This paper proposes a hybrid infrastructure-to-vehicle (I2V) communication framework to support future 6G-enabled intelligent transportation systems (ITS) in smart cities. Leveraging existing LED streetlighting infrastructure, the system simultaneously delivers energy-efficient illumination and high-speed wireless connectivity. The proposed scheme integrates visible light communication (VLC) with a complementary terahertz (THz) antenna array to overcome VLC limitations under high ambient light and adverse weather conditions. Key contributions include the design of a VLC/THz access network, seamless integration with lighting infrastructure, a proposed switching-combination (PSC) mechanism, and a physical layout optimization strategy. Using a grid search method, thousands of configurations were evaluated to maximize lighting coverage, received power, signal-to-noise ratio (SNR), signal-to-interference-and-noise ratio (SINR), and minimize outage probability. Results show that optimized lighting coverage improves from 35% to 97%, while hybrid communication coverage increases from 49% to 99.9% at the same power level. Under extreme environmental conditions, the hybrid system maintains up to 99% coverage, compared to 69% with VLC alone. These results demonstrate the scalability, cost-efficiency, and practicality of the proposed system for next-generation ITS deployment.

In this paper, we introduce a stochastic shape optimization technique for base-station placement in cellular wireless communication networks. We formulate the data-driven facility location problem in a gradient-based framework and propose an algorithm that computes stochastic gradients efficiently via nearest-neighbor evaluations on Voronoi diagrams. This enables the use of Shannon-entropy objectives that promote balanced coverage and yield more than two orders of magnitude reduction in per-iteration runtime compared to a conventional integral-based optimization that assumes full knowledge of the under-lying density, making the proposed approach practical for real deployments. We highlight the requirements of facility location balancing problems with the introduction of the Adjusted Entropy Ratio and show a significant improvement in load balancing compared to the baseline algorithms, particularly in scenarios where baseline algorithms fall short in subdividing crowded areas for more equitable coverage. A downlink telecom evaluation with realistic propagation and interference models further shows that the proposed method configuration substantially improves user-rate fairness and load balance. Our results also show that Self-Organizing Maps (SOMs) provide an effective initialization by capturing the structure of the users’ location data.

The extension of elastic optical network (EON) technologies to multi-band transmission (MB-EON) promises enhanced spectral efficiency, greater throughput, and long-term cost benefits for telecom operators. However, designing such networks presents challenges, particularly in optimizing physical parameters like optical power and quality of transmission (QoT) across different frequency bands. This paper introduces a methodology for optimal span-by-span power allocation using two hyper-accelerated power optimization (HPO) modes: flat launch power (FLP) and flat received power (FRP). This methodology significantly accelerate network power optimization while ensuring service stability in scenarios such as changes in network parameters, QoT degradation due to aging, and network re-optimization or upgrading. Through a comprehensive comparison, we find that FRP notably improves signal flatness and GSNR/OSNR, particularly in the S-band, contributing to a network-wide throughput increase in the order of 12% to 75%. Additionally, we demonstrate that HPO applied to global power optimization is simpler and more cost-effective than when applied to local methods for large-scale networks.

Cell-free massive MIMO (CF-mMIMO) network and its user-centric (UC) version are considered as promising techniques for the next generations of wireless networks. However, fronthaul and backhaul assignments are challenging issues in these networks. In this paper, energy efficiencies of uplink transmission for the CF- and UC-mMIMO networks are studied, wherein access points (APs) are connected to aggregation nodes (ANs) through radio frequency (RF) and/or free-space optic (FSO) fronthauls, and the ANs are connected to a central processing unit via fiber backhauls. The achievable data rates are derived by taking into account the effects of hardware non-ideality at the APs and ANs, FSO alignment and weather conditions. To have a robust and energy-efficient network, especially in the presence of FSO misalignment and adverse weather conditions, first, a cognitive RF–FSO fronthaul assignment algorithm is proposed at the cost of sharing the available RF bandwidth between the access and fronthaul links. Then, optimal power allocations at the users and APs are investigated, and two analytical approaches are proposed to solve the non-convex optimization problem. Through numerical results, we have discussed how utilizing the cognitive RF–FSO fronthaul assignment achieves higher energy efficiency compared to that of FSO-only, RF-only, or simultaneously using RF and FSO fronthaul links, e.g., achieving up to$198\%$higher energy efficiency under unfavorable conditions. Moreover, the effects of FSO misalignment, weather conditions, and power allocations on the networks’ performances are discussed.

