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Communication networks are becoming increasingly important in military operations, with task fields like time-critical targeting, covert special operations, command and control, training, and logistics, all relying heavily on the communication network and its services. On the other hand, commercial communication has dramatically transformed our society and the way we communicate. The newest network mode at present, 5G and beyond (5GB), is characterized by high speed, low latency, high reliability, and high communication density. Although the use of 5GB commercial networks for defense agencies can offer greater flexibility and efficiency, they also face a new challenge that requires high standards of network protection and harsh working conditions and environments. In this paper, we discuss the significance of communication networks in several potential military applications, particularly for warfare, training/drilling, logistics, and special mission-specific stations. We present the communication trends adopted in military applications. Then, we open up various 5GB key performance indexes and their use cases for the military communication systems. We also elaborate on unique challenges of the military communication networks that are unlikely to be resolved via commercial 5GB research. The various 5GB enabling technologies for military communication systems are discussed. Lastly, we present and analyze 5GB new radio for the private military communication under C-band.

With the ubiquitous growth of Internet-of-things (IoT) devices, current low-power wide-area network (LPWAN) technologies will inevitably face performance degradation due to congestion and interference. The rule-based approaches to assign and adapt the device parameters are insufficient in dynamic massive IoT scenarios. For example, the adaptive data rate (ADR) algorithm in LoRaWAN has been proven inefficient and outdated for large-scale IoT networks. Meanwhile, new solutions involving machine learning (ML) and reinforcement learning (RL) techniques are shown to be very effective in solving resource allocation in dense IoT networks. In this article, we propose a new concept of using two independent learning approaches for allocating spreading factor (SF) and transmission power to the devices using a combination of a decentralized and centralized approach. SF is allocated to the devices using RL for contextual bandit problem, while transmission power is assigned centrally by treating it as a supervised ML problem. We compare our approach with existing state-of-the-art algorithms, showing a significant improvement in both network level goodput and energy consumption, especially for large and highly congested networks.

Hybrid visible light communication and radio frequency (VLC/RF) networks provide combined benefits of both visible light and radio frequency technologies. This is an eminent technology for providing 5G services in an indoor environment. Because of small coverage range, multiple VLC cells co-exist in a close proximity. For efficiently utilizing resources of multiple VLC access points (VLC-APs), a different approach than conventional multi RF-APs based resource allocation schemes is required due to unique characteristics of light. In this paper, we provide an optimized solution for combined power and slot allocation in hybrid VLC/RF networks with the objective of maximizing sum rate for a downlink communication scenario. The resources are optimized on frame by frame basis, instead of per request basis. This frame-based optimization reduces the frequency of optimization and also improves the utilization of the resources. The non-convex problem of joint slot and power allocation is divided into less complex sub problems and solved iteratively. The performance of the joint slot and power allocation (JSPA) scheme has been analyzed for data rate and fairness factor for varying values of network size and minimum quality of service (QoS) requirements. The analysis exhibits on average more than 65% improvement in both fairness factor and average sum rate as compared to single VLC-AP based scheme (SV O RF), in which single VLC-AP is utilized for a user and RF-AP is utilized to offload traffic when VLC resources are not sufficient. Moreover, we propose a hybrid user-centeric and network-centeric (H-Uc/Nc) scheme for slot allocation which entails far less computational complexity. Although, H-Uc/Nc scheme performs inferior to optimized scheme, however, it exhibits much better performance than (SV O RF).

Electroencephalogram (EEG) is one of the fundamental tools for analyzing the behavior of brain and particularly helpful for treatment of epilepsy and detection of associated seizures. For long-term recording of EEG signals, current research is heading towards simple, unobtrusive and ambulatory devices with a small number of channels. The primary contribution of this paper is to assess the performance difference between the seizure detection results using features from all channels versus only the channels in/around the temporal region. For this purpose, we develop a supervised seizure detection algorithm that uses time domain features extracted sequentially for every 1-second epoch. By using this algorithm, we obtained sensitivity values of 0.95 and 0.92, specificity values of 0.99 and 0.99 and false positive per hour values as 0.16 and 0.21 for all 23 channels and 10 temporal region channels, respectively. These results show that restricting the EEG analysis to temporal region results only in a graceful and gradual degradation of classifier performance. We conclude that EEG ambulatory devices with a montage local to the temporal region could demonstrate satisfactory performance. This presents a promising way forward for the use of ambulatory devices with compact wearable design.

