Space–Time Diversity Enhancements Using Collaborative Communications

Mitran, Patrick; Ochiai, Hideki; Tarokh, Vahid

doi:10.1109/tit.2005.847731

Cited by 177 publications

(130 citation statements)

References 24 publications

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“…It then re-encodes the message with its own codebook and transmits for the remaining time that the source transmits [93,134,136]. One can show that this dynamic DF scheme achieves the diversity-multiplexing pairs (d, r) satisfying…”

Section: Dynamic Decode-and-forwardmentioning

confidence: 99%

Cooperative Communications

Kramer¹,

2006

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This article reviews progress in cooperative communication networks. Our survey is by no means exhaustive. Instead, we assemble a representative sample of recent results to serve as a roadmap for the area. Our emphasis is on wireless networks, but many of the results apply to cooperation in wireline networks and mixed wireless/wireline networks. We intend our presentation to be a tutorial for the reader who is familiar with information theory concepts but has not actively followed the field. For the active researcher, this contribution should serve as a useful digest of significant results. This article is meant to encourage readers to find new ways to apply the ideas of network cooperation and should make the area sufficiently accessible to network designers to contribute to the advancement of networking practice. Overview IntroductionThe classic representation of a communication network is a graph, as in Figure 1.1, with a set of nodes and edges. The nodes usually represent devices such as a router, a wireless access point, or a mobile telephone. The edges usually represent communication links or channels, for example a fiber-optic cable or a wireless link. Both the devices and the channels may have constraints on their operation. For example, a router might have limited processing power, or perhaps it can accept data from only a few of its ports simultaneously. A fiber-optic link has a limited bandwidth (which can be quite large!). A wireless phone, on the other hand, has limited battery resources and likely wishes to conserve energy. A wireless link can have rapid time variations arising from mobility and multipath propagation of signals. Some of these properties are collected in Table 1.1 and are described in more detail in this text.The purpose of a communication network is to enable the exchange of messages between its nodes. These messages, as generated by an application, are organized into data packets. In the traditional model of a network, the nodes operate as store-and-forward packet routers that transmit packets over point-to-point links. However, this model is unnecessarily restrictive as it ignores two important possibilities:• Node Coding: Nodes can combine, or encode, any of their received information and symbol streams.• Broadcasting: Nodes overhear the transmissions of other nodes from which they are not required to receive messages.Node coding is possible in any network, while the ability to overhear transmissions is a property of the physical communication channel. In particular, wireless devices inherently broadcast information in that a signal to a particular receiving node can be overheard by other nodes. Typically, the wireless nodes treat these overheard signals as interference and the system provides mechanisms to mitigate this interference. For example, many second generation cellular phones employ code division multiple access (CDMA) to permit signal decoding in 274 Overview the presence of interference [185]. As a second example, 802.11× wireless LANs employ a media access...

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Section: Dynamic Decode-and-forwardmentioning

confidence: 99%

Cooperative Communications

Kramer¹,

2006

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“…And the optimum 1 is decreasing as SNR increasing when the relay location is near the source. In Fig.5, the achievable rates are observed with effective SNR, which is defined in equation (19) and (20). The figure shows that the achievable rate under the instantaneous constraint performs better than under the average constraint for the distance ratio L=1 and L=10.…”

Section: Fig3 B's Ratio With Different Relay Locationsmentioning

confidence: 98%

Achievable rate for amplify and forward relay system at low SNR

Ding

Kwon

2012

2012 International Conference on Computing, Networking and Communications (ICNC)

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The following faculty members have examined the final copy of this thesis for form and content, and recommend that it be accepted in partial fulfillment of the requirement for the degree of Master of Science with a major in Electrical Engineering. Thanks are also due to my fellow lab workers who helped guide me in good and bad times. _________________________________vi ABSTRACT In this thesis, the achievable rate at low SNR is established for Amplify-and-Forward (AF) cooperative system with a source node, a destination node and a relay node. To characterize the effect of relay locations, an aggregate channel model which consists of both long-term path loss and short-term path loss fading is used. To consider the effect of channel information at the relay, average and instantaneous power constraints are applied respectively when evaluating the achievable rate. As analytic solutions to the achievable rate seem difficult to obtain, approximations for the achievable rate, the optimal power loadings, the optimal amplification coefficients for both types of power constraints are derived in this thesis.vii

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“…An alternative approach that bases on flooding the network with a message that shall be transmitted is presented in [29,40]. In the opportunistic large array (OLA) method neighbouring nodes function as relay nodes that retransmit a received signal various times.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Transmission in Wireless Sensor Networks by a (1 + 1)-EA

Sigg

Beigl

2008

2008 Eighth International Workshop on Applications and Services in Wireless Networks (Aswn 2008)

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With collaborative transmission we propose a novel transmission scheme that utilises constructive interference between transmitted signals of wireless sensor nodes. Similar to cooperative transmission approaches we are able to drastically extend the transmission range of a wireless sensor network. We show that synchronisation of received signal components is feasible without inter-node communication. Our approach is capable of synchronising a virtually arbitrary number of signal components. The underlying scenario is modelled as a black-box optimisation problem and is solved by a (1 + 1) evolution strategy. The method is optimal in the sense that any other evolutionary search approach with equal mutation probability has an expected asymptotic optimisation time that is at least of the same order.

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Space–Time Diversity Enhancements Using Collaborative Communications

Cited by 177 publications

References 24 publications

Cooperative Communications

Cooperative Communications

Achievable rate for amplify and forward relay system at low SNR

Collaborative Transmission in Wireless Sensor Networks by a (1 + 1)-EA

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