Results 1  10
of
24
Cell Association and Interference Coordination in Heterogeneous LTEA Cellular Networks
"... Abstract—Embedding pico/femto basestations and relay nodes in a macrocellular network is a promising method for achieving substantial gains in coverage and capacity compared to macroonly networks. These new types of basestations can operate on the same wireless channel as the macrocellular netwo ..."
Abstract

Cited by 45 (0 self)
 Add to MetaCart
(Show Context)
Abstract—Embedding pico/femto basestations and relay nodes in a macrocellular network is a promising method for achieving substantial gains in coverage and capacity compared to macroonly networks. These new types of basestations can operate on the same wireless channel as the macrocellular network, providing higher spatial reuse via cell splitting. However, these basestations are deployed in an unplanned manner, can have very different transmit powers, and may not have traffic aggregation among many users. This could potentially result in much higher interference magnitude and variability. Hence, such deployments require the use of innovative cell association and intercell interference coordination techniques in order to realize the promised capacity and coverage gains. In this paper, we describe new paradigms for design and operation of such heterogeneous cellular networks. Specifically, we focus on cell splitting, range expansion, semistatic resource negotiation on thirdparty backhaul connections, and fast dynamic interference management for QoS via overtheair signaling. Notably, our methodologies and algorithms are simple, lightweight, and incur extremely low overhead. Numerical studies show that they provide large gains over currently used methods for cellular networks. Index Terms—Intercell interference management, femtocells I.
Performance Limits of Greedy Maximal Matching in Multihop Wireless Networks
"... In this paper, we characterize the performance limits of an important class of scheduling schemes, called Greedy Maximal Matching (GMM), for multihop wireless networks. For simplicity, we focus on the wellestablished nodeexclusive interference model, although many of the stated results can be rea ..."
Abstract

Cited by 17 (1 self)
 Add to MetaCart
In this paper, we characterize the performance limits of an important class of scheduling schemes, called Greedy Maximal Matching (GMM), for multihop wireless networks. For simplicity, we focus on the wellestablished nodeexclusive interference model, although many of the stated results can be readily extended to more general interference models. The study of the performance of GMM is intriguing because although a lower bound on its performance is well known, empirical observations suggest that this bound is quite loose, and that the performance of GMM is often close to optimal. In fact, recent results have shown that GMM achieves optimal performance under certain conditions. In this paper, we provide new analytic results that characterize the performance of GMM through the topological properties of the underlying graphs. To that end, we generalize a recently developed topological notion called the local pooling condition to a far weaker condition called the σlocal pooling. We then define the localpooling factor on a graph, as the supremum of all σ such that the graph satisfies σlocal pooling. We show that for a given graph, the efficiency ratio of GMM (i.e., the ratio of the throughput of GMM to that of the optimal) is equal to its localpooling factor. Further, we provide results on how to estimate the localpooling factor for arbitrary graphs and show that the efficiency ratio of GMM is no smaller than d ∗ /(2d ∗ −1) in a network topology of maximum nodedegree d ∗. We also identify specific network topologies for which the efficiency ratio of GMM is strictly less than 1. I.
Stochastic Network Utility Maximization A tribute to Kelly’s paper published in this journal a decade ago
"... ..."
(Show Context)
Distributed Throughput Maximization in Wireless Networks via Random Power Allocation
"... Abstract—We consider throughputoptimal power allocation in multihop wireless networks. The study of this problem has been limited due to the nonconvexity of the underlying optimization problems, that prohibits an efficient solution even in a centralized setting. We take a randomization approach t ..."
Abstract

