation algorithm is able to provide the best spectral efficiency performance in any network parameter configuration, while reducing the estimation normalized mean-square error compared to conventional statistical CSI.

In this paper, we investigate a novel representation of a band-limited and continuous-time signal using a sequence of impulses or events. To begin with, we evaluate the mean squared error between the original signal and its reconstructed version assuming that: (i) the signal samples are transmitted at the Shannon channel capacity; (ii) the signal representation using the cosine phase method is done before its transmission. The proposed approach is then compared with an existing approach based on the signal transmission of quantized samples. Finally, we provide some representative examples through Monte Carlo simulations, consistently revealing the superiority of the proposed signal representation approach over the existing one regarding transmission bandwidth and peak-to-peak value of the original signal.

The acquisition of accurate channel state information (CSI) is of utmost importance since it provides performance improvement for wireless communication systems. However, acquiring accurate CSI, which can be done through channel estimation or channel prediction, is an intricate task due to the complexity of the time-varying and frequency selectivity of the wireless environment. To this end, we propose an efficient machine learning (ML)-based technique for channel prediction in orthogonal frequency-division multiplexing (OFDM) sub-bands. The novelty of the proposed approach lies in the training of channel fading samples used to estimate future channel behavior in selective fading.

The operations of unmanned aerial vehicles (UAVs) are susceptible to cybersecurity risks, mainly because of their firm reliance on the Global Positioning System (GPS) and radio frequency (RF) sensors. GPS and RF sensors are vulnerable to potential threats, such as spoofing attacks that can cause the UAVs to behave erratically. Since these threats are widespread and potent, it is imperative to develop effective intrusion detection systems. In this paper, we propose an image-based intrusion detection system for detecting GPS spoofing cyberattacks based on a deep learning methodology. We combine convolutional neural networks with Principal Component Analysis (PCA) to reduce the dimensionality of the dataset features, data augmentation to increase the size and diversity of the training dataset, and transfer learning to improve the proposed model’s performance with limited data to design a fast, accurate, and general method. Extensive numerical experiments demonstrate the effectiveness of the proposed solution carried out using benchmark datasets. We achieved an accuracy of 100% within a running time of 120.64 s at 0.3529 ms latency and a detection time of 2.035 s in the case of the training dataset. Further, using this trained model, we achieved an accuracy of 99.25% within a detection time of 2.721 s on an unseen dataset that was unrelated to the one used for training the model. In contrast, other models, such as Inception-v3, showed lower accuracy on unseen datasets. However, Inception-v3 performance improved significantly after Bayesian optimization, with the Tree-structured Parzen Estimator reaching 99.06% accuracy. Our results demonstrate that the proposed image-based intrusion detection method outperforms the existing solutions while providing a general model for detecting cyberattacks included in unseen datasets.

This paper presents a novel framework for intrusion detection specially designed for cyberattacks, such as Denial-of-Service, Distributed Denial-of-Service, Distributed Reflection Denial-of-Service, Brute Force, Botnets, and Sniffing, on vehicles that are situated in the Internet of Vehicles environment. We propose an intrusion detection system based on machine learning that is capable of detecting abnormal behavior by examining network traffic to find unusual data flows. In this paper, we have presented a strategy for intrusion detection through a careful evaluation and selection of the most effective techniques for the following steps of the machine learning process: (i) data preprocessing by using Z-score normalization that preserves the data distribution for the proposed method and handles outliers; (ii) feature selection by using a regression model that simplifies the model complexity and reduces the execution time; and (iii) model selection and training – Random Forest, Extreme Gradient Boosting, Categorical Boosting, Light Gradient Boosting Machine – with hyperparameter optimization to control the behavior in the training phase and to prevent overfitting. The effectiveness of the proposed solution is demonstrated by extensive numerical experiments carried out using the well-known standard datasets CIC-IDS-2017, CSE-CIC-IDS-2018, and CIC-DDoS-2019, both separately and merged. We achieved a high accuracy above 99.8% within a running time of 46.9 s and 0.24 s detection time for the three combined intrusion detection system datasets, thereby showing that the proposed intrusion detection system outperforms the previous methods introduced in the literature.

This work introduces a new perspective for physical media sharing and radio access in multiuser communication by jointly considering (i) the meaning of the transmitted message and (ii) its function at the end user. Specifically, we have defined a scenario where multiple users (sensors) employ an event-trigger transmission scheme to communicate their own state value concerning a predetermined event. On the receiver side, there is an alarm monitoring system, whose function is to decide whether such a predetermined event has happened in a certain time period and, if yes, in relation to which user. The proposed solution leverages the lack of explicit use of a predetermined spectrum resource – which defines an energy slot – to infer that no event has happened, thus building an implicit communication channel. When an event occurs at a given sensor, a sequence of “use” and “lack of use” instances for such a spectrum resource will be transmitted as a sequence of “on” and “off” values with respect to the occupancy of the energy slot. The receiver’s role is to detect such a sequence to identify that an event has happened and in which sensor. This direct map from the data acquisition to the transmission is the basis of semantic-functional communication. The challenge of this scheme is to construct a coding–decoding scheme where the receiver can effectively recover the states of the different sensors (in terms of the occurrence of the events) with high fidelity. We have demonstrated the high efficacy of the proposed nonorthogonal solution when the transmitters select a random code, and the receiver only detects whether the energy slot is used (i.e., there is no actual demodulation so that there is no packet collision when two or more sensors are transmitting). We have shown the proposed method leads to a better event transmission efficiency than an adaptation of well-known techniques like the orthogonal-based TDMA and the random-access-based slotted ALOHA when the same number of limited resources is employed. Remarkably, for almost all studied cases, the proposed method asymptotically achieves 100% efficiency and 0% error probability, while consistently outperforming TDMA and slotted ALOHA variations.

