Syed Imtiaz Hussain

Partial Relay Selection in Underlay Cognitive Networks with Fixed Gain Relays

In a communication system with multiple cooperative relays, selecting the best relay utilizes the available spectrum more efficiently. However, selective relaying poses a different problem in underlay cognitive networks compared to the traditional cooperative networks due to interference thresholds to the primary users. In most cases, a best relay is the one which provides the maximum end-to-end signal to noise ratio (SNR). This approach needs plenty of instantaneous channel state information (CSI). The CSI burden could be reduced by partial relay selection. In this paper, a partial relay selection scheme is presented and analyzed for an underlay cognitive network with fixed gain relays operating in the vicinity of a primary user. The system model is adopted in a way that each node needs minimal CSI to perform its task. The best relay is chosen on the basis of maximum source to relay link SNR which then forwards the message to the destination. We derive closed form expressions for the received SNR distributions, system outage, probability of bit error and average channel capacity of the system. The derived results are confirmed through simulations.

Performance analysis of selective cooperation in underlay cognitive networks over Rayleigh channels

Underlay cognitive networks should follow strict interference thresholds to operate in parallel with primary networks. This constraint limits their transmission power and eventually the area of coverage. Therefore, it is very likely that the underlay networks will make use of relays to transmit signals to the distant secondary users. In this paper, we propose a secondary relay selection scheme which maximizes the end-to-end signal to noise ratio (SNR) for the secondary link while keeping the interference levels to the primary network below a certain threshold. We derive closed form expressions for the probability density function (PDF) of the SNR at the secondary destination, average bit error probability and outage probability. Analytical results are verified through simulations which also give insight about the benefits and tradeoffs of the selective cooperation in underlay cognitive networks. It is shown that, in contrast to non-cognitive selective cooperation, this scheme performs better in low SNR region for cognitive networks.

Performance analysis of relay selection schemes in underlay cognitive networks with Decode and Forward relaying

In underlay cognitive networks with regenerative relaying, secondary users operating with primary user are adhering to stringent interference constraint which limits their transmission power and coverage area. In order not to violate this interference limit, underlay network will make the use of relays to transmit signal over the secondary network. The retransmitting relay will be selected among the available secondary users; hence, relay selection becomes more challenging due to strict interference limits. Relay selection is based not only on interference limit, but also on threshold constraint which ensure the satisfactory reception of the signal by the relays and the destination and which is an important criterion since the relaying protocol used is Decode and Forward (DF). This paper proposes three new relay selection schemes based on relay to destination link and relay to primary link qualities. We derive closed form expressions of the probability density function (PDF) of the SNR at the secondary destination, the outage probability and the average bit error probability. Analytical results are validated by simulations and they provide comparative analysis of the different relay selection scheme proposed herein.

Reactive relay selection in underlay cognitive networks with fixed gain relays

Best relay selection is a bandwidth efficient technique for multiple relay environments without compromising the system performance. The problem of relay selection is more challenging in underlay cognitive networks due to strict interference constraints to the primary users. Generally, relay selection is done on the basis of maximum end-to-end signal to noise ratio (SNR). However, it requires large amounts of channel state information (CSI) at different network nodes. In this paper, we present and analyze a reactive relay selection scheme in underlay cognitive networks where the relays are operating with fixed gains near a primary user. The system model minimizes the amount of CSI required at different nodes and the destination selects the best relay on the basis of maximum relay to destination SNR. We derive close form expressions for the received SNR statistics, outage probability, bit error probability and average channel capacity of the system. Simulation results are also presented to confirm the validity of the derived expressions.

Best relay selection using SNR and interference quotient for underlay cognitive networks

Cognitive networks in underlay settings operate simultaneously with the primary networks satisfying stringent interference limits. This condition forces them to operