Multiple-input multiple-output (MIMO) systems find immense potential and applicability in the long term evolution (LTE), 5G, Internet of Things (IoT), vehicular ad hoc networks (VANETs), and tactical communication systems. Jamming poses significant communication hindrance as well as security risks to the wireless communication systems. The achievable rate and reliability are the two most compromised aspects of a wireless link under such severe jamming. Owing to the high capacity and reliability of MIMO systems, they are increasingly used in tactical and critical applications. Therefore, it becomes essential to assess and enhance their sustenance under hostile jamming scenarios. To this end, we address the rate and reliability requirements of a MIMO OFDM system and propose a novel rate-reliability beamformer transceiver design for highly reliable and spectrally efficient operation under the most detrimental jamming attacks. We consider the disguised all band and multiband jamming where the jammer continuously attempts to mimic the legit transmissions. Additionally, we evaluate the rate and reliability performance under barrage jamming.The significant contributions of the proposed rate-reliability beamformer scheme are: (i) achieves a minimum of 2 orders of magnitude better reliability in contrast to the state-of-the-art, (ii) outperforms the state-of-the-art scheme by 1.4× with regards to achievable spectral efficiency, (iii) a very low complexity (O (|Q|)) decoder is presented, and (iv) first work to evaluate the performance of state-of-the-art transmit diversity scheme under hostile jamming attacks.

In this paper, we present the preliminary work towards providing a complete end-to-end solution that can connect survivors of a disaster with each other and public safety authorities using a completely self-sufficient ad hoc network. Accordingly, we develop a Heterogeneous Efficient Low Power Radio (HELPER) that acts as a WiFi (Wireless Fidelity) access point for end-users to connect using custom application. These HELPERs then coordinate with each other to form a LoRa based ad hoc network. The proposed solution will use a distributed optimized cross-layer routing algorithm that aims to maximize the network lifetime. This aspect is critical especially in energy-limited scenarios after a disaster. Some of the envisioned services include text and voice messages, live map updates, ability to send distress messages (like 911 calls) to authorities. HELPER network can also be used by authorities to remotely monitor the connectivity of the affected area, alert users of imminent dangers and share resource information. We intend to provide resources (code and instructions) that will enable the researchers of the community to set up a HELPER in a cost-effective (<; $ 150) manner using commercial off-the-self components and advance it further. Overall, we hope this technology will become instrumental in improving the efficiency and effectiveness of public safety activities.

A low cost, portable, robust jammer detection, and localization device is proposed and developed in this work. Intentional or unintentional use of jammers is detrimental to the seamless operation of emergency rescue and public safety missions as it disrupts the critical communication devices. The proposed device employs robust parallel detection algorithms based on Kurtosis and FRactional Fourier Transform (FRFT) to detect the most common types of Radio Frequency Interference (RFI) that affects critical communication signals. As part of preliminary performance analysis, the proposed detection technique is compared to the conventional energy detectors (employed in many commercial interference detectors) and shown to achieve significant improvement (~ 40dB) in probability of detection. The developed device is envisioned to revolutionize the low cost, portable spectrum interference monitoring sector.

The upcoming 5G (5th Generation) networks demand high-speed and high spectral-efficiency communications to keep up with the proliferating traffic demands. To this end, Massive multiple-input multiple-output (MIMO) techniques have gained significant traction owing to its ability to achieve these without increasing bandwidth or density of base stations. The preexisting space-time block code (STBC) designs cannot achieve a rate of more than 1 for more than two transmit antennas while preserving the orthogonality and full diversity conditions. In this paper, we present Jagannath codes - a novel complex modulation STBC, that achieves a very high rate of 2 for three and four transmit antennas. The presented designs achieve full diversity and overcome the previously achieved rates with the three and four antenna MIMO systems. We present a detailed account of the code construction of the proposed designs, orthogonality and full diversity analysis, transceiver model and conditional maximum likelihood (ML) decoding. In an effort to showcase the improvement achieved with the presented designs, we compare the rates and delays of some of the known STBCs with the proposed designs. The effective spectral efficiency and coding gain of the presented designs are compared to the Asymmetric Coordinate Interleaved design (ACIOD) and Jafarkhani code. We presented an effective spectral efficiency improvement by a factor of 2 with the proposed Jagannath codes. Owing to the full diversity of the presented designs, we demonstrate significant coding gains (6 dB and 12 dB) with the proposed designs.

