With the proliferation of cellular networks, there has been an unprecedented surge in multimedia mobile data traffic. Managing this massive data traffic has become increasingly intricate due to limited resource availability. To address this limitation and for improved resource utilization in cellular networks, multicast transmission is considered a promising technology due to its capability to serve multiple users via shared radio resources. However, as we show in this article, a blanket switching from unicast to multicast transmission without consideration to the channel condition of users may not achieve the most efficient result. In order to achieve an efficient resource utilization in cellular networks, we propose a DYnamic Resource allocation scheme for converged Unicast and Multicast (DY-RUM) transmissions considering the channel condition of users. The scheme has been proposed in the context of the Fifth Generation (5G) mobile communication system. To implement this scheme, we use the Open Radio Access Network (O-RAN) architecture for the 5G system, which provides a suitable setting for the scheme. We also present simulation results affirming the effectiveness of the proposed scheme in improving resource utilization for converged unicast-multicast transmissions in 5G.

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.

We design and analyse filter bank multicarrier (FBMC) offset quadrature amplitude modulation (OQAM)-based millimeter wave (mmWave) hybrid multiple-input multiple-output (MIMO) systems. Furthermore, a novel channel estimation model is conceived for quasi-static mmWave hybrid MIMO-FBMC-OQAM (mmH-MFO) systems that reconfigures the radio-frequency (RF) circuitry during the transmission of zero symbols. Subsequently, a Bayesian learning (BL) technique is proposed for sparse channel estimation, which relies on multiple measurement vectors combined with selective subcarrier grouping for enhanced estimation. Additionally, an online BL based Kalman filter (OBL-KF) is designed for sparse channel tracking in doubly-selective mmH-MFO systems. Then the Bayesian Cramér-Rao lower bounds (BCRLBs) are derived for characterizing the performance of the proposed frequency-selective and doubly-selective channel estimation techniques. Finally, a limited feedback based algorithm relying on beamspace channel estimates is proposed for hybrid precoder/combiner design. The accuracy of our analytical results is confirmed by our simulation results.

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.

Dual Connectivity (DC) is one of the key techniques to harness the potential of heterogeneous cellular networks. However, 3rd Generation Partnership Project (3GPP) has introduced disparate mechanisms for DC support in different Radio Access Technologies (RATs), bringing complexity to the network nodes in a Multi-RAT Radio Access Network (RAN). Moreover, DC support requires an exchange of high volume of control information between these network nodes. To address these issues, in this paper an SDN based architecture for Multi-RAT RAN is proposed. The proposed architecture brings simplicity to the network interactions, allows flexibility in the network and helps the network in performing load balancing and mobility management functions effectively. We also demonstrate a reduction in the control signaling and an improvement in system performance compared to legacy architecture.

To address the demand of exponentially increasing end users, efficient use of limited spectrum is a necessity. For this, spectrum allocation among co-existing operators in licensed and unlicensed spectrum band is required to cater to the temporal and spatial traffic variations in the wireless network. In this paper, we consider multiple operator spectrum allocation problem via auctions. The classical Vickrey-Clarke-Groves (VCG) approach provides a strategy-proof and social welfare maximizing auction at the cost of high computational complexity which makes it intractable for practical implementation. We propose a sealed bid auction for spectrum allocation, which is computationally tractable and can hence be applied as per the dynamic load variations of the network. We show that the proposed algorithm is strategy-proof. Simulation results are presented to exhibit the performance comparison of the proposed algorithm and the VCG mechanism.

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.

Dual Connectivity (DC) is a technique proposed to address the problem of increased handovers in heterogeneous networks. In DC, a foreground User Equipment (UE) with multiple transceivers has a possibility to connect to a Macro eNodeB (MeNB) and a Small cell eNodeB (SeNB) simultaneously. In downlink split bearer architecture of DC, a data radio bearer at MeNB gets divided into two; one part is forwarded to the SeNB through a non-ideal backhaul link to the UE, and the other part is forwarded by the MeNB. This may lead to an increase in the total delay at the UE since different packets corresponding to a single transmission may incur varying amounts of delays in the two different paths. Since the resources in the MeNB are shared by background legacy users and foreground users, DC may increase the blocking probability of background users. Moreover, single connectivity to the small cell may increase the blocking probability of foreground users. Therefore, we target to minimize the average delay of the system subject to a constraint on the blocking probability of background and foreground users. The optimal policy is computed and observed to contain a threshold structure. The variation of average system delay is studied for changes in different system parameters.

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.

