Integrated access and backhaul (IAB) is an exciting new paradigm currently being investigated in the context of fifth generation (5G) millimeter wave (mm-wave) cellular networks, where the backhaul network (BN) shares the same resource and infrastructure used by the radio access network (RAN). While the IAB design will enable denser deployment of small cell base stations (SBSs) without the support of fiber backhaul, it will also lead to entirely new design challenges, e.g. the resource partitioning at the base stations (BSs) between the access and backhaul links. In order to study the design trends of this new paradigm, we develop a new stochastic geometry- based analytical framework for a two-tier mm-wave HetNet with IAB where the macro BSs (MBSs) provide wireless backhaul to the SBSs. For this network, we derive the downlink rate coverage probability. Our analysis reveals that offloading users from MBSs to SBSs may not provide significant rate improvements as it would if the SBSs were supported by fiber backhaul. We also show that due to the IAB design, there is a limit of densification of the SBSs beyond which no significant rate improvement is observed.

In wireless networks, many problems can be formulated as subset selection problems where the goal is to select a subset from the ground set with the objective of maximizing some objective function. These problems are typically NP-hard and hence solved through carefully constructed heuristics, which are themselves mostly NP-complete and thus not easily applicable to large networks. On the other hand, subset selection problems occur in slightly different context in machine learning (ML) where the goal is to select a subset of high quality yet diverse items from a ground set. This balance in quality and diversity is often maintained in the ML problems by using determinantal point process (DPP), which endows distributions on the subsets such that the probability of selecting two similar items is negatively correlated. While DPPs have been explored more generally in stochastic geometry (SG) to model inter-point repulsion, they are particularly conducive for ML applications because the parameters of their distributions can be efficiently learnt from a training set. In this paper, we introduce a novel DPP-based learning (DPPL) framework for efficiently solving subset selection problems in wireless networks. The DPPL is intended to replace the traditional optimization algorithms for subset selection by learning the quality-diversity trade-off in the optimal subsets selected by an optimization routine. As a case study, we apply DPPL to the wireless link scheduling problem, where the goal is to determine the subset of simultaneously active links which maximizes the network-wide sum-rate. We demonstrate that the proposed DPPL approaches the optimal solution with significantly lower computational complexity than the popular optimization algorithms used for this problem in the literature.

With the emergence of integrated access and backhaul (IAB) in the fifth generation (5G) of cellular networks, backhaul is no longer just a passive capacity constraint in cellular network design. In fact, this tight integration of access and backhaul is one of the key ways in which 5G millimeter wave (mm-wave) heterogeneous cellular networks (HetNets) differ from traditional settings where the backhaul network was designed independently from the radio access network (RAN). With the goal of elucidating key design trends for this new paradigm, we develop a stochastic geometry-based analytical framework for a millimeter wave (mm-wave) two-tier HetNet with IAB where only the macro BSs (MBSs) have fiber access to the core network and the small cell BSs (SBSs) are wirelessly backhauled by the MBSs over mm-wave links. For this network, we derive the downlink rate coverage probability for two types of resource allocations at the MBS: 1) integrated resource allocation (IRA): where the total bandwidth (BW) is dynamically split between access and backhaul, and 2) orthogonal resource allocation (ORA): where a static partition is defined for the access and backhaul communications. Our analysis concretely demonstrates that offloading users from the MBSs to SBSs may not provide similar rate improvements in an IAB setting as it would in a HetNet with fiber-backhauled SBS. Our analysis also shows that it is not possible to improve the user rate in an IAB setting by simply densifying the SBSs due to the bottleneck on the rate of wireless backhaul links between MBS and SBS.

Owing to its flexibility in modeling real-world spatial configurations of users and base stations (BSs), the Poisson cluster process (PCP) has recently emerged as an appealing way to model and analyze heterogeneous cellular networks (HetNets). Despite its undisputed relevance to HetNets-corroborated by the models used in the industry-the PCP's use in performance analysis has been limited. This is primarily because of the lack of analytical tools to characterize the performance metrics, such as the coverage probability of a user connected to the strongest BS. In this paper, we develop an analytical framework for the evaluation of the coverage probability, or equivalently the complementary cumulative density function (CCDF) of signal-tointerference-and-noise ratio (SINR), of a typical user in a K-tier HetNet under a max power-based association strategy, where the BS locations of each tier follow either a Poisson point process (PPP) or a PCP. The key enabling step involves conditioning on the parent PPPs of all the PCPs, which allows us to express the coverage probability as a product of sum-product and probability generating functionals (PGFLs) of the parent PPPs. In addition to several useful insights, our analysis provides a rigorous way to study the impact of the cluster size on the SINR distribution, which was not possible using the existing PPP-based models.

