Vincent Wong

The next generation of wireless systems aims to provide services with higher data rates, greater reliability, and lower latency compared to their predecessors (e.g., the fifth generation (5G) wireless systems). To facilitate the support of such services, new emerging technologies, such as edge intelligence and terahertz (THz) band communication, will be incorporated into wireless systems. Additionally, network slicing technology is inherited from 5G to enable the support of services with diverse quality of service (QoS) requirements within a shared network infrastructure. To fully harness the potential of these technologies and improve the performance of wireless systems, it is necessary to employ intelligent resource allocation algorithms using machine learning (ML) techniques within such systems. ML-based algorithms can adapt well to the network dynamics and can provide desired solutions in a timely manner. In this thesis, we employ various ML techniques to optimize wireless system performance for three specific problems. First, we consider a radio resource slicing problem to support enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services in a shared radio access network (RAN). To solve this multi-timescale problem, we propose a hierarchical deep learning framework. Second, we study federated learning (FL) algorithm as an enabler for edge intelligence and aim to improve its performance under heterogeneous data and device settings. To tackle this problem, we propose a personalized FL algorithm with optimized masking vectors called PerFedMask. Third, we consider 360° video streaming in a multi-user THz wireless system with multiple multi-antenna access points (APs). We propose a content-based viewport prediction framework to determine which video tiles should be sent to the users. Additionally, we propose a hierarchical deep reinforcement learning (DRL) framework to optimize the bitrate selection of the video tiles and the beamforming vectors at the APs. Simulation results show that compared with the benchmarks, our proposed ML-based approaches can achieve a higher aggregate throughput in the RAN slicing problem, a higher test accuracy with a lower average number of trainable parameters for FL under heterogeneous settings, and a higher quality of experience (QoE) for the users watching 360° videos in a THz wireless system.

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With the rapid development of wireless network-enabled applications, the beyond fifth generation (B5G) wireless systems are required to support a large number of mobile and Internet of things (IoT) devices. Moreover, the growing demand for applications with high data rate requirements, including virtual reality (VR), brings new challenges to the B5G wireless systems. While several emerging physical layer and medium access control techniques, including grant-free multiple access (GFMA), intelligent reflecting surface (IRS), and rate-splitting (RS), introduce additional degrees of freedom (DoF) to the B5G wireless systems, novel resource allocation algorithms are required to fully exploit their potentials. In this thesis, we propose deep reinforcement learning (DRL)-based algorithms to efficiently optimize the DoF and improve the performance of B5G wireless systems.First, we propose a distributed pilot sequence selection scheme for GFMA systems. The proposed scheme maximizes the aggregate throughput by mitigating pilot sequence selection collisions. In the proposed scheme, a distributed pilot sequence selection policy is obtained by using a multiagent DRL technique. Second, we propose a joint user scheduling, phase shift control, and beamforming optimization algorithm for IRS-aided systems. We formulate a joint optimization problem for maximizing the aggregate throughput and achieving the proportional fairness in IRS-aided systems. The proposed algorithm exploits neural combinatorial optimization (NCO) to determine user scheduling, and uses curriculum learning (CL) and deep deterministic policy gradient (DDPG) to optimize the beamforming vectors and IRS phase shifts. Third, we propose a novel IRS-aided RS VR streaming system. We formulate an optimization problem for maximizing the achievable bitrate of the 360-degree video subject to the quality of experience (QoE) constraints of the users. We propose a deep deterministic policy gradient with imitation learning (Deep-GRAIL) algorithm, in which we leverage DRL and the human expert knowledge to optimize the IRS phase shifts, RS parameters, beamforming vectors, and bitrate selection of 360-degree videos. Simulation results show that the proposed DRL-based algorithms improve the performance of B5G wireless systems by efficiently optimizing the DoF. Our results also demonstrate the effectiveness of empowering the DRL techniques with the human expert knowledge of wireless systems.

