The advent of Ethereum 2.0 has introduced significant changes, particularly the shift to Proof-of-Stake consensus. This change presents new opportunities and challenges for arbitrage. Amidst these changes, we introduce BriDe Arbitrager, a novel tool designed for Ethereum 2.0 that leveragesBribery-driven attacks toDelay block production and increase arbitrage gains. The main idea is to allow malicious proposers to delay block production by bribing validators/proposers, thereby gaining more time to identify arbitrage opportunities. Through analysing the bribery process, we design an adaptive bribery strategy. Additionally, we propose a Delayed Transaction Ordering Algorithm to leverage the delayed time to amplify arbitrage profits for malicious proposers. To ensure fairness and automate the bribery process, we design and implement a bribery smart contract and a bribery client. As a result, BriDe Arbitrager enables adversaries controlling a limited ($\lt 1/4$) fraction of the voting powers to delay block production via bribery and arbitrage more profit. Extensive experimental results based on Ethereum historical transactions demonstrate that BriDe Arbitrager yields an average of 8.78 ETH (16,687.88 USD) daily profits. Furthermore, our approach does not trigger any slashing mechanisms and remains effective even under Proposer Builder Separation and other potential mechanisms will be adopted by Ethereum.

We consider decentralized federated learning (DFL) in unmanned aerial vehicle (UAV) networks where UAVs collaboratively train their machine learning (ML) models in a serverless peer-to-peer manner without sharing local data. We focus on three challenges affecting the performance and feasibility of UAV-aided DFL: i) communication inefficiency, ii) dynamics, heterogeneity and energy constraints of UAV networks, and iii) high synchronization overheads. To address these challenges, we propose an asynchronous DFL (A-DFL) model for UAV networks and design a novel distributed, adaptive and energy-aware model compression method based on the gradient sparsification. In this method, UAVs communicate asynchronously and apply the time-varying and non-identical compression parameters to adjust to a dynamic, heterogeneous environment. This reduces synchronization overheads and improves the communication efficiency given the strict battery constraints of UAVs. We show that our method can be formulated as a Markov potential game where the UAVs act as the players which decide on their compression parameters and the number of training data samples used for model updates. We prove that our game admits a dominant pure-strategy Nash equilibrium (NE) that maximizes its potential function and develop a new sparsified A-DFL algorithm enabling every UAV to reach its dominant strategy independently, in polynomial time. We then prove that the proposed algorithm converges to the Pareto-optimal NE representing the most efficient solution of our game. Using extensive simulations, we verify that our algorithm outperforms the state-of-the-art methods in terms of the key evaluation metrics of DFL.

In sharded blockchains, peers are divided into smaller groups (shards) that generate and verify blocks in parallel, offering enhanced throughput and reduced delays. These properties make sharded blockchains a promising solution for secure data management in Internet of Things (IoT) systems. Particularly, geolocation-based sharded blockchains assign geographically proximate peers to the same shard, enabling faster IoT transaction processing. Yet, peers in each shard can easily collude to falsely accept/reject blocks. To resolve this issue, in this paper, we propose a robust reputation-based shard inspection framework. The framework adopts the shard inspection mechanism where a group of inspectors selected from the most reputable peers randomly verify blocks in each shard. This enables avoiding collusion attacks and enhancing the security of each shard. However, additional block verifications during the inspection process can incur significant block delays and energy overheads. To reduce these overheads, we formulate an optimization problem that jointly determines the number of inspectors and the inspection interval to maximize the system utility, which is proportional to the blockchain throughput and energy consumption. We then develop a distributed algorithm that enables dividing the optimization problem into sub-problems solvable independently by each shard. Experimental results show that our framework can maximize the system utility, while maintaining high levels of security in each shard.

