The vast amount of content generated in the Meta verse and unpredictable user demands make real-time optimization of communication, computing, and caching increasingly challenging. These issues highlight the need for intelligent mechanisms that reduce redundant content transmission and improve resource efficiency. To address this, joint semantic aware caching and rendering schemes that leverage content similarity are proposed to enable reusability across Metaverse environments. The goal is to optimize user-server associations, caching, and rendering decisions to efficiently utilize network resources, thereby maximizing resource savings and service quality. Reusing content across heterogeneous Metaverse environments, however, requires a learning algorithm capable of adapting to diverse task settings. To this end, a lifelong learning–based algorithm, Deep-Centralized ELLA (DC-ELLA), incorporating dictionary learning is developed to accommodate diverse user requests by dynamically extracting knowledge from different semantic environments. Simulation results show that the proposed caching and rendering schemes significantly outperform traditional approaches, while DC-ELLA enhances convergence speed and stability, demonstrating superior performance in dynamic scenarios. By exploiting knowledge and content from prior requests, the approach achieves scalable adaptation to new Metaverse environments.

In multi-domain multi-technology network slicing (NS), different service providers within the same and/or different administrative domains are involved in meeting a service level agreement (SLA). In fact, the SLA requirements must be dynamically decomposed into portions that each domain can support based on their network condition. This paper presents a framework to design federated multi-domain NS in which providers collaborate to set policies for SLA decomposition, task offloading, routing, and resource sharing across domains. First, a centralized scheme is proposed as a benchmark where a cross-domain orchestrator decomposes the SLA and performs multi-path routing and resource allocation in negotiation with the domain controllers. Then, we adopt decomposition theory to delegate decomposing the SLA to each domain controller and create multi-domain network slices in a fully distributed manner. Through consistency pricing, we enforce fair collaboration between domains to share resources and perform task-split offloading and multi-path multi-domain routing. We evaluate our approach using typical sixth-generation (6G) real-world examples. Our results show that our approach achieves two to four times higher utility compared to schemes without collaboration and/or SLA decomposition.

As the Metaverse evolves with developments in AI, semantic communication, edge computing, and blockchain, it encounters challenges in adapting to dynamic environments and meeting rising communication and computation needs. In this article, we propose a semantic-aware UAV-based architecture tailored to the dynamic Metaverse that leverages UAV swarms consisting of a collection UAV and edge UAV servers. By mapping different semantic features of Metaverse environments, such as amount of tasks, arrival rate, throughput, and latency requirements, we jointly optimize the mobility, task allocation, and resource allocation in a dynamic Metaverse system. First, a particle swarm optimization-based collection-edge mobility algorithm (PSO-CEMA) is designed to optimize the mobility of UAV servers. Second, to facilitate timely and stable task allocation with reduced complexity, we propose a dual-queue system and a Lyapunov drift function-based dynamic programming task allocation algorithm (LDF-DPTAA). Then, we adopt lifelong learning and design a collection-edge joint training and processing algorithm (LL-CJTPA) to optimize the dynamic allocation of computational resources in the swarm. Finally, we integrate our algorithms into a PSO-LDF-LL algorithm to serve the dynamic Metaverse system. Simulation results show that our approach effectively optimizes UAV servers’ positions and task allocation, significantly reduces the training time when facing new tasks, and enhances the stability and efficiency of the network in dynamic settings while reducing congestion.

Ensuring covert and secure communication remains a challenge in the evolving landscape of wireless communications. This paper presents a covert cognitive mobile edge computing network (CCMEC) in which secondary nodes (Alice) aim to transmit securely and offload computing tasks to secondary edge computing nodes (Bob) in the presence of multiple primary wardens (Willie). A two-stage connectivity probing and activation scheme is developed to maximize the data transmitted under covertness and minimize the energy consumption within a latency bound. The proposed scheme combines a distributed probing phase to find secure available connections based on the activity of primary and secondary nodes and a centralized activation phase to jointly optimize power allocation, channel, and Bob selection. The problem is solved by a Restless Multi-Armed Bandit (RMAB) framework with Whittle index optimization. Simulation results show the effectiveness of our approach in achieving covert communication compared to existing solutions.

