In fifth generation (5G) and beyond networks, the millimeter-wave frequency band is expected to cater to the ever increasing user demand for enhanced data rates. However, the denser deployments in mmwave with varying user velocities, will lead to frequent handovers (HOs), resulting in degradation of quality of service (QoS) for the end user in terms of both the number of HOs and lower throughput. Non-orthogonal multiple access (NOMA) is one of the promising radio access techniques for throughput enhancement in 5G and beyond networks. Motivated by this, we analyze the performance of a hybrid OMA-NOMA system considering the imperfections in successive interference cancellation (SIC) for various HO algorithms. We propose two HO algorithms: Algorithm 1 - the HO decision is based on the orthogonal multiple access (OMA) signal to interference-plus noise ratio (SINR) in the control plane, whereas, NOMA is only used for rate enhancements in the data plane; and Algorithm 2 - NOMA-based rates are taken into consideration for the HO decision. We compare the proposed algorithms with benchmark OMA based algorithms. Through extensive simulation we show that the proposed algorithms result in significant enhancement in throughput and reduction in the average number of HOs in the system.

Millimeter-waves’ propagation characteristics create opportunities for spatio-temporal spectrum sharing in a variety of contexts, including cognitive spectrum sharing (CSS). However, CSS with omnidirectional sensing is not efficient at mmWave frequencies due to their directional transmission, as this limits secondary networks’ ability to access the spectrum. This inspired us to create an analytical approach using stochastic geometry to examine the implications of directional sensing in millimeter-wave (mmWave) CSS. We explore a cognitive network where multiple secondary transmitter-receiver pairs coexist with multiple primary transmitter-receiver pairs. The positions of the secondary transmitters are modelled using a Poisson point process with corresponding secondary receivers located around them. A threshold on directional transmission is imposed on each secondary transmitter to limit its interference at the primary receiver. We derive the medium-access-probability of a secondary user along with the fraction of the secondary transmitters active at a time-instant. To understand cognition’s feasibility, we derive the primary and secondary links’ coverage probabilities. We provide various design insights. For example, we investigate the interference-threshold’s optimal value while ensuring both link’s coverage and its dependence on various parameters. We find that directionality improves both links’ performance. Further, allowing location-aware secondary-directionality can help achieve similar coverage for all secondary links.

In this work, capacity based optimal best beam set selection methods in MIMO channel condition are presented. Proposed methods are suitable for existing Verizon 5G specifications \cite{VzW} and also scalable to upcoming 3GPP NR\cite{NR} mmWave communications systems. Proposed methods are aimed at improving the performance, robustness and computational efficiency of the user equipment (UE) system where beam measurements are intended. Two methods namely, a two stage capacity maximization (CM) based method and a hybrid power and capacity maximization based method are proposed herein. Capacity based solutions are preferred over received signal strength (RSS) based methods in view of its superior performance in presence of unknown interference and noise. Hybrid method exploits the sparse channels inherent in beamformed mmWave systems. Also, it is preferable as it complies with the evolving 5G communication requirements which need estimation of additional alternative MIMO transmit and receive beam pairs apart from the serving beams to achieve higher throughput by overcoming random beam blockage and misalignment. Efficacy and performance of the proposed beam-selection methods is compared with existing RSS based method. Also computational complexity of the proposed methods are analyzed and numerical results comparing them are presented. Advantages of the capacity based methods proposed are demonstrated through analysis and simulation results.

The volume of mobile data traffic has been driven to an unprecedented high level due to the proliferation of smartphones/mobile devices that support a wide range of broadband applications and services, requiring a next generation mobile communication system, i.e., the fifth generation (5G). Millimeter wave (mmWave) bands can offer much larger available spectrum bandwidth and thus are considered as one of the most promising approaches to significantly boost the capacity in 5G NR. However, devices and network radio nodes operating on mmWave bands suffer from phase noise and without correction of phase noise, the performance of the network could potentially suffer significant losses. In this paper, we investigate the effects of phase noise and provide comprehensive solutions to track the phase noise by using phase tracking reference signals (PT-RS), as currently standardized in 3GPP Release 15. The design aspects such as PT-RS pattern, interference randomization, multi-TRP operation, etc., are investigated and evaluation results are also provided.

Beamforming is the key enabler for wireless communications in the mmWave bands. 802.11 ad and WiGig are wireless technologies that currently use the 60 GHz unlicensed mmWave spectrum via beamforming techniques. Given the widespread study and potential deployments of LTE in the 5GHz unlicensed band, it is likely that 5G systems will be considered for potential deployments in the 60GHz unlicensed spectrum (apart from other unlicensed bands). Therefore, 5G systems must coexist with 802.11 ad and WiGig in this spectrum. The first step to achieve this co-existence is to find the directions in which the nodes using these incumbent technologies are communicating and eliminate those directions to avoid interference. In this paper, we use multi-armed bandits-based algorithms to blindly detect the beam directions (both along azimuth and elevation i.e., 3D-beamforming) used for communication by the incumbents. Numerical results show the superior performance of these algorithms over techniques that are commonly employed in such blind settings.

