In today's wireless networks, a variety of Radio Access Technologies (RATs) are present. However, each RAT being controlled individually leads to suboptimal utilization of network resources. Due to the remarkable growth of data traffic, interworking among different RATs is becoming necessary to overcome the problem of suboptimal resource utilization. Users can be offloaded from one RAT to another based on loads of different networks, channel conditions and priority of users. We consider the optimal RAT selection problem in a Fifth Generation (5G) New Radio (NR)-Wireless Fidelity (WiFi) network where we aim to maximize the total system throughput subject to constraints on the blocking probability of high priority users and the offloading probability of low priority users. The problem is formulated as a Constrained Markov Decision Process (CMDP). We reduce the effective dimensionality of the action space by eliminating the provably suboptimal actions. We propose low-complexity online heuristics for RAT selection which can operate without the knowledge regarding the statistics of system dynamics. Network Simulator-3 (ns-3) simulations reveal that the proposed algorithms outperform traditional RAT selection algorithms under realistic network scenarios including user mobility.

To support the data requirements of exponentially increasing number of cellular users, Internet of Things (IoT) devices, and enterprises, wireless cellular networks are undergoing significant architectural enhancements. The heterogeneous network architecture is one such advancement, which can augment the capacity of cellular networks through the addition of femto/pico (small) cells. However, fiber connectivity to each small cell is not feasible. In such scenarios, wireless backhaul enables connectivity between small cells and core network (CN). Integrated Access and Backhaul (IAB) has emerged as a solution in 5G network, where wireless backhauling is supported. In IAB networks, IAB-donors are connected to the CN through fiber connectivity, and the multiple IAB-nodes are associated with IAB-donors through wireless backhaul. IAB Nodes can support small cells and provide last mile connectivity to users and IAB-donors act as wireless backhaul provider. For efficient utilization of the spectrum in wireless backhaul, we design an auction-based mechanism to allocate resources dynamically across IAB-nodes considering the spatial and temporal variation of the network traffic. Moreover, using Monte Carlo simulations, we show that the proposed mechanism achieves optimal social welfare.

IAB is a feasible and economical solution to deploy ultra-dense cells in the 5G networks, where access and wireless backhaul links share the same spectrum. IAB eliminates the need to connect ultra-dense cells to the core network through the wired backhaul. However, mmWave backhauling and multihop topology imposes new constraints on the 5G IAB network that become a hindrance to its effective performance. In this paper, we elaborate on cell selection and present a few cell selection policies designed explicitly for IAB networks. Unlike the popular RSRP based policy that may lead to load unbalance in the IAB network, the proposed policies are devised after considering the backhaul constraints and end-to-end performance requirements. The performance of these policies is investigated using system-level simulations. These policies have shown tremendous improvement in achieving cell-edge throughput while maintaining comparable average UE throughput. The policies provide better load balancing and topology in the IAB network than the RSRP based policy.

Proportional Fair (PF) is a scheduling technique to maintain a balance between maximizing throughput and ensuring fairness to users. Dual Connectivity (DC) technique was introduced by the 3rd Generation Partnership Project (3GPP) to improve the mobility robustness and system capacity in heterogeneous networks. In this paper, we demonstrate the utility of DC in improving proportional fairness in the system. We propose a low complexity centralized PF scheduling scheme for DC and show that it outperforms the standard PF scheduling scheme. Since the problem of dual association of users for maximizing proportional fairness in the system is NP-hard, we propose three heuristic user association schemes for DC. We demonstrate that DC, along with the proposed PF scheme, gives remarkable gains on PF utility over single connectivity and performs almost close to the optimal PF scheme in heterogeneous networks.