With an intention to provide robust broadband connection to rural areas, Government of India is laying out optical fiber cables across the country connecting the rural offices (named Gram Panchayat) under the initiative BharatNet. This work explores the feasibility of implementing a middle-mile network from these Gram Panchayats to the nearby villages, where wireless clusters will be set up, using LTE-Advanced over TV White Spaces owing to excellent propagation characteristics of the frequencies in TV UHF band. A proportionally fair radio resource allocation over the middle-mile network is proposed that uses Coordinated Multipoint Technology offered by Long Term Evolution — Advanced (LTE-A) which satisfies the broadband requirement. The simulation results have shown that for a cell radius of Coordination Region below 5 km, a rate of 1 Mbps per end-user for rural population density of India is achievable, which is more than the 512 Kbps target rate prescribed by the Telecom Regulatory Authority of India (TRAI).

Mobility management, in heterogeneous cellular deployments, has become more complex due to the proliferation of small cells, resulting in increased number of cell crossings. In addition, user mobility impacts the HandOver (HO) performance and therefore, mobility state of the user needs to be taken into account in the HO parameter selection. The Mobility State Estimation (MSE) procedure, as specified by the Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) standard, is used to classify UEs in normal, medium and high mobility states, respectively, based on the handover rate. This information is subsequently used to determine the appropriate handover parameters for each UE. In this paper, we propose an enhancement to the MSE procedure which exploits the fact that the trajectory of the mobile user impacts the estimation of mobility state. Our proposed algorithm improves the accuracy of the estimation procedure while ensuring backward compatibility with the legacy MSE procedure. We further improve the mobility performance by combining our algorithm with the enhanced MSE procedure available in the literature, which gives consideration to both successful and failed HO events in the mobility state estimation.

We determine the approximate cumulative distribution function (CDF) of the uplink signal to interference ratio (SIR) at the base station (BS) when D2D communications are underlaid in a cellular network with uniformly distributed users, in a shadow fading environment. Initially, we determine the probability density function (PDF) of the received normalized powers at the BS and then compute the PDF of the SIR semi-analytically. Next we approximate the normalized interference as a lognormal random variable by extending Fenton-Wilkinson's moment matching method to arrive at an approximate closed form expression of the SIR's CDF. The results show that the first method approximates the CDF well but at the cost of computational complexity. The second method approximates the CDF in the lower SIR region and is easy to implement.

Abstract This paper investigates the radio resource management (RRM) issues in a heterogeneous macro‐femto network. The objective of femto deployment is to improve coverage, capacity, and experienced quality of service of indoor users. The location and density of user‐deployed femtos is not knowna‐priori. This makes interference management crucial. In particular, with co‐channel allocation (to improve resource utilization efficiency), RRM becomes involved because of both cross‐layer and co‐layer interference. In this paper, we review the resource allocation strategies available in the literature for heterogeneous macro‐femto network. Then, we propose a self‐organized resource allocation (SO‐RA) scheme for an orthogonal frequency division multiple access based macro‐femto network to mitigate co‐layer interference in the downlink transmission. We compare its performance with the existing schemes like Reuse‐1, adaptive frequency reuse (AFR), and AFR with power control (one of our proposed modification to AFR approach) in terms of 10 percentile user throughput and fairness to femto users. The performance of AFR with power control scheme matches closely with Reuse‐1, while the SO‐RA scheme achieves improved throughput and fairness performance. SO‐RA scheme ensures minimum throughput guarantee to all femto users and exhibits better performance than the existing state‐of‐the‐art resource allocation schemes.Copyright © 2014 John Wiley & Sons, Ltd.

Integration of IEEE 802.11 wireless local area networks with the 3GPP based cellular networks has attracted significant interest in recent releases of 3GPP. But the current scanning procedures used for the discovery of WLANs, implemented in most smartphones, is very inefficient due to unnecessary scan operations. In this paper, we have proposed modified scanning algorithms to reduce the amount of undesirable scans. In the proposed schemes, the UE schedules its scanning operations with assistance from the cellular network on the operator deployed WLAN network and offload policies. The simulation results in ns-3, with additional modules implemented for ANDSF and IEEE 802.11u for 3GPP-WLAN interworking, have shown significant improvement in energy consumption along with better association time for the users.