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With the proliferation of the Internet of things (IoT) applications, it becomes essential for wireless cellular networks to support energy-efficient communication for an increasing number of IoT devices. In this thesis, we develop resource allocation algorithms and propose novel preamble design for enhancing the connection density in IoT systems. First, we propose a non-orthogonal multiple access (NOMA) scheme for narrowband IoT (NB-IoT) systems. This scheme allows multiple IoT devices to simultaneously access either one subcarrier (single-tone mode) or a bond of contiguous subcarriers (multi-tone mode). We formulate joint subcarrier and power allocation problems for both modes to maximize the connection density. The formulated problems are nonconvex mixed integer programming problems. We optimally solve the formulated problems using mixed integer linear programming transformation and difference of convex programming. We also propose low-complexity algorithms to solve both problems in a suboptimal manner. Second, we propose a communication mode selection scheme for IoT devices that can communicate using either active transmission or energy-efficient short-range backscattering. In the active transmission mode, the IoT devices can transmit data using power-domain NOMA. In the backscattering mode, nearby user equipment (UE) devices are used as relays that receive the backscattered signals from the IoT devices and forward them to the base station (BS). We formulate a connection density maximization problem to select the communication mode for each IoT device. The optimal algorithm, which solves the formulated binary integer programming problem, incurs exponential computational complexity. Hence, we propose a low-complexity suboptimal algorithm to solve the problem. Third, we propose a larger set of random access preambles by considering all possible combinations of aggregating two Zadoff-Chu preamble sequences. Decoding the aggregate preambles is challenging because the receiver needs to decode two preambles where each one is allocated half of the transmit power. We propose two receiver architectures for preamble decoding. The first architecture only requires minor changes to the conventional preamble receiver architecture. The second architecture exploits a deep neural network (DNN). Simulation results demonstrate that the proposed schemes in this thesis can enhance the capability of wireless cellular networks to support a higher connection density in IoT systems.

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The electric utilities industry is undergoing a major paradigm shift, driven by an aging physical infrastructure as well as concerns for carbon emissions. The migration to the digital space with information and communications technology (ICT) as well as the need to integrate more sustainable energy sources have raised new challenges for the legacy power systems. Energy storage systems (ESSs) can help to address the aforementioned challenges and to facilitate the transition to the future smart grid infrastructure. Early deployment of front-of-the-meter utility-scale ESSs have proven to be valuable in providing alternative service options that can benefit the bulk power systems.With the fast declining capital and operating cost, there is a rapid growth in behind-the-meter ESSs adoption. In this thesis, we focus on the application of such ESSs in the low voltage networks and investigate their potential use cases for customers and electric utilities alike. It addresses several specific challenges that exist in the smart grid infrastructure and leverages the unique characteristics of the ESSs to provide solutions for end consumers, the system operator, and storage owners.For end consumers, we design a privacy protection solution at the customer premises based on data obfuscation approach. A household load hiding scheme is developed by exploiting the opportunistic use of the electric vehicles and household appliances to minimize customer’s privacy leakage. For the system operator, we design a frequency regulation scheme based on bi-level optimization that takes into account the ESSs’ operation economics. A decentralized control algorithm is developed to allow the system operator to align with the ESSs on the frequency regulation decisions. For storage owners, we design a market participation strategy to maximize the revenue from providing frequency regulation service. A decision-making framework is developed that allows the storage owners to optimize its operation decisions by anticipating the effect of such decisions on the market clearing outcomes. Our simulation results demonstrate that the applications designed in this thesis by leveraging the behind-the-meter ESSs in the low voltage networks can provide significant benefits for customers and electric utilities alike.