Heterogeneous Internet of Things (IoT) networks enabled by Unmanned Aerial Vehicles (UAVs) operate in various protocols and spectrum bands (e.g., WiFi, LoRa, Zigbee) to collect and offload data generated from heterogeneous sensors. However, achieving timely and secure communications is challenging due to uncertain data generation, mobility, and location of wardens. This paper presents a collaborative framework that exploits cross-technology communications to achieve covertness constraints. The aim is to minimize the age of covert information (AoCI) and energy consumption by jointly optimizing data scheduling, power allocation, and offloading decisions by collaborating with UAVs. A multi-agent actor-critic algorithm that incorporates federated learning and attention mechanisms (cluster-MAAC-attention) is presented to solve the previous problem. Our simulation results show that our algorithm reduces the worst AoCI by 4 times, and reduces the penalty by 20 times compared to existing schemes.

Dynamic network architectures that utilize communication, computing, and storage resources at the wireless edge are key to delivering emerging services in next-generation networks (e.g., AR/VR, 3D video, intelligent cars, etc). Network slicing can be significantly enhanced by including dynamically available resources throughout the fog/edge/cloud continuum and using mmWave/THz bands. However, network slicing of dynamic multi-tier computing networks remains under-explored. In this paper, we present a self-learning end-to-end network slicing mechanism (SELF-E2E-NS) that facilitates collaboration between the Infrastructure Provider (InP) and tenants to slice their subscribers’ resources (i.e., radio, computing, and storage) as fog resources. To adapt to the uncertain availability of resources at the edge and minimize the risk of non-satisfying service level agreements (SLAs), our slicing mechanism has two operational modes. Operational mode 1 is for joint network slicing (JNS) in which the InP infrastructure is augmented with fog resources and jointly sliced to meet high throughput and delay tolerant requirements. Operational mode 2 is for independent network slicing (INS) in which the InP infrastructure and fog resources are sliced separately to achieve high throughput, low-latency, and high-reliability requirements. Our schemes leverage mmWave/THz, fog/edge/cloud computing, and caching to achieve new service requirements. We design a DQ-E2E-JNS algorithm that uses Deep Dueling network and a MAAC-E2E-INS algorithm based on multi-agent actor-critic, which incorporate service-aware pricing feedback and fog trading matching, respectively. These algorithms find the optimal slice request admission and collaboration policy that maximizes the long-term revenue of the InP and tenants for each mode. The simulation results show that our novel slicing mechanism can serve up to 4 times more requests and effectively exploits different spectrum bands and fog resources to improve revenue and performance.

Heterogeneous Internet of Things (IoT) networks, which operate using various protocols and spectrum bands like WiFi, Bluetooth, Zigbee, and LoRa, bring many opportunities to collaborate and achieve timely data collection. However, several challenges must be addressed due to heterogeneous data patterns, coverage, spectrum bands, and mobility. This paper introduces a cross-technology IoT network architecture design that facilitates collaboration between service providers (SPs) to share their spectrum bands and offload computing tasks from heterogeneous IoT devices using multi-protocol mobile gateways (M-MGs). The objective is to minimize the age of information (AoI) and energy consumption by jointly optimizing collaboration between M-MGs and SPs for bandwidth allocation, relaying, and cross-technology data scheduling. A pricing mechanism is presented to incentivize different levels of collaboration and matching between M-MGs and SPs. Given the uncertainty due to mobility and task requests, we design a cross-technology federated matching algorithm (CT-Fed-Match) based on a multi-agent actor-critic approach in which M-MGs and SPs learn their strategies in a distributed manner. Furthermore, we incorporate federated learning to enhance the convergence of the learning process. The numerical results demonstrate that our CT-Fed-Match-RC algorithm with cross-technology and relaying collaboration reduces the AoI by 30 times and collects 8 times more packets than existing approaches.

In this paper, we present a multi-domain network slicing scheme for satellite-terrestrial edge computing networks (STECNs) that admits different slice configurations. Each slice is configured to include terrestrial-air, terrestrial-satellite, terrestrial-air-satellite, or terrestrial-air-satellite-gateway domain topologies. However, the multi-domain nature of STECNs makes slicing especially challenging since the cross-domain orchestrator has no knowledge of the resource availability in different domains. Our goal is to design an algorithm that builds a belief in resource availability to jointly optimize the slice configuration, service level agreement (SLA) decomposition, routing, and resource allocation. We model the slice/resource availability as a Markov process to track the probability of achieving the SLA per configuration. To solve the multi-domain slicing problem, the cross-domain orchestrator interacts with the configuration coordinator to define an index-based slice configuration policy based on restless multi-armed bandits (RMABs), which is aware of the network traffic. The configuration coordinator decomposes the SLA and each domain controller solves the optimum routing and resource allocation. Our slicing scheme is evaluated using five typical application scenarios for STECNs. Simulation results show that our scheme achieves six times higher reward than agnostic schemes and efficiently performs multi-domain slicing with low complexity.