The design and implementation of a novel distributed deadline-based routing and spectrum allocation algorithm for tactical ad-hoc networks is reported in this article. Different traffic classes including text, voice, surveillance video, and threat alert among others need to be handled by these networks. Each of these traffic classes have different quality of service (QoS) based deadline requirements. Additionally, these networks are characterized by dynamic channel and traffic conditions that vary with time and location. Even under these conditions, it is critical to receive packets before the deadline expires to make rapid decisions in the battlefield. Therefore, a tactical ad-hoc network should be able to adapt to these requirements and maximize the number of packets delivered to the destination within the specified deadline. A distributed deadline-based routing and spectrum allocation algorithm is designed to maximize the utilization of the available resources and ensure delivery of packets within the deadline constraints. To this end, a weighted virtual queue (VQ) that is used to construct the network utility function is defined. Accordingly, the optimal session, next hop, transmit power, and frequency is determined by the distributed algorithm to ensure efficient utilization of the available resources. Hence, maximizing the delivery of packets to the intended destination within the specified deadline. The 49 node simulation shows up to 35 percent improvement in effective throughput and 26 percent improvement in reliability as compared to joint ROuting and Spectrum Allocation algorithm (ROSA), which does not adapt according to the deadline requirements of the data flowing through the network. As a secondary objective, this work advances the state of the art of the experimental cross-layer framework to address the challenges involved in having such cross-layer algorithms implemented on a testbed. The required flexibility to change the transmission parameters on-the-fly is provided by the proposed framework. The network is designed to enable the data exchange between neighbors using custom designed control packets (which might be different for different algorithms) since this information is critical for nodes to perform optimization. Cross-layer optimization is achieved by means of data management and control entities that enable information exchange between layers. The practicality of the proposed solution was proven by having the novel algorithm implemented on a five-node software defined radio testbed which leverages the proposed cross-layer framework. In contrast to ROSA, the proposed algorithm demonstrated up to 17 percent improvement in terms of throughput and reliability. The performance improvement achieved is expected to increase on a larger network deployment.

In this paper, we propose a novel distributed deadline-based routing and spectrum allocation algorithm for tactical ad-hoc networks. The proposed algorithm will enable nodes to adapt to various deadline requirements unique to each traffic classes. A tactical ad-hoc network needs to handle a variety of data flowing through the network including voice, surveillance video, threat alert among others. Each of these traffic classes may have different quality of service (QoS) based deadline requirements. It is critical to receive these packets before the deadline expires to make crucial decisions in the battlefield. Therefore, the network should be able to adapt to these requirements and maximize the effective throughput. Accordingly, a distributed deadline-based routing and spectrum allocation algorithm is designed to maximize the utilization of the available resources to ensure delivery of packets within the deadline constraints. The simulations show up to 35 % improvement in effective throughput and 26 % improvement in reliability as compared to the routing and spectrum allocation algorithm (ROSA).

In this paper, we address the constrained resource allocation problems arising in the context of spectrum sharing in cognitive radio networks utilizing a multi-dimensional formulation. Given the activity of the primary users (PUs), we consider multiple objectives and constraints, viz., sum rate, fairness, number of active secondary users (SUs), power consumption, and quality of service requirements (of both PUs and SUs). The three dimensions for the optimization task are the assignment of power, frequency, and antenna directionality to various SUs. Efficient heuristic algorithms are developed for five variations of the NP-hard optimization problems. Solution quality tradeoffs are shown for three algorithms, viz., convex relaxation with tree pruning, convex relaxation with gradual removal, and a genetic algorithm (GA); results show that the GA provides a reasonable balance between solution quality and computational effort. The multi-objective problems are solved using a modification of the NSGA-II evolutionary algorithm, obtaining a set of Pareto-optimal solutions under computational constraints.