Can we optimally attack networks (in terms of disrupting the ability of the nodes in the network from communicating) when the network topology is unknown? In this paper, we show that it is not always possible to do so when the network topology is unknown a priori. Specifically, we develop multi armed bandit-based techniques that enable the attacker to learn the best network attack strategies and also discuss the potential limitations that the attacker faces in such blind scenarios.

In this paper, we study jamming attacks against wireless networks. Specifically, we consider a network of base stations (BSs) or access points (APs) and investigate the impact of a fixed number of jammers that are randomly deployed according to a Binomial point process. We investigate the network performance in terms of: 1) the outage probability and 2) the error probability of a victim receiver in the downlink of this wireless network. We derive analytical expressions for both these metrics and discuss in detail how the jammer network must adapt to the various wireless network parameters in order to effectively attack the victim receivers. For instance, we will show that with only 1 jammer per BS/AP: 1) the outage probability of the wireless network can be increased from 1% (as seen in the non-jamming case) to 80% and 2) when retransmissions are used, the jammers cause the effective network activity factor (and hence the interference among the BSs) to be doubled. Furthermore, we show that the behavior of the jammer network as a function of the BS/AP density is not obvious. In particular, a non-trivial behavior is seen, which indicates that the number of jammers required to attack the wireless network must scale with the BS density only until a certain value beyond which it decreases. In the context of error probability of the victim receiver, we study whether or not some recent results related to jamming in the point-to-point link scenario can be extended to the case of jamming against wireless networks. Numerical results are presented to validate all the theoretical inferences presented.

In this paper, we analyze jamming against wireless networks from an error probability perspective. Specifically, we investigate the impact of a fixed number of jammers against a network of base stations (BS) or access points (AP). We first derive analytical expressions for the error probability of a victim receiver in the downlink of this wireless network and later study whether or not some recent results related to jamming in the point-to-point link scenario can be extended to the case of jamming against wireless networks.

Distributed storage systems (DSSs) in the presence of an external wiretapper are considered. A DSS is parameterized by (n, k, d), in which the data are stored across n nodes (each with storage capacity α), and must be recoverable by accessing the contents stored on any k out of n nodes. If a node fails, any d ≥ k out of (n - 1) nodes help in the repair (regeneration) of the failed node (by sending dβ units of repair data, where β ≤ α), so that the data can still be recovered from the DSS. For such a (n, k, d)-DSS, security from the two types of wiretappers is investigated: 1) Type-I (node data) wiretapper, which can read the data stored on any ℓ <; k nodes and 2) Type-II (repair data) wiretapper, which can read the data that is used to repair a set of ℓ failed nodes. The focus of this paper is on the optimal tradeoff between the storage (α) and the repair bandwidth (dβ) in presence of a Type-I/Type-II wiretapper and the practically relevant constraint of exact repair in which a failed node must be replaced by its exact replica. In this paper, several new results and outer bounds for the storage-versus-exact-repair-bandwidth tradeoff(s) are obtained for the Type-I and Type-II security problems. Furthermore, new outer bounds are presented for the Type-II problem, which hold for general (n, k, d, ℓ) parameters. It is shown that these outer bounds strictly improve upon the existing cutset-based outer bounds. The key technical contribution of this paper is in developing novel information theoretic converse proofs for these problems. From our optimal characterization results, we show that in a Type-II setting, the only efficient point in the storage-versus-exact-repair-bandwidth tradeoff is the minimum bandwidth regenerating (MBR) point corresponding to α = dβ. This is in sharp contrast to the Type-I setting in which the optimal tradeoff allows a spectrum of operating points beyond the MBR point.

Can an intelligent jammer learn and adapt to unknown environments in an electronic warfare-type scenario? In this paper, we answer this question in the positive, by developing a cognitive jammer that adaptively and optimally disrupts the communication between a victim transmitter-receiver pair. We formalize the problem using a multiarmed bandit framework where the jammer can choose various physical layer parameters such as the signaling scheme, power level and the on-off/pulsing duration in an attempt to obtain power efficient jamming strategies. We first present online learning algorithms to maximize the jamming efficacy against static transmitter-receiver pairs and prove that these algorithms converge to the optimal (in terms of the error rate inflicted at the victim and the energy used) jamming strategy. Even more importantly, we prove that the rate of convergence to the optimal jamming strategy is sublinear, i.e., the learning is fast in comparison to existing reinforcement learning algorithms, which is particularly important in dynamically changing wireless environments. Also, we characterize the performance of the proposed bandit-based learning algorithm against multiple static and adaptive transmitter-receiver pairs.

