This paper studies the performance of a wireless powered backscatter communication network consisting of an exciter, several randomly distributed backscattering devices (BDs) and a receiver. We consider selection/scheduling schemes, where a BD is selected for information transmission. The proposed schemes are based on random, end-to-end (e2e) signal-to-noise ratio (SNR) and Euclidean distance. By using stochastic geometry tools, we derive analytical expressions for the outage probability of each selection scheme. Our results show that the scheme based on the e2e SNR achieves the best performance. We also present a cluster-based selection scheme and provide some simulation results. The derived analytical framework provides useful insights on the design of such networks.

Radio resource management (RRM) relaxation has been introduced in 3GPP NR Release 16 as a key feature for optimizing UE power saving. It can be applied for UEs under “low mobility” and “cell edge” criteria. In this study, we investigate its benefit in addition to discontinuous reception (DRX) for reduced capability (RedCap) new radio (NR) UEs, a new type of low-cost device, for which the power saving is a key requirement. We provide comprehensive simulation results that quantify energy saving by applying RRM relaxation for stationary and pedestrian RedCap UEs with different “cell edge” criteria and DRX settings. In addition, we evaluate the impact in terms of handover failures and packet delay. The simulation analysis shows that dynamic adjustment of “cell edge” criteria of RRM relaxation is a more power-efficient solution that can achieve up to 64% to 96% RRM measurement energy saving.

Uplink power control plays a significant role in maintaining a good signal quality at the serving cell while minimizing interference to neighboring cells, thus maximizing the system performance. Traditionally, a single value open-loop power control (OLPC) parameter, P0, is configured for all the user equipments (UEs) in a cell, and often same setting is used for similar cells. Recent studies have demonstrated that optimal P0depends on many factors, which yields a complex multidimensional optimization problem and there are no efficient approaches known to solve it under practical system-level settings. In this paper, we propose a solution based on reinforcement learning (RL) where each BS autonomously adjusts its P0setting to maximize its throughput performance. As compared to conventional sub-optimal approach, our solution encompasses a smart clustering of UEs, where each cluster specifies its own P0. The proposed solution is evaluated by extensive system level simulations, where our results demonstrate a potential performance enhancement as compared to the baseline proposals.

Caching at mobile devices and leveraging cooperative device-to-device (D2D) communications are two promising approaches to support massive content delivery over wireless networks while mitigating the effects of interference. To show the impact of cooperative communication on the performance of cache-enabled D2D networks, the notion of device clustering must be factored in to convey a realistic description of the network performance. In this regard, this paper develops a novel mathematical model, based on stochastic geometry and an optimization framework for cache-assisted coordinated multi-point (CoMP) transmissions with clustered devices. Devices are spatially distributed into disjoint clusters and are assumed to have a surplus memory to cache files from a known library, following a random probabilistic caching scheme. Desired contents that are not self-cached can be obtained via D2D CoMP transmissions from neighboring devices or, as a last resort, from the network. For this model, we analytically characterize the offloading gain and rate coverage probability as functions of the system parameters. An optimal caching strategy is then defined as the content placement scheme that maximizes the offloading gain. For a tractable optimization framework, we pursue two separate approaches to obtain a lower bound and a provably accurate approximation of the offloading gain, which allows us to obtain optimized caching strategies. Remarkably, if we replace the obtained expression for offloading gain with its lower bound, we can find a suboptimal caching strategy that is not only described via analytical formulas but can also show an improvement over the state-of-the-art caching schemes. Results reveal that cooperative transmission becomes more appealing in denser D2D caching networks and adverse interference conditions, which is the case of the imminent Internet of Things (IoT) and ns era.