In an offload-capable Long Term Evolution (LTE)Wireless Fidelity (WiFi) Heterogeneous Network (HetNet), we consider the problem of maximization of the total system throughput under voice user blocking probability constraint. The optimal policy is threshold in nature. However, computation of optimal policy requires the knowledge of the statistics of system dynamics, viz., arrival processes of voice and data users, which may be difficult to obtain in reality. Motivated by the Post-Decision State (PDS) framework to learn the optimal policy under unknown statistics of system dynamics, we propose, in this paper, an online Radio Access Technology (RAT) selection algorithm using Relative Value Iteration Algorithm (RVIA). However, the convergence speed of this algorithm can be further improved if the underlying threshold structure of the optimal policy can be exploited. To this end, we propose a novel structureaware online RAT selection algorithm which reduces the feasible policy space, thereby offering lesser storage and computational complexity and faster convergence. This algorithm provides a novel framework for designing online learning algorithms for other problems and hence is of independent interest. We prove that both the algorithms converge to the optimal policy. Simulation results demonstrate that the proposed algorithms converge faster than a traditional scheme. Also, the proposed schemes perform better than other benchmark algorithms under realistic network scenarios.

Multicast transmission has been gaining importance as an efficient means of delivering bandwidth hungry video content and is expected to become an integral part of cellular networks worldwide. This has resulted in the need for generalized allocation algorithms that are capable of handling multiple multicast and unicast services with different Quality of Service (QoS) requirements. In this paper, we propose such a Vickrey-Clarke-Groves (VCG) auction based resource allocation and pricing algorithm. The proposed algorithm takes allocation decisions based on the QoS requirements of the end users for maximizing the system social utility. Even though the users in a multicast group are served on the same PRB, requirements of each individual user are taken into consideration while making the allocation decisions. The proposed algorithm ensures that the users report their true valuations of the system resources. VCG auctions provide a general framework for designing truthful optimal mechanisms. However, in many cases, VCG auction turns out to be NP-hard. In this paper, we propose an efficient, polynomial time implementation of the proposed VCG mechanism. Using simulations, we show that the algorithm successfully meets the unique demands of all unicast and multicast services.

Today's wireless networks consist of a multitude of Radio Access Technologies (RATs), each being controlled individually, leading to suboptimal utilization of network resources. However, the unprecedented growth of data traffic is creating the need for an efficient inter-working of various RATs to circumvent the problem of suboptimal utilization of resources. Application of Software Defined Networking (SDN) principles enables the control and management of various RATs in a unified way. In this paper, we specifically focus on the inter-working between Long Term Evolution (LTE) and Wireless Fidelity (WiFi). We propose an SDN based architecture for a network comprising LTE Base Stations (BSs) and WiFi Access Points (APs). Users can be offloaded from one RAT to another based on different criteria, viz., user priority and channel state of users. We consider the problem of optimal RAT selection to maximize the total system throughput subject to constraints on the blocking probability of high priority users and the offloading probability of high priority users and formulate it as a Constrained Markov Decision Process (CMDP). We propose a low-complexity RAT selection algorithm which does not require the knowledge of the statistics of system dynamics. To conduct experiments, we develop a Network Simulator-3 (ns-3) based evaluation platform in accordance with the SDN principles. Experimental results demonstrate that the proposed algorithm provides a near-optimal performance.

Device to Device (D2D) communication is expected to play a major role for enhancing system capacity in the fifth generation wireless networks. The gains are expected due to the possibility of reusing resources allocated to the cellular users (CUs) for the D2D underlay network. This allows for the resource reuse in the same cell and thus may lead to a significant interference. The key challenge is to devise resource allocation schemes for the D2D communication that does not adversely affect CUs' communication. In this paper, our aim is to propose a polynomial time proportionally fair resource allocation scheme for D2D users that respects the rate requirements of the CUs. The proposed scheme can potentially work with any resource allocation scheme for CUs and can adapt to the time and location varying channel conditions. Unlike most of the previous work, our scheme allows for allotting more than one resource block to a D2D pair. The performance of the proposed scheme is validated through the simulations.

In a Heterogeneous Network (HetNet) comprising of multiple Radio Access Technologies (RATs), a user can be associated with a particular RAT and can be steered to other RATs in a seamless manner. To handle the rapid growth of data traffic, offloading of mobile data to Wireless Fidelity (WiFi) has been proposed in a Long Term Evolution (LTE) based HetNet. In this paper, we consider an optimal RAT selection problem in an offload-capable LTE-WiFi system with an objective of maximizing the total system throughput subject to a constraint on the voice user blocking probability. An on-line algorithm for optimal RAT selection is proposed based on a Relative Value Iteration Algorithm (RVIA) centric Q-learning approach. The proposed algorithm can be implemented without any explicit knowledge of arrival processes of voice and data users. Simulation results are presented to exhibit the convergence behavior of the proposed scheme to the optimal policy.