We study decentralized federated learning (DFL) in edge computing networks where edge nodes (ENs) collaboratively train their artificial intelligence (AI) models in a serverless manner without sharing local data. We consider the following critical DFL challenges: i) scarce bandwidth resources of ENs; ii) dynamic, heterogeneous edge environment; iii) incentive provisioning and complex tradeoffs between the DFL performance and training costs. To resolve these challenges, we develop a new model compression method where ENs utilize dynamic, non-identical compression rates to improve the communication efficiency of DFL under time-varying, heterogeneous resource constraints. We show that our method can be formulated as a graphical Markov potential game where ENs act as players deciding on their compression factors and the number of data samples used for model updates. Each EN is incentivized to participate in DFL through rewards based on the EN's contribution to training. We prove that our game has a dominant pure-strategy Nash equilibrium (NE) maximizing its potential function and propose a dynamic distributed compression algorithm in which each EN can find its dominant strategy independently. We show that this algorithm converges to the Pareto-optimal NE, representing the most efficient solution of our game enhancing the DFL performance with minimal costs.

We study the problem of ultra-reliable and low-latency slicing in multi-access edge computing (MEC) systems for the next-generation internet of things (IoT) and mobile applications operating in the space-air-ground integrated network. The network has a dynamic topology formed by multiple non-stationary nodes with unstable communication links and unreliable processing/transmission resources. Each node can be in one of two hidden states: i) reliable – in which the node generates no data errors and no losses; ii) unreliable – when the node can generate/propagate random data errors/losses. Solving this problem is difficult, as it represents the non-deterministic polynomial-time (NP) hard non-concave non-smooth stochastic maximization problem which depends on the unknown hidden nodes’ states and private information about local, dynamic parameters of each node, which is known only to this node, and not to other nodes. To address these challenges, we develop a new deep learning (DL) model based on the message passing graph neural network (MPNN) to estimate hidden nodes’ states. We then propose a novel algorithm based on the online alternating direction method of multipliers (ADMM) – an extension of the well-known classical “static” ADMM to dynamic settings, where our slicing problem can be solved distributedly, in real time, without revealing local (private) information of the nodes. We show that our algorithm converges to a global optimum of the slicing problem and has a good consistent performance even in highly-dynamic, unreliable scenarios.

We consider a multi-operator multi-access edge computing (MEC) network for applications with dependent tasks. Each task includes jobs executed based on logical precedence modelled as a directed acyclic graph, where each vertex is a job, each edge – precedence constraint, such that the job can be started only after its preceding jobs are completed. Tasks are executed by MEC servers with the assistance of workers – nearby edge devices. Each MEC server acts as a master deciding on jobs assigned to its workers. The master's decision problem is complex, as its workers can be associated with other masters in proximity. Thus, the available workers' resources depend on job assignments of all neighboring masters. Yet, as masters select their decisions simultaneously, no master knows concurrent decisions of its neighbors. Besides, some masters can belong to competing operators that have no incentives to exchange information about their decisions. To address these challenges, we formulate a novel framework based on the graphical stochastic Bayesian game, where masters play under uncertainty about their neighbors' decisions. We prove that the game admits a perfect Bayesian equilibrium (PBE), and develop new Bayesian reinforcement learning and Bayesian deep reinforcement learning algorithms enabling each master to reach the PBE independently.

We consider the problem of resource slicing in the 6thgeneration multi-access edge computing (6G-MEC) network. The network includes many non-stationary space-air-ground-sea nodes with dynamic, unstable connections and resources, where any node can be in one of two hidden states: i) reliable – when the node generates/propagates no data errors; ii) unreliable – when the node can generate/propagate random errors. We show that solving this problem is challenging, since it represents a non-deterministic polynomial-time (NP) hard dynamic combinatorial optimization problem depending on the unknown distribution of hidden nodes’ states and time-varying parameters (connections and resources of nodes) which can only be observed locally. To tackle these challenges, we develop a new deep learning (DL) model based on the message passing graph neural network (MPNN) to estimate hidden nodes’ states in dynamic network environments. We then propose a novel algorithm based on the integration of MPNN-based DL and online alternating direction method of multipliers (ADMM) – extension of the well-known classical “static” ADMM to dynamic settings, where the slicing problem is solved distributedly, in real time, based on local information. We prove that our algorithm converges to a global optimum of our problem with a superior performance even in the highly-dynamic, unreliable scenarios.