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In future heterogeneous wireless networks, machine-type communication (MTC) devices require the access of wireless cellular networks. However, the Long Term Evolution (LTE) networks, which are designed for human users, may not be able to handle a large number of bursty random access requests from MTC devices. We propose a scheme that uses both access class barring (ACB) and timing advance information to reduce random access overload in MTC systems. Given the number of backlogged MTC devices, we formulate an optimization problem to determine the optimal ACB parameter, which maximizes the expected number of MTC devices successfully served in each random access slot. We present a closed-form approximate solution and propose an algorithm to estimate the number of backlogged MTC devices to improve the practicability of the proposed scheme. Besides, the data traffic demand of mobile users is significant in future communication networks. In heterogeneous wireless networks, mobile devices close to each other can also communicate in a device-to-device (D2D) manner to transfer digital objects (e.g., videos). However, the opportunity that mobile users download their interested objects from neighbors is transient. We propose an expected available duration (EAD) metric to evaluate the opportunity that an object can be downloaded from neighbors. The EAD metric takes into account the pairwise connectivity of users, social influence between users, diffusion of digital objects, and the time that users would like to wait for D2D data offloading. To download more data from neighbors, a mobile user can first download the available object that has the smallest EAD. Moreover, for resource allocation in future wireless cellular networks with the cloud radio access network (C-RAN) architecture, we model user’s utility by a sigmoidal function of signal-to-interference-plus-noise ratio (SINR) to capture the diminishing utility returns for very small or very large SINRs in real-time applications (e.g. video streaming). Our objective is maximizing the aggregate utility of users while taking into account the imperfectness of channel state information, limited backhaul capacity of C-RAN, and minimum quality of service requirements. We propose an efficient resource allocation algorithm which outperforms a baseline scheme for weighted system sum rate maximization.

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Energy management is of prime importance for power system operators to enhance the use of the existing and new facilities, while maintaining a high level of reliability. In this thesis, we develop analytical models and efficient algorithms for energy management programs in transmission and distribution networks. First, we study the optimal power flow (OPF) in ac-dc grids, which is a non-convex optimization problem. We use convex relaxation techniques and transform the problem into a semidefinite program (SDP). We derive the sufficient conditions for zero relaxation gap and design an algorithm to obtain the global optimal solution. Subsequently, we study the security-constrained unit commitment (SCUC) problem in ac-dc grids with generation and load uncertainty. We introduce the concept of conditional value-at risk to limit the net power supply shortage. The SCUC is a nonlinear mixed-integer optimization problem. We use ℓ₁-norm approximation and convex relaxation techniques to transform the problem into an SDP. We develop an algorithm to determine a near-optimal solution. Next, we target the role of end-users in energy management activities. We study demand response programs for residential users and data centers. For residential users, we capture their coupled decision making in a demand response program with real-time pricing as a partially observable stochastic game. To make the problem tractable, we approximate the optimal scheduling policy of the residential users by the Markov perfect equilibrium (MPE) of a fully observable stochastic game with incomplete information. We develop an online load scheduling learning algorithm to determine the users’ MPE policy. Last but not least, we focus on the demand response program for data centers in deregulated electricity markets, where each data center can choose a utility company from multiple available suppliers. We model the data centers’ coupled decisions of utility company choices and workload scheduling as a many-to-one matching game with externalities. We characterize the stable outcome of the game, where no data center has an incentive to unilaterally change its strategy. We develop a distributed algorithm that is guaranteed to converge to a stable outcome.

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Due to the growing number of wireless communication devices and emerging bandwidth-intensive applications, the demand of data usage is increasing rapidly. Utilizing various radio access technologies and multiple frequency bands in wireless networks can provide efficient solutions to meet the growing demand of data. These techniques are promising for the fifth generation (5G) wireless communication systems. However, to fully exploit their benefits, spectrum and spatial reuse, power saving, throughput and utility enhancement are crucial issues. In this thesis, we propose different resource allocation algorithms to address the aforementioned issues in wireless communication networks. First, we study the resource allocation problem for a hybrid overlay/underlay cognitive cellular network. We propose a hybrid overlay/underlay spectrum access mechanism to improve the spectrum and spatial reuse. We formulate the resource allocation problem as a coalition formation game among femtocell users, and analyze the stability of the coalition structure. We propose an efficient algorithm based on the solution concept of recursive core. The proposed algorithm achieves a stable and efficient spectrum allocation. Next, we study the resource allocation problem for multimedia content delivery in millimeter wave (mmWave) based home networks. We characterize different usage scenarios of multimedia content delivery. We formulate a joint power and channel allocation problem, which captures the spectrum and spatial reuse of mmWave communications, based on a network utility maximization framework. The problem is a non-convex mixed integer programming (MIP) problem. We reformulate the non-convex MIP problem into a convex MIP problem and propose a resource allocation algorithm based on the outer approximation method. We also develop an efficient heuristic algorithm which has a substantially lower complexity than the outer approximation based algorithm. Finally, we study full-duplex relay-assisted device-to-device (D2D) communication in mmWave based wireless networks. To design an efficient relay selection and power allocation scheme, we formulate a multi-objective combinatorial optimization problem, which balances the trade-off between power consumption and system throughput. The problem is transformed into a weighted bipartite matching problem. We then propose a joint relay selection and power allocation algorithm, which can achieve a Pareto optimal solution in polynomial time.