In this paper, the privacy of wireless transmissions is improved through an efficient technique termed DDM and is subsequently assessed in terms of the measure of information leakage. Recently, a variation of DDM termed LPDDM has attracted significant attention as a prominent secure transmission method owing to its ability to improve the privacy of wireless communications. Roughly speaking, this modulation operates by randomly selecting the transmitting antenna from an antenna array whose radiation pattern is known. Thereafter, the modulator adjusts the constellation phase so as to ensure that only the legitimate receiver recovers the information. To begin with, we highlight some privacy boundaries inherent to the underlying system. In addition, we propose features that the antenna array must meet in order to increase the privacy of a wireless communication system. Last, we adopt a uniform circular monopole antenna array with equiprobable transmitting antennas in order to assess the impact of DDM on information leakage and the BER.

Dual-polarized rate-splitting multiple access (RSMA) with polarization multiplexing has appeared recently as an attractive downlink transmission technique for dual-polarized massive multiple-input multiple-output (MIMO) systems. Dual-polarized RSMA does not require successive interference cancellation (SIC), which avoids practical issues of imperfect SIC decoding. Nevertheless, depolarization phenomena can still degrade the performance of this featured technique. In this work, we exploit the advanced capabilities of a dual-polarized intelligent reflecting surface (IRS) for unleashing an enhanced RSMA polarization multiplexing. To optimize the IRSs, we develop a multi-objective Frank-Wolfe-based algorithm for jointly mitigating cross-polar interference and improving the reception of common and private data streams at multiple users. Representative simulation examples demonstrate that dual-polarized IRS-MIMO-RSMA can efficiently mitigate detrimental depolarization phenomena and outperform conventional systems.

The polarization domain provides an extra degree of freedom (DoF) for improving the performance of multiple-input multiple-output (MIMO) systems. This paper takes advantage of this additional DoF to alleviate practical issues of successive interference cancellation (SIC) in rate-splitting multiple access (RSMA) schemes. Specifically, we propose three dual-polarized downlink transmission approaches for a massive MIMO-RSMA network under the effects of polarization interference and residual errors of imperfect SIC. The first approach implements polarization multiplexing for transmitting the users’ data messages, which removes the need to execute SIC in the reception. The second approach transmits replicas of users’ messages in the two polarizations, which enables users to exploit diversity through the polarization domain. The third approach, in its turn, employs the original SIC-based RSMA technique per polarization, and this allows the BS to transmit two independent superimposed data streams simultaneously. An in-depth theoretical analysis is carried out, in which we derive tight closed-form approximations for the outage probabilities of the three proposed approaches. Accurate approximations for the ergodic sum-rates of the two first schemes are also derived. Simulation results validate the theoretical analysis and confirm the effectiveness of the proposed schemes. For instance, under low to moderate cross-polar interference, the results show that, even under high levels of residual SIC error, our dual-polarized MIMO-RSMA strategies outperform the conventional single-polarized MIMO-RSMA counterpart. It is also shown that the performance of all RSMA schemes is impressively higher than that of single and dual-polarized massive MIMO systems employing non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) techniques.

Aiming at overcoming practical issues of successive interference cancellation (SIC), this paper proposes a dual-polarized rate-splitting multiple access (RSMA) technique for a downlink massive multiple-input multiple-output (MIMO) net-work. By modeling the effects of polarization interference, an in-depth theoretical analysis is carried out, in which we derive tight closed-form approximations for the outage probabilities and ergodic sum-rates. Simulation results validate the accuracy of the theoretical analysis and confirm the effectiveness of the proposed approach. For instance, under low to moderate cross-polar interference, our results show that the proposed dual-polarized MIMO-RSMA strategy outperforms the single-polarized MIMO-RSMA counterpart for all considered levels of residual SIC error.

This paper focuses on improving the resource allocation algorithm in terms of packet delivery ratio (PDR), i.e., the number of successfully received packets sent by end devices (EDs) in a long-range wide-area network (LoRaWAN). Setting the transmission parameters significantly affects the PDR. Employing reinforcement learning (RL), we propose a resource allocation algorithm that enables the EDs to conFigure their transmission parameters in a distributed manner. We model the resource allocation problem as a multi-armed bandit (MAB) and then address it by proposing a two-phase algorithm named MIX-MAB, which consists of the exponential weights for exploration and exploitation (EXP3) and successive elimination (SE) algorithms. We evaluate the MIX-MAB performance through simulation results and compare it with other existing approaches. Numerical results show that the proposed solution performs better than the existing schemes in terms of convergence time and PDR.

With the emerge of the Internet of Things (IoT), localization within indoor environments has become inevitable and has attracted a great deal of attention in recent years. Several efforts have been made to cope with the challenges of accurate positioning systems in the presence of signal interference. In this paper, we propose a novel deep learning approach through Gradient Boosting Enhanced with Step-Wise Feature Augmentation using Artificial Neural Network (AugBoost-ANN) for indoor localization applications as it trains over labeled data. For this purpose, we propose an IoT architecture using a star network topology to collect the Received Signal Strength Indicator (RSSI) of Bluetooth Low Energy (BLE) modules by means of a Raspberry Pi as an Access Point (AP) in an indoor environment. The dataset for the experiments is gathered in the real world in different periods to match the real environments. Next, we address the challenges of the AugBoost-ANN training which augments features in each iteration of making a decision tree using a deep neural network and the transfer learning technique. Experimental results show more than 8% improvement in terms of accuracy in comparison with the existing gradient boosting and deep learning methods recently proposed in the literature, and our proposed model acquires a mean location accuracy of 0.77 m.

In this paper, we propose an intelligent analog beam selection strategy in a terahertz (THz) band beamspace multiple-input multiple-output (MIMO) system. First inspired by transfer learning, we fine-tune the pre-trained off-the-shelf GoogleNet classifier to learn analog beam selection as a multi-class mapping problem. Simulation results show 83% accuracy for the analog beam selection, which subsequently results in 12% spectral efficiency (SE) gain over the existing counterparts. For a more accurate classifier, we replace the conventional rectified linear unit (ReLU) activation function of the GoogleNet with the recently proposed Swish and retrain the fine-tuned GoogleNet to learn analog beam selection. It is numerically indicated that the fine-tuned Swish-driven GoogleNet achieves 86% accuracy, as well as 18% improvement in achievable SE, over the similar schemes. Eventually, a strong ensembled classifier is developed to learn analog beam selection by sequentially training multiple fine-tuned Swish-driven GoogleNet classifiers. According to the simulations, the strong ensembled model is 90% accurate and yields 27% gain in achievable SE in comparison with prior methods.