We study the problem of distributed computing in themulti-operator multi-access edge computing(MEC) network fordependent tasks. Every task comprises severalsub-taskswhich are executed based on logical precedence modelled as adirected acyclic graph. In the graph, each vertex is a sub-task, each edge – precedence constraint, such that a sub-task can only be started after all its preceding sub-tasks are completed. Tasks are executed by MEC servers with the assistance of nearby edge devices, so that the MEC network can be viewed as adistributed“primary-secondary node” system where each MEC server acts as aprimary node(PN) deciding on sub-tasks assigned to itssecondary nodes(SNs), i.e., nearby edge devices. The PN's decision problem is complex, as its SNs can be associated with otherneighboringPNs. In this case, the available processing resources of SNs depend on the sub-task assignment decisions of all neighboring PNs. Since PNs are controlled by different operators, they do not coordinate their decisions, and each PN is uncertain about the sub-task assignments of its neighbors (and, thus, the available resources of its SNs). To address this problem, we propose a novel framework based on agraphical Bayesian game, where PNs play under uncertainty about their neighbors’ decisions. We prove that the game has aperfect Bayesian equilibrium(PBE) yieldingunique optimal values, and formulate newBayesian reinforcement learningandBayesian deep reinforcement learningalgorithms enabling each PN to reach the PBE autonomously (without communicating with other PNs).

Consortium blockchains have attracted considerable interest from academia and industry due to their low-cost installation and maintenance. However, typical consortium blockchains can be easily attacked by colluding block validators because of the limited number of miners in the systems. To address this problem, in this paper, we propose a novel block validation framework to enhance blockchain security. In the framework, the block validations are assisted and implemented by various lightweight nodes, e.g., edge devices, in addition to the typical blockchain miners. This improves the blockchain security but can cause an increased block validation delay and, thereby, reduced blockchain throughput. To tackle this challenge, we propose an effective method to select lightweight nodes based on their computing powers to maximize the blockchain throughput, and prove the uniqueness of the optimal nodes selection strategy. Security analysis and simulation results from the deployed consortium blockchain platform show that the proposed framework achieves higher throughput and security than the existing consortium blockchain models.

We formulate a novel framework to improve security and utility of the coded multi-access edge computing (MEC) network for Internet of Things (IoT) applications where multiple edge servers (ESs) jointly process raw IoT data to obtain the final network output. To correctly recover the final output even when some processing outputs produced by malicious or malfunctioning ESs are erroneous, the network utilizes coded distributed computing (CDC) that enhances security by adding computational redundancy to the data processed by ESs. Within the framework, we propose an advanced approach to address limitations of contemporary CDC-based systems related to their inability to guarantee security when the number of malicious ESs is large and reduced network utility due to redundant computations. In this approach, the processing loads are allocated to ESs based on deep learning (DL) algorithms to identify the unknown ESs types (faithful or malicious) and minimize the load of malicious ESs, thereby optimizing security and utility. The proposed DL algorithms adopt the message passing neural network (NN) a generalized graph NN with lower complexity and faster convergence than conventional NNs. We prove that our framework yields the optimal security and utility, and verify its superior performance compared with the state-of-the-art schemes.

We design a novel encoding model based on Lagrange coded computing (LCC) for private, secure, and resilient distributed mobile edge computing (MEC) systems, where multiple base stations (BSs) act as “masters” offloading their computations to edge nodes acting as “workers”. A two-fold objective of the scheme is: i) efficient allocation of computing tasks to the workers; ii) providing the workers with appropriate incentives to complete their tasks. As such, each master must decide on its offloading requests to the workers including the allocated tasks and service fees to be paid. This problem is complex due to the following reasons: i) masters can be privately-owned or managed by different operators, i.e., there is no communication and no coordination among them; ii) workers are heterogeneous non-dedicated nodes with limited and nondeterministic transmission and computing resources. As a result, the masters must compete for constrained resources of workers in a stochastic partially-observable environment. To address this problem, we define the interactions between masters and workers as a direct stochastic first-price-sealed-bid (FPSB) auction. To analyze the auction, we represent it as a stochastic Bayesian game and develop a Bayesian learning framework to perfect the auction solution.