The exponential growth in global mobile data traffic, especially with regards to the massive deployment of devices envisioned for fifth generation (5G) mobile networks, has given impetus to exploring new spectrum opportunities to support the new traffic demands. The millimeter wave (mmWave) frequency band is considered as a potential candidate for alleviating the spectrum scarcity. Moreover, the concept of multi-tier networks has gained popularity, especially for dense network environments. In this paper, we deviate from the conventional multi-tier networks and employ the concept of control-data separation architecture (CDSA), which comprises of a control base station (CBS) overlaying the data base station (DBS). We assume that the CBS operates on the sub-6 GHz single band, while the DBS possesses a dual-band mmWave capability, i.e., 26 GHz unlicensed band, and 60 GHz licensed band. We formulate a multi-objective optimization (MOO) problem, which jointly optimizes conflicting objectives: the spectral efficiency (SE) and the energy efficiency (EE). The unique aspect of this paper includes the analysis of a joint radio resource allocation algorithm based on Lagrangian Dual Decomposition (LDD) and we compare the proposed algorithm with the maximal-rate (maxRx), dynamic sub-carrier allocation (DSA) and joint power and rate adaptation (JPRA) algorithms to show the performance gains achieved by the proposed algorithm.

In a sensor, mesh, or ad hoc network, different transmissions take place between multiple source-destination pairs, which is referred to as data flows. In this paper, each data flow is associated with a certain duty cycle schedule to avoid collisions of multiple flows in an energy-efficient manner. Cooperative routes in the network are assumed to be linear and strip-shaped, where each hop or a level contains multiple nodes, and the distance between the nodes is kept constant. We assume all nodes in a cooperative route, which corresponds to one source-destination pair, follow the same duty cycle schedule, while schedules of different routes are orthogonal in time to avoid interference between routes. Therefore, when a new route is formed, it is important to select a duty cycle schedule that is not already in use by the nodes participating in the new route. As part of route set-up, an orthogonal frequency division multiplexing packet travels from the source to the destination, identifying the schedules in use by the nodes along the path. Our objective in this paper is to analyze the probabilities of detection for this method of detecting schedules, which is useful to gain insights into duty cycling-based medium access protocol design in the presence of multiple data flows. To this end, the successive transmissions in a linear network are modeled using an irreducible discrete time Markov chain. Also, three different approaches to detect a certain schedule are investigated, which are distinguished by the binary integration process with multiple sub-carriers corresponding to the schedule. We derive the probability transition matrix for the Markov chain based on different distributions of the received signal energy, the left eigenvector of which yields the probability of detection.

Continuous phase modulation (CPM) is a power and bandwidth efficient modulation scheme used in cellular, personal, and satellite communications among other applications. If the modulation index of the CPM waveform used by the transmitter is unavailable at the receiver, serious performance degradation may occur. This paper investigates the problem of modulation index estimation for partial response continuous phase modulation schemes. Specifically, we propose two novel estimators for the CPM signal observed in an additive white Gaussian noise channel. A non-data aided method of moments (MoM) estimator based upon the fourth-order cumulants and a data-aided best linear unbiased estimator (BLUE) based upon an approximate linear phase model of the received CPM signal have been proposed. Modified Cramer-Rao lower bound is derived for the partial response CPM schemes to assess the performance of the estimators. We also perform numerical simulations to compare the mean-squared error (MSE) performance of the proposed MoM and BLUE estimators with previously proposed estimators in the literature. It is shown that the MoM estimator exhibits good MSE performance at low signal-to-noise ratio (SNR) while the BLUE estimator performs very well at high SNRs. Moreover, the proposed estimators have lower complexity than existing methods and are based upon non-iterative algorithms.

Hybrid networks consisting of both millimeter wave (mmWave) and microwave $(\mu W)$ capabilities are strongly contested for next-generation cellular communications. A similar avenue of current research is device-to-device (D2D) communications, where users establish direct links with each other rather than using central base stations. However, a hybrid network, where D2D transmissions coexist, requires special attention in terms of efficient resource allocation. This paper investigates dynamic resource sharing between network entities in a downlink transmission scheme to maximize energy efficiency (EE) of the cellular users (CUs) served by either $(\mu W)$ macrocells or mmWave small cells while maintaining a minimum quality-of-service (QoS) for the D2D users. To address this problem, first, a self-adaptive power control mechanism for the D2D pairs is formulated, subject to an interference threshold for the CUs while satisfying their minimum QoS level. Subsequently, an EE optimization problem, which is aimed at maximizing the EE for both CUs and D2D pairs, has been solved. Simulation results demonstrate the effectiveness of our proposed algorithm, which studies the inherent tradeoffs between system EE, system sum rate, and outage probability for various QoS levels and varying densities of D2D pairs and CUs.