Sharing the licensed frequency spectrum among mobile network operators (MNOs) is a promising approach to improve licensed spectrum utilization. In this paper, we model and analyze a non-orthogonal spectrum sharing system consisting of seller and buyer MNOs where buyer MNOs can lease several licensed sub-bands from different seller MNOs. All base stations (BSs) owned by a buyer MNO can also utilize various licensed sub-bands simultaneously, which are also used by other buyer MNOs. To reduce the interference that a buyer MNO imposes on one seller MNO sharing its licensed sub-band, this buyer MNO has a limitation on the maximum interference caused to the corresponding seller MNO’s users. We assume each MNO owns its BSs and users whose locations are modeled as two independent homogeneous Poisson point processes. Applying stochastic geometry, we derive expressions for the downlink signal-to-interference-plus-noise ratio coverage probability and the average rate of both seller and buyer networks. The numerical results validate our analysis with simulation and illustrate the effect of the maximum interference threshold on the total sum rate of the network.

End-to-End (E2E) network slicing enables wireless networks to provide diverse services on a common infrastructure. Each E2E slice, including resources of radio access network (RAN) and core network, is rented to mobile virtual network operators (MVNOs) to provide a specific service to end-users. RAN slicing, which is realized through wireless network virtualization, involves sharing the frequency spectrum and base station antennas in RAN. Similarly, in core slicing, which is achieved by network function virtualization, data center resources such as commodity servers and physical links are shared between users of different MVNOs. In this paper, we study E2E slicing with the aim of minimizing the total energy consumption. The stated optimization problem is non-convex that is solved by a sub-optimal algorithm proposed here. The simulation results show that our proposed joint power control, server and link allocation (JPSLA) algorithm achieves 30% improvement compared to the disjoint scheme, where RAN and core are sliced separately.

In this paper, we propose an accurate two-phase millimeter-Wave (mmWave) beamspace channel tracking mechanism. Particularly in the first phase, we train an extreme reservoir machine (ERM) for tracking the historical features of the mmWave beamspace channel and predicting them in upcoming time steps. Towards a more accurate prediction, we further fine-tune the ERM by means of Xavier initializer technique, whereby the input weights in ERM are initially derived from a zero mean and finite variance Gaussian distribution, leading to 49% degradation in prediction variance of the conventional ERM. The proposed method numerically improves the achievable spectral efficiency (SE) of the existing counterparts, by 13%, when signal-to-noise-ratio (SNR) is 15dB. We further investigate an ensemble learning technique in the second phase by sequentially incorporating multiple ERMs to form an ensembled model, namely adaptive boosting (AdaBoost), which further reduces the prediction variance in conventional ERM by 56%, and concludes in 21% enhancement of achievable SE upon the existing schemes at SNR = 15dB.

This paper presents new analytical results for evaluating the ALOHA-like multi-class random access wireless network’s performance. The proposed model is motivated by the growth of low-power wireless networks that employ random access protocols. In particular, we compare our analytical formulation with system-level simulations of Long Range (LoRa) technology. We show that the proposed formulation provides an accurate approximation of LoRaWAN performance capturing its main trade-offs. The main contributions are (i) an extensive analysis of the impact of different LoRa spreading factors (SFs) allocation strategies, including area intersection among SFs, which is little explored in the literature and represents the optimal approach under some conditions; and (ii) the optimal proportion of users that maximizes the network throughput for each class and for each allocation strategy considered in the paper.

Abstract Learning methods have been increasingly used in power engineering to perform various tasks. In this paper, a fault selection procedure in double-circuit transmission lines employing different learning methods is accordingly proposed. In the proposed procedure, the discrete Fourier transform (DFT) is used to pre-process raw data from the transmission line before it is fed into the learning algorithm, which will detect and classify any fault based on a training period. The performance of different machine learning algorithms is then numerically compared through simulations. The comparison indicates that an artificial neural network (ANN) achieves remarkable accuracy of 98.47%. As a drawback, the ANN method cannot provide explainable results and is also not robust against noisy measurements. Subsequently, it is demonstrated that explainable results can be obtained with high accuracy by using rule-based learners such as the recently developed quantitative association rule mining algorithm (QARMA). The QARMA algorithm outperforms other explainable schemes, while attaining an accuracy of 98%. Besides, it was shown that QARMA leads to a very high accuracy of 97% for highly noisy data. The proposed method was also validated using data from an actual transmission line fault. In summary, the proposed two-step procedure using the DFT combined with either deep learning or rule-based algorithms can accurately and successfully perform fault selection tasks but indicating remarkable advantages of the QARMA due to its explainability and robustness against noise. Those aspects are extremely important if machine learning and other data-driven methods are to be employed in critical engineering applications.

Network function virtualization (NFV) is a promising technology to make 5G networks flexible and agile. NFV decreases operators’ OPEX and CAPEX by decoupling the physical hardware from the functions they perform. In NFV, users’ service request can be viewed as a service function chain (SFC) consisting of several virtual network functions (VNFs) which are connected through virtual links. Resource allocation in NFV is done through a centralized authority called NFV Orchestrator (NFVO). This centralized authority suffers from some drawbacks such as single point of failure and security. Blockchain (BC) technology is able to address these problems by decentralizing resource allocation. The drawbacks of NFVO in NFV architecture and the exceptional BC characteristics to address these problems motivate us to focus on NFV resource allocation to users’ SFCs without the need for an NFVO. To this end, we assume there are two types of users: users who send SFC requests (SFC requesting users) and users who perform mining process (miner users). For SFC requesting users, we formulate NFV resource allocation (NFV-RA) problem as a multi-objective problem to minimize the energy consumption and utilized resource cost, simultaneously. To address this problem, we propose an Approximation-based Resource Allocation algorithm (ARA) using Majorization-Minimization approximation method to convexify NFV-RA problem. Furthermore, due to the high complexity of ARA algorithm, we propose a low complexity Hungarian-based Resource Allocation (HuRA) algorithm using Hungarian algorithm for server allocation. Through the simulation results, we show that our proposed ARA and HuRA algorithms achieve near-optimal performance with lower computational complexity. Also, ARA algorithm outperforms the existing algorithms in terms of number of active servers, energy consumption, and average latency. Moreover, the mining process is the foundation of BC technology. In wireless networks, mining is performed by resource-limited mobile users. Since the mining process requires high computational complexity, miner users cannot perform it alone. So, in this article, we assume that miner users can perform mining process with participating of other users. For mining process, the problem of minimizing the energy consumption and cost of users’ processing resources is formulated as a linear programming problem that can be optimally solved in polynomial time.