5G is expected to serve diverse applications and users due to the popularity of Internet of Things (IoT), big data and industrial applications. Many of these IoT and industrial applications have inherent loss tolerance that can be used to enable energy efficient uplink communication. The uplink energy efficient system will increase the battery life of devices enabling new use cases in industrial IoT. In this paper, we map the effects of application loss tolerance to the rate requirements of the user. We then mathematically model an energy minimization problem for the uplink user association and resource allocation in heterogeneous networks. We aim to provide acceptable quality of service (QoS) with improved energy efficiency by exploiting the loss tolerance and bandwidth expansion simultaneously. A distributed uplink joint user association and resource allocation strategy for uplink energy per bit minimization is presented. We conduct extensive simulation based study for a heterogeneous network to evaluate the performance of our proposed schemes. Average energy per bit consumption in the proposed scheme is -74 dB compared to -53 dB in state-of-the-art channel individual offset (CIO) scheme.

In this work, we investigate user equipment (UE) positioning assisted by deep learning (DL) in 5G and beyond networks. As compared to state of the art positioning algorithms used in today's networks, radio signal fingerprinting and machine learning (ML) assisted positioning requires smaller additional feedback overhead; and the positioning estimates are made directly inside the radio access network (RAN), thereby assisting in radio resource management. The conventional positioning algorithms will be used as back-up for the environments with high variability in conditions; but ML-assisted positioning serves as more efficient and simpler technique to provide better or similar positioning accuracy. In this regard, we study ML-assisted positioning methods and evaluate their performance using system level simulations for an outdoor scenario in Lincoln park Chicago. The study is based on the use of raytracing tools, a 3GPP 5G NR compliant system level simulator and DL framework to estimate positioning accuracy of the UE. The use of raytracing tool and system level simulator helps avoid expensive drive test measurements in practical scenarios. Our proposed mechanism is a first step towards more proactive mobility management in future networks. We evaluate and compare performance of various DL models and show mean positioning error in the range of 1-1.5m for the best DL configuration with appropriate system feature-modeling.

An increasing number of emerging applications, e.g., Internet of Things (IoT), vehicular communications, augmented reality, and the growing complexity due to the interoperability requirements of these systems, lead to the need to change the tools used for the modeling and analysis of those networks. Agent-based modeling (ABM) as a bottom-up modeling approach considers a network of autonomous agents interacting with each other, and therefore represents an ideal framework to comprehend the interactions of heterogeneous nodes in a complex environment. Here, we investigate the suitability of ABM to model the communication aspects of a road traffic management system as an example of an IoT network. We model, analyze, and compare various medium access control (MAC) layer protocols for two different scenarios, namely uncoordinated and coordinated. Besides, we model the scheduling mechanisms for the coordinated scenario as a high-level MAC protocol by using three different approaches: 1) centralized decision maker (DM); 2) DESYNC; and 3) decentralized learning MAC (L-MAC). The results clearly show the importance of coordination between multiple DMs in order to improve the information reporting error and spectrum utilization of the system.

Caching at mobile devices and leveraging device-to-device (D2D) communication are two promising approaches to support massive content delivery over wireless networks. The analysis of cache-enabled wireless networks is usually carried out by assuming that devices are uniformly distributed, however, in social networks, mobile devices are intrinsically grouped into disjoint clusters. In this regards, this paper proposes a spatiotemporal mathematical model that tracks the service requests arrivals and account for the clustered devices geometry. Two kinds of devices are assumed, particularly, content clients and content providers. Content providers are assumed to have a surplus memory which is exploited to proactively cache contents from a known library, following a random probabilistic caching scheme. Content clients can retrieve a requested content from the nearest content provider in their proximity (cluster), or, as a last resort, the base station (BS). The developed spatiotemporal model is leveraged to formulate a joint optimization problem of the content caching and spectrum partitioning in order to minimize the average service delay. Due to the high complexity of the optimization problem, the caching and spectrum partitioning problems are decoupled and solved iteratively using the block coordinate descent (BCD) optimization technique. To this end, an optimal and suboptimal solutions are obtained for the bandwidth partitioning and probabilistic caching subproblems, respectively. Numerical results highlight the superiority of the proposed scheme over conventional caching schemes under equal and optimized bandwidth allocations. Particularly, it is shown that the average service delay is reduced by nearly 100% and 350%, compared to the Zipf and uniform caching schemes under equal bandwidth allocations, respectively.