With multimedia becoming the most dominant part of data traffic, many situations arise where the same content is required to be transmitted to many or all users in a cell. We propose the use of multicast transmission for dealing with such situations. With multicast transmission, we can accommodate more users in the available resources simultaneously. We propose grouping and resource allocation schemes for multicast transmissions in LTE. We also compare the performance of unicast and multicast transmission in terms of resource utilization at the evolved NodeB (eNB). We have considered two different methods for multicast group formation with fixed and variable group sizes. Allocation of resources is performed using a greedy scheme and a Linear Programming (LP) relaxation based approach. A number of scenarios become feasible by the use of multicast which are otherwise infeasible if usual unicast transmissions are used.

Optimizing energy consumption is paramount to sustain the growth of cellular networks. One of the approaches to reduce energy consumption is traffic dependent operation of networks. The traffic demand experienced by the network fluctuates over the duration of a day. Therefore, during the periods of low traffic, we may re-configure the network to trade the excess capacity for energy reduction. For example, we may modulate the BS transmit power (Cell Zooming) so as to maintain the desired QoS. However, determining an optimal network configuration is known to be computationally hard. Along with Cell Zooming, it is essential to also consider channel re-assignment, for, when the transmit powers of BSs are changed, the channels allocated to BSs must also be suitably changed. Considering therefore channel allocations also as state variables, the search space over which the optimization should be performed blows up, further complicating the problem. In this paper, we propose a framework to address this problem. The proposed algorithm is suitable for such large search spaces, while the framework is general enough to admit several QoS requirements and sophisticated power consumption models. The underlying mathematical formulation is also applicable in other contexts, and is of independent interest as well.

In wireless networks, the design of radio resource sharing mechanisms is complicated by the complex interference constraints among the various links. In their seminal paper (IEEE Trans. Autom. Control, vol. 37, no. 12, pp. 1936-1948), Tassiulas and Ephremides introduced Maximum Weighted Scheduling, a centralized resource sharing algorithm, and proved its optimality. Since then, there have been extensive research efforts to devise distributed implementations of this algorithm. Recently, distributed adaptive CSMA scheduling schemes have been proposed and shown to be optimal, without the need of message passing among transmitters. However, their analysis relies on the assumption that interference can be accurately modeled by a simple interference graph. In this paper, we consider the more realistic and challenging signal-to-interference-plus-noise ratio (SINR) interference model. We present distributed scheduling algorithms that: 1) are optimal under the SINR interference model; and 2) do not require any message passing. These algorithms are based on a combination of a simple and efficient power allocation strategy referred to as Power Packing and randomization techniques. The optimality of our algorithms is illustrated in various traffic scenarios using numerical experiments.

A transmitter powered by a renewable energy source becomes self sustainable. In this paper, we consider the broadcast channel with a transmitter and N receivers. The transmitter is powered by a renewable energy source and has finite battery capacity. Transmitter requires power Pito transmit a packet to ithuser. In this setting, our objective is to minimize the expected backlog at the transmitter while accounting for randomness in the arrival and the recharge processes. We formulate the problem as an infinite horizon Markov Decision Process (MDP) problem and obtain the structural properties of an optimal policy. These structural properties provide valuable insights for designing close to optimal policies that are computationally efficient for real life implementations. In special cases, we provide complete description of an optimal policy.

We address an issue of scheduling for uplink realtime flows in WiMAX networks. Real-time flows require hard delay and throughput guarantees. Thus, each packet has a strict deadline and the packets delayed beyond their respective deadlines are dropped. Real-time applications can sustain some loss gracefully. Alternatively, they require some minimum throughput guarantee. Most of the existing scheduling schemes for WiMAX networks are either designed to optimize long term performance (without considering packet drops on account of deadline violations) or require packet level information for scheduling and thus are suitable for downlink flows. Hence, we propose a simple scheduling scheme based on the concept of slot fairness. We evaluate the proposed scheme using extensive simulations and show that the proposed scheme outperforms the existing schemes in terms of fairness and system throughput.