In this paper, we study the problem of minimizing the uplink aggregate transmit power subject to the users' minimum data rate and peak power constraint on each sub-channel for multi-cell wireless networks. To address this problem, a distributed sub-optimal joint power and rate control algorithm called JPRC is proposed, which is applicable to both non-orthogonal frequency-division multiple access (NOMA) and orthogonal frequency-division multiple access (OFDMA) schemes. Employing JPRC, each user updates its transmit power using only local information. Simulation results illustrate that the JPRC algorithm can reach a performance close to that obtained by the optimal solution via exhaustive search, with the NOMA scheme achieving a 59% improvement on the aggregate transmit power over the OFDMA counterpart. It is also shown that the JPRC algorithm can outperform existing distributed power control algorithms.

Networked industrial systems capitalize on recent advancements in sensing, communications, computing and storage to improve productivity, operational and cost efficiency. The proliferation of effective techniques for knowledge extraction drive a paradigm shift in industrial environments and provide a fertile ground for enhanced process monitoring and control capabilities. In an effort to shed light on industrial data management operations, this paper presents two different approaches for dealing with information processing tasks of aggregated sensor measurements. Such tasks constitute part of an end-to-end process monitoring solution which is implemented in an open-source platform following a modular, scalable and interpretable procedure. A mapping of the industrial data processing components to the operational principles and architecture of a cyber-physical system reveals useful insights for an automated supervision of critical processes and workflows.

Telecommunication service providers have to guarantee acceptable speech quality during a phone call to avoid a negative impact on the users’ quality of experience. Currently, there are different speech quality assessment methods. ITU-T Recommendation G.107 describes the E-model algorithm, which is a computational model developed for network planning purposes focused on narrowband (NB) networks. Later, ITU-T Recommendations G.107.1 and G.107.2 were developed for wideband (WB) and fullband (FB) networks. These algorithms use different impairment factors, each one related to different speech communication steps. However, the NB, WB, and FB E-model algorithms do not consider wireless techniques used in these networks, such as Multiple-Input-Multiple-Output (MIMO) systems, which are used to improve the communication system robustness in the presence of different types of wireless channel degradation. In this context, the main objective of this study is to propose a general methodology to incorporate wireless network parameters into the NB and WB E-model algorithms. To accomplish this goal, MIMO and wireless channel parameters are incorporated into the E-model algorithms, specifically into the $I_{e,eff}$ and $I_{e,eff,WB}$ impairment factors. For performance validation, subjective tests were carried out, and the proposed methodology reached a Pearson correlation coefficient (PCC) and a root mean square error (RMSE) of 0.9732 and 0.2351, respectively. It is noteworthy that our proposed methodology does not affect the rest of the E-model input parameters, and it intends to be useful for wireless network planning in speech communication services.

The dominance of distributed energy resources in microgrids and the associated weather dependence require flexible protection. They include devices capable of adapting their protective settings as a reaction to (potential) changes in the state of the system. Communication technologies have a key role in this system, since the reactions of the adaptive devices shall be coordinated. This coordination imposes strict requirements: communications must be available and ultrareliable with bounded latency in the order of milliseconds. This article reviews the state of the art in the field and provides a thorough analysis of the main related communication technologies and optimization techniques. We also present our perspective on the future of communication deployments in microgrids, indicating the viability of 5G wireless systems and multiconnectivity to enable adaptive protection.

In this paper, a backscatter cooperation (BC) scheme is proposed for non-orthogonal multiple access (NOMA) downlink transmission. The key idea is to backscatter the surplus power of the received downlink signals at one user to enhance the reception of the user who cannot recover its information. To evaluate the performance of the proposed BC-NOMA scheme, three benchmark schemes are introduced, which include the non-cooperation (NC)-NOMA scheme, the conventional relaying (CR)-NOMA scheme, and the incremental relaying (IR)-NOMA scheme. For all these schemes, the outage performance, the expected rate, and the diversity-multiplexing trade-off (DMT) performance are analyzed. Additionally, the optimal power allocation to maximize the expected rate of the BC-NOMA scheme in the high signal-to-noise ratio region is derived. The theoretical results show that the proposed BC-NOMA can enhance both transmission reliability (outage performance) and transmission effectiveness (expected rate) compared with NC-NOMA. Furthermore, representative numerical results are presented to validate the theoretical results. It is shown that unlike the two relaying-based cooperative schemes (CR/IR-NOMA), which improve the outage performance at the cost of transmission rate, the proposed BC-NOMA scheme can enhance the transmission reliability without impairing the spectrum efficiency, which makes backscattering an appealing solution to cooperative NOMA downlinks.