The objective of this work is to design and implement a novel framework for automatic signal classification techniques (FACT) for software defined radios (SDR) capable of classifying multiple signals simultaneously. The focus of this work is to create a modular classification framework to facilitate the testing and implementation of new classification methods. The framework is divided into three parts: (i) Sensor resource manager (SRM), which performs the initial signal detection, preprocessing and the delegation of secondary receivers to the corresponding signals of interest (SOIs); (ii) Modulation classifier block (MCB), which takes the received signal from SRM and performs the required modulation classification and (iv) Data library and statistical block contains all the templates required to perform classification, thresholds for signal detection and also known parameters of expected signals. To prove the feasibility of the framework, FACT is implemented and tested on a Universal Software Radio Peripheral (USRP) test bed using GNU radio signal processing toolkit. We evaluate the performance of signal detection based on the probability of detection (Pd) in varying signal-to-noise ratios (SNR). Additionally, the USRP based experiments demonstrate FACT operating as a single unit, preforming both blind detection and classification of multiple SOIs using different classification methods.

A Distributed Battle Manager (DBM) system based on Concurrent MACro Partially Observable Markov Decision Processes (CMAC-POMDP) is proposed to manage multiple competing missions. Implementation details are presented for a semi-autonomous management of multiple ad-hoc teams of UAVs operating under extreme uncertainty in a contested environment. The proposed solution is semi-autonomous since command and control teams perform target confirmation, weapon selection, weapon release, and bomb damage assessment. Our proposed approach decomposes the resource management requirements into specialized mission timelines. There would be one adaptive mission timeline for each system objective, and each timeline would be represented by a POMDP. At any time, the DBM would be controlling multiple concurrent timelines that are competing for system resources. Each timeline is solved independently to obtain the optimal MDP solution parameters. These solution parameters are then combined using linear programming with constraints to obtain a suboptimal system solution. The resulting suboptimal solutions provide satisfactory real-time system performance. Under extreme uncertainty, it is preferable to maximize robustness utilizing an adequately performing solution as opposed to finding an optimal but brittle solution. Performance results of the core CMAC-POMDP algorithm are presented for a system implemented to manage multiple heterogeneous sensors addressing multiple simultaneous threats. This simpler resource management system utilizes the same timeline for all of the threats. Simulations demonstrate better than real-time performance even for large numbers of threats. Furthermore, computational requirements are shown to grow linearly with the number of threats. Extension to multiple simultaneous timelines for the DBM system is straightforward.

We analyze the potential for using multiple transmission dimensions in a cognitive radio network (CRN) in terms of the probability of successful communications. We consider a random CRN where the users are randomly distributed in an area of interest. The users can be mobile. We derive the successful communication probability (SCP) with respect to transmit power for different density of primary users and secondary users by including different transmission dimensionalities such as time, frequency and antenna directionality. It is shown that using multiple transmission dimensions improves the SCP significantly. The advantage of using directional antennas for spatial reuse and increased range is also shown in terms of improved SCP.

We propose a modified Bayesian Cramér-Rao lower bound (BCRLB) for nonlinear tracking applications where the prediction distribution conditioned on past measurements is used as the prior. The novelty of the proposed modified BCRLB comes from the fact that it utilizes past measurements, therefore it is specific to the current realization of the track which makes it a useful online tool that can be used for real-time sensor management. The computation of our proposed modified BCRLB is not analytically tractable except under very restricted conditions. Therefore, we also develop a particle based numerical computation method for our modified BCRLB so that this new bound can be easily calculated in real-time using the particles already available from the underlying particle filter which is used to track the target. We show by simulations that our developed numerical computation method approaches to its true analytical value as the number of particles in the particle filter increases.

Efficient localization of a ballistic missile is an important task in missile defense problems. This paper formulates and solves the sensor placement problem for efficient estimation of the missile location. The first part of this paper develops a mathematical framework for the localization of the missile using multiple sensors based on Cramer-Rao lower bound (CRLB) analysis. We derive the Fisher information matrix to facilitate the evaluation of estimation accuracy. The second part of the paper presents an evolutionary algorithm for obtaining the sensor placements. Simulation results show that the evolutionary algorithm outperforms a greedy sensor placement algorithm and obtains sensor placements with very low estimation error.