In Internet-of-Vehicles (IoV), smart vehicles can efficiently process various sensing data through federated learning (FL) - a privacy-preserving distributed machine learning (ML) approach that allows collaborative development of the shared ML model without any data exchange. However, traditional FL approaches suffer from poor security against the system noise, e.g., due to low-quality trained data, wireless channel errors, and malicious vehicles generating erroneous results, which affects the accuracy of the developed ML model. To address this problem, we propose a novel FL model based on the concept of Lagrange coded computing (LCC) - a coded distributed computing (CDC) scheme that enables enhancing the system security. In particular, we design the first L-CoFL (Lagrange coded FL) model to improve the accuracy of FL computations in the presence of lowquality trained data and wireless channel errors, and guarantee the system security against malicious vehicles. We apply the proposed L-CoFL model to predict the traffic slowness in IoV and verify the superior performance of our model through extensive simulations.

We analyze the information exchange and interactions among the major components, i.e., people, things, data, and processes, of the Internet of Everything (IoE) system, where raw data generated by people and things must be processed to obtain relevant higher level information that can be utilized by IoE processes for decision making and actions. Accordingly, the value of information obtained in the IoE system depends on the Age of Information (AoI)—time elapsed from the moment when raw data is generated to the moment when the data is processed and delivered to the processes. To reduce the AoI, the system is realized in the multiaccess edge computing network, where data can be processed by the edge devices (EDs) in proximity to people, things, and processes. The system security and resilience are further enhanced through coded distributed computing when each data input of EDs is encoded with a specific encoding function so that the final result of data processing by EDs can be recovered even if some processing outputs of EDs are erroneous or delayed. We then define a stochastic optimization problem where the AoI, security, and resilience are optimized jointly to maximize the expected long-term system payoff—difference between the value of information and data processing costs. Since this problem is hard to solve directly due to hidden information about the correctness of processing outputs returned by EDs, we develop a machine learning (ML) framework to obtain the problem solution.

This paper integrates Software-Defined Networking (SDN) and Information -Centric Networking (ICN) framework to enable low latency-based stateful routing and caching management by leveraging a novel forwarding and caching strategy. The framework is implemented in a clean- slate environment that does not rely on the TCP/IP principle. It utilizes Pending Interest Tables (PIT) instead of Forwarding Information Base (FIB) to perform data dissemination among peers in the proposed IC-SDN framework. As a result, all data exchanged and cached in the system are organized in chunks with the same interest resulting in reduced packet overhead costs. Additionally, we propose an efficient caching strategy that leverages in- network caching and naming of contents through an IC-SDN controller to support off- path caching. The testbed evaluation shows that the proposed IC-SDN implementation achieves an increased throughput and reduced latency compared to the traditional information-centric environment, especially in the high load scenarios.

We develop a new Lagrange coded blockchain model for Internet-of-Things (IoT) systems based on Lagrange coded computing (LCC). In the model, a mining task assigned to a blockchain node (BN) is encoded with a specific encoding function. Thus, the final result, i.e., newly generated block or block verification result, can be decoded even when only some mining outputs returned by BNs are correct, while other outputs are erroneous or discarded due to delays. To be decoded correctly, the number of mining outputs returned prior to decoding must be at least a given decoding threshold. Then, security against malicious BNs and resilience against stragglers can be guaranteed if the number of mining tasks allocated to BNs is not less than the sum of decoding threshold, number of stragglers, and double of the number of malicious BNs. Unlike other IoT blockchains and LCC-based methods showing enhanced throughput but yielding poor security, our model can improve throughput without compromising on security. This is achieved through optimized load allocations when the higher loads (two or more mining tasks) are allocated to the fastest BNs leading to: 1) increased number of mining outputs returned prior to decoding required to meet the decoding threshold and 2) increased number of allocated mining tasks to strengthen security and resilience. To overcome the limitation of our model related to higher loads and, hence, higher mining costs to BNs, we develop a contract-theoretic mechanism that incentivizes each BN to complete its mining task through joint load and transaction fee allocations.