Cited by 9 (1 self)
 Add to MetaCart
(Show Context)
Abstract—We consider throughputoptimal power allocation in multihop wireless networks. The study of this problem has been limited due to the nonconvexity of the underlying optimization problems, that prohibits an efficient solution even in a centralized setting. We take a randomization approach to deal with this difficulty. To this end, we generalize the randomization framework originally proposed for input queued switches to an SINR ratebased interference model. Further, we develop distributed power allocation and comparison algorithms that satisfy these conditions, thereby achieving (nearly) 100% throughput. We illustrate the performance of our proposed power allocation solution through numerical investigation and present several extensions for the considered problem. Index Terms—Power allocation, wireless scheduling, capacity region, graphbased interference model, SINR interference model. I.
Greedy Maximal Matching: Performance Limits for Arbitrary Network Graphs Under the Nodeexclusive Interference Model
"... Greedy Maximal Matching (GMM) is an important scheduling scheme for multihop wireless networks. It is computationally simple, and has often been numerically shown to achieve throughput that is close to optimal. However, to date the performance limits of GMM have not been well understood. In partic ..."
Abstract

Cited by 8 (0 self)
 Add to MetaCart
Greedy Maximal Matching (GMM) is an important scheduling scheme for multihop wireless networks. It is computationally simple, and has often been numerically shown to achieve throughput that is close to optimal. However, to date the performance limits of GMM have not been well understood. In particular, although a lower bound on its performance has been well known, this bound has been empirically found to be quite loose. In this paper, we focus on the wellestablished nodeexclusive interference model and provide new analytical results that characterize the performance of GMM through a topological notion called the localpooling factor. We show that for a given network graph with singlehop traffic, the efficiency ratio of GMM (i.e., the worstcase ratio of the throughput of GMM to that of the optimal) is equal to its localpooling factor. Further, we estimate the localpooling factor for arbitrary network graphs under the nodeexclusive interference model and show that the d ∗ 2d∗−1 efficiency ratio of GMM is no smaller than in a network topology of maximum nodedegree d ∗. Using these results, we identify specific network topologies for which the efficiency ratio of GMM is strictly less than 1. We also extend the results to the more general scenario with multihop traffic, and show that GMM can achieve similar efficiency ratios when a flowregulator is used at each hop.
Crosslayer Optimization for Wireless Networks with Deterministic Channel Models
"... Abstract—Existing work on crosslayer optimization for wireless networks adopts simple physicallayer models, i.e., treating interference as noise. In this paper, we adopt a deterministic channel model proposed in [11, 12], a simple abstraction of the physical layer that effectively captures the eff ..."
Abstract

Cited by 8 (4 self)
 Add to MetaCart
Abstract—Existing work on crosslayer optimization for wireless networks adopts simple physicallayer models, i.e., treating interference as noise. In this paper, we adopt a deterministic channel model proposed in [11, 12], a simple abstraction of the physical layer that effectively captures the effect of channel strength, broadcast and superposition in wireless channels. Within the Network Utility Maximization (NUM) framework, we study the crosslayer optimization for wireless networks based on this deterministic channel model. First, we extend the wellapplied conflict graph model to capture the flow interactions over the deterministic channels and characterize the feasible rate region. Then we study distributed algorithms for general wireless multihop networks. The convergence of algorithms is proved by Lyapunov stability theorem and stochastic approximation method. Further, we show the convergence to the bounded neighborhood of optimal solutions with probability one under constant steps and constant update intervals. Our numerical evaluation validates the analytical results. I.
On Stability Region and Delay Performance of LinearMemory Randomized Scheduling for TimeVarying Networks
"... Abstract—Throughput optimal scheduling policies in general require the solution of a complex and often NPhard optimization problem. Related literature has shown that in the context of timevarying channels, randomized scheduling policies can be employed to reduce the complexity of the optimization ..."
Abstract

Cited by 6 (3 self)
 Add to MetaCart
Abstract—Throughput optimal scheduling policies in general require the solution of a complex and often NPhard optimization problem. Related literature has shown that in the context of timevarying channels, randomized scheduling policies can be employed to reduce the complexity of the optimization problem but at the expense of a memory requirement that is exponential in the number of data flows. In this paper, we consider a LinearMemory Randomized Scheduling Policy (LMRSP) that is based on a pickandcompare principle in a timevarying network with N onehop data flows. For general ergodic channel processes, we study the performance of LMRSP in terms of its stability region and average delay. Specifically, we show that LMRSP can stabilize a fraction of the capacity region. Our analysis characterizes this fraction as well as the average delay as a function of channel variations and the efficiency of LMRSP in choosing an appropriate schedule vector. Applying these results to a class of Markovian channels, we provide explicit results on the stability region and delay performance of LMRSP. I.
Power Controlled Network Protocols for MultiRate Ad Hoc Networks
 IEEE Trans. Wireless Commun
, 2009
"... Abstract—In this paper, we propose for MultiRate ad hoc networks a crosslayer design using Power Control, called MRPC. MRPC consists of two parts. First, we propose a MultiRate Power Controlled MAC protocol, called MRPCMAC. By carefully controlling the transmission power, it can enable concurre ..."
Abstract