In this paper, we investigate the practical implication of employing virtual massive multiple-input-multiple output (MIMO) based distributed decision fusion (DF) for collaborative wideband spectrum sensing (WSS) in a cognitive radio (CR)-like network. Towards that end, an indoor-only measurement campaign has been conducted to capture the propagation statistics of a 4 × 64 massive MIMO system with one authorized primary user (PU) and 4 unauthorized secondary users (SUs) transmitting simultaneously over a 20 MHz band divided into 1200 subcarriers. The frequency subcarriers belong to an Orthogonal-frequency-division-multiplexing (OFDM)-like set-up without the addition of cyclic prefix (CP) to the transmit symbols. Measurements are accumulated for different relative positions of the SUs which are analyzed to extract fading, shadowing, noise and interference power statistics. Log-likelihood ratio (LLR) based fusion rule and three different sets of sub-optimum fusion rules along with their time-reversed versions are formulated for combining decisions on the availability of each subcarrier transmitted by the SUs. The extracted channel characteristics are incorporated in both analytical and simulated performance analysis of the devised fusion rules for comparison and testing the validity of distributed DF in realistic collaborative WSS scenario.

In this paper, we aim at maximizing the offloading gain for a clustered cache-enabled device-to-device (D2D) network whose devices adopt probabilistic caching and cooperative transmission. Devices with surplus memory probabilistically cache a content from a known library. A requested content is either brought from the device's local cache, cooperatively transmitted from catering devices, or downloaded from the network. For this network, we derive a closed-form expression for the offloading gain and formulate the offloading maximization problem. In order to simplify the objective function and obtain a tractable expression, we derive a lower bound on the offloading gain, for which a suboptimal solution is obtained when considering a special case. Results reveal that the obtained suboptimal solution can achieve up to 12% increase in the offloading gain compared to the Zipf's caching technique.

Providing connectivity to aerial users, such as cellular-connected unmanned aerial vehicles (UAVs) or flying taxis, is a key challenge for tomorrow's cellular systems. In this paper, the use of coordinated multi-point (CoMP) transmission along with caching for providing seamless connectivity to aerial users is investigated. In particular, a network of clustered cache-enabled small base stations (SBSs) serving aerial users is considered in which a requested content by an aerial user is cooperatively transmitted from collaborative ground SBSs. For this network, a novel upper bound expression on the coverage probability is derived as a function of the system parameters. The effects of various system parameters such as collaboration distance and content availability on the achievable performance are then investigated. Results reveal that, when the antennas of the SBSs are tilted downwards, the coverage probability of a high-altitude aerial user is upper bounded by that of a ground user regardless of the transmission scheme. Moreover, it is shown that for a low signal-to-interference-ratio (SIR) threshold, CoMP transmission improves the coverage probability for aerial users from 10% to 70% under a collaboration distance of 200 m.

We formulate and solve the energy minimization problem for a clustered device-to-device (D2D) network with cache-enabled mobile devices. Devices are distributed according to a Poisson cluster process (PCP) and are assumed to have a surplus memory which is exploited to proactively cache files from a library. Devices can retrieve the requested files from their caches, from neighboring devices in their proximity (cluster), or from the base station as a last resort. We minimize the energy consumption of the proposed network under a random probabilistic caching scheme, where files are independently cached according to a specific probability distribution. A closed form expression for the D2D coverage probability is obtained. The energy consumption problem is then formulated as a function of the caching distribution, and the optimal probabilistic caching distribution is obtained. Results reveal that the proposed caching distribution reduces energy consumption up to 33% as compared to caching popular files scheme.

A key aspect of the fifth-generation wireless communication network will be the integration of different services and technologies to provide seamless connectivity. In this paper, we consider using massive multiple-input multiple-output (mMIMO) to provide backhaul links to a dense deployment of self-backhauling (s-BH) small cells (SCs) that provide cellular access within the same spectrum resources of the backhaul. Through a comprehensive system-level simulation study, we evaluate the interplay between access and backhaul and the resulting end-to-end user rates. Moreover, we analyze the impact of different SCs deployment strategies, while varying the time resource allocation between radio access and backhaul links. We finally compare the above mMIMO-based s-BH approach to a mMIMO direct access (DA) architecture accounting for the effects of pilot reuse schemes, together with their associated overhead and contamination mitigation effects. The results show that dense SCs deployments supported by mMIMO s-BH provide significant rate improvements for cell-edge users (UEs) in ultra-dense deployments with respect to mMIMO DA, while the latter outperforms mMIMO s-BH from the median UEs' standpoint.

