Resource Allocation and Cross-Layer Control in Wireless Networks

Georgiadis, Leonidas; Neely, Michael J.; Tassiulas, Leandros

doi:10.1561/9781933019697

Cited by 517 publications

(35 citation statements)

References 139 publications

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“…In practice, packet arrivals and departures vary at a time scale smaller than n. This case implies that definitions that are slightly different from the definition in (4) are also possible [8]. Such differences, however, are inconsequential for the subsequent analysis, and (4) has been chosen for mathematical simplicity.…”

Section: Modeling Preliminariesmentioning

confidence: 99%

“…This definition is referred to as "strong stability" in [8]. For queues to be stable, the following necessary condition needs to be satisfied:…”

Section: Modeling Preliminariesmentioning

confidence: 99%

“…The objective will entail concave and increasing so-called utility functions U m (•), which are commonly used in resource allocation tasks (not only restricted to communication systems) and account for the "social" utility (reward) to which a specific resource (here,ā m ) gives rise. Sum-utility maximization has frequently been employed by scheduling, medium access control layer, and networking algorithms; for example, see [8], [11], [18], and the references therein. Utility functions U m (ā m ) are chosen to be increasing (so that solutions that allow for higher arrival rates are promoted) and concave (so that the marginal utility for each user terminal diminishes as its rate increases), which offers a means of effecting fairness among different users.…”

Section: Problem Formulation and Design Approachmentioning

confidence: 99%

“…In these works, the solution of a properly formulated optimization problem dictates how resources are allocated, whereas the structure of the solution typically suggests the design of signaling protocols. In the second category, approaches build on dynamic backpressure policies, pioneered by Tassiulas and Ephremides [32]; for example, see [6], [8], [9], [27], and [31]. These works rely on adaptive control tools (the so-called Lyapunov optimization) and aim at stabilizing queues of the wireless network.…”

Section: Introductionmentioning

confidence: 99%

“…Such queues correspond not only to actual queues where packets are buffered before transmission but also to virtual queues whose role is to account for QoS requirements. Most existing works have focused on the impact of the cross-layer features on higher layers, and fewer works have addressed the design of cross-layer networking algorithms that take into account instantaneous fading; see [4], [7], [8], [15], [17], and [28] for some of the exceptions. In this context, this paper aims at optimally designing crosslayer resource allocation algorithms for fading wireless cellular networks under the following operating conditions.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Cross-Layer Resource Allocation in Cellular Networks Using Channel- and Queue-State Information

Marqués

Lopez-Ramos

Giannakis

et al. 2012

IEEE Trans. Veh. Technol.

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Algorithms that jointly allocate resources across different layers are envisioned to boost the performance of wireless systems. Recent results have revealed that two of the most important parameters that critically affect the resulting cross-layer designs are channel-and queue-state information (QSI). Motivated by these results, this paper relies on stochastic convex optimization to develop optimal algorithms that use instantaneous fading and queue length information to allocate resources at the transport (flow-control), link, and physical layers. Focus is placed on a cellular system, where an access point exchanges information with different users over flat-fading orthogonal channels. Both uplink and downlink setups are considered. The allocation strategies are obtained as the solution of a constrained utility maximization problem that involves average performance metrics. It turns out that the optimal allocation at a given instant depends on the instantaneous channel-state information (CSI) and Lagrange multipliers, which are associated with the quality-of-service (QoS) requirements and the operating conditions of the system. The multipliers are estimated online using stochastic approximation tools and are linked with the window-averaged length of the queues. Capitalizing on those links, queue stability and average queuing delay of the developed algorithms are characterized, and a simple mechanism is devised to effect delay priorities among users.

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Section: Modeling Preliminariesmentioning

confidence: 99%

“…This definition is referred to as "strong stability" in [8]. For queues to be stable, the following necessary condition needs to be satisfied:…”

Section: Modeling Preliminariesmentioning

confidence: 99%

Section: Problem Formulation and Design Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimal Cross-Layer Resource Allocation in Cellular Networks Using Channel- and Queue-State Information

Marqués

Lopez-Ramos

Giannakis

et al. 2012

IEEE Trans. Veh. Technol.

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Satisfying strict deadlines for cellular Internet of Things through hybrid multiple access

Gamgam

Karasan

2023

Trans Emerging Tel Tech

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Latency‐constrained aspects of cellular Internet of Things (IoT) applications rely on Ultra‐Reliable and Low Latency Communications (URLLC), which highlight research on satisfying strict deadlines. In this study, we address the problem of latency‐constrained communications with strict deadlines under average power constraint using Hybrid Multiple Access (MA), which consists of both Orthogonal MA (OMA) and power domain Non‐Orthogonal MA (NOMA) as transmission scheme options. We aim to maximize the timely throughput, which represents the average number of successfully transmitted packets before deadline expiration, where expired packets are dropped from the buffer. We use Lyapunov stochastic optimization methods to develop a dynamic power assignment algorithm for minimizing the packet drop rate while satisfying time average power constraints. Moreover, we propose a flexible packet dropping mechanism called Early Packet Dropping (EPD) to detect likely to become expired packets and drop them immediately. Numerical results show that Hybrid MA improves the timely throughput compared to conventional OMA by up to 46%$$ 46\% $$ and on average by more than 21%$$ 21\% $$. With EPD, these timely throughput gains improve to 53%$$ 53\% $$ and 24.5%$$ 24.5\% $$, respectively.

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Smart Data Center

Khan

2015

Handbook on Data Centers

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Resource Allocation and Cross-Layer Control in Wireless Networks

Cited by 517 publications

References 139 publications

Optimal Cross-Layer Resource Allocation in Cellular Networks Using Channel- and Queue-State Information

Optimal Cross-Layer Resource Allocation in Cellular Networks Using Channel- and Queue-State Information

Satisfying strict deadlines for cellular Internet of Things through hybrid multiple access

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