With the increasing network densification, it has become exceedingly difficult to provide traditional fiber backhaul access to each cell site, which is especially true for small cell base stations (SBSs). The increasing maturity of millimeter wave (mmWave) communication has opened up the possibility of providing high-speed wireless backhaul to such cell sites. Since mmWave is also suitable for access links, the third generation partnership project (3GPP) is envisioning an integrated access and backhaul (IAB) architecture for the fifth generation (5G) cellular networks in which the same infrastructure and spectral resources will be used for both access and backhaul. In this paper, we develop an analytical framework for IAB-enabled cellular network using which we provide an accurate characterization of its downlink rate coverage probability. Using this, we study the performance of two backhaul bandwidth (BW) partition strategies, (i) equal partition: when all SBSs obtain equal share of the backhaul BW, and (ii) load-based partition: when the backhaul BW share of an SBS is proportional to its load. Our analysis shows that depending on the choice of the partition strategy, there exists an optimal split of access and backhaul BW for which the rate coverage is maximized. Further, there exists a critical volume of cell-load (total number of users) beyond which the gains provided by the IAB-enabled network disappear and its performance converges to that of the traditional macro-only network with no SBSs.

The growing complexity of heterogeneous cellular networks (HetNets) has necessitated a variety of user and base station (BS) configurations to be considered for realistic performance evaluation and system design. This is directly reflected in the HetNet simulation models proposed by standardization bodies, such as the 3rd Generation Partnership Project (3GPP). Complementary to these simulation models, stochastic geometry-based approach, modeling the locations of the users, and the K tiers of BSs as independent and homogeneous Poisson point processes (PPPs), has gained prominence in the past few years. Despite its success in revealing useful insights, this PPP-based K-tier HetNet model is not rich enough to capture spatial coupling between user and BS locations that exists in real-world HetNet deployments and is included in 3GPP simulation models. In this paper, we demonstrate that modeling a fraction of users and arbitrary number of BS tiers alternatively with a Poisson cluster process (PCP) captures the aforementioned coupling, thus bridging the gap between the 3GPP simulation models and the PPP-based analytic model for HetNets. We further show that the downlink coverage probability of a typical user under maximum signal-tointerference-ratio (SIR) association can be expressed in terms of the sum-product functionals over PPP, PCP, and its associated offspring point process, which are all characterized as a part of our analysis. We also show that the proposed model converges to the PPP-based HetNet model as the cluster size of the PCPs tends to infinity. Finally, we specialize our analysis based on general PCPs for Thomas and Matérn cluster processes. Special instances of the proposed model closely resemble the different configurations for BS and user locations considered in 3GPP simulations.

With the increasing network densification, it has become exceedingly difficult to provide traditional fiber backhaul access to each cell site, which is especially true for small cell base stations (SBSs). The increasing maturity of millimeter wave (mm-wave) communication has opened up the possibility of providing high-speed wireless backhaul to such cell sites. Since mm-wave is also suitable for access links, the third generation partnership project (3GPP) is envisioning an integrated access and backhaul (IAB) architecture for the fifth generation (5G) cellular networks in which the same infrastructure and spectral resources will be used for both access and backhaul. In this paper, we develop an analytical framework for IAB-enabled cellular network using which its downlink rate coverage probability is accurately characterized. Using this framework, we study the performance of three backhaul bandwidth (BW) partition strategies: 1) equal partition: when all SBSs obtain equal share of the backhaul BW; 2) instantaneous load-based partition: when the backhaul BW share of an SBS is proportional to its instantaneous load; and 3) average load-based partition: when the backhaul BW share of an SBS is proportional to its average load. Our analysis shows that depending on the choice of the partition strategy, there exists an optimal split of access and backhaul BW for which the rate coverage is maximized. Further, there exists a critical volume of cell-load (total number of users) beyond which the gains provided by the IAB-enabled network disappear and its performance converges to that of the traditional macro-only network with no SBSs.

In this paper we develop a comprehensive analytical framework for a cellular network enhanced with in-band device-to-device (D2D) communication capability where the D2D links reuse the downlink resources of the cellular links. The locations of the cellular base stations (BSs) are modeled as a Poisson point process (PPP). The user positions are modeled as a Thomas cluster process (TCP) to capture the inherent tendency of users to be located at close proximity to each other. The bandwidth allocated to D2D transmission is fixed and to cellular transmission is dynamic dependent on the load (the number of users) served by the macro BS. The users inside a cluster can either establish a D2D connection with a potential D2D transmitter (Tx) containing the file of interest within the same cluster if it is located closer than certain distance threshold or otherwise connects to the cellular network. By increasing this distance threshold, cellular traffic can be offloaded to D2D connections which in turn increases the interference from simultaneously active D2D Txs. We characterize the downlink rate coverage probability for this setup. Our analysis shows that there exists an optimum distance threshold for which rate coverage is maximized.