This paper presents a compact circuit design for implementing full-duplex relays serving network architectures envisioned for 5G applications. The proposed design prevents the transmit signal from interfering with the received signal through signal inversion. Signal inversion is accomplished through a compact design based on parametric amplifier circuit which operates simultaneously in two different modes, re-transmit and demodulate. This design also has an added advantage of being capable of generating phase difference between the transmitted and received signals by controlling the local oscillator signal. The validity of the design is evaluated against an example Smart Grid architecture, where it is employed to function as amplify-and-forward full-duplex relays/repeaters for serving several communication links. Simulation results indicate that full-duplex mode outperforms half-duplex one in terms of average channel capacity as well as bit error rate irrespective of the position of the relays with respect to distance from source/destination.

Proactive wireless caching and device to device (D2D) communication have emerged as promising techniques for enhancing users' quality of service and network performance. In this paper, we propose a new architecture for D2D caching with inter-cluster cooperation. We study a cellular network in which users cache popular files and share them with other users either in their proximity via D2D communication or with remote users using cellular transmission. We characterize the network average delay per request from a queuing perspective. Specifically, we formulate the delay minimization problem and show that it is NP-hard. Furthermore, we prove that the delay minimization problem is equivalent to the minimization of a non-increasing monotone supermodular function subject to a uniform partition matroid constraint. A computationally efficient greedy algorithm is proposed which is proven to be locally optimal within a factor (1- e-1) ≈ 0.63 of the optimum. We analyze the average per request throughput for different caching schemes and conduct the scaling analysis for the average sum throughput. We show how throughput scaling depends on video content popularity when the number of files grows asymptotically large. Simulation results show a delay reduction of 45% to 80% compared to a D2D caching system without inter-cluster cooperation.

We investigate the energy efficient packet scheduling and power allocation problem for services which require reliable communication to guarantee a certain quality of experience. We establish links between the average transmit power and the reliability of data transfer, which depends on both the average amount of data transfer and short-term rate guarantees. We consider a slow-fading point-to-point channel without channel state information at the transmitter side (CSIT). In the absence of CSIT, the slow fading channel has an outage probability associated with every transmit power. As a function of data loss tolerance parameters, and minimum rate and peak power constraints, we formulate an optimization problem that adapts rate and power to minimize the average transmit power for the user equipment. Then, a relaxed optimization problem is formulated where the transmission rate is assumed to be fixed for each packet transmission. We use Markov chain to model constraints of the optimization problem. The corresponding problem is not convex for both of the formulated problems, therefore a stochastic optimization technique, namely, the simulated annealing algorithm, is used to solve them. The numerical results quantify the effect of various system parameters on average transmit power and show significant energy savings when the service has less stringent requirements on timely and reliable communication.

Proactive wireless caching and D2D communication have emerged as promising techniques for enhancing users' quality of service and network performance. In this paper, we propose a new architecture for D2D caching with inter- cluster cooperation. We study a cellular network in which users cache popular files and share them with other users either in their proximity via D2D communication or with remote users using cellular transmission. We characterize the network average delay per request from a queuing perspective. Specifically, we formulate the delay minimization problem and show that it is NP-hard. Furthermore, we prove that the delay minimization problem is equivalent to minimization of a non-increasing monotone supermodular function subject to a partition matroid constraint. A computationally efficient greedy algorithm is proposed which is proven to be locally optimal within a factor 2 of the optimum. Simulation results show more than 45% delay reduction compared to a D2D caching system without inter-cluster cooperation.