Supporting real-time applications is paramount to sustaining the growth of wireless networks. Real time applications require strict delay guarantee, i.e., a packet delayed beyond certain predefined value is dropped. Fortunately, depending on the codec used, real-time applications can sustain some loss gracefully. Aim of an admission control algorithm is to make sure that when a new flow is admitted, its and other existing flows' packet loss on account of deadline violation is below their respective acceptable limit. The problem of admission control has been studied extensively for wireline networks. However, this analysis does not extend to wireless case on account of fading. Here, we consider a wireless network with TDMA based MAC, and for this network obtain a scalable admission control algorithm.

Consider a wireless system where a transmitter may send data to a set of receivers, or on various channels, experiencing random time-varying fading. The transmitter can send data to a single receiver or on a single channel at a time and may adapt its transmission power to the radio conditions of the chosen receiver/channel. Its objective is to implement a strategy defining at each time how to select the receiver/channel and transmission power, so as to maximize its throughput, i.e., its average sending rate, under an average power constraint. The optimization problem is easy when the fading conditions of all the receivers/channels are known. In many situations however, the instantaneous fading conditions are not known a priori, instead they have to be acquired, i.e., receivers/channels have to be probed, which consumes resources (time, spectrum, energy) in proportion of the number of probed receivers/channels. Hence, the transmitter may choose not to acquire the radio conditions of all the receivers/channels so as to spare resources for actual transmissions. In this paper, we aim at characterizing a joint probing, receiver/channel selection and power control strategy maximizing throughput. We provide an adaptive algorithm converging to the throughput optimal strategy. This algorithm may be used in a wide class of wireless systems with limited information, such as broadcast systems without a priori knowledge of the instantaneous Channel-State Information (CSI). But it can be also used to solve dynamic spectrum access problems such as those arising in cognitive radio systems, where secondary users can access large parts of the spectrum, but have to discover which portions of the spectrum offer more favorable radio conditions or less interference from primary users.

In Wireless LANs, users may adapt their transmission rates depending on the radio conditions of their links so as to maximize their throughput. Recently, there has been a significant research effort in developing distributed rate adaptation schemes. Unlike previous works that mainly focus on channel tracking, this paper characterizes the optimal reaction of a rate adaptation protocol to the contention information received from the MAC. We formulate this problem analytically. We study both competitive and cooperative user behaviors. In the case of competition, users selfishly adapt their rates so as to maximize their own throughput, whereas in the case of cooperation they adapt their rates so as to maximize the overall system throughput. We show that the Nash Equilibrium reached in the case of competition is inefficient (i.e. the price of anarchy goes to infinity as the number of users increases), and provide insightful properties of the socially optimal rate adaptation schemes. We find that recently proposed collision-aware rate adaptation algorithms decrease the price of anarchy. We also propose a novel collision-aware rate adaptation algorithm that further reduces the price of anarchy.

In this paper, we consider the problem of throughput maximization in an infrastructure based WLAN. We demonstrate that most of the proposed protocols though perform optimally for connected network (no hidden terminals), their performance is worse than even that of standard IEEE 802.11 in presence of hidden terminals. Here we present a stochastic approximation based algorithm that not only provide optimum throughput in a fully connected network but also when hidden nodes are present.

In a multi-access fading channel, dynamic allocation of bandwidth, transmission power and rates is an important aspect to counter the detrimental effect of time-varying nature of the channel. Most of the existing work on dynamic resource allocation assumes capacity achieving codes for various signaling schemes like TDMA, FDMA, CDMA and successive decoding. For the capacity achieving codes, the rate achievable by the user is log(1+SNR), where SNR denotes the signal to noise ratio of the user at the receiver side. However, codes that are used in practice have a finite gap to capacity, i.e., the achievable rate is log(1+SNR/Gamma) for Gamma > 1. The exact value of Gamma depends on the coding strategy and the desired bit error rate. Many existing resource allocation techniques that are optimal for capacity achieving codes perform sub-optimally in presence of the coding gap. For example, successive decoding does not always minimize the sum power required for providing the desired rate to each of the users for Gamma > 1. The problem of minimizing the sum power while guaranteeing the required rate to each of the users is important for both real-time and non real-time applications, and is addressed here. We obtain the resource allocation that is optimal for the above problem in presence of the coding gap.