One of the principal underlying assumptions of current approaches to the analysis of heterogeneous cellular networks (HetNets) with random spatial models is the uniform distribution of users independent of the base station (BS) locations. This assumption is not quite accurate, especially for user-centric capacity-driven small cell deployments where low-power BSs are deployed in the areas of high user density, thus inducing a natural correlation in the BS and user locations. In order to capture this correlation, we enrich the existing K-tier Poisson point process (PPP) HetNet model by considering user locations as Poisson Cluster Process with the BSs at the cluster centers. In particular, we provide the formal analysis of the downlink coverage probability in terms of a general density function describing the locations of users around the BSs. The derived results are specialized for two cases of interest: 1) Thomas cluster process, where the locations of the users around BSs are Gaussian distributed and 2) Matérn cluster process, where the users are uniformly distributed inside a disc of a given radius. Tight closed-form bounds for the coverage probability in these two cases are also derived. Our results demonstrate that the coverage probability decreases as the size of user clusters around BSs increases, ultimately collapsing to the result obtained under the assumption of PPP distribution of users independent of the BS locations when the cluster size goes to infinity. Using these results, we also handle mixed user distributions consisting of two types of users: 1) uniformly distributed and 2) clustered around certain tiers.

Current approaches to the analysis of heterogeneous cellular networks (HetNets) with random spatial models assume users to be distributed according to a homogeneous Poisson Point Process (PPP) independently of the base station (BS) locations. In reality, however, current deployments are capacity-driven, which correlates the BS and user locations. In this paper, we develop tools for the downlink analysis of HetNets with general nonuniform user distributions by enriching the K-tier PPP HetNet model. Instead of being PPP distributed, the user locations are modeled by a Poisson cluster process with the cluster centers being the BSs. In particular, we provide the first formal analysis of the downlink coverage probability in terms of a general density function describing the locations of users around the BSs. All the results are specialized to a particular case of a Thomas cluster process, where the locations of the users around BSs are Gaussian distributed. Our results concretely demonstrate that the coverage probability decreases with the increasing variance of the user location distribution, ultimately collapsing to the result for the PPP user distribution when the variance goes to infinity.

This paper proposes a novel fuzzy rule-based penalty function approach for solving single-objective nonlinearly constrained optimization problems. Of all the existing state-of-the-art constraint handling techniques, the conventional method of penalty can be easily implemented because of its simplicity but suffers from the lack of robustness. To mitigate the problem of parameter dependency, several forms of adaptive penalties have been suggested in literature. Instead of identifying a complex mathematical function to compute the penalty for constraint violation, we propose a Mamdani type IF-THEN rule-based fuzzy inference system that incorporates all the required criteria of self-adaptive penalty without formulating an explicit mapping. Effectiveness of the proposed constrained optimization algorithm has been empirically validated on the basis of the standard optimality theorems from the literature on mathematical programming. Simulation results show that fuzzy penalty not only surpasses its existing counterpart i.e., self adaptive penalty, but also remain competitive against several other standard as well as currently developed complex constraint handling strategies.

The recently developed Kinect sensor has opened a new horizon to Human-Computer Interface (HCI) and its native connection with Microsoft's product line of Xbox 360 and Xbox One video game consoles makes completely hands-free control in next generation of gaming. Games that requires a lot of degree of freedoms, especially the driving control of a car in racing games is best suitable to be driven by gestures, as the use of simple buttons does not scale to the increased number of assistive, comfort, and infotainment functions. In this paper, we propose a Mamdani type-I fuzzy inference system based data processing module which effectively takes into account the dependence of actual steering angle with the distance of two palm positions and angle generated with respect to the sagittal plane. The FIS output variable controls the duration of a virtual “key-pressed” event which mocks the users pressing of actual keys assigned to control car direction in the original game. The acceleration and brake(deceleration) of the vehicle is controlled using the relative displacement of left and right feet. The proposed experimental setup, interfacing Kinect and a desktop based racing game, has shown that the virtual driving environment can be easily applied to any games belonging to this particular genre.

In this paper, a multiobjective evolutionary algorithm based on decomposition (MOEA/D) is proposed to solve the unit commitment (UC) problem as a multiobjective optimization problem (MOP) considering minimizing cost and emission as the multiple objectives. Since UC problem is a mixed-integer optimization problem, a hybrid strategy is integrated within the framework of MOEA/D such that genetic algorithm (GA) evolves the binary variables, while differential evolution (DE) evolves the continuous variables. Further, a novel nonuniform weight-vector distribution (NUWD) strategy is proposed and an ensemble algorithm based on combination of MOEA/D with uniform weight-vector distribution (UWD) and NUWD strategy is implemented to enhance the performance of the presented algorithm. Extensive case studies are presented on different test systems and the effectiveness of the hybrid strategy, the NUWD strategy, and the ensemble algorithm is verified through stringent simulated results. Further, exhaustive benchmarking against the algorithm proposed in the literature is presented to demonstrate the superiority of the proposed algorithm.

Dynamic Constrained Optimization Problems (DCOP) are a unique class of optimization problems where the objective function as well as the constraint functions change with respect to time. Conventional DCO algorithms involve Genetic Algorithms (GAs) accompanied by a separate constraint-handling technique e.g., a repair method, or a penalty function. However, ordinary repair methods with elitism significantly decrease the diversity of the population during the exploitation stage and penalty functions cannot properly deal with disconnected feasible regions. In this paper, we propose a new approach based on the Gravitational Search Algorithm as well as a modified version of a repair method that produces improved results. The proposed approach incorporates knowledge-reusing and knowledge-restarting in order to produce a quick recovery and faster convergence.