In this work, we address the relay selection problem for the wireless powered communication networks, where the relays harvest energy from the source radio frequency signals. A single source- destination pair is considered without a direct link. The connecting relay nodes are equipped with storage batteries of infinite size. We assume that the channel state information (CSI) on the source-relay link is available at the relay nodes. Depending on the availability of the CSI on the relay-destination link at the relay node, we propose different relay selection schemes and evaluate the outage probability. The availability of the CSI at the relay node on the relay- destination link considerably improves the performance due to additional flexibility in the relay selection mechanism. We numerically quantify the performance for the proposed schemes and compare the outage probability for fixed and equal number of wireless powered forwarding relays.

We propose a novel Licensed Shared Access (LSA) spectrum allocation framework in this paper. The spectrum is made available to the licensee mobile network operators (MNOs) at specific time instants, in a specific service area, and for a specific time period. We propose a spectrum allocation algorithm which aims at providing resources to the MNOs in such a way that they can utilize them to serve their users and the resource allocation is fair at the same time. We also introduce a penalty mechanism whose output is a reduced spectrum allocation for the MNOs which violate the LSA spectrum use regulations. Our proposed algorithms are evaluated numerically and our results show that we can both guarantee fairness in spectrum allocation and penalize the MNOs that misbehave in regards to the LSA spectrum usage limitations imposed by the incumbent.

This paper proposes a new system architecture for Licensed Shared Access (LSA) wireless networks, as well as novel band management techniques for fair and ranking-based spectrum allocation. The proposed architecture builds upon recently standardized and regulatory-accepted LSA systems and stems from the work done in the EU-funded project ADEL. Two new resource allocation algorithms are introduced and their behaviour is validated via system-level simulations.

Energy harvesting has emerged as a promising technique which helps to increase the sustainability of wireless networks. In this paper, we consider a network with a single source, single destination and a single relay equipped with multiple antennas. We develop an optimization framework that exploits the energy harvested from the source radio frequency signals with smart antenna selection schemes at the relay node. Our main target is to minimize the source power and the antennas' circuit power jointly subject to quality of service constraints on the rate. To overcome the computational complexity of the optimization problem, we propose two special case schemes, namely Fixed Source Power Antenna Selection (FSP-AS) and All Receive Antenna Selection (AR-AS). Also, we suggest two sub-optimal heuristic schemes with low complexity and compare their performance with the optimization problem solutions numerically. The simulation results show the gain of our optimal scheme in terms of energy efficiency, which can be up to 80% as compared to solutions proposed in the literature.

In this study, the authors discuss relay selection schemes with the objective to minimise outage probability for a network consisting of a single source, multiple relays and a single destination. The relays are powered by radio frequency signals from the source. For a successful transmission, at least one of the relay nodes should be able to decode the source signals and have enough energy to relay the information to the destination. The authors assume that a relay node cannot decode information and harvest energy from the source signals simultaneously. The authors formulate an optimisation problem to minimise outage probability for the system. The relay selection scheme and the outage performance depend on the availability of the channel state information (CSI) on the source‐relay and the relay‐destination links. Based on the availability of the CSI on the relay‐destination link, the authors propose relay selection schemes for different scenarios and evaluate the performance numerically. The results show that the availability of the CSI on the relay‐destination link at the relay node helps to improve the outage performance considerably. The authors characterise the outage probability for the schemes analytically; and numerically compute the optimal number of relays which provide the optimal outage performance for a given scheme.

In this paper, we study and analyze cooperative cognitive radio networks with arbitrary number of secondary users (SUs). Each SU is considered a prospective relay for the primary user (PU) besides having its own data transmission demand. We consider a multi-packet transmission framework that allows multiple SUs to transmit simultaneously because of dirty-paper coding. We propose power allocation and scheduling policies that optimize the throughput for both PU and SU with minimum energy expenditure. The performance of the system is evaluated in terms of throughput and delay under different opportunistic relay selection policies. Toward this objective, we present a mathematical framework for deriving stability conditions for all queues in the system. Consequently, the throughput of both primary and secondary links is quantified. Furthermore, a moment generating function approach is employed to derive a closed-form expression for the average delay encountered by the PU packets. Results reveal that we achieve better performance in terms of throughput and delay at lower energy cost as compared with equal power allocation schemes proposed earlier in the literature. Extensive simulations are conducted to validate our theoretical findings.