Satellite-airborne-terrestrial edge computing networks (SATECNs) emerge as a global solution for Internet of Things (IoT) applications in 6G. However, their highly dynamic nature with uncertain varying topology and network traffic makes their management and control more challenging. In this paper, we consider a scenario in which IoT devices, UAVs, and satellites with different edge computing capabilities make decisions to balance the freshness of information and energy consumption. Since SATECNs are highly dynamic and data freshness optimization requires timely decisions, we present a new lifelong learning computing resource allocation algorithm (LL-SATEC) that adapts to the environment by exploiting knowledge transfer between devices in different layers in SATECNs and previous experience. Lifelong learning is a promising machine learning algorithm that learns continuously without requiring a new training phase and avoids catastrophic forgetting. Our goal is to find computing resource allocation policies online for IoT devices, UAVs, and satellites that optimize the overall average age-of-information and energy trade-off. Numerical results show that our approach significantly accelerates learning compared to traditional reinforcement learning algorithms, achieves three times lower AoI and energy consumption in just a few iterations, and avoids catastrophic forgetting.

Satellite-Airborne-Terrestrial Networks (SATNs) are expected to provide communication and edge-computing services for a plethora of IoT applications. However, preserving the freshness of information is challenging since it requires timely data collection, bandwidth, and offloading decisions across different administrative domains. In this paper, we aim to optimize the age of information (AoI) and energy consumption tradeoff when serving multiple traffic classes in SATNs. A cross-domain federated computation offloading algorithm (Fed-SATEC-Off) is presented in which different service providers (SPs) collaborate to allocate the bandwidth while unmanned aerial vehicles (UAVs) and satellites make decisions to collect, relay, and offload the computing tasks. Given the requirements of each traffic class, the optimum collaborative strategies between SPs, UAVs, and satellites are obtained together with the computation offloading topology. Our algorithm is based on multi-agent actor-critic and incorporates federated learning to improve the convergence of the learning process. The numerical results show that Fed-SATEC-Off reduces the AoI by factor 4 and achieves faster convergence than existing approaches.

Satellite–terrestrial edge computing networks (STECNs) emerged as a global solution to support multiple Internet of Things (IoT) applications in 6G networks. The enabling technologies to slice STECNs, such as software-defined networking (SDN), satellite edge computing (EC), and network function virtualization (NFV) are key to realizing this vision. In this article, we survey and analyze network slicing (NS) solutions for STECNs. We discuss slice management and orchestration for different STECNs integration architectures, satellite EC, mmWave/THz, and artificial intelligence solutions to make NS adaptive. In addition, we identify challenges and open issues to slice STECNs. In particular, resilient NS is crucial for essential and critical services. Network failures are unavoidable in large networks and can cause significant disruptions in NS, compromising many services. To this end, we present a resilient NS design to cope with failures and guarantee service continuity which is agnostic to the integration architecture and inherently multidomain. Further, we present strategies to achieve resilient networking and slicing in STECNs, including planning and provisioning of redundant network resources, design rules for service level agreement decomposition, and cross-domain solutions to detect and mitigate failures. Finally, promising future research directions are highlighted. This article provides valuable guidelines for slicing STECNs and will benefit key sectors, such as smart healthcare, e-commerce, Industrial IoT, education, and among others.

Enabling timely data collection in heterogeneous IoT networks under different protocols and spectrum bands (e.g., WiFi, Bluetooth, Zigbee, LoR$a$) is crucial to implementing large-scale IoT systems. This paper presents a federated matching framework for heterogeneous IoT networks in which an intermediate layer of multi-protocol mobile gateways (M-MGs) is deployed by different service providers (SPs) to collect and relay data from IoT objects and perform computing tasks. The aim is to develop collaborative strategies between M-MGs and SPs to minimize the average weighted sum of the age-of-information and energy consumption. A novel collaborative framework based on a 2-level multi-protocol multi-agent actor-critic (MP-MAAC) is presented, where M-MGs and SPs can learn the interactive strategies through their own observations. The M-MGs strategies include the selection of IoT objects for data collection, execution, and offloading t o S Ps' a ccess points while SPs decide on the spectrum allocation. Moreover, we incorporate federated matching (Fed-Match) into the multi-agent collaborative framework to improve the convergence of the learning process. The numerical results show that our Fed-Match algorithm reduces the Aol by factor 4, collects twice more packets than existing approaches and establishes design principles for the stability of the training process.