A dual-polarized intelligent reflecting surface (IRS) can contribute to a better multiplexing of interfering wireless users. In this paper, we use this feature to improve the performance of dual-polarized massive multiple-input multiple-output (MIMO) with non-orthogonal multiple access (NOMA) under imperfect successive interference cancellation (SIC). By considering the downlink of a multi-cluster scenario, the IRSs assist the base station (BS) to multiplex subsets of users in the polarization domain. Our novel strategy alleviates the impact of imperfect SIC and enables users to exploit polarization diversity with near-zero inter-subset interference. To this end, the IRSs are optimized to mitigate transmissions originated at the BS from the interfering polarization. The formulated optimization is transformed into quadratically constrained quadratic sub-problems, which makes it possible to obtain the optimal solution via interior-points methods. We also derive analytically a closed-form expression for the users’ ergodic rates by considering large numbers of reflecting elements. This is followed by representative simulation examples and comprehensive discussions. The results show that when the IRSs are large enough, the proposed scheme always outperforms conventional massive MIMO-NOMA and MIMO-OMA systems even if SIC error propagation is present. It is also confirmed that dual-polarized IRSs can make cross-polar transmissions beneficial to the users, allowing them to improve their performance through diversity.

This paper investigates the achievable sum rate and the outage capacity of generalized frequency division multiplexing systems (GFDMs) with minimum mean-square error (MMSE) receivers over frequency-selective Rayleigh fading channels. To this end, a Gamma-based approximation approach for the probability density function of the signal-to-interference-plus-noise ratio is presented, based on which accurate analytical formulations for the achievable sum rate and outage capacity are proposed. The accuracy of our analysis is corroborated through Monte Carlo simulation assuming different GFDM parameters. Illustrative numerical results are depicted in order to reveal the impact of the key system parameters, such as the number of subcarriers, number of subsymbols, and roll-off factors, on the overall system performance.

In this paper, we propose a novel successive sub-array activation (SSAA) diversity scheme for a massive multiple-input multiple-output (MIMO) system in combination with non-orthogonal multiple access (NOMA). A single-cell multi-cluster downlink scenario is considered, where the base station (BS) sends redundant symbols through multiple transmit sub-arrays to multi-antenna receivers. An in-depth analytical analysis is carried out, in which an exact closed-form expression for the outage probability is derived. Also, a high signal-to-noise ratio (SNR) outage approximation is obtained and the system diversity order is determined. Our results show that the proposed scheme outperforms conventional full array massive MIMO setups.

A backscatter cooperation (BC) mechanism is pro-posed for power-domain non-orthogonal multiple access (NOMA) downlink transmission. The key idea is to enable one user to split and then backscatter part of its received signals to improve the reception at another user. To evaluate its performance, three benchmark schemes are introduced. They are non-cooperation (NC)-NOMA, conventional relaying (CR)-NOMA, and incremental relaying (IR)-NOMA. For all these schemes, the analytical expressions of the minimum total power to avoid information outage are derived, based on which their respective outage performance, expected rates, and diversity-multiplexing trade-off (DMT) are investigated. Analytical results show that the proposed BC-NOMA strictly outperforms NC-NOMA in terms of all the three metrics. Furthermore, theoretical analyses are validated via Monte-Carlo simulations. It is shown that compared with CR/IR-NOMA, only the proposed BC-NOMA scheme can enhance the transmission reliability without impairing the transmission rate.

Peer-to-peer (p2p) electricity exchange can reduce energy wastage and improve capacity utilization in microgrids with renewable energy based electricity production. In such microgrids, called community microgrids, an important problem is to enable customers to share and transact their energy in a rational and acceptable manner. In this paper, electricity exchanges are modeled using co-operative game-theoretic concepts. We assume that all the participants of a community microgrid, which is connected to an external grid, co-operate to form a single (grand) coalition. Using the concept of marginal contributions (MC), we had previously proposed a fair methodology to distribute the total profits of the coalition among its participants. Here, we extend the methodology by considering two scenarios. First, we analyze the case where the community microgrid sometimes collectively produces more electricity than its total load and sells this excess production to an aggregator. Secondly, we consider self-sufficiency as an important objective of the community microgrid. We then use MC to derive a new, simple, and scalable allocation formula to distribute the profits among the participants fairly. To demonstrate our methodology, we applied it to a microgrid in Austin, Texas, USA. The newly proposed methodology saved ≈4.55% as compared to the old originally proposed methodology.

Internet of Things (IoT) refers to the interconnection of physical objects via the Internet. It utilizes complex back-end systems that need different capabilities depending on the requirements of the system. IoT has already been used in various applications, such as agriculture, smart home, health, automobiles, and smart grids. There are many IoT platforms, each of them capable of providing specific services for such applications. Finding the best match between application and platform is, however, a hard task as it is difficult to understand the implications of small differences between platforms. This article builds on previous work that has identified 21 important factors of an IoT platform, which were verified by the Delphi method. We demonstrate here how these factors can be used to discriminate between five well-known IoT platforms, which are arbitrarily chosen based on their market share. These results illustrate how the proposed approach provides an objective methodology that can be used to select the most suitable IoT platform for different business applications based on their particular requirements.

Automated decision making is used routinely throughout our every-day life. Recommender systems decide which jobs, movies, or other user profiles might be interesting to us. Spell checkers help us to make good use of language. Fraud detection systems decide if a credit card transactions should be verified more closely. Many of these decision making systems use machine learning methods that fit complex models to massive datasets. The successful deployment of machine learning (ML) methods to many (critical) application domains crucially depends on its explainability. Indeed, humans have a strong desire to get explanations that resolve the uncertainty about experienced phenomena like the predictions and decisions obtained from ML methods. Explainable ML is challenging since explanations must be tailored (personalized) to individual users with varying backgrounds. Some users might have received university-level education in ML, while other users might have no formal training in linear algebra. Linear regression with few features might be perfectly interpretable for the first group but might be considered a black-box by the latter. We propose a simple probabilistic model for the predictions and user knowledge. This model allows to study explainable ML using information theory. Explaining is here considered as the task of reducing the “surprise” incurred by a prediction. We quantify the effect of an explanation by the conditional mutual information between the explanation and prediction, given the user background.