Opportunistic scheduling is a key mechanism for improving the performance of wireless systems. However, this mechanism requires that transmitters are aware of channel conditions (or CSI, Channel State Information) to the various possible receivers. CSI is not automatically available at the transmitters, rather it has to be acquired. Acquiring CSI consumes resources, and only the remaining resources can be used for actual data transmissions. We explore the resulting trade-off between acquiring CSI and exploiting channel diversity to the various receivers. Specifically, we consider a system consisting of a transmitter and a fixed number of receivers/users. An infinite buffer is associated to each receiver, and packets arrive in this buffer according to some stochastic process with fixed intensity. We study the impact of limited channel information on the stability of the system. We characterize its stability region, and show that an adaptive queue length-based policy can achieve stability whenever doing so is possible. We formulate a Markov Decision Process problem to characterize this queue length-based policy. In certain specific and yet relevant cases, we explicitly compute the optimal policy. In general case, we provide a scheduling policy that achieves a fixed fraction of the system's stability region. Scheduling with limited information is a problem that naturally arises in cognitive radio systems, and our results can be used in these systems.

In broadcast fading channel, channel variations can be exploited through what is referred to as multi-user diversity and opportunistic scheduling for improving system performance. To achieve the gains promised by this kind of diversity, the transmitter has to accurately track the channel variations of the various receivers, which consumes resources (time, energy, bandwidth), and thus reduces the resources remaining for effective data transmissions. The transmitter may decide not to acquire or probe the channel conditions of certain receivers, either because these receivers are presumably experiencing severe fading, or because the transmitter wishes to spare resources for data transmissions. It may also decide to transmit to a receiver without probing its channel; in such cases, the transmitter guesses the channel state, which often results in a reduction of the transmission rate compared to when the transmitter knows the channel state. Ultimately, the transmitter has to decide to which receiver it should transmit. In this paper, we identifying the joint probing and transmission strategies realizing the optimal trade-off between the channel state acquisition and the effective data transmission. The objective is to maximize the system throughput. Finally, we propose several extensions of the proposed strategy, including a scheme to maximize the system utility and a scheme to ensure the system stability.

The question of providing throughput guarantees through distributed scheduling, which has remained an open problem for some time, is addressed in this paper. It is shown that a simple distributed scheduling strategy, maximal scheduling, attains a guaranteed fraction of the maximum throughput region in arbitrary wireless networks. The guaranteed fraction depends on the ldquointerference degreerdquo of the network, which is the maximum number of transmitter-receiver pairs that interfere with any given transmitter-receiver pair in the network and do not interfere with each other. Depending on the nature of communication, the transmission powers and the propagation models, the guaranteed fraction can be lower-bounded by the maximum link degrees in the underlying topology, or even by constants that are independent of the topology. The guarantees are tight in that they cannot be improved any further with maximal scheduling. The results can be generalized to end-to-end multihop sessions. Finally, enhancements to maximal scheduling that can guarantee fairness of rate allocation among different sessions, are discussed.

A power optimal scheduling algorithm that guarantees desired throughput and bounded delay to each user is developed for fading multi-access multi-band channels that can model many important practical systems including OFDM system and flat and frequency selective multi-access channels. The optimization is over the joint space of all rate allocation and coding strategies. The proposed scheduling assigns rates on each band based only on the current system state, and subsequently uses optimal multi-user signaling to achieve these rates. The scheduling is computationally simple, and hence scalable. Due to uplink-downlink duality, all the results extend in straightforward fashion to the broadcast channels.

Recently, network coding emerged as a promising technology that can provide significant improvements in throughput and energy efficiency of wireless networks, even for unicast communication. Often, network coding schemes are designed as an autonomous layer, independent of the underlying Phy and MAC capabilities and algorithms.Consequently, these schemes are greedy, in the sense that all opportunities of broadcasting combinations of packets are exploited. We demonstrate that this greedy design principle may in fact reduce the network throughput. This begets the need for adaptive network coding schemes. We further show that designing appropriate MAC scheduling algorithms is critical for achieving the throughput gainsexpected from network coding. In this paper, we propose a general framework to develop optimal and adaptive joint network coding and scheduling schemes. Optimality is shown for various Phy and MAC constraints. We apply this framework to two different network coding architectures: COPE, a scheme recently proposed in [7], and XOR-Sym, a new scheme we present here. XOR-Sym is designed to achieve a lower implementation complexity than that of COPE, and yet to provide similar throughput gains.