The exponential back-off mechanism, proposed for reducing MAC- layer contention in the 802.11 standard, is sub-optimal in terms of the network throughput. This back-off mechanism and its improved variants are especially inefficient under unknown dynamics such as packet arrivals and user entry/exit. In this paper, we formulate the problem of optimizing this back-off mechanism as a Markov decision process, and propose online learning algorithms to learn the optimal back-off schemes under unknown dynamics. By exploiting the fact that some components of the system dynamics (such as protocol states) are known because the users follow the common 802.11 protocol, we propose a post-decision state (PDS)- based learning algorithm to speed up the learning process. Compared to traditional Q-learning algorithms, the advantages of the proposed online learning algorithm are that 1) it exploits partial information about the system so that less information needs to be learned in comparison to other learning algorithms, and 2) it removes the necessity for action exploration which usually impedes the learning process of conventional learning algorithms (such as Q-Learning). We prove the optimality of the proposed PDS-based learning algorithm and via numerical results demonstrate the improvement over existing protocols and Q-learning in terms of throughput and convergence speed. We first address this problem from a single-user perspective and later describe the challenges involved and present new insights into the multi-user learning scenarios, especially in cases where the MDP models of the users are coupled with each other.

Can an intelligent jammer learn and adapt to unknown environments in an electronic warfare-type scenario? In this paper, we answer this question in the positive, by developing a cognitive jammer that disrupts the communication between a victim transmitter-receiver pair. We formalize the problem using a novel multi-armed bandit framework where the jammer can choose various physical layer parameters such as signaling scheme, power level and the on-off/pulsing duration in an attempt to obtain power efficient jamming strategies. We first present novel online learning algorithms to maximize the jamming efficacy against static transmitter-receiver pairs i.e., the case when the victim does not change its communication technique despite the presence of interference. We prove that our learning algorithm converges to the optimal jamming strategy. Even more importantly, we prove that the rate of convergence to the optimal jamming strategy is sub-linear, i.e. the learning is fast, which is important in dynamically changing wireless environments. Also, we characterize the performance of the proposed bandit-based learning algorithm against adaptive transmitter-receiver pairs.

It is well-known that non-line-of-sight (NLOS) propagation degrades the accuracy of localization in indoor and harsh environments. In such environments, source nodes either do not have connections with a sufficient number of anchor nodes due to strong attenuation or have very poor range estimates due to NLOS propagation. Cooperative localization, in which a source node communicates with both anchor nodes and other source nodes, can benefit such scenarios by providing additional measurements that can significantly improve the localization performance. While most of the earlier works have studied static node localization in noncooperative networks, cooperative tracking of mobile nodes in NLOS environments is still an open problem. To address this open problem, in this work we develop an technique for cooperative tracking applicable to NLOS environments. More specifically, we develop a novel node tracking algorithm based on semidefinite programming (SDP) which has the ability to mitigate NLOS propagation. Our simulations show that the proposed SDP estimator outperforms the classic extended Kalman filter for cooperative networks in NLOS environments.

This paper presents a new preprocessing stage that allows for the reliable classification of digital amplitude-phase modulated signals in a practical scenario where: 1) the classifier has no knowledge of the timing (symbol transition epochs) of the received signal; 2) the noise added in the channel is non-Gaussian; and 3) the fading experienced by the signal is frequency selective. The proposed preprocessor, which is based on the Gibbs sampling algorithm, is used to acquire timing information and to estimate the channel state and noise distribution parameters blindly, i.e., without knowledge of the received symbol sequence and the modulation scheme used. With the obtained estimates, in a second processing stage, the signal is then classified by using an appropriate (likelihood- or feature-based) classification algorithm. To quantify the performance of the proposed preprocessor, the probability of correct classification obtained by using the preprocessor with different classification algorithms is presented. It is shown that, by using the proposed preprocessor, modulation classification algorithms can perform well compared with clairvoyant classifiers assumed to be symbol synchronous with the received signal and to have perfect knowledge of the channel state and noise distribution.