Next-generation networks will provide intelligent infrastructure and management using machine learning. In real-world applications, demand for resources and performance within a service class may vary over time. Infrastructure providers choose which requests to accept with the goal of long-term profit maximization – a process known as slice admission control. In this paper, we envision a dynamic system with varying service requests attributes in urgency, duration, and amount of resources (i.e., computing, network, and storage). Further, we develop a dynamic pricing model that is responsive to demand and supply resulting in demand and supply reserve shaping. Then, we propose a solution to the slice admission control problem by using a reinforcement learning approach with Deep-Q Networks, where the state of the system is modeled using an array of parameters, similar to the input matrix in computer vision. Results show that our computer vision-inspired approach is capable of learning how the better policy to navigate this complex environment by selecting service requests that maximize the provider’s long-term profit.

The sixth-generation (6G) of wireless communications systems will significantly rely on fog/edge network architectures for service provisioning. To realize this vision, AI-based fog/edge enabled reinforcement solutions are needed to serve highly stringent applications using dynamically varying resources. In this paper, we propose a cognitive dynamic fog/edge network where primary nodes (PNs) temporarily share their resources and act as fog nodes (FNs) for secondary nodes (SNs). Under this architecture, that unleashes multiple access opportunities, we design distributed fog probing schemes for SNs to search for available connections to access neighbouring FNs. Since the availability of these connections varies in time, we develop strategies to enhance the robustness to the uncertain availability of channels and fog nodes, and reinforce the connections with the FNs. A robustness control optimization is formulated with the aim to maximize the expected total long-term reliability of SNs’ transmissions. The problem is solved by an online robustness control (ORC) algorithm that involves online fog probing and an index-based connectivity activation policy derived from restless multi-armed bandits (RMABs) model. Simulation results show that our ORC scheme significantly improves the network robustness, the connectivity reliability and the number of completed transmissions. In addition, by activating the connections with higher indexes, the total long-term reliability optimization problem is solved with low complexity.

To accommodate increasingly diverse traffic demands in future 6G wireless networks, intelligent and dynamic network slicing schemes are needed to exploit available edge/fog resources (i.e., radio, computing, storage resources). In this paper, we present a new dynamic edge/fog network slicing scheme (EFNS) in which tenants can temporarily lease back to the Infrastructure Provider (InP) unused resources to serve demands exceeding its current resources in stock. To efficiently use the resources, tenants can also lease their subscribers' terminals when idle to act as fog nodes augmenting the infrastructure and service capability of the InP. Our goal is to find an optimal slice request admission policy, which includes slices with augmented resources, to maximize the long-term revenue of the InP. A semi-Markov decision process is used to model the arrival of slice requests by taking into account the dynamics of users' demands and availability of resources. To find the optimal policy under uncertain resource demands, a Q-learning (Q-EFNS) algorithm is developed. Additionally, to improve the convergence time and reduce the computational complexity of Q-learning in large-scale scenarios, a Deep reinforcement learning (DQ-EFNS) algorithm and an enhancement based on a Deep Dueling (Dueling DQ-EFNS) algorithm are presented. Finally, simulation results show improvements in the range between 20% to 60% by our algorithms compared to conventional fixed network slicing when only 8% of the subscribers act as fog nodes. Besides, by using Dueling DQ-EFNS, the optimal slice request admission policy is obtained in just a few iterations.

Dynamic spectrum sharing between licensed incumbent users (IUs) and unlicensed wireless industries has been well recognized as an efficient approach to solving spectrum scarcity as well as creating spectrum markets. Recently, both US and European governments called a ruling on opening up spectrum that was initially licensed to sensitive military/federal systems. However, this introduces serious concerns on operational privacy (e.g., location, time, and frequency of use) of IUs for national security concerns. Although several works have proposed obfuscation methods to address this problem, these techniques only rely on syntactic privacy models, lacking rigorous privacy guarantee. In this paper, we propose a comprehensive framework to provide real-time differential location privacy for sensitive IUs. We design a utility-optimal differentially private mechanism to reduce the loss in spectrum efficiency while protecting IUs from harmful interference. Furthermore, we strategically combine differential privacy with another privacy notion, expected inference error, to provide double shield protection for IU's location privacy. Extensive simulations are conducted to validate our design and demonstrate significant improvements in utility and location privacy compared with other existing mechanisms.