We consider a public blockchain realized in the mobile edge computing (MEC) network, where the blockchain miners compete against each other to solve the proof-of-work puzzle and win a mining reward. Due to limited computing capabilities of their mobile terminals, miners offload computations to the MEC servers. The MEC servers are maintained by the service provider (SP) that sells its computing resources to the miners. The SP aims at maximizing its long-term profit subject to miners' budget constraints. The miners decide on their hash rates, i.e., computing powers, simultaneously and independently, to maximize their payoffs without revealing their decisions to other miners. As such, the interactions between the SP and miners are modeled as a stochastic Stackelberg game under private information, where the SP assigns the price per unit hash rate, and miners select their actions, i.e., hash rate decisions, without observing actions of other miners. We develop a hierarchical learning framework for this game based on fully- and partially-observable Markov decision models of the decision processes of the SP and miners. We show that the proposed learning algorithms converge to stable states in which miners' actions are the best responses to the optimal price assigned by the SP.

In this article, we study the pricing and resource management in the Internet of Things (IoT) system with blockchain-as-a-service (BaaS) and mobile-edge computing (MEC). The BaaS model includes the cloud-based server to perform blockchain tasks and the set of peers to collect data from local IoT devices. The MEC model consists of the set of terrestrial and aerial base stations (BSs), i.e., unmanned aerial vehicles (UAVs), to forward the tasks of peers to the BaaS server. Each BS is also equipped with an MEC server to run some blockchain tasks. As the BSs can be privately owned or controlled by different operators, there is no information exchange among them. We show that the resource management and pricing in the BaaS-MEC system are modeled as a stochastic Stackelberg game with multiple leaders and incomplete information about actions of leaders/BSs and followers/peers. We formulate a novel hierarchical reinforcement learning (RL) algorithm for the decision makings of BSs and peers. We also develop an unsupervised hierarchical deep learning (HDL) algorithm that combines deep $Q$ -learning (DQL) for BSs with the Bayesian deep learning (BDL) for peers. We prove that the proposed algorithms converge to stable states in which the peers' actions are the best responses to optimal actions of BSs.

We propose a fully distributed system architecture and a scalable self-organized sharding scheme for the Internet-of-Things (IoT) blockchains that can guarantee system security without reducing its throughput. In the system, the IoT devices are supported by the set of blockchain peers that gather, process, verify, and store the blocks of IoT transaction records. To support communications among peers, the system is realized in the mobile-edge computing (MEC) network. We design a new consensus mechanism in which each peer votes on the outputs of each block task in its shard. The peer's voting power is computed from its reputation, i.e., trustworthiness in the system. By adopting a reputation-based coalitional game model, we formulate a novel self-organized shard formation algorithm in which each peer acts as a rational player aiming to maximize both its payoff and the coalitional reputation. We prove that the algorithm converges to the reputation-based stable shard structure, i.e., a structure that maximizes the payoff and coalitional reputation of each peer without negatively affecting other peers. The algorithm shows a superior performance in terms of system security and throughput when compared to state-of-the-art sharding schemes and reputation-based blockchains.