Reliable detection of the onset of epileptic seizures has seen renewed interest over the past few years, owing to several factors including, the global push toward digital health-care, the advancements in signal processing techniques, and the increased computational power of machines. A reliable automatic system could result in tremendous improvement in the quality of life of epilepsy patients. This paper presents dynamic mode decomposition (DMD), a data-driven dimensionality reduction technique, originally used in fluid mechanics, as an instrument for epileptic seizure detection from scalp electroencephalograph (EEG) data. DMD is employed in this paper to measure power of signals in different frequency bands. These subband-powers, along with signal curve lengths, are used as features for training random under-sampling boost decision-tree classifier. Post-processing measures ensure an acceptable balance between false positives and true positives. The proposed algorithm has been tested over a thousand hours of EEG data from two different data sets, the CHB-MIT data set and the KU Leuven data set, giving sensitivity values of 0.87 and 0.88, respectively, and specificity values of 0.99 for both the data sets.

The deployment of heterogeneous networks (HetNets) inevitably demands the design of interference management techniques to elevate the overall network performance. This paper presents a novel interference mitigation technique known as reverse frequency allocation (RFA), which provides an efficient resource allocation compared with the other state-of-the-art techniques. RFA reverses the transmission direction of interferers, thereby minimizing the cross-tier interference. Eventually, better coverage as well as increased data rates are achieved by providing complementary spectrum to the macro and pico users. In this paper, we present a tractable approach for modeling HetNets under the proposed RFA scheme. Specifically, we employ well known tools from stochastic geometry to derive closed-form expressions for the coverage probability and rate coverage in two-tier cellular network employing RFA and its variants. The modeling is performed using two approaches; first, where the base stations and users are modeled as independent Poisson point processes (PPPs) and second, the interference is approximated using the fluid model. It is shown that the results obtained from the PPP model are accurate for higher values of path loss exponents, while the results from fluid model are useful for smaller values of path loss exponents. The plausibility of model is validated through the Monte-Carlo simulations and the network performance is evaluated in terms of coverage probability, coverage rate, and outage capacity. The results demonstrate that 2-RFA yields outage capacity gains of 13% as compared with the soft fractional frequency reuse scheme, whereas, the performance gains can be further improved by 14% by employing the proposed variants of RFA.

Current resource allocation techniques in cellular networks are largely based on single-slope path loss model, which falls short in accurately capturing the effect of physical environment. The phenomenon of densification makes cell patterns more irregular; therefore, the multi-slope path loss model is more realistic to approximate the increased variations in the links and interferences. In this paper, we investigate the impacts of multi-slope path loss models, where different link distances are characterized by different path loss exponents. We propose a framework for joint user association, power and subcarrier allocation on the downlink of a heterogeneous network (HetNet). The proposed scheme is formulated as a weighted sum rate maximization problem, ensuring the users' quality-of-service requirements, namely users' minimum rate, and the base stations' (BSs) maximum transmission power. We then compare the performance of the proposed approach under different path loss models with demonstrate the effectiveness of dual-slope path loss model in comparison to the single-slope path loss model. Simulation results show that the dual-slope model leads to significant improvement in network's performance in comparison to the standard single-slope model by accurately approximating the path loss exponent dependence on the link distance. Moreover, it improves the user offloading from macrocell BS to small cells by connecting the users to nearby BSs with minimal attenuation. It has been shown that the path loss exponents significantly influence the user association lying across the critical radius in the case of the dual-slope path loss model.

In this paper, a strip-shaped multi-hop ad hoc network is analyzed using a spatial Poisson point process (PPP) and stochastic geometry. The decode-and-forwardprotocol is considered for transmission over the multi-hop network where cooperative communications is employed at each hop. An analytical expression for the probability density function of the received power at an arbitrary node is derived, given a set of nodes transmits in the previous hop, which is further used to characterize the coverage performance of the network. The received power at a node becomes a doubly stochastic process owing to random path loss and a Rayleigh fading channel. The notions of one-hop success probability and coverage range are analyzed for various network parameters. An algorithm for conserving energy is also proposed by considering PPP thinning and its performance in terms of the fraction of energy saved is quantified. It is shown that the proposed algorithm is more energy efficient as compared with an independent thinning algorithm.