With network slicing, physical networks are partitioned into multiple virtual networks tailored to serve different types of service with their specific requirements. In order to optimize the utilization of network resources for delay-critical applications, we propose a new multi-domain network virtualization framework based on a novel multipath multihop delay model. This framework encompasses a novel hierarchical orchestration mechanism for mapping network slices onto physical resources and a mechanism for dynamic slice resizing. The main idea is to locally redefine the delay requirements on each network domain depending on the conditions in the rest of the network. Delays larger than threshold (debt) are allowed in certain domains if there is a possibility to compensate such excessive delays in other segments of the network that can transmit the messages with less latency (credit). This tradeoff or delay threshold redefinition on different segments of the route is referred to as network latency equalization. For performance comparison, minimum cost routing with latency constraints is used as a baseline. We show that our approach enables significantly better utilization of the network resources measured in the number of slices with the same latency requirements that can be accommodated in the network.

We develop and analyze a new user-centric networking model for ubiquitous spectrum sharing where every user can share and use the spectrum under uncertainty of their traffic models. In this concept, users when connected to the Internet (wired/wireless) can dynamically serve as access points for other users in their vicinity. For this reason, the concept is referred to as user-centric distributed spectrum sharing. Each user in spectrum sharing mode utilizes a part of its available spectrum for its own traffic and remaining part to share with users in spectrum demanding modes. The model is designed as an operator supervised double-Stackelberg game with network operators, access points, and users as main players. We study network reliability and latency of the system under uncertainty of users' traffic patterns. The numerical results show that the proposed model, depending on different settings, can significantly improve both profit and utility for network operators and users, respectively. Furthermore, network reliability is significantly improved depending on the network parameters for both users and operators.

The next generation of cellular networks (5G) will provide dense millimeter-wave backhaul architectures to wirelessly forward heterogeneous data traffic in a multihop fashion. In this paper, we present a general optimization framework for the design of delay-aware (DA) policies in multihop wireless networks, providing proportional prioritization of traffic. We develop three throughput-optimal DA algorithms (BP-DA, BPE-DA, and HD-DA) for joint dynamic routing and dynamic link-scheduling problems with good to optimal average network delay performance. Our DA framework considers both the classical back-pressure (BP) and the recent heat-diffusion (HD) algorithms, since queue back-pressure algorithms are being considered for mmWave backhauling management. We provide analytical results for the throughput-optimality of the proposed policies and average delay minimization of HD-DA within the class of DA policies. These are policies that make decisions at each timeslot based only on current channel state, current network queue sizes, and flow priorities. We discuss the applications of our proposals to backhaul management of mmWave cellular networks in light of recent works in the literature. Finally, we present extensive simulations of our proposed algorithms, which confirm the theoretical results and show how the algorithms effectively differentiate data traffic in terms of delay while satisfying flow rate requirements.

Dynamic network architectures (DNAs) have been developed under the assumption that some terminals can be converted into temporary access points (APs) anytime when connected to the Internet. In this paper, we consider the problem of assigning a group of users to a set of potential APs with the aim to maximize the downlink system throughput of DNA networks, subject to total transmit power and users' quality of service (QoS) constraints. In our first method, we relax the integer optimization variables to be continuous. The resulting non-convex continuous optimization problem is solved using successive convex approximation framework to arrive at a sequence of second-order cone programs (SOCPs). In the next method, the selection process is viewed as finding a sparsity constrained solution to our problem of sum rate maximization. It is demonstrated in numerical results that while the first approach has better data rates for dense networks, the sparsity oriented method has a superior speed of convergence. Moreover, for the scenarios considered, in addition to comprehensively outperforming some well-known approaches, our algorithms yield data rates close to those obtained by branch and bound method.