As the Internet usage on mobile networks is increasing at an alarming rate, operators need techniques to provide more capacity and bandwidth. One solution proposed by the 3rd Generation Partnership Project (3GPP) is to offload data to other access networks. The 3GPP architecture is designed to work along with non 3GPP access networks like Wireless Local Area Networks (WLANs). IP Flow Mobility (IFOM) is one method standardized, that enables offloading of data to these alternate accesses. IFOM enabled User Equipment (UEs) have multiple interfaces which allows them to connect to different access networks such as Long Term Evolution (LTE) and WLANs simultaneously. Thus concurrent flows with different traffic types can exist over these networks, and selective and seamless switching of an IP flow from one access network to the other is feasible. Our objective in this paper, is to investigate the benefits of IFOM on UE throughput in Heterogeneous Networks (HetNets) for different traffic types and latencies under diverse network environments, loads and fading conditions. We have carried out extensive ns-3 simulations. Our simulation results show that the IFOM enabled UEs experience high throughput at latencies below 18 ms for Voice over Internet Protocol (VoIP) and 50 ms for video and higher throughput with lower latencies in the proximity of WLANs. We further propose an algorithm to switch traffic flows for IFOM enabled UEs so that, the average per UE throughput is maximized, to provide a better Quality of service (QoS). We carry out simulations with the same and achieve a significant 14% to 30% increase in the average per UE throughput under varying load conditions.

Unlike in the developed countries, TV white spaces are abundant in India and rural broadband coverage is negligible. Government of India has an ambitious plan to connect 250,000 rural offices (Gram Panchayat) using optical fiber based point of presence (PoP); this will allay, but not solve the rural broadband coverage problem. In this work, a middle-mile multihop mesh network operating in the TV UHF band is presented as a solution to providing seamless connectivity between the Gram Panchayat and village users. This network can coexist with TV broadcasting via the License Shared Access mechanism. The main contribution of this paper is a throughput optimization tool. Given the demography information of rural areas, the desired broadband speed and contention ratio, available frequency, location of fiber PoP, and propagation path-loss models of the TV UHF band - our optimization tool computes optimal power and routing for the multihop mesh network. The analysis is performed at the physical layer. Time division multiple access and spatial reuse of frequency are used for interference management in this work. Simply put, given demography information, our optimization tool determines at what power will broadband coverage will be feasible in the rural areas. This tool's design is separable and it can be utilized in other areas of the world, where demography and path-loss models can be replaced. Our optimization tool will be exemplified using results from one demographic region in India.

As a part of Fourth Generation (4G) wireless communication system, a user can associate with any radio access technology (RAT) when there are multiple RATs available and can move seamlessly among them. To handle the explosive growth of traffic in cellular network, the idea of mobile data traffic offloading to Wireless Fidelity (WiFi) has been proposed. In this paper, we focus on mobile data offload assisted optimal association problem in a heterogeneous network. The problem has been formulated under the framework of constrained Markov decision process (CMDP). Value iteration and gradient descent algorithm has been used to determine the optimal policy. We aim to maximize the expected average per-user throughput of the system subject to a constraint on blocking probability of voice users. In our model, we consider the possibility of mobile data user offload from one RAT to another during association or departure of a user. Optimal policy has been found to follow a threshold structure. Numerical results are presented to show how the average per-user throughput of the system varies under different load conditions.

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.

The use of small cells has been proposed to increase system capacity by installation of base stations close to user location. Proximity of the base station with the user equipment also implies lesser power requirement for transmitting the same information. Thus one may expect improvement in energy efficiency. But installing a large number of base stations can also lead to an increase in the total energy consumption of the system. To combat this, mechanisms have been proposed to switch OFF these base stations at times of low load. In this paper, we consider the problem of finding the fraction of base stations that can be switched OFF while maintaining quality of service (measured in terms of the average waiting time of users), for given load conditions. We also obtain the optimal switch OFF pattern. We do this in two steps. First, we determine the optimal ON-OFF pattern of base stations and user-base station association policy for a fixed fraction of base stations to be switched OFF. Then, we find the maximum fraction of base stations that can be switched OFF for given load conditions.

In this paper, we discuss the problem of non-collocated coexistence of WiFi and 4G technologies such as WiMAX and LTE due to adjacent channel interference. The existing literature has many solutions and schemes to address the problem of shared channel coexistence and adjacent channel coexistence on multi-radio platforms. Results for Non-collocated coexistence in adjacent channels in wireless remain very scattered and few. Radio devices operating on Broadband Wireless Access (BWA) 4G wireless technologies like IEEE 802.16 (WiMAX) and LTE-A require very low noise floor. BWA spectrum allocations in 2.3 GHz and 2.5GHz have resulted in these networks to be very close to 2.4 GHz ISM band used by WiFi. We show, with measurements on our test-bed and from existing results, that the low-cost filters on WiFi devices are not very effective in controlling the out-of-band emissions to satisfy the low noise floor requirements of 4G. We propose schemes to mitigate the problem of adjacent channel interference by a time sharing mechanism across technologies by protecting packet receptions on both IEEE 802.11 and the IEEE 802.16 side. We demonstrate the effectiveness of our scheme to protect WiMAX packets by ensuring a controlled silence zone in the WiFi network using a test-bed. We also show that there is very limited adverse impact, due to the use of our scheme, on the system throughput of the non-collocated WiFi network operating in the adjacent channel.