Wireless powered communication network (WPCN) is an emerging area of research where energy is transferred from the access point to the mobile terminals in the downlink and information is transferred in the uplink. In the context of WPCN, we study the effects of applying different downlink/uplink scheduling schemes on the system performance in terms of achieved system throughput and fairness. In contrast to conventional wireless networks, where data scheduling determines the system sum rate and fairness behaviour, downlink energy scheduling contributes equally in WPCNs. We propose fairness based downlink energy transfer and compare different combinations of downlink and uplink scheduling schemes. Furthermore, we propose a new metric for downlink energy transfer for the special case of finite energy buffer and evaluate its effect on the achieved system throughput and fairness. Our numerical results show that a complete throughput fairness cannot be achieved as long as fairness is not employed in energy transfer in downlink regardless of the uplink scheduling scheme.

Quality of service (QoS) for a network is characterized in terms of various parameters specifying packet delay and loss tolerance requirements for the application. The unpredictable nature of the wireless channel demands for application of certain mechanisms to meet the QoS requirements. Traditionally, medium access control (MAC) and network layers perform these tasks. However, these mechanisms do not take (fading) channel conditions into account. In this paper, we investigate the problem using cross layer techniques where information flow and joint optimization of higher and physical layer is permitted. We propose a scheduling scheme to optimize the energy consumption of a multiuser multi-access system such that QoS constraints in terms of packet loss are fulfilled while the system is able to maximize the advantages emerging from multiuser diversity. Specifically, this work focuses on modeling and analyzing the effects of packet buffering capabilities of the transmitter on the system energy for a packet loss tolerant application. We discuss low complexity schemes which show comparable performance to the proposed scheme. The numerical evaluation reveals useful insights about the coupling effects of different QoS parameters on the system energy consumption and validates our analytical results.

In this paper, we propose a relay selection strategy for randomly distributed multiple relays. A single relay is selected for transmitting signal from a fixed source to a fixed destination which requires minimum total transmit power. The lack of perfect channel state information (CSI) at the relay-destination link has been taken into consideration while selecting the relay. We consider the relay movement with low mobility features and evaluate the performance based on the total transmission power requirement. In addition, relay selection region is obtained to improve the overall performance of the system. Simulation results show that a similar performance in terms of total transmit power can be achieved at lower complexity if we select the relay from a specific relay selection region as compared to considering all the relays in the network.

In this paper, we study and analyze cooperative cognitive radio networks with multiple secondary users (SUs). Each SU is considered a prospective relay for the primary user (PU) besides having its own data demand. The proposed scheme leverages the spectral efficiency of the system via allowing two SUs to transmit simultaneously thanks to dirty-paper coding. We propose a power allocation policy that minimizes the average transmitted power at each SU. Moreover, we are concerned with enhancing the throughput of both primary and secondary links. Towards this objective, we investigate multiple opportunistic relay selection policies. We develop a mathematical framework for deriving stability conditions for the queues involved in the system using outage probabilities. Results reveal that we achieve better performance in terms of average power and throughput as compared to uniform power allocation schemes proposed earlier in literature. Extensive numerical simulations are conducted to validate our theoretical findings.

In this paper, we consider energy efficient multiuser scheduling. Packet loss tolerance of the applications is exploited to minimize average system energy. There is a constraint on average packet drop rate and maximum number of packets dropped successively (bursty loss). A finite buffer size is assumed. We propose a scheme which schedules the users opportunistically according to the channel conditions, packet loss constraints and buffer size parameters. We assume imperfect channel state information at the transmitter side and analyze the scheme in large user limit using stochastic optimization techniques. First, we optimize system energy for a fixed buffer size which results in a corresponding statistical guarantee on successive packet drop. Then, we determine the minimum buffer size to achieve a target (improved) energy efficiency for the same (or better) statistical guarantee. We show that buffer size can be traded effectively to achieve system energy efficiency for target statistical guarantees on packet loss parameters.

