Cooperative Diversity for Wireless Networks. Dr. Noha Ossama El-Ganainy Lecturer, Arab Academy of Science and Technology Alexandria, Egypt.

Biography PhD degree of Electrical Communications, Faculty of Engineering, Alexandria University, Alexandria, Egypt, Worked for different institutions in Egypt. More than 15 publications in international journals and conferences. Won the young scientist awards 2011 from URSI GA 2011 “Union Radio Scientific Internationale”

Presentation Outlines.  Developments of cellular systems.  Next generation systems requirements.  Cooperative diversity: the smart solution.  Discussions and conclusions.

Developments of Cellular Systems.  2G 2.5G 3G 4G. 3G Services: Mobile TV Video on demand Video conferencing Location-based services 2G and 2.5G Services Voice Messaging Image Transmission 4G Services Mobile Internet Ultra Connectivity Adaptive and Smart systems

Next Generation Systems Requirements.  Next generation systems are challenged with the growing demand for high-rate, high-quality wireless services.  Advanced algorithms are recommended to increase the data rate and to guarantee the quality-of-service QOS desired by each media class.  It is also essential to efficiently allocate the network resources to improve the transmission rate and capacity.  Advanced signal processing, adaptive techniques, and using various forms of diversity are highly recommended.

Spatial Diversity  Provided independently faded versions of the same signals at the receiver which enhances the detection.  It combats the channel deteriorations and the deep fades  Results in more efficient performance compared to any other signal processing tool.

MIMO Transmissions MIMO Transmissions  They provided the spatial diversity but hard to implement for single terminals.  Widely used and served in the development of a number of communication systems.

Cooperative Communications  Allows single-antenna mobiles to share their antennas in a manner that creates a virtual MIMO systems.  Gain the benefits of MIMO transmissions with no additional cost to the network.  Numerous theoretical models of cooperative signaling were proposed.  Can serve, in aware transmissions, to efficiently use the available network resources.  We are concerned in wireless networks, of cellular or ad-hoc variety, where the wireless terminal increase their quality of service via cooperation.

Historical Background  Is a development of the classical concept of Relay channels introduced by T. A.Cover and El-Gamal in  Was a model of a three-node networks consisting of a source, a destination, and a relay.  The Relay unique role is to help the source. The capacity was studied under AWGN channel.  While in a cooperative environment the users act as both information sources as well as relays.  The studies are interested in transmission in a fading channel.

Cooperative Communications

 Cooperative communication provides independently faded versions of the transmitted signal at the ultimate receiver.  Single-antenna mobiles in a multi-user framework are allowed to share their antennas and generate a virtual multiple- antenna transmitter.

Cooperative Communications Requirements  The base station ties-up a number of users as user-partner, pairs are highlighted.  The base station must separately receive the original and relayed data.  In cellular systems, hardware requirements are essential at the terminals as they receive down-link and up-link transmissions.  Half-Duplex and Full-Duplex.

Different Cooperative Signaling  Amplify-and-Forward: o Each user receives, amplifies, and retransmits a noisy version of the partner’s signal. o The destination combines the information sent by the user and partner to make a final decision on the transmitted bit. o The destination must have efficient estimation process to equalize the effect of the inter-user channel. Amplify-and-Forward

Different Cooperative Signaling  Coded Cooperation: o Integrates cooperation into channel coding, d ifferent portions of each user’s codeword is sent via two independent fading path (users). o Requires efficient code design.

Different Cooperative Signaling  Decode-and-Forward: o The partner is assigned to detect/estimate the user’s signal and forward it to the destination after encoding it. o The destination must have access to the inter-user channel coefficient to do optimal decoding. o Adaptive signaling is possible, at low SNR the partner can switch to non-cooperative mode.

Different Cooperative Signaling

 Compress-and-Forward: The partner is allowed to compress the user’s signal and forward it to the destination without decoding the signal.