Collaboration among neighbouring eNBs in radio resource allocation, in the absence of a centralized control unit, is one of the challenges raised from the flat architecture suggested for the Long Term Evolution (LTE) networks. This paper investigates the system performance of a collaborative resource allocation scheme, in a scenario that consists of two tiers of collaborative Regions (CoR), and considers the gain achieved from the eNB collaboration and performance degradation due to the interference from neighbouring eNBs. Our results indicate that interference introduced from the cells outside the collaborating cluster can have significant impact on the system performance. However, Monte Carlo simulation based performance analysis demonstrates the effectiveness of collaborative resource allocation among adjacent eNBs for the LTE networks.

We consider the problem of UpLink (UL) scheduling in relay-assisted Long Term Evolution (LTE) networks and propose a Buffer-based Channel dependent Scheduler (BCS) for the same. Our objectives are to minimize packet loss due to buffer overflow and increase resource utilization efficiency while ensuring fairness among users. This is achieved by utilizing the Buffer Status Report (BSR) available in Third Generation Partnership Project (3GPP)-LTE. The proposed scheduler comprise three-phases. Phase I partitions the resources between eNB (Base Station in LTE) and Relay Nodes (RNs) while Phase II, between different RNs. This resource partitioning is entailed by Phase III which implements buffer-based channel dependent resource allocation. We perform simulations to illustrate reduction in packet drop probability and increment in resource utilization efficiency when BSR is incorporated in scheduling decision. Further, our simulations show that BCS provides higher degree of fairness and lower outage probability compared to opportunistic and round robin scheduler. We also investigate the impact of buffer based resource partitioning on packet drop probability.

In this paper, we consider the problem of energy-efficient uplink scheduling with delay constraint for a multiuser wireless system. We address this problem within the framework of constrained Markov decision processes (CMDPs) wherein one seeks to minimize one cost (average power) subject to a hard constraint on another (average delay). We do not assume the arrival and channel statistics to be known. To handle state-space explosion and informational constraints, we split the problem into individual CMDPs for the users, coupled through their Lagrange multipliers; and a user selection problem at the base station. To address the issue of unknown channel and arrival statistics, we propose a reinforcement learning algorithm. The users use this learning algorithm to determine the rate at which they wish to transmit in a slot and communicate this to the base station. The base station then schedules the user with the highest rate in a slot. We analyze convergence, stability, and optimality properties of the algorithm. We also demonstrate the efficacy of the algorithm through simulations within IEEE 802.16 system.

A light-trail is a generalization of a lightpath such that multiple nodes can take part in communication along the path. A light-trail exhibits properties of dynamic provisioning, optical multicasting and sub-wavelength grooming and architecturally is analogous to a shared wavelength optical bus with an Out-Of-Band (OOB) control channel. The bus feature results in a node that has a large pass-through loss, and hence restricts the size of a light-trail to metro environments. Within a bus the OOB control channel allows for dynamic real-time arbitration. Due to this limitation, it is difficult to extend the light-trail concept to regional and core networks. In this paper we propose a method to provide multihop communication in light-trails thereby relaxing the limitation in hop count, as well as enhancing reach of communications. We propose node architecture and protocol requirements for creating Multi-hop Light-trails (MLTs). We then discuss design issues for MLTs in regional area networks through problem formulation. A simulations study validates MLTs.

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 this paper, we consider a non-saturated IEEE 802.11 based wireless network. We use a three-way fixed point to model the node behavior with Bernoulli packet arrivals and determine closed form expressions for the distribution of the time spent between two successful transmissions in an isolated network. The results of the analysis have been verified using extensive simulations in QualNet. The methodology presented in the paper is novel and we believe that the analysis like ours can be used as an approximation to model the behavior of sub-components of a larger mesh or hybrid network.

In this paper we propose polling based uplink scheduling schemes for TCP based applications in a multipoint- to-point fixed broadband IEEE 802.16 network. Our schemes adapt the transmission rates between subscriber stations (SSs) and the base station (BS) dynamically using adaptive modulation. We ensure fairness among the SSs via a credit-based approach in which an SS that misses a chance to transmit due to bad channel gets more weightage when the channel favors scheduling. We also propose a method to compute an optimal polling interval that aims to maximize slot utilization and TCP throughput. We demonstrate through exhaustive simulations that the proposed schedulers maximize link utilization, provide long-term fairness and minimize contraction of TCP congestion window. Implementation of the proposed schemes requires a cross-layer based feedback protocol stack at the BS and SSs.