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In the current electrical grid, utility companies have begun to use demand side management (DSM) programs and time-of-use (TOU) pricing schemes to shape the residential load profile. However, it is difficult for the residential users to respond to the pricing signal and manually manage the operation of various household appliances. Hence, the autonomous energy consumption scheduling of residential loads and electric vehicles (EVs) is necessary for the users to benefit from the DSM programs. In this thesis, we propose different algorithms to schedule the energy consumption of residential loads and EVs, and provide ancillary services to the electrical grid. First, we study the DSM for areas with high photovoltaic (PV) penetration. Since many rooftop PV units can be integrated in the distribution network, the voltage rise issue occurs when the reverse power flow from the households to the substation is significant. We use stochastic programming to formulate an energy consumption scheduling problem, which takes into account the voltage rise issue and the uncertainty of the power generation from PV units. We propose an algorithm by solving the formulated problem and jointly shave the peak load and reduce the reverse power flow. Subsequently, we study using the EVs to provide the frequency regulation service. We formulate a problem to schedule the hourly regulation capacity of the EVs using the probabilistic robust optimization framework. Our formulation takes into account the limited battery capacity of the EVs and the uncertainty of the automatic generation control (AGC) signal. An efficient algorithm is proposed to solve the formulated problem based on duality. Last but not least, we study the market participation of an aggregator which coordinates a fleet of EVs to provide frequency regulation service to an independent system operator (ISO). The two-settlement market system (i.e., the day-ahead market (DAM) and real-time market) is considered. We analyze two types of DAMs based on the market rules of New York ISO and California ISO. We formulate a problem to determine the bid for the aggregator in the DAM using stochastic program and conditional value at risk. Efficient algorithms are proposed to tackle the formulated problem.

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To achieve a high level of reliability and robustness in power systems, the grid is usually designed for the peak demand rather than the average demand. This usually results in an under-utilized system. Demand side management (DSM) programs can be adopted to shape the load pattern of the users to better utilize the available power generation capacity and to prevent installing new generation and transmission infrastructures.In this thesis, we propose different algorithms for DSM.First, we focus on the problem of maximizing the social welfare of the users.We consider a scenario where the users are equipped with automated control units and are able to make price-responsive decisions. We propose a Vickrey-Clarke-Groves (VCG) mechanism to maximize the social welfare of the users.Subsequently, we focus on developing a novel automated load scheduling algorithm to minimize the energy expenses of the user.The proposed algorithm takes into account the effects of the load uncertainties in future time slots. Moreover, the operational constraints of different types of appliances including must-run appliances, and interruptible and non-interruptible controllable appliances are studied. Next, we study how the utility company can set price values for different times of a day such that the peak-to-average ratio (PAR) of the load is minimized.We also consider the effects of the uncertainty regarding the price-responsiveness of the users.To simulate the likely behavior of the users in response to different price values for different times of the day, we propose the use of a system simulator unit. We propose two pricing algorithms based on stochastic approximation aiming to minimize the PAR of the aggregate load. Finally, we consider systems with high penetration of renewable energy resources.To tackle the reverse power flow problem associated with these systems, we propose a joint load scheduling and trading algorithm.This algorithm encourages the users to sell their excess generation to their neighboring users which mitigates the reverse power flow problem.