Communication technologies supply the blood for smart city applications. In view of the ever-increasing wireless traffic generated in smart cities and our already congested radio access networks (RANs), we have recently designed a data transportation network, the vehicular cognitive capability harvesting network (V-CCHN), which exploits the harvested spectrum opportunity and the mobility opportunity offered by the massive number of vehicles traveling in the city to not only offload delay-tolerant data from congested RANs but also support delay-tolerant data transportation for various smart-city applications. To make data transportation efficient, in this paper, we develop a spectrum-aware (SA) data transportation scheme based on Markov decision processes. Through extensive simulations, we demonstrate that, with the developed data transportation scheme, the V-CCHN is effective in offering data transportation services despite its dependence on dynamic resources, such as vehicles and harvested spectrum resources. The simulation results also demonstrate the superiority of the SA scheme over existing schemes. We expect the V-CCHN to well complement existing telecommunication networks in handling the exponentially increasing wireless data traffic.

Tactile Internet (TI) will generate a variety of 5G-enabled use cases with different requirements for latency, throughput and reliability. In this paper, we propose a flexible cloud-based radio access network (FRAN) for TI, where traffic of user equipments (UEs) can be temporary offloaded from the operator-provided networks to user- provided networks if needed. Such a concept enables dynamic network slicing (DNS) where the network architecture is temporally augmented with slices of infrastructure borrowed from user provided network. FRAN is able to support Tactile applications without any basic change to the hardware/software infrastructure in the network. We model DNS system as a two-layer/slice traffic- aware resource allocation framework, where every layer uses a separate two-step matching game in order to serve Tactile users (TUs) and low- priority users (LUs). We propose a subgame Nash stable and two-sided exchange stable concepts as solutions of the proposed two-layer traffic-aware resource allocation. Our numerical results show that network operators and UEs (either TUs or LUs) significantly benefit from deploying the FRAN rather than conventional cloud-based radio access networks (CRANs).

In this paper, we consider a recent cellular network connection paradigm, known as a user-provided network (UPN), where users share connectivity and act as an access point for other users. To incentivize user participation in this network, we allow the users to trade their data plan and obtain a profit by selling and buying leftover data capacities (caps) from each other. We formulate the data trading association between buyers and sellers as a matching game. In this game, buyers and sellers rank each other based on preference functions that capture the buyers' demand for data and QoS requirements, the data available for purchase from the sellers and energy resources. We show that these preferences are interdependent and influenced by existing network-wide matching. For this reason, the game can be classified as a one-to-many matching game with externalities. To solve the game, we propose a distributed algorithm that combines notions from matching theory and market equilibrium. The algorithm enables the players to self-organize into a stable matching and ensures dynamic adaptation of price to data demand and supply. The properties of the resulting matching are discussed. We also calculate operator gains and the benchmark price that will encourage users to join the UPN. Simulation results show that the proposed algorithm yields average utility per user improvements of up to 25% and 50% relative to random matching and worst case utility, respectively.

A new paradigm in wireless network access is presented and analyzed. In this concept, certain classes of wireless terminals can be turned temporarily into an access point (AP) anytime while connected to the Internet. This creates a dynamic network architecture (DNA) since the number and location of these APs vary in time. In this paper, we present a framework to optimize different aspects of this architecture. First, the dynamic AP association problem is addressed with the aim to optimize the network by choosing the most convenient APs to provide the quality-of-service (QoS) levels demanded by the users with the minimum cost. Then, an economic model is developed to compensate the users for serving as APs and, thus, augmenting the network resources. The users' security investment is also taken into account in the AP selection. A preclustering process of the DNA is proposed to keep the optimization process feasible in a high dense network. To dynamically reconfigure the optimum topology and adjust it to the traffic variations, a new specific encoding of genetic algorithm (GA) is presented. Numerical results show that GA can provide the optimum topology up to two orders of magnitude faster than exhaustive search for network clusters, and the improvement significantly increases with the cluster size.

Future wireless networks are envisioned to integrate multi-hop multi-operator multi-technology (m3) components in order to meet the increasing traffic demand at an acceptable price for subscribers. The performance of such a network depends on the multitude of parameters defining traffic statistics, network topology/technology, channel characteristics, and business models for multi-operator cooperation. So far, most of these aspects have been separately addressed in the literature. Since the above parameters are mutually dependent and simultaneously present in a network, for a given channel and traffic statistics, a joint optimization of technology and business model parameters is required. In this paper, we present such joint models of complex wireless networks and introduce optimization with parameter clustering to solve the problem in a tractable way for large number of parameters. By parameter clustering, we compress the optimization vector and significantly simplify system implementation, and hence, the algorithm is referred to as the compressed control of wireless networks. Two distinct parameter compression techniques are introduced, namely, parameter absorption and parameter aggregation. Numerical results obtained in this way demonstrate clear maximum in the network utility as a function of the network topology parameters. The results, for a specific network with traffic offloading, show that the cooperation decisions between the multiple operators will be significantly influenced by the traffic dynamics. For typical example scenarios, the optimum offloading price varies by factor 3 for different traffic patterns, which justifies the use of dynamic strategies in the decision process. Besides, if user availability increases by multi-operator cooperation, network capacity can be increased up to 50% and network throughput up to 30%-40%.