In this paper, we propose a novel successive sub-array activation (SSAA) diversity scheme for a massive multiple-input multiple-output (MIMO) system in combination with non-orthogonal multiple access (NOMA). Considering a single-cell multi-cluster downlink scenario, where the base station (BS) sends redundant symbols through multiple transmit sub-arrays to multi-antenna receivers, a low-complexity two-stage beamformer, that is constructed based only on the long-term channel statistical information, is proposed. An in-depth analytical analysis is carried out, in which an exact closed-form expression for the outage probability is derived. A high signal-to-noise ratio (SNR) outage approximation is obtained and the system diversity order is determined. The ergodic sum-rate is also investigated, in which a closed-form solution is evaluated considering a particular case. Numerical and simulation results are provided to validate the analytical analysis and to demonstrate the performance superiority of the proposed SSAA scheme. For example, our results show that the proposed system operating with SSAA outperforms conventional full array massive MIMO setups.

This paper addresses multi-user multi-cluster massive multiple-input-multiple-output (MIMO) systems with non-orthogonal multiple access (NOMA). Assuming the downlink mode, and taking into consideration the impact of imperfect successive interference cancellation (SIC), an in-depth analytical analysis is carried out, in which closed-form expressions for the outage probability and ergodic rates are derived. Subsequently, the power allocation coefficients of users within each sub-group are optimized to maximize fairness. The considered power optimization is simplified to a convex problem, which makes it possible to obtain the optimal solution via Karush-Kuhn-Tucker (KKT) conditions. Based on the achieved solution, we propose an iterative algorithm to provide fairness also among different sub-groups. Simulation results alongside with insightful discussions are provided to investigate the impact of imperfect SIC and demonstrate the fairness superiority of the proposed dynamic power allocation policies. For example, our results show that if the residual error propagation levels are high, the employment of orthogonal multiple access (OMA) is always preferable than NOMA. It is also shown that the proposed power allocation outperforms conventional massive MIMO-NOMA setups operating with fixed power allocation strategies in terms of outage probability.

This paper investigates the performance of multi- cluster multi-user massive multiple-input multiple- output (MIMO) networks with non-orthogonal multiple access (NOMA) and a dual-polarized antenna array. Considering the downlink mode in which a single base station communicates with multiple users, a precoder design is proposed with the aim to maximize the number of user groups that are simultaneously served within a cluster. A closed- form expression for the outage probability is derived, based on which an asymptotic analysis is carried out and the diversity gain is determined. Moreover, the outage sum-rate is also examined. Representative numerical examples are presented along with insightful discussions. Our results show that the proposed dual-polarized MIMO-NOMA design outperforms conventional single-polarized systems, even for high cross-polar interference.

This paper assesses the performance of a private LTE wireless network in an industrial automation setup. Our goal is to study the impact of communication latency in a robot arm position control application when the position feedback signal is transmitted via a commercial LTE wireless link. An Ethernet link scenario is considered as our benchmark. We show that even under very specific conditions (no network load, high signal power), the wireless link cannot achieve a satisfactory performance. The proposed demonstration provides a simple visualization of how LTE network latency disturbs the robot behavior, leading to undesired overshoots, oscillations and even stopping its movements. These results demonstrate that even a private LTE network is still incapable of providing the low latency required by industry automation applications, which shall be incorporated in the upcoming 5G wireless systems.

Non-uniform, event-driven sampling of signals can be advantageous for different applications. In this paper, we focus on event-based sampling strategy for electricity metering purposes. Specifically, we propose an improvement in the enhanced event-driven metering (EDM) technique introduced by Simonov et al. Our solution provides additional flexibility on the types of measurements to be sent, by including the option to reduce the sending of consecutive measurements. Numerical results are presented for 4 different open databases of electricity consumption and consistently show that, in relation to the other options, our proposed strategy leads to both: (i) reduction in the amount of measurements sent, and (ii) improvements on the signal reconstruction by decreasing its reconstruction error. These two aspects are extremely useful in a scenario of massive deployment of measurement devices.

In this paper, we consider a wireless-powered communication network with transmit antenna selection (TAS), where an energy-limited multi-antenna information source, powered by a dedicated power beacon, communicates with a mobile user (MU). The MU is equipped with a single-antenna and its mobility is characterized by the well-known random waypoint mobility model. Differently from previous works which considered only static scenarios, this paper aims to investigate wireless power and information transfer in the scenario with a random mobile user under Nakagami-m fading. To this end, exact analytical expressions for the outage probability, average delay-limited throughput, ergodic capacity, and average delay-tolerant throughput are derived. The analytical results are compared with Monte-Carlo simulations in order to validate the analysis and provide useful insights on the impact of different parameters on the system performance.

This paper aims to design and evaluate the performance of multi-cluster multi-user dual-polarized massive multiple-input multiple-output (MIMO) systems with non-orthogonal multiple access (NOMA). Assuming the downlink mode in which a single base station communicates with multiple users, with all terminals being equipped with multiple co-located dual-polarized antennas, two precoder designs are proposed: (i) the first one aims to maximize the number of user groups that are simultaneously served within a cluster; and (ii) the second approach aims to provide further improvements compared to the first one by exploring polarization diversity. Closed-form expressions for the outage probability are derived for both approaches, based on which the respective asymptotic studies are carried out and the diversity gains are determined. The ergodic sum-rates are also derived. Representative numerical examples are presented along with insightful discussions. For instance, our results show that the proposed dual-polarized MIMO-NOMA designs outperform conventional single-polarized systems, even for high cross-polar interference. Simulation results are plotted to corroborate the analytical framework and analysis.

This work assesses the performance of secure machine-type communication networks composed of a legitimate pair of devices communicating in the presence of an eavesdropper. We evaluate the impact of legitimate source's arrival traffic in the design of secure communication protocol. Our approach is based on the secrecy outage probability framework, which identifies the security level of transmissions. We then characterize the secrecy transmission rate that includes the arrival traffic, evaluating its impact on the secrecy performance of the network. We introduce ON-OFF adaptive and non-adaptive transmission schemes that maximizes both the secure effective capacity and the maximum average arrival rate at the source node. Our numerical results provide insights on the interplay between the different traffic originated from MTC devices and security in the system.