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To meet the growing demand of mobile data service with limited radio resources, the cellular architecture has evolved from single-cell networks towards multi-cell networks. In multi-cell networks, the spectrum is reused by multiple adjacent cells to increase the spectral efficiency. As a trade-off, interference is introduced among the cells, which limits the achievable data rates for users who experience significant inter-cell interference. In this thesis, multi-cell coordination is applied to mitigate interference, and several resource allocation mechanisms are proposed to improve the system performance for various multi-cell networks. First, a downlink scheduling mechanism is proposed for a multi-cell multiple-input multiple-output (MIMO) network. This mechanism dynamically selects the users to be scheduled and the corresponding MIMO transmission strategy to optimize a utility function. Both centralized and distributed algorithms are developed, and an efficient rate adjustment method is proposed to improve the system throughput when the channel state information (CSI) is imperfect. Next, a network configuration mechanism is developed for two-tier macro-femto networks. In this mechanism, coordination is applied for different network configuration processes such that access control, spectrum allocation and power management are performed sequentially at the base stations and users, respectively. This mechanism is modeled as a multi-stage decision making process and the desired decisions are obtained using a multi-level optimization approach. Finally, coordination among multiple service providers for resource sharing is studied in cloud-based radio access networks (C-RANs). A multi-timescale resource sharing mechanism is designed. This mechanism employs a threshold-based policy to control the inter-cell interference, and defines a new metric for providing resource sharing guarantee for each service provider. It consists of resource allocation processes that are performed at different time scales to deal with traffic demand variation. The proposed mechanism addresses the issue of user mobility by employing a mobility prediction method when optimizing the resource sharing decisions. The performance of the mechanisms proposed in this thesis are evaluated via computer simulations. It is shown that these mechanisms substantially improve the performance for the corresponding multi-cell networks.

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The unreliability of wireless mesh networks creates challenge in designing high performance wireless networks in terms of network throughput, end-to-end delay, and fairness provisioning. In this thesis, the goal is to improve the network performance in terms of these metrics. We explore several techniques such as multipath routing, channel coding, network coding, and interference alignment. We consider resource allocation both in terms of average data rate provisioning and scheduling policies in a time slot basis.First, we propose data rate and channel code rate allocation algorithms for networks with multiple paths to maximize the network throughput while all users can fairly exploit the network resources. We study the effect of adaptive and non-adaptive channel coding schemes. We also consider the end-to-end delay that each network flow experiences for data transmission. For that purpose, we formulate the problem of decreasing the end-to-end delay for network flows while improving the network throughput. Simulation results show that we can decrease the delay at the cost of a slight decrease in network throughput. We also formulate a data rate allocation problem in networks with network coding. Simulation results show that considering link reliabilities in the network coding design dramatically increases the network performance.Data rate allocation algorithms provide the average data rates at which the source must transmit data. They do not determine scheduling on a time slot basis. To address that, we consider transmission scheduling in wireless networks. We also compare the suggested algorithm with a centralized optimal data rate allocation algorithm to verify that our algorithm follows the optimal solution. Through simulations, we show that fairness provisioning leads to higher network performance. We show that the suggested algorithm outperforms the current algorithms in the literature in terms of both network throughput and fairness provisioning.Finally, we consider transmission scheduling in wireless multi-input multi-output (MIMO) systems. We formulate the problem of joint scheduling, interference alignment, and admission control in those networks and use Lyapunov stability theory to solve it. We also develop a heuristic approach to solve the problem in a semi-distributed manner.