The paper proposes a novel framework based on the contract theory and Lyapunov optimization for content sharing in a wireless content delivery network (CDN) with edge caching and device-to-device (D2D) communications. The network is partitioned into a set of clusters. In a cluster, users can share contents via D2D links in coordination with the cluster head. Upon receiving the content request from any user in its cluster, the cluster head either delivers the content itself or forwards the request to another node, i.e., a base station (BS) or another user in the cluster. The content access at the BS and in each cluster is modeled as a queuing system, where arrivals represent the content requests directed to respective nodes. The objective is to assign content delivery nodes to stabilize all queues while minimizing the time-averaged network cost given incomplete information about content sharing costs of the users and unknown distribution of the network state defined by users' locations and their cached/requested content. The proposed framework allows the users to truthfully reveal their content sharing expenditures, minimize the time-averaged network cost and stabilize the queuing system representing the CDN. Based on this framework, a distributed content access and delivery algorithm where the node assignments are made by every cluster head independently is developed. It is shown that the algorithm converges to the optimal policy with the trade-off in total queue backlog and achieves a superior performance compared with some other D2D content sharing policies.

We present a novel game-theoretic (GT) and reinforcement learning (RL) framework for computational offloading in the mobile edge computing (MEC) network operated by multiple service providers (SPs). The network is formed by MEC servers installed at stationary base stations (BSs) and unmanned aerial vehicles (UAVs) deployed as quasi-stationary BSs. Since computing powers of MEC servers are limited, the BSs in proximity can form coalitions with shared data processing resources to serve their users more efficiently. However, as BSs can be privately owned or controlled by different SPs, in any coalition, the BSs: 1) take only the actions that maximize their long-term payoffs and 2) do not coordinate their actions with other BSs in the coalition. That is, inside each coalition, BSs act in an independent and self-interested manner. Therefore, the interactions among BSs cannot be described by conventional coalitional games. Instead, the network operation is modeled by a two-level hierarchical model. The upper level is a cooperative game that defines the process of coalition formation. The lower level comprises the set of noncooperative subgames to represent a self-interested and independent behavior of BSs in coalitions. To enable each BS to select a coalition and decide on its action maximizing its long-term payoff, we propose two algorithms that combine coalition formation with RL and prove that these algorithms converge to the states where the coalitional structure is strongly stable and the strategies of BSs are in the mixed-strategy Nash equilibrium (NE).

We present a novel game, called evolutionary Bayesian coalition formation game, to model and analyze the problem of distributed resource sharing in a multi-cell device-to-device (D2D) enabled cellular network where the rationality of the players, i.e., device pairs, is bounded, e.g., due to limited information. Each player can make its decision on the channel to access with and without coordination. In the former case, the player works in D2D mode. In the latter case, the player forms a coalition with some other players and they connect to one base station in cellular mode. In this case, the player realizes its action after observing the actions of other players. Unlike classical coalition formation games where the player decides on its coalition to form by estimating its payoff, in the proposed game, the player forms a coalition and selects an action based on its current population state which is updated using a simple and scalable learning algorithm. We prove that the evolutionary coalition formation process converges to the unique equilibrium that induces a stable coalitional agreement. The proposed process is applied to a long-term evolution-advanced network where it shows a superior performance compared with other baseline resource sharing strategies.

This paper investigates the problem of distributed resource sharing in a device-to-device enabled heterogeneous network, where the various device pairs choose their transmission channels, modes, base stations (BSs), and power levels without any control by the BSs based only on the locally-observable information. This problem is represented as a Bayesian coalition formation game, where the players (device pairs) create coalitions to maximize their long-term rewards with no prior knowledge of the values of potential coalitions and the types of their members. To minimize these uncertainties, a novel Bayesian reinforcement learning (RL) model is derived. In this model, the players update (through repeated coalition formation) their beliefs about the types and coalitional values to reach a stable coalitional agreement. The proposed Bayesian RL-based coalition formation algorithms are implemented in a long-term evolution advanced network and evaluated using simulations. The algorithms show a superior performance when compared with other relevant resource allocation schemes and achieve near-optimal results after a relatively small number of RL iterations.