We consider a class of queueing referred to as generalized constrained queueing networks which form the basis of several different communication and informa- tion systems. These consist of a collection of queues such that only certain sets of queues can be concurrently served. When- ever a queue is served, the system receives a certain reward. Dif- ferent rewards are obtained for serving different queues, and fur- thermore, the reward obtained for serving a queue depends on the set of concurrently served queues. We demonstrate that the depen- dence of the rewards on the schedules alter fundamental relations between performance metrics like throughput and stability. Specif- ically, maximizing the throughput is no longer equivalent to max- imizing the stability region; we therefore need to maximize one subject to certain constraints on the other. Since stability is crit- ical for bounding packet delays and buffer overflow, we focus on maximizing the throughput subject to stabilizing the system. We design provably optimal scheduling strategies that attain this goal by scheduling the queues for service based on the queue lengths and the rewards provided by different selections. The proposed scheduling strategies are however computationally complex. We subsequently develop techniques to reduce the complexity and yet attain the same throughput and stability region. We demonstrate that our framework is general enough to accommodate random re- wards and random scheduling constraints.

We investigate the fairness and throughput properties of a simple distributed scheduling policy, maximal scheduling, in the context of a general ad-hoc wireless network. We design a fully distributed algorithm that combines a token generation scheme with maximal scheduling policy so as to attain max-min fair rates within the feasible region of maximal scheduling. We next present throughput guarantees of maximal scheduling that quantify the performance loss of each session due to the use of local information based scheduling. We show that the performance loss for each session depends on the maximum “interference degree” in its neighborhood. We also demonstrate that the performance penalties can not be localized any further.

The goal of this correspondence is to minimize delay in real-time multiple-access channel (MAC) layer multicast by exploiting the broadcast nature of wireless medium and limited loss tolerance of the applications. Multiple transmissions of a packet at the MAC layer significantly reduces the delay than that when only one transmission is allowed. But each additional transmission consumes additional power and increases network load. Therefore, the goal is to design a policy that judiciously uses the limited transmission opportunities so as to deliver each packet in the minimum possible time to the required number of group members. The problem is an instance of the stochastic shortest path problem, and using this formulation computationally simple, closed-form transmission strategies have been obtained in important special cases

Many real-time applications require one to many (multicast) communication. Real time applications can gracefully accommodate some loss but require low delay. We minimize the delay in real-time MAC layer multicast by exploiting the broadcast nature of wireless medium and limited loss tolerance of the applications. We show that multiple transmissions of a packet at the MAC layer significantly reduces the delay than that when only one transmission is allowed. But each additional transmission consumes additional power and increases network load. Therefore, our goal is to design a policy that judiciously uses the limited transmission opportunities so as to deliver each packet in minimum possible time to the required number of group members. We show that the problem is an instance of the stochastic shortest path problem, and using this formulation obtain a computationally simple, closed form transmission strategy in important special cases. Numerical computations show that only a small number of transmissions, if used judiciously, are sufficient to minimize the delay subject to loss constraint.

We design transmission strategies for medium access control (MAC) layer multicast that maximize the utilization of available bandwidth. Bandwidth efficiency of wireless multicast can be improved substantially by exploiting the feature that a single transmission can be intercepted by several receivers at the MAC layer. The multicast nature of transmissions, however, changes the fundamental relations between the quality of service (QoS) parameters, throughput, stability, and loss, e.g., a strategy that maximizes the throughput does not necessarily maximize the stability region or minimize the packet loss. We explore the tradeoffs among the QoS parameters, and provide optimal transmission strategies that maximize the throughput subject to stability and loss constraints. The numerical performance evaluations demonstrate that the optimal strategies significantly outperform the existing approaches.

Bandwidth efficiency of wireless multicast can be improved substantially by exploiting the fact that several receivers can be reached at the MAC layer by a single transmission. The multicast nature of the transmissions, however, introduces several design.