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In this thesis, we present several random access algorithms for medium access control in wireless networks. Optimization theory, game theory, and dynamic programming are applied in the analysis and the design of these algorithms. First, we study the problem of multi-channel random access using the signal-to-interference-plus-noise-ratio (SINR) model in cognitive radio networks. We formulate it as a network utility maximization (NUM) problem, and propose a distributed algorithm that converges to a near-optimal solution. Moreover, we apply coalitional game theory to study the incentive issues of rational user cooperation in a given channel under the SINR model. Next, we consider a wireless local area network (WLAN) with rational users, who may strategically declare their access categories (ACs) not intended for their applications in order to gain some unfair shares of the network resources. We propose to use the Vickrey-Clarke-Groves (VCG) mechanism to motivate each user to declare truthfully its actual AC to the access point (AP). In order to implement the VCG mechanism with concave, step, and quasi-concave utility functions, we propose an enumeration algorithm to obtain the global optimal solution of the formulated non-convex NUM problem. To extend the aforementioned work on single-channel random access in WLANs, we focus on sigmoidal utility functions. We propose a subgradient algorithm to solve the formulated NUM problem using the dual decomposition method. If the sufficient conditions on link capacities are satisfied, the algorithm obtains the optimal solution. Finally, we consider the vehicular ad hoc networks. We study the problem of random access in a drive-thru scenario, where roadside APs are installed on a highway to provide temporary Internet access for vehicles. We first consider the single-AP scenario with random vehicular traffic, and propose a dynamic optimal random access (DORA) algorithm that aims to minimize the total transmission cost of a vehicle. We determine the conditions under which the optimal transmission policy has a threshold structure, and propose an algorithm with a lower computational complexity. Then, we consider the multiple-AP scenario with deterministic vehicular traffic arrival due to traffic estimation. A joint DORA is proposed to obtain the optimal transmission policy.

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Wireless sensor networks (WSNs) are composed of inexpensive sensor devices called sensor nodes. Sensors have limited power supply, computational capabilities, and memory. Different types of sensors can measure either temperature, light, sound, or pressure from the environment. Because the sensors have short transmission range, the generated data are gathered via multihop transmissions at a central processor called a sink. In this thesis, we propose several power efficient algorithms for WSNs.First, we formulate the lexicographically optimal commodity lifetime routing problem. We propose the lexicographically optimal node lifetime algorithm, which is suitable for practical implementation. Simulation results show that our proposed algorithm can increase the network lifetime compared to other schemes in the literature.Second, we study the problem of supporting multicast traffic in WSNs with network coding. We formulate the maximum-lifetime minimum-resource coding subgraph problem to study the lifetime-resource tradeoff. Results show that the network lifetime can be substantially increased using our algorithm.Next, we consider the problem of designing feedback mechanisms for WSNs using random linear network coding (RLNC). For an intermediate node, we determine the time at which the node can stop transmission of a particular flow. We propose novel link-by-link and end-to-end feedback mechanisms for RLNC with buffer sharing. Simulation results show that link-by-link feedback is more power-efficient compared to end-to-end feedback.Then, we study the passive loss inference problem in WSNs using RLNC. By inspecting the contents of packets at the sink, the sink can estimate the path loss rates from the sources and intermediate nodes. We propose a passive loss inference with RLNC algorithm. Simulation results show that our algorithm can identify the status of a higher number of links compared to a Bayesian inference algorithm.Finally, we study the problem of cardinality estimation in radio frequency identification systems with several readers. We introduce exclusive estimators to estimate the number of tags located exclusively in the zone of a reader. Then, we propose cardinality estimation algorithms. Our simulation results show that the variance of our proposed estimation algorithms increases linearly with the number of readers while it increases exponentially for existing algorithms.

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This thesis studies several research problems in the area of wireless local area networks (WLANs)with an objective of improving network efficiency, quality-of-service and user satisfactions. TheI E E E 802.11 Working Group has been under rapid development and expansion in recent yearsfollowing the successful deployment of the 802.11 network around the globe. The thesis workhas been striving to study several key problems in these developments and propose effectiveschemes to improve network performance. The original 802.11 standard presents a simple androbust design, but has relatively low data rate and lacks QoS support. The recent 802.11estandard and the 8 0 2 . l ln proposals aim to significantly improve the network performance interms of QoS and throughput. In this thesis, an analytical model of I E E E 802.11e WLANsis first presented. With the help of this throughput model, an admission control scheme for amulti-hop 802.11e W L A N is proposed. To fully utilize the high data rate provided by 802.11n,the performance improvement of the M A C protocol by frame aggregation is studied. Twoframe aggregation techniques, namely A - M P D U (MAC Protocol Data Unit Aggregation) andA - M S D U (MAC Service Data Unit Aggregation) are considered. Furthermore, a comprehensivenetwork setup is studied where the QoS requirements of the 802.11e M A C and the MIMOphysical layer of 8 0 2 . l ln are both considered. Cross-layer design schemes are proposed forWLANs under two different M A C protocols: the carrier sense multiple access with collision avoidance (CSMA/CA)-based 802.11e M A C , and the slotted Aloha M A C . Lastly, the thesisstudies the problem of cooperative transmission in a wireless ad-hoc network with extensionsto the 802.11 M A C protocols. A complete system framework is proposed for wireless adhocnetworks utilizing two different cooperative relaying techniques at the physical layer: therepetition coding and the space-time coding. In the data link layer, two medium access controlprotocols are proposed to accommodate the corresponding physical layer cooperative diversityschemes. In the network layer, diversity-aware routing protocols are proposed to determine therouting path and the relaying topology. Improvements in network performance for the proposedschemes are validated with numerical and simulation tests.