Decode-and-Forward Algorithm.  During odd intervals, the user and partner send their information to each other and to the destination. Also, they are assigned to detect/estimate the partner’s information.  During even intervals, all user’s transmitted signal is a combination of its own data and the partner’s information estimate each spread by the appropriate code.

Inter-User Channel  The value of P e12 affects the estimation of the partner’s data which has the potential to control the efficiency of the cooperation process.

Decode-and-Forward Algorithm.

Odd Duration  The received signal at the destination during the odd interval is  While the received signal at the partner is

Partner detector  During the odd intervals the partner’s estimate and the P e of the transmitted bit are

Even Duration  The received signal at the destination during the even interval is

The Receiver Model  The destination begins by calculating the soft decision statistics for both intervals, which results in

The Receiver Model  The destination combines the information extracted during both intervals to obtain the transmitted bit  The MAP detector is used to extract b 1 given y  The probability of detecting b 1 given y is

The Optimal Detector  The optimal detector is found to be

The Sub-Optimum Detector Model  The optimal detector is complex and doesn't have a closed-form expression for the resulting probability of bit error.  A sub-optimal detector ‘modified λ -MRC’ is proposed instead.  The information received during the even duration is waited by.

Optimum vs Sub-Optimal Detector  For perfect inter-user P e12, the optimal detector reduces to the sub-optimal model.  The -MRC is simple and computationally undemanding.  It has a closed form expression which provides a simulation-free analysis.  The -MRC may run in a blind mode, and is may be calculated blindly.

Optimum vs Sub-Optimal Detector  As P e12 increases, the equivalence between the two models disappears.  For some transmissions conditions, a performance loss will take place.

The Sub-Optimum Detector Model

The Weighting Factor The Weighting Factor  Is used to weight the information received from the partner before the combining stage.  Is a measure of the destination confidence of the partner’s transmitted bit.  Ranging from 0 to 1 and is dependent on the inter-user channel error P e12.  Controls the efficiency of cooperatrion.

The Weighting Factor The Weighting Factor  The value of P e12 affects the estimation of the partner’s data which is reflected on the value of the proper.  0  1

The Probability of Error  The P e is given by;

The Probability of Error  The destination wants to use the value of that minimizes Pe for given transmission conditions.  The destination may not have access to the value of P e12, an adaptive estimation and feedback from the users is essential.  For given transmission conditions, the maximum possible performance is found by making use of an “optimal” value of (found) numerically.

P e vs P e12 The performance analysis of the cooperative algorithm in terms of the probability of error for different values of inter-user channel

To Cooperate or Not to Cooperate?

 Power Tradeoff M ore power is may be needed to provide cooperation? The baseline power will be reduced due to diversity. Smart power allocation is used to efficiently utilize the power resources.  Rate Tradeoff Is cooperation causing losses of rate in the system? Due to the spectral efficiency improvement, the channel code rates is may be increased.  Cost Is positively approved by several studies.

Discussions and Conclusions  The cooperative communications concept provides the benefits of MIMO transmission at no additional cost to the network.  It provides higher capacity and enhanced throughput compared to non-cooperative transmissions.  It efficiently allocates the network resources which improves the network capabilities and enhances the overall performance.

Discussions and Conclusions  Decreased sensitivity to channel variations.  Security the user’s data has to be encrypted before transmission, the partner can detect the user’s data without understanding it.  Complexity of Mobile Receiver Increased security, signal separation.  How to decide the partnership?  Partners assignments and reassignments

References A. Nosratinia, T. Hunter, and A. Hedayat, “Cooperation Communication in Wireless Networks,” IEEE Communication Magazine, October 2004, pp. 74–80. Noha O. El-Ganainy and Said E. El-Khamy, “A New Practical Receiver for a Decode-and-Forward Cooperative CDMA Systems based on a Blind λ - Combiner,” Progress in Electromagnetic Research Letters PIERL, Issue #28, page 23-36, 2012.

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