This paper proposes a shared precomputed restoration (SPR) mechanism for link failures in translucent elastic optical networks (EONs). We present SPR with a heuristic algorithm that aims to minimize a cost function. The cost function depends on the number of transceivers and frequency slots (FSs) used to establish a working/protection lightpath (LP) with a tunable parameter that determines the weight of the number of transceivers and FSs in the cost function. SPR precalculates a protection LP for all links of each working LP. As a result, non-link-disjoint working LPs can share protection spectrum and transceivers on newly eased conditions. Like shared backup path protection (SBPP) and dedicated protection (1+1), SPR guarantees single link failure recovery. Our simulation results reveal that SPR outperforms SBPP in terms of recovered bandwidth in multiple link failures. Furthermore, SPR uses fewer transceivers compared to SBPP and 1+1 protection.

With the emergence of various applications with diverse transmission requirements, optical networks must support multiclass service provisioning and prioritization. This paper studies serving multiclass requests in elastic optical networks by defining appropriate controlling parameters (CPs), which can be learned by an artificial neural network (ANN). These CPs can be tuned to allocate resources according to the service class priorities. We propose two routing, modulation, and spectrum assignment (RMSA) mechanisms working together to establish data and flow-oriented connections. Two physical layer impairment methods are leveraged to realize connection provisioning by considering the quality of transmission limitations. The RMSA methods are equipped with four CPs to handle the time and frequency resource consumption competition between request classes. An ANN-based regression model is used to determine the value of each CP for preset request classes’ blocking ratios. Exhaustive simulations are performed and analyzed to scrutinize the impact of each CP on the network status. Simulations reveal the effectiveness of ANN decision-making in policy imposition of allocating resources to each request class.

Future heterogeneous wireless networks (HetNets) should provide massive connectivity for a large number of devices. In such networks, the problem of user association and multiple access management is of paramount importance. This paper formulates a statistical representation of the user association problem using users' probability density function. In other words, this paper is a bridge between the dynamic mixed-integer formulation of the user association used in relaxation, game theory, and reinforcement learning approaches and the static user association considered in analytical stochastic geometry methods. To this aim, a novel representation independent of the number of users is derived from the combinatorial user association formulation. Interestingly, we show that for fair user association, the statistical representation is a multi-objective optimization. The first objective is to maximize the network throughput with fairness consideration, and the second objective is to optimize the load balancing in terms of the Shannon entropy. Based on this representation, we introduce an algorithm to optimize user association using the first-order derivative formula. We propose a method that can optimize individual base stations' bias factors inside each tier of a HetNet. Numerical results show that the statistical representation closely tracks the stochastic behavior of the dynamic association. The proposed optimization approach improves the 10% outage rate of a two-tier network by 19% and enhances the load balancing by reducing the load of macro base stations by 22%.

In this paper, a novel method is proposed to study the tradeoff between energy efficiency (EE) of small-cell users and unmanned aerial vehicles (UAV) users in multi-cell orthogonal frequency division multiple access (OFDMA)-based networks. Contrary to the prior works that only maximize the EE of the UAV network subject to some constraints on transmit power of UAV users, we formulate a multi-objective optimization problem (MOOP) that jointly maximize the EE of small-cell and UAV users while guaranteeing the minimum rate for UAV users as well as maximum transmit powers for the corresponding small-cell and UAV BSs. The proposed MOOP is transformed into a single optimization problem (SOOP) by the weighted Tchebycheff approach. Then, an iterative technique is used to optimize alternatively subchannels and transmission powers of small-cell and UAV networks at each step. Numerical results show that a substantial performance gain can be obtained over the existing solutions.

Elastic Optical Network (EON) is a promising solution to address the high capacity, low latency, and flexibility requirements of the upcoming 5th-generation (5G) networks. Furthermore, Multi-Core Fibers (MCFs) and Space Division Multiplexing (SDM) technique can be utilized to overcome the capacity limitation of the conventional Single Mode Fibers (SMFs). On the other hand, Quantum Key Distribution (QKD) is an effective solution to address the security issues in 5G transport networks. In this paper, we investigate the performance of QKD over elastic optical networks with multi-core fibers and address the resource allocation problem for quantum and classical channels of QKD (QChs and CChs) and conventional data channels (DChs). To do so, we calculate the background noise caused by different noise sources and accordingly calculate the Secret Key Rate (SKR) in quantum channels. Then, we propose an Integer Linear Programming formulation and a heuristic algorithm to allocate network resources (spectrum, core, and links) to QChs, CChs, and DChs, with the objective of maximizing the secret key rate and minimizing the number of utilized frequency slots (FSs). Finally, we evaluate the proposed ILP and heuristic algorithm in terms of SKR and the number of utilized FSs. In our simulations, we consider core and metro topologies, fixed and distance adaptive launch power for classical signals, different fiber specifications, and different assumptions regarding the relative locations of quantum and classical channels in a multi-core fiber.