We consider the problem of scheduling users on the downlink of a Time Division Multiplexed(TDM) system with constraints on the average packet delays over a fading wireless channel. Our objective is to maximize the sum throughput with constraints on the user delays. Due to the difficulty in computing optimal policy, we propose a suboptimal scheduling algorithm which is based on computing appropriate indices and scheduling the user with the highest index. Our simulations for the IEEE 802.16 system indicate that our algorithm satisfies the delay constraints of the users and is highly throughput efficient.

In this paper, we consider the problem of energy efficient scheduling under average delay constraint for a single user fading channel. We propose a new approach for on-line implementation of the optimal packet scheduling algorithm. This approach is based on reformulating the value iteration equation by introducing a virtual state called post-decision state. The resultant value iteration equation becomes amenable to online implementation based on stochastic approximation. This approach has an advantage that an explicit knowledge of the probability distribution of the channel state as well as the arrivals is not required for the implementation. We prove that the on-line algorithm indeed converges to the optimal policy.

We consider the point-to-point link scheduling problem in Spatial Time Division Multiple Access (STDMA) wireless ad hoc networks, motivate the use of spatial reuse as performance metric and provide an explicit characterization of spatial reuse. We assume uniform transmission power at all nodes and propose an algorithm based on a graph model of the network as well as Signal to Interference and Noise Ratio (SINR) computations. Our algorithm achieves higher spatial reuse than existing algorithms, without compromising on computational complexity.

In this paper, we propose a power optimal opportunistic scheduling scheme for a multiuser single hop time division multiple access (TDMA) system. We formulate the problem of minimizing average transmission power with different minimum information theoretic rate constraints for individual users. We suggest a stochastic approximation based scheme to implement the policy and argue the convergence of this algorithm. Finally, we extend the power optimal scheduling algorithm for providing temporal fairness among users.

Active queue management (AQM) policies are mechanisms for congestion avoidance, which pro-actively drop packets in order to provide an early congestion notification to the sources. We propose a new adaptive prediction based approach for congestion estimation (APACE) in active queue management that predicts the instantaneous queue length at a future time using adaptive filtering techniques. We compare the performance of APACE with other existing AQM schemes like RED (random early detection), SRED (stabilized RED), AVQ (adaptive virtual queue) and PAQM (predictive AQM) in networks having both single and multiple bottleneck links. We show that the performance of APACE in terms of the stability of the instantaneous queue is comparable to that of PAQM, which is one of the best AQM strategies. Moreover, APACE performs better than PAQM in terms of link utilization and in networks with multiple bottleneck links. APACE is not very sensitive to parameter settings, adapts quickly to changes in traffic and can achieve a given delay or packet loss by varying just one parameter, thus giving the network operator a better tool to manage congestion and improve the performance of the network.

Internet protocol (IP) address lookup is one of the major performance bottlenecks in high-end routers. This paper presents an architecture for an IP address lookup engine based on programmable finite-state machines (FSMs). The IP address lookup problem can be translated into the implementation of a large FSM. Our hardware engine is then used to implement this FSM using a structured approach, in which the large FSM is broken down into a set of smaller FSMs which are then mapped into reconfigurable hardware blocks. The design of our hardware engine is based on a regular and well structured architecture, which is easy to scale. Our simulation results demonstrate that the FSM based architecture can easily scale to wire speed performance at OC-192 rates. Unlike previous approaches, the performance of our architecture is not constrained by memory bandwidth and is, therefore, in principle scalable with very large scale integration technology.

Real time voice applications typically produce uniformly spaced voice packets and faithful reconstruction demands that these be played out at the same intervals. Best effort packet networks, however, produce variable delays on different packets and the receiver is required to buffer the received packets before playout. Excessive buffering delays deteriorate the system performance for interactive audio and so intelligent algorithms that keep this delay minimum while maintaining an acceptable packet loss have to be employed. In this research, we develop a new "/spl alpha/-adaptive" algorithm which offers considerable reduction in delays compared to existing algorithms, especially for low packet losses. A generic jitter control procedure is also proposed which may be used with any buffering algorithm to enhance its jitter performance without significantly affecting the delay loss tradeoff. Further, an existing algorithm based on Normalized Least Mean Squares filter is discussed and modifications are proposed for its practical implementation. All suggestions are supported by simulations on Internet delay traces.