With the shortage of spectrum in conventional cellular frequencies, millimeter-wave (mmWave) bands are being widely considered for use in next-generation networks. Multihop relaying is likely to play a significant role in mmWave cellular systems for self backhauling, range extension and improved robustness from path diversity. However, designing scheduling policies for these systems is challenging due to the need to account for both adaptive directional transmissions and dynamic time-division duplexing schedules, which are key enabling features of mmWave systems. This paper considers the problem of joint scheduling and congestion control in a multihop mmWave network using a Network Utility Maximization (NUM) framework. Interference is modeled with an exact model and two auxiliar simplified models: actual interference (AI), with a graph-based calculation of the Signal to Interference plus Noise Ratio (SINR) depending on dynamic link activity and directivity, as well as upper and lower bounds computed from worst-case interference (WI) and interference free (IF) approximations. Throughput and utility optimal policies are derived for all interference models (AI, WI and IF) with both deterministic Maximum Weighted and randomized Pick and Compare scheduling algorithms, jointly with decentralized Dual Congestion Control. Results are evaluated with numerical simulations, using accurate mmWave channel and beamforming gain approximations based on measurement campaigns.

Benefits of multi-operator spectrum sharing in wireless networks heavily depend on the traffic misbalance in the networks belonging to different operators. In this paper, we study the likelihood that such misbalance occurs in networks with high traffic dynamics. An extensive business portfolio for heterogeneous networks is presented to analyse the benefits due to multi-operator cooperation for spectrum sharing. High resolution pricing models are developed to dynamically facilitate price adaptation to the system state. By using queuing theory, we quantify the operators' gains in cooperative arrangements as opposed to non-cooperative independent operation. In addition, Markov model is used that can handle wider range of different distributions of traffic arrivals and service rates. A tractable analysis and quantitative results are provided for those gains as a function of the number of cooperating operators. Under the condition that there is a traffic underflow in one band, it has been shown that with capacity aggregation model, the operator operating in other band can take advantage of additional channels with probability close to 1. In capacity borrowing/leasing model, this advantage is not unconditional, and there is a risk that the operator leasing the spectra will suffer temporary packet losses. When cognitive models are used in a network with high traffic dynamics, 50–70% of the spectra may be lost due to channel corruptions caused by the return of primary users. The gains of traffic offloading from a cellular network to a WLAN are quantified by an equivalent increase in opportunistic capacity proportional to the ratio of aggregate coverage of cellular networks and WLANs. The results provide guidelines for business decision in multi-operator network management.

In this paper, we present an economic model for offloading data from subscribers of a large scale cellular operator to a small scale WLAN in a multi-hop cellular environment. We make use of a hexagonal tessellation deployed with relay elements to model the multi-hop capability. An incentive-based model helps to determine the behavior of the cellular and WLAN operator, as the cellular operator decides to offload its users depending upon the price charged by the WLAN operator for each offloaded user. The simulated results quantify the benefits of collaboration between the operators in terms of the offload ratio, network efficiency, and revenue gains.

With increasing mobile data demand there is a push towards heterogeneous networks. Small-scale operators (SSOs) of WLANs are becoming more prevalent, while Mobile Network Operators (MNOs) seek an outlet for their customers' data usage. These conditions prompt the need for an effective relationship between the two parties for the purpose of offloading cellular data traffic to WLANs in a way that is economically beneficial to all involved. This paper presents a model of such a relationship, in which the SSO sets a strategic offloading price per subscriber and the MNO chooses how many subscribers it wants to offload in order to minimize its costs. The application of this model is simulated in a real-world WLAN deployment in Oulu, Finland. Our findings can be used by both MNOs and SSOs to make informed network deployment decisions, even before engaging in an offloading relationship.