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The forthcoming fourth generation (4G) heterogeneous wireless networks are a mixture ofoverlapped networks using different wireless access technologies and addressing differentneeds from the users. Due to mobility, the users are able to switch connections amongnetworks and hence to perform the so-called horizontal and vertical handoffs. The presentthesis makes contributions to the field of mobility management with focus on handoffmanagement and connection admission control in heterogeneous wireless networks. Twodifferent integrated heterogeneous systems are investigated: 1) the interworking of cellularnetworks with wireless local area networks (WLANs) based on the I E E E 802.11 standard;2) the interworking of cellular networks with wireless metropohtan area networks basedon the I E E E 802.16e standard. To this end, first we develop a novel vertical handoffdecision algorithm by modeling the vertical handoff problem as a Markov decision process.Our model considers the important tradeoff among the quality of service (QoS) of theconnection and the signaling cost of performing a vertical handoff. We also take theconnection duration into consideration for the handoff decision. Second, we propose ananalytical model for admission control in c e l l u l a r / W L A N interworking and investigate thecombination of different admission control policies. Our model considers mobihty of users,capacity and coverage of each network, admission policies, and QoS in terms of blockingand dropping probabilities. We introduce the concept of policy functions to model theadmission control policies and formulate two different revenue maximization problems.T h i r d , we extend the virtual partitioning technique w i t h preemption for admission controlin cellular/802.16e interworking. We propose admission control algorithms for each typeof connection request. We also describe the expected mobility scenario in such integratedsystem. Finally, to achieve joint design at the connection-level and packet-level, a jointQoS optimization approach is used.

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This thesis presents several simple, robust, and optimal resource management schemes for multihop wireless access networks with the main focus on multi-channel wireless mesh networks (MC WMNs). In this regard, various resource management optimization problems are formulatedarid efficient algorithms are proposed to solve each problem. First, we consider the channel assignment problem in MC-WMNs and formulate different resource management problems withinthe general framework of network utility maximization (NUM). Unlike most of the previouslyproposed channel assignment schemes, our algorithms can not only assign the orthogonal (i.e.,non-overlapped) channels, but also partially overlapped channels. This better utilizes the available frequency spectrum as a critical resource in MC-WMNs. Second, we propose two distributedrandom medium access control (MAC) algorithms to solve a non-convex NUM problem at theMAC layer. The first algorithm is fast, optimal, and robust to message loss and delay. It alsoonly requires a limited message passing among the wireless nodes. Using distributed learningtechniques, we then propose another NUM-based MAC algorithm which achieves the optimalperformance without frequent message exchange. Third, based on our results on random MAC,we develop a distributed multi-interface multi-channel random access algorithm to solve the NUM problem in MC-WMNs. Different from most of the previous channel assignment schemes in the literature, where channel assignment is intuitively modeled in the form of combinatorial and discrete optimization problems, our scheme is based on formulating a novel continuous optimization model. This makes the analysis and implementation significantly easier. Finally, we consider the problem of pricing and monetary exchange in multi-hop wireless access networks, where each intermediate node receives a payment to compensate for its offered packet forwarding service. In this regard, we propose a market-based wireless access network model with two-fold pricing. It uses relay-pricing to encourage collaboration among the access points. It also uses interference pricing to leverage optimal resource management. In general, this thesis widely benefits from several mathematical techniques as both modeling and solution tools to achieve simple, robust, optimal, and practical resource management strategies for future wireless access networks.

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