This paper explores the problem of joint mode selection, spectrum management, power control, and interference mitigation for device-to-device (D2D) communication underlaying a Long Term Evolution-Advanced (LTE-A) network. We consider a dynamic mode selection scenario, in which the modes (D2D or cellular) of the devices depend on optimal allocations. To improve the quality of service (QoS) for the users, the optimization objective in a corresponding problem is formulated in terms of buffer size of user equipments (UEs), which is estimated based on buffer status information collected by the UEs. The realizations of a resource allocation approach presented in the paper include its real-time and non-real-time implementations, as well as two modifications applicable to a standard LTE-Direct (LTE-D) network. Performance of the proposed algorithms has been evaluated using the OPNET-based simulations. All algorithms show improved performance in terms of mean packet end-to-end delay when compared to most relevant schemes proposed earlier.

We study the problem of autonomous operation of the device-to-device (D2D) pairs in a heterogeneous cellular network with multiple base stations (BSs). The spectrum bands of the BSs (that may overlap with each other) comprise the sets of orthogonal wireless channels. We consider the following spectrum usage scenarios: 1) the D2D pairs transmit over the dedicated frequency bands and 2) the D2D pairs operate on the shared cellular/D2D channels. The goal of each device pair is to jointly select the wireless channel and power level to maximize its reward, defined as the difference between the achieved throughput and the cost of power consumption, constrained by its minimum tolerable signal-to-interference-plus-noise ratio requirements. We formulate this problem as a stochastic non-cooperative game with multiple players (D2D pairs) where each player becomes a learning agent whose task is to learn its best strategy (based on the locally observed information) and develop a fully autonomous multi-agent Q-learning algorithm converging to a mixed-strategy Nash equilibrium. The proposed learning method is implemented in a long term evolution-advanced network and evaluated via the OPNET-based simulations. The algorithm shows relatively fast convergence and near-optimal performance after a small number of iterations.

We explore the problem of resource allocation in a Third Generation Partnership Project (3GPP) long-term evolution (LTE) based cognitive radio network (CRN). The network model consists of a number of service providers (SPs) with fixed licensed spectrum bands. The network offers the wireless services to two types of users: primary and secondary. The primary users (PUs) get prioritized access to the licensed spectrum bands. The secondary users (SUs) are served on the best-effort (non-prioritized) basis. In this paper we consider the specific design features of LTE radio interface associated with the uplink spectrum access, scheduling process, and limited control channel capacity of the LTE system. We establish the relation between the number of users in the system, and the scheduling delay (which is the largest contributor to the packet end-to-end delay in LTE network). Using these results, we propose a simple algorithm to assign the spectrum for the SUs without violating the quality of service (QoS) requirements of the PU, and implement it in an LTE-based CRN. Consistent performance of the algorithm is verified using OPNET-based simulations.

In this paper we present a novel dynamic bandwidth allocation technique in which different base stations share the total available spectrum to maximize the quality of service (QoS) in the network, and show the implementation of this technique in a cognitive 3rd Generation Partnership Project Long Term Evolution (3GPP LTE) network. Assuming, that each base station is characterized by a concave increasing utility and a positive weight, we conduct a weighted utility maximization framework, and develop a simple prediction-based bandwidth allocation algorithm. To deal with heterogeneous network applications we propose to deploy the approach used in optimal flow and congestion control (OFC) where the resources are assigned based on speed of load increase. Using the appropriate load indictors, the algorithm first identifies the base stations with increasing (decreasing) load, and then decrease (increase) the channel utilization of base stations with increased (decreased) load using weighted proportional fairness criterion.

Priority based packet transmission techniques are commonly used in communication networks to support multimedia services. In wireless networks mainly type of service or queue measurement based packet transmission priority techniques are used. This paper introduces a novel two stage traffic prediction and type of service based priority technique for an infrastructure based Wireless Local Area Network. The developed algorithm alters the priority of transmission queues and services in a radio access network based on the predicted traffic volume and the conventional type of service priority technique for multimedia packet transmissions. Simulation results show that our proposed algorithm improves the QoS of multimedia traffic significantly. An OPNET based simulation model has been developed to obtain the performance results for a multiple access points based wireless infrastructure network.