When considering a multicell scenario with nonuniform traffic distribution in multihop wireless networks, the search for the optimum topology becomes an NP-hard problem. For such problems, exact algorithms based on exhaustive search are only useful for small toy models, so heuristic algorithms such as genetic algorithms (GA) must be used in practice. For this purpose, we present a novel sequential genetic algorithm (SGA) to optimize the relaying topology in multihop cellular networks aware of the intercell interference and the spatial traffic distribution dynamics. We encode the topologies as a set of chromosomes and special crossover and mutation operations are proposed to search for the optimum topology. The performance is measured by a fitness function that includes the throughput, power consumption and delay. Improvement in the fitness function is sequentially controlled as newer generations evolve and whenever the improvement is sufficiently increased the current topology is updated by the new one having higher fitness. Numerical results show that SGA provides both high performance improvements in the system and fast convergence (at least one order of magnitude faster than exhaustive search) in a dynamic network environment. We also demonstrate the robustness of our algorithm to the initial state of the network.

In this paper, we present a new approach to optimization of multicast in multihop cellular networks. We apply a hexagonal tessellation for inner partitioning of the cell into smaller subcells of radius$r$. Subcells may be several orders of magnitude smaller than, e.g., microcells, resulting in what we refer to as a nanoscale network model (NSNM), including a special nanoscale channel model (NSCM) for this application. For such tessellation, a spatial interleaving SI MAC protocol is introduced for context-aware interlink interference management. The directed flooding routing protocol (DFRP) and interflooding network coding (IFNC) are proposed for such a network model including intercell flooding coordination (ICFC) protocol to minimize the intercell interference. By adjusting the radius of the subcell$r$, we obtain different hopping ranges that directly affect the throughput, power consumption, and interference. With$r$as the optimization parameter, in this paper we jointly optimize scheduling, routing, and power control to obtain the optimum tradeoff between throughput, delay, and power consumption in multicast cellular networks. A set of numerical results demonstrates that the NSNM enables high-resolution optimization of the system and an effective use of the context awareness.

In this paper, we present a joint optimization of power control, scheduling and relaying topology for multi-hop cellular networks aware of the intercell interference. To reduce the intercell interference in adjacent cells, cooperative diversity relaying scheme (COOR) is used in the system to reduce the transmission power needed to meet the same SINR threshold as the conventional relaying scheme (CONR). The resulting intercell interference management (I2M) protocols using COOR and CONR schemes will be referred to as I2M-COOR, and I2M-CONR respectively. In this complex scenario, for a given spatial traffic distribution we find the optimum scheduling and relaying topology in order to reduce the overall interference in the network and achieve a proper tradeoff between throughput and power allocation. Numerical results show that I2M-COOR offers an improvement in the network throughput of at least 150% and a reduction of power consumption of at least 130% compared to I2M-CONR. The optimum topology consumes in average 3 times less power than non-optimum options and the variation in throughput between the non-optimum solutions is by factor 5 inferior with respect to the optimum topology.

In this paper we define the jointly optimum topology for the duplex transmission (uplink/downlink) in multihop cellular networks which is aware of the intercell interference and a protocol that reconfigures the optimum topology based on the observation of the temporal traffic in the network. In addition we also consider the application of network coding in cellular networks to combine the uplink and downlink transmissions and incorporate it into the optimum bidirectional relaying with intercell interference awareness resulting in a comprehensive solution for 4G common air interface.

Cognitive wireless networks benefit from context awareness which is integrated in decision making process in different layers. In this paper, we modify the MAC layer transmission permission probability, for elastic traffic users, in such a way to discourage the transmissions from/to the users at the border of the cell where transmissions cause significant interference in adjacent cell. This will be referred to as spatial traffic shaping. By using the absorbing Markov chain theory, we provide, for such a concept, analytical models to analyze system performance, mainly probability of successful channel assignment and message delivery delay. The analysis shows that in the cellular network, with channel spatial reuse factor equal to one, the probability of successful channel assignment close to one can be achieved with acceptable message delivery delay.

The optimization of the relaying topology in multihop cellular network should provide the answer to the question who is transmitting to whom, and when, in such a way to insure the best system performance. In the case of temporally and spatially varying traffic distribution the optimal topology will also vary in time and an efficient way for topology control is needed in order to maximize the system performance. In this paper we present an algorithm for efficient relaying topology control, which is aware of the intercell interference, requiring coordinated action between the cells and resulting in multicell jointly optimal relaying topology. Numerical results demonstrate that an adaptive relaying topology control provides the network utility improvements and presents the framework for quantifying these improvements for spatially and temporally varying traffic.