This paper analysis the energy efficiency of an unlicensed wireless network in which retransmission is possible if the transmitted message is decoded in outage. A wireless sensor network is considered in which the sensor nodes are unlicensed users of a wireless network which transmit its data in the uplink channel used by the licensed users. Poisson point process is used to model the distributions of the nodes and the interference caused by the licensed users for the sensor nodes. After finding the optimal throughput in the presence of retransmissions, we focus on analyzing the total power consumption and energy efficiency of the network and how retransmissions, network density and outage threshold affects the energy efficiency of the network.

This paper analyzes the throughput of an unlicensed wireless network where messages decoded in outage may be retransmitted. We assume that some wireless devices such as sensors are the unlicensed users, which communicate in the licensed uplink channel. In this case, the licensed users that interfere with the unlicensed transmissions devices are mobile devices whose spatial distribution are assumed to follow a Poisson point process with respect to a reference unlicensed link. We investigate how the number of allowed retransmissions and the spectrum efficiency jointly affect the throughput in [bits/s/Hz] of a reference unlicensed link for different licensed network densities, constrained by a given required error rate. The optimal throughput is derived for this case as a function of the network density. We also prove that the optimal constrained throughput can always reach the unconstrained optimal value. Our numerical results corroborate those of the analytical findings, also illustrating how the number of allowed retransmissions that leads to the optimal throughput changes with the error rate requirements.

This paper provides an in-depth analysis of the event-based metering strategy proposed by Simonov et al. This strategy is an alternative to the traditional periodic (time-based) metering where the power demand is average in fixed time periods (e.g. every 15 minutes). The event-based approach considers two thresholds that trigger an event, one related to the (instantaneous) power demanded, other to the accumulated energy consumed. The original work assumed these thresholds fixed for the measurements. Our present contribution relaxes this assumption by proposing a method to set the thresholds from the percentage of the peak power consumption over the period under analysis. This approach, in contrast to the time-based and the fixed thresholds, better captures the actual power demanded when different households with diverse power demand profiles are studied.In this sense, our method provides a more efficient way to store electricity demand data while maintaining the estimation error (in relation to the real-time power demand) under acceptable values. Numerical examples presented to illustrate the advantage and possible drawbacks of the proposed method.

This paper proposes a novel analytical framework based on the Fox H-function distribution. To test its efficacy we focus on a study case where we derive closed form expressions for the distribution of the end-to-end signal to noise ratio, and corresponding outage probability for the dual-hop amplify-and-forward configuration over Weibull-fading channels with channel state information. An extensive simulation campaign was carried out to corroborate the proposed approach.

Data aggregation is a promising approach to enable massive machine-type communication. This paper focuses on the aggregation phase where a massive number of machine-type devices (MTDs) transmit to aggregators. By using non-orthogonal multiple access (NOMA) principles, we allow several MTDs to share the same orthogonal channel in our proposed hybrid access scheme. We develop an analytical framework based on stochastic geometry to investigate the system performance in terms of average success probability and average number of simultaneously served MTDs, under imperfect successive interference cancellation (SIC) at the aggregators, for two scheduling schemes: random resource scheduling and channel-aware resource scheduling (CRS). We identify the power constraints on the MTDs sharing the same channel to attain a fair coexistence with purely orthogonal multiple access (OMA) setups. Then, power control coefficients are found, so that these MTDs perform with similar reliability. We show that under high access demand, the hybrid scheme with CRS outperforms the OMA setup by simultaneously serving more MTDs with reduced power consumption.

This paper investigates a hybrid network configuration in which full-duplex base stations serve half-duplex users on both Uplink and Downlink simultaneously. Users are modeled as a homogeneous Poisson point process while the channel is modeled as a composite fading with correlated Log-normal shadowing and Nakagami-m fading. We characterize the signal-to-interference-ratio at an user of interest, and then evaluate how the network performs in terms of outage probability. We account for the cross-correlation between the user of interest and a random co-site interferer within range. Thus, we provide a valuable insight on on how the hybrid network performs under the assumption of correlated shadowing. We show that when the aforesaid correlation is low, the user of interest can achieve higher data rate at expense of high outage; however, if the distance to the serving base station is short and the cross correlation is high, a satisfactory data rate can be sustained at low outage.

This letter deals with the secrecy capacity of the radio channel in interference-limited regime. We assume that interferers are uniformly scattered over the network area according to a Point Poisson Process and the channel model consists of path-loss, log-normal shadowing and Nakagami-m fading. Both the probability of non-zero secrecy capacity and the secrecy outage probability are then derived in closed-form expressions using tools of stochastic geometry and higher-order statistics. Our numerical results show how the secrecy metrics are affected by the disposition of the desired receiver, the eavesdropper and the legitimate transmitter.

The problem of throughput optimization in decentralized wireless networks with spatial randomness under queue stability and packet loss constraints is investigated in this paper. Two key performance measures are analyzed, namely the effective link throughput and the network spatial throughput. Specifically, the tuple of medium access probability, coding rate, and maximum number of retransmissions that maximize each throughput metric is analytically derived for a class of Poisson networks, in which packets arrive at the transmitters following a geometrical distribution. Necessary conditions so that the effective link throughput and the network spatial throughput are stable and achievable under bounded packet loss are determined, as well as upper bounds for both cases by considering the unconstrained optimization problem. Our results show in which system configuration stable achievable throughput can be obtained as a function of the network density and the arrival rate. They also evince conditions for which the per-link throughput-maximizing operating points coincide or not with the aggregate network throughput-maximizing operating regime.

We introduce a model for capturing the effects of beam misdirection on coverage and throughput in a directional wireless network using stochastic geometry. In networks employing ideal sector antennas without sidelobes, we find that concavity of the orientation error distribution is sufficient to prove monotonicity and quasi-concavity (both with respect to antenna beamwidth) of spatial throughput and transmission capacity, respectively. Additionally, we identify network conditions that produce opposite extremal choices in beamwidth (absolutely directed versus omni-directional) that maximize the two related throughput metrics. We conclude our paper with a numerical exploration of the relationship between mean orientation error, throughput-maximizing beamwidths, and maximum throughput, across radiation patterns of varied complexity.