Jamming attacks can significantly impact the performance of wireless communication systems, and can lead to significant overhead in terms of re-transmissions and increased power consumption. This paper considers the problem of optimal jamming over an additive white Gaussian noise channel. We derive the optimal jamming signal for various digital amplitude-phase-modulated constellations and show that it is not always optimal to match the jammer's signal to the victim signal in order to maximize the error probability at the victim receiver. Connections between the optimum jammer obtained in this analysis and the well-known pulsed jammer, popularly analyzed in the context of spread spectrum communication systems, are illustrated. The gains obtained by the jammer when it knows the victim's modulation scheme and uses the optimal jamming signals obtained in this paper as opposed to conventional additive white Gaussian noise jamming are evaluated in terms of the additional signal power needed by the victim receiver to achieve the same error rates under these two jamming strategies. We then extend these findings to obtain the optimal jamming signal distribution: 1) when the victim uses an orthogonal frequency-division multiplexing (OFDM)-modulated signal and 2) when there is multiple jammer attacking a single victim transmitter-receiver pair. Numerical results are presented in all the above cases to validate the theoretical inferences presented.

Jamming attacks can significantly impact the performance of wireless communication systems, and can lead to significant overhead in terms of re-transmissions and increased power consumption. This paper considers the problem of optimal jamming over an additive white Gaussian noise channel. We derive the optimal jamming signal for various digital amplitude-phase modulated constellations. We show that it is not always optimal to match the jammer's signal to the victim signal in order to maximize the error probability at the victim receiver. Connections between the optimum jammer obtained in this analysis and the well-known pulsed jammer, popularly analyzed in the context of spread spectrum communication systems are illustrated. Further, we evaluate the value of the knowledge of the victim's modulation schemes by comparing the performance of the optimal jamming signals with conventional additive white Gaussian noise jamming. Numerical results are presented in order to validate the theoretical inferences presented.

Distributed storage systems (DSS) in the presence of a passive eavesdropper are considered in this paper. A typical DSS is characterized by 3 parameters (n, k, d) where, a file is stored in a distributed manner across n nodes and can be recovered entirely from any k out of n nodes. Whenever a node fails, d ∈ [k, n) nodes help in repairing the failed node. The focus of this work is on the exact repair capabilities of a DSS, where a failed node is replaced with an identical node. Securing this DSS from passive eavesdropping attacks is studied in this paper. The eavesdropper is capable of wiretapping the repair process of a subset of nodes in the storage system. The main contribution of this paper is the optimal characterization of the secure storage-vs-exact-repair-bandwidth tradeoff region which prior to this work was unknown. We focus on the simplest nontrivial instances of this problem, namely (n, k, d) = (3, 2, 2) and (4, 3, 3), and present novel information-theoretic converse proofs that validate these optimal tradeoff regions.

The 3-user multiple-input single-output (MISO) broadcast channel (BC) with hybrid channel state information at the transmitter (CSIT) is considered. In this framework, there is perfect and instantaneous CSIT from a subset of users and delayed CSIT from the remaining users. We present new results on the sum degrees of freedom (DoF) of the 3-user MISO BC with hybrid CSIT. In particular, for the case of 2 transmit antennas, we show that with perfect CSIT from one user and delayed CSIT from the remaining two users, the optimal sum DoF is 5/3. For the case of 3 transmit antennas and the same hybrid CSIT setting, it is shown that a higher sum DoF of 9/5 is achievable and this result improves upon the best known bound. Furthermore, with 3 transmit antennas, and the hybrid CSIT setting in which there is perfect CSIT from two users and delayed CSIT from the third one, a novel scheme is presented which achieves 9/4 sum DoF. Our results also reveal new insights on how to utilize hybrid channel knowledge for multi-user scenarios.

Jamming attacks can significantly impact the performance of wireless communication systems. In addition to reducing the capacity, such attacks may lead to insurmountable overhead in terms of retransmissions and increased power consumption. In this paper, we consider the multiple-input single-output (MISO) broadcast channel (BC) in the presence of a jamming attack in which a subset of the receivers can be jammed at any given time. Further, countermeasures for mitigating the effects of such jamming attacks are presented. The effectiveness of these antijamming countermeasures is quantified in terms of the degrees-of-freedom (DoF) of the MISO BC under various assumptions regarding the availability of the channel state information (CSIT) and the jammer state information at the transmitter (JSIT). The main contribution of this paper is the characterization of the DoF region of the two user MISO BC under various assumptions on the availability of CSIT and JSIT. Partial extensions to the multiuser broadcast channels are also presented.

Jamming attacks can significantly impact the performance of wireless communication systems, and lead to insurmountable overhead in terms of re-transmissions and increased power consumption. In this paper, we consider the multi-user multiple-input single-output (MISO) broadcast channel (BC) in the presence of jamming attacks in which a subset of the users can be selectively jammed at any given time. We present countermeasures for mitigating the effects of such jamming attacks. The effectiveness of these anti-jamming countermeasures is quantified in terms of the degrees-of-freedom (DoF) of the MISO BC under various assumptions regarding the availability of the channel state information (CSIT) and the jammer state information at the transmitter (JSIT).