Energy efficient communication has emerged as one of the key areas of research due to its impact on industry and environment. Any potential degree of freedom (DoF) available in the system should be exploited smartly to design energy efficient systems. This paper proposes a framework for achieving energy efficiency for the data loss tolerant applications by exploiting the multiuser diversity and DoFs available through the packet loss pattern. For a real time application, there is a constraint on the maximum number of packets to be dropped successively that must be obeyed. We propose a channel-aware energy efficient scheduling scheme which schedules the packets such that the constraint on the average packet drop rate and the maximum number of successively dropped packets is fulfilled for the case when a finite number of unscheduled packets can be buffered. We analyze the scheme in the large user limit and show the energy gain due to buffering on the proposed scheme.

This work addresses scheduling in a cognitive radio scenario where a minimum throughput for the downlink primary network (PN) is guaranteed to each user with an associated violation probability (probability of not obtaining the guaranteed throughput). The primary network is surrounded by multiple downlink secondary networks, each aiming to maximize its network throughput. Scheduling in PN is performed independent of the secondary networks. Some information about the PN is available at the central scheduler that is responsible for scheduling the secondary networks. The contribution of this work is to apply a novel scheduler to the PN which is more robust to QoS degradations resulting from the secondary networks than other state of the art schedulers. This is validated by numerical simulations of the cognitive radio network.

We address the problem of energy efficient scheduling over fading channels for the loss tolerant applications. The proposed scheduling scheme allows dropping of a certain predefined proportion of data packets on the transmitter side. However, there is a hard constraint on the maximum number of successively dropped packets. The scheduler exploits average data loss tolerance to reduce the average system energy expenditure while fulfills the hard constraint on the number of successively dropped packets. We explore the effect of average and successive packet loss constraints on the system energy and characterize the regions where one parameter is more critical as compared to the other in the sense of achieving better energy efficiency. The scheme is analyzed using asymptotically large user limit and the optimized channel¿-dependent dropping thresholds are computed by using combinatorial optimization techniques. The numerical results illustrate the energy efficiency of the scheme as a function of the average and successive packet drop parameters.

We address the problem of energy efficient scheduling for the loss tolerant applications by exploiting the multiuser diversity. The proposed scheduling scheme allows dropping of a certain predefined proportion of data packets on the transmitter side. However, there is a hard constraint on the maximum number of successively dropped packets. The scheduler exploits average data loss tolerance to reduce the average system energy expenditure while fulfills the hard constraint on successively dropped packets. We analyze the scheme using asymptotically large user limit. The numerical results illustrate the energy efficiency of the scheme as a function of the average packet drop probability and the maximum permitted successively dropped packets parameters.

In this work an opportunistic scheduling scheme is presented and analyzed for a multiuser system. The objective of the proposed scheme is to minimize the system transmit energy in the presence of a hard deadline delay constraint for the individual packets. In the large system limit, the scheme is modeled and analyzed in the scenario when arriving packets have associated deadlines which vary from packet to packet. We introduce transmission thresholds that depend on channel quality and number of time slots left before a packet reaches its hard deadline. These thresholds are optimized such that they reflect the interaction of deadline delay and channel variation, and result in a minimum system energy. The results demonstrate the saving in energy for a system where the applications have individual packet deadline delay constraints.

In this work, an opportunistic scheduling scheme for a large multiuser system is proposed. A group of users with good channels are scheduled simultaneously for data transmission and separated by means of superposition coding. The proposed scheduling scheme is analyzed in the large system limit. Random packet arrivals are modeled as constant arrivals with random content size. Transmission thresholds are optimized in such a way that the system energy is minimized while obeying a strict upper bound on the packet delay. We find that the state space representations of systems with either constant or random arrivals are equivalent. Thus, the thresholds optimized for constant arrivals in earlier work are valid for systems with random arrivals as well. Furthermore, we address the option of intentional packet dropping and the trade-off between packet drop rate and required system energy.