Cited by 6 (1 self)
 Add to MetaCart
(Show Context)
Abstract—In this paper, we propose for MultiRate ad hoc networks a crosslayer design using Power Control, called MRPC. MRPC consists of two parts. First, we propose a MultiRate Power Controlled MAC protocol, called MRPCMAC. By carefully controlling the transmission power, it can enable concurrent transmissions, which is otherwise impossible for the IEEE 802.11 standard. Second, we propose a MultiRate Power Controlled Routing protocol, called MRPCRouting. Different from traditional routing protocols, MRPCRouting is not intended to find endtoend paths, rather, it determines the next hop right before transmitting packets at the MAC layer. In this protocol, it uses the Effective Transport Capacity as the routing metric such that short links with high bandwidth are preferred and more concurrent transmissions can be enabled. Having these coupled power controlled MAC protocol and routing protocol, MRPC can greatly improve the spatial reuse and the network throughput. Simulation results also show MRPCMAC, MRPCRouting, and especially MRPC, can improve the network throughput significantly. Index Terms—Wireless ad hoc networks; cross layer design; transmission power control. I.
MAC Scheduling with Low Overheads by Learning Neighborhood Contention Patterns
, 2007
"... Aggregate traffic loads and topology in multihop wireless networks may vary slowly, permitting MAC protocols to ‘learn’ how to spatially coordinate and adapt contention patterns. Such an approach could reduce contention, leading to better throughput. To that end we propose a family of MAC schedulin ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
Aggregate traffic loads and topology in multihop wireless networks may vary slowly, permitting MAC protocols to ‘learn’ how to spatially coordinate and adapt contention patterns. Such an approach could reduce contention, leading to better throughput. To that end we propose a family of MAC scheduling algorithms and its general conditions, if satisfied, ensure latticethroughputoptimality (i.e., achieving any ratepoint on a uniform discretelattice within the throughputregion). This general framework for latticethroughputoptimality allows us to design MAC protocols which meets various objectives and conditions. In this paper, as instances of such a latticethroughputoptimal family, we propose distributed, synchronous contentionbased scheduling algorithms under graph and physical interference model that (i) is latticethroughputoptimal, (ii) does not require node location information, and (iii) has a signaling complexity that does not depend on network size. Thus, it is amenable to simple implementation, and is robust to network dynamics such as topology and load changes. Further, we propose a heuristic, which belongs to the proposed throughputoptimal family, for achieving faster convergence, leading to a better transient throughput.
Towards a System Theoretic Approach to Wireless Network Capacity in Finite Time and Space
"... Abstract—In asymptotic regimes, both in time and space (network size), the derivation of network capacity results is grossly simplified by brushing aside queueing behavior in nonJackson networks. This simplifying doublelimit model, however, lends itself to conservative numerical results in finite ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
(Show Context)
Abstract—In asymptotic regimes, both in time and space (network size), the derivation of network capacity results is grossly simplified by brushing aside queueing behavior in nonJackson networks. This simplifying doublelimit model, however, lends itself to conservative numerical results in finite regimes. To properly account for queueing behavior beyond a simple calculus based on average rates, we advocate a system theoretic methodology for the capacity problem in finite time and space regimes. This methodology also accounts for spatial correlations arising in networks with CSMA/CA scheduling and it delivers rigorous closedform capacity results in terms of probability distributions. Unlike numerous existing asymptotic results, subject to anecdotal practical concerns, our transient one can be used in practical settings: for example, to compute the time scales at which multihop routing is more advantageous than singlehop routing. I.