In this paper, a new approach toward the optimization of Access Point (AP) placement in cellular Visible light Communication (VLC) networks is proposed based on the projected gradient algorithm. The objective of the optimal placement problem is to maximize the average throughput of the network subject to constraints on the minimum illumination level and the minimum rate of the users. This optimization framework gives the enhanced AP deployment in case of users with static, nomadic, or completely mobile behavior. Taking the distribution of users, the receiver's field of view, reflection from walls and interference from neighboring APs into account makes the deployment problem complicated. To solve the arising intricate optimization problem, we derive analytical expressions for gradients of the objective and use them in the gradient ascent algorithm. The proposed adaptive gradient projection method then realizes the constraints. This method relaxes the projection onto a high dimensional space to planar projections, which are implemented using efficient tools from computational geometry. The proposed method eliminates the need for an intractable exhaustive search to find the optimal placement of VLC APs in cellular VLC networks, while it gives either exact optimum or very close approximations to the optimal placement. It is shown, with the aid of properties of convolution of parametric concave functions, that in some practical cases the objective function is unimodal and has no local optimum. Simulation results show a significant improvement in the throughput, SINR and outage probability of the system when the access points are deployed according to their optimized placement, determined by the algorithm. On the other hand, proper constraints on the minimum achievable rate and minimum illumination level improve the worst-case performance of the network to the desired extent.

In this paper, we investigate multi-quality of service (QoS) routing and spectrum assignment in elastic optical networks (EONs) by utilizing network state information in terms of bandwidth usage and holding time of established connections. Toward this aim, two types of traffic, namely big data traffic (BDT) requests, and fixed rate flow requests (FLRs) are considered, where BDTs can tolerate a pre-determined initial delay while FLRs must be served immediately without any delay. We assume that both frequency and time usage of optical bandwidth is slotted, and accordingly optical resources are defined in a two-dimensional (2D) time/frequency domain, where the smallest resource is specified with its time and frequency slot number. Furthermore, we define two controlling coefficients determining the ratio of time and spectrum slots which can be assigned to BDTs. By selecting various values for these coefficients, network manager will be able to support different class of services and change the relative priorities of different classes. In addition, for each coefficient we obtain a threshold determining the upper bound on the number of time or frequency slots allocated to BDTs. Our results highlight the relation between the blocking probability and the value of these thresholds. In particular, the results show that assigning smaller resource blocks will result into lower blocking probability for both FLRs and BDTs. In addition, the maximum initial delay plays an important role in the blocking probability of BDTs.

In this paper, we formulate an integer linear programming (ILP) to perform multicast routing and spectrum assignment (MRSA) in elastic optical networks, which serves jointly a set of multicast requests. In this formulation, all physical layer restrictions including modulation level assignment, maximum number of multicast capable nodes (MCNs), and maximum splitting degree (MSD) of MCNs, are considered. In addition, we modify the proposed joint ILP to serve multicast requests one-by-one, which is referred to as a separate ILP. Furthermore, we present three heuristic algorithms for MRSA, namely distance-based MRSA (DMRSA), congestion-based MRSA (CMRSA), and mixed CMRSA/DMRSA, which are applicable in both static and dynamic operation scenarios. In CMRSA and DMRSA, the link length and the amount of occupied spectrum are considered as the cost function of multicast routing, respectively; and in mixed CMRSA/DMRSA, a combination of normalized link length and normalized occupied spectrum is considered as the cost function. The comparison of ILPs and heuristic algorithms in static operation reveals that the joint ILP, as the benchmark, gives the optimum solution while has the most computational complexity. Furthermore, the separate ILP has lower complexity at the cost of consuming slightly more spectrum. Unless the DMRSA method, which has the worst performance, the gap between the other two heuristic algorithms and the ILPs is negligible. Furthermore, simulation results of dynamic operation scenarios reveal that mixed CMRSA/DMRSA outperforms other two heuristics algorithms in terms of blocking probability.