This paper introduces a novel approach to evaluate the performance of ad hoc networks based on their spatial capacity, defined as the maximum spatial spectral efficiency supported by the network while a zero outage probability is guaranteed for all communication links. Specifically, the spatial capacity together with upper and lower bounds is derived in closed-form for networks where transmitters follow a Poisson point process and their respective receivers are located at a fixed distance. In this scenario, the spatial capacity is achieved using a rate adaptation technique that adjusts the link spectral efficiency in accordance with the distance between the receivers and their closest interferers. Besides, the spatial capacity is analytically proved to be always greater than or equal to the maximum spatial spectral efficiency achieved when a fixed spectral efficiency and an unbounded outage probability are considered. Numerical results show that the spatial-capacity-achieving setting leads to a spatial spectral efficiency about 125% higher than the one reached with the fixed spectral efficiency strategy.

In this paper, a new measure of outage-constrained network area spectral efficiency, coined as stable transmission capacity, is introduced, which is achievable under finite delay and queue-length stability. Specifically, this framework extends the transmission capacity formulation to scenarios with packet retransmissions and transmitters' queues with independent packet arrivals. This approach is applied to single-hop wireless networks with nodes being spatially distributed as Poisson point process, slotted ALOHA medium access protocol, packet arrivals following a geometrical distribution, and bounded number of retransmissions. The stable transmission capacity is then obtained as the solution of a constrained optimization problem with respect to the probability that a transmitter access the network, the link spectral efficiency, and the maximum number of retransmissions. Using the unconstrained stable transmission capacity as an upper bound, it is shown under which operating points and network parameters such limit can be achieved. Our numerical results also evince how the spatial density and the arrival process affect the network performance.

In this work we investigate whether it is preferable to have a large number of short single-hop links or a small number of long single-hops in a multi-hop wireless network. We derive analytical expressions to compute the metric aggregate multi-hop information efficiency under different hopping strategies, and analyze the trade-off involving robustness of single-hop links, interference and hopping strategy.

In this paper we investigate the transmission capacity of wireless networks when packet retransmissions are allowed. We consider networks modeled as a homogeneous Poisson point process operating under different medium access control schemes, namely unslotted and slotted ALOHA, and CSMA with carrier sensing at the transmitter and with carrier sensing at the receiver. For these scenarios, we derive analytical expressions to compute the maximum number of retransmissions attempts that leads to the optimal transmission capacity. Numerical results based on our formulation show that CSMA with carrier sensing at the receiver (asynchronous transmissions) reaches the highest maximum transmission capacity when traffic intensity is low, while slotted ALOHA (synchronous transmissions) is the best choice when traffic intensity is high.

In this contribution, we consider the performance of the CSMA MAC protocol in multi-hop ad hoc networks in terms of "aggregate multi-hop information efficiency". Our model consists of a wireless network where transmitter nodes are distributed according to a homogeneous 2-D Poisson point process, and packets are generated following a Poisson distribution. Each packet is forwarded to its destination a fixed distance away from the source through an arbitrary number of hops. Approximate analytical expressions are derived for the outage probability of CSMA in its various incarnations, considering different values for the sensing threshold (related to the backoff decision) and the required communication threshold (which determines a correct packet reception). The aggregate multi-hop information efficiency is evaluated as a function of the transmission density, the communication rate, the maximum number of permitted backoffs and retransmissions, and the number of hops. Our results indicate the existence of optimal operating points for achieving maximal efficiency, and a basis is thus established for the optimization of the system parameters in order to improve the performance of multi-hop ad hoc networks.

The transmission capacity (TmC) of an ad hoc network measures the area spectral efficiency (in bits/sec × Hz×m2) of successful transmissions as a function of the required transmission rate in single-hop links ¿, under the assumption that the average density of active links ¿atin the network is given. In reality, however, the probability that a node wishing to transmit becomes active is conditioned on the availability of a receiving peer. Consequently, ¿atis not a given parameter but rather a function of topological parameters. In this paper, we employ stochastic-geometric tools to obtain an expression of ¿atas a function of the transmission range d and network density ¿, and apply the result to evaluate the transmission capacity of autonomous interference-limited networks. We then use the TmC to study the impact of closest-neighbor, furthest-neighbor and random-neighbor hopping strategies on the performance of such networks. It is shown that amongst these alternatives, the closest-neighbor strategy always achieves the highest transmission capacity. Furthermore, it is found that the advantage of closest-neighbor hopping is more significant in networks with high densities, large transmission ranges and/or higher required rates, where interference is the dominant limiting factor. In noninterference-limited networks, however, the three strategies are equivalent.

We introduce multi-hop aggregate information efficiency (MIEA), a comprehensive metric that captures several performance-affecting factors of wireless ad hoc networks in a unified formulation. This metric is then employed to analyze such networks with respect to their spectral efficiencies, network loads, and hopping strategies. The analysis reveals that the hopping strategy that achieves maximum information efficiency is that of multiple short hops with no more than a single packet retransmission allowed at each hop, as opposed to the alternative of fewer long-haul hops with multiple packet retransmissions. The implementation of that preferred strategy withstanding, it is found furthermore that the most efficient networks typically exhibit about 65 % of link outage probability, which corroborates similar findings obtained in different network settings and using different metrics. Bearing in mind that link outage is a function not only of deterministic parameters such as node density, but also of design parameters such as modulation, our analysis also shows that the modulation scheme that optimizes the aggregate information efficiency is in fact a function of node density. In that respect, our metric and method is shown to be useful to determining the modulation scheme that optimizes the performance of a network with a certain node density.

In this paper we evaluate the effects of modulation and coding schemes on the performance of ad hoc networks. The analysis is based on analytical expressions derived for the aggregate information efficiency and the average packet delay in ad hoc networks. Modulation and error correcting coding directly affect the trade-offs involving spectral efficiency, interference immunity and channel reuse in a wireless network. Results of numerical analysis show that lower modulation orders and high coding rates are preferable when one wants to maximize the aggregate information efficiency and minimize the average packet delay.