To cope with the unprecedented growth of mobile data traffic, we investigate the performance gains obtained from unifying coverage-centric 4G mobile networks and capacity-centric fiber-wireless (FiWi) broadband access networks based on data-centric Ethernet technologies with resulting fiber backhaul sharing and WiFi offloading capabilities. Despite recent progress on backhaul-aware 4G studies with capacity-limited backhaul links, the performance-limiting impact of backhaul latency and reliability has not been examined in sufficient detail previously. In this paper, we evaluate the maximum aggregate throughput, offloading efficiency, and in particular, the delay performance of FiWi enhanced LTE-Advanced (LTE-A) heterogeneous networks (HetNets), including the beneficial impact of various localized fiber-lean backhaul redundancy and wireless protection techniques, by means of probabilistic analysis and verifying simulation, paying close attention to fiber backhaul reliability issues and WiFi offloading limitations due to WiFi mesh node failures as well as temporal and spatial WiFi coverage constraints. We use recent and comprehensive smartphone traces of the PhoneLab data set to verify whether the previously reported assumption that the complementary cumulative distribution function of both WiFi connection and interconnection times fit a truncated Pareto distribution is still valid. In this paper, we put a particular focus on the 5G key attributes of very low latency and ultra-high reliability and investigate how they can be achieved in FiWi enhanced LTE-A HetNets. Furthermore, given the growing interest in decentralization of future 5G networks (e.g., user equipment assisted mobility), we develop a decentralized routing algorithm for FiWi enhanced LTE-A HetNets.

To cope with the unprecedented growth of mobile data traffic, we investigate the performance gains obtained from unifying coverage-centric 4G mobile networks and capacity-centric fiber-wireless (FiWi) broadband access networks based on data-centric Ethernet technologies with resulting fiber backhaul sharing and WiFi offloading capabilities. Despite recent progress on backhaul-aware 4G studies with capacity-limited backhaul links, the performance-limiting impact of backhaul latency and reliability has not been examined in sufficient detail previously. In this paper, we evaluate the maximum aggregate throughput, offloading efficiency, and in particular the delay performance of FiWi enhanced LTE-A heterogeneous networks (HetNets), including the beneficial impact of various localized fiber-lean backhaul redundancy and wireless protection techniques, by means of probabilistic analysis and verifying simulation, paying close attention to fiber backhaul reliability issues and WiFi offloading limitations due to WiFi mesh node failures as well as temporal and spatial WiFi coverage constraints.

In this paper, we shed some light on the latency and reliability issues of mobile backhaul networks, which have been largely ignored in the past, and examine their impact on LTE-A heterogeneous networks (HetNets). Specifically, we propose a backhaul-aware user association algorithm for fiber-wireless (FiWi) enhanced LTE-A HetNets. The performance limiting factors of state-of-the-art fiber backhaul infrastructures are highlighted and a variety of solutions are described. To mitigate the vulnerability of the backhaul against fiber cuts, we introduce different advanced protection techniques. Accounting for the given conditions of the backhaul in terms of delay and reliability, we present a distributed load-balancing algorithm for user association in FiWi-LTE HetNets. The proposed algorithm is analyzed and evaluated numerically by comparing its performance with state-of-the-art alternative approaches in terms of average delay, blocking probability, average achievable throughput, and service interruption percentage. The obtained results demonstrate that our algorithm outperform its counterparts in terms of delay and service interruption percentage, while its average achievable throughput is the same as that of a backhaul-unaware alternative solution. In addition, the blocking probability of the proposed backhaul-aware load-balancing method is shown to be higher than that of backhaul-unaware ones.

In this paper, we analyze the exact node- and network-wise operation scenarios in elastic optical networks (EONs). First, the busy/idle patterns of optical spectrum in a stand-alone node are modeled by utilizing a continuous Markov chain. In the node-wise perspective, four operation scenarios are investigated, which are determined based on the spectrum allocation methods. We present an algorithmic procedure to derive the global balance equations of the corresponding Markov chains for all operation scenarios. Furthermore, the network-wise operation is assessed by analyzing the end-to-end blocking probability for two operation modes, with and without spectrum conversion capabilities at the intermediate nodes. Because the computational complexity of exact model increases exponentially versus the number of spectrum slots, we present two approximate alternatives. The results of the exact models, approximations, and verifying simulations are compared for small scale problem. Comparison reveals that the exact model and simulation match very well. In addition, the accuracy of both approximations is acceptable. The approximate solutions are also examined under large scale scenarios by considering simulation results as a benchmark. The accuracy of the first approximation is not degraded in large scale cases, as opposed to the second one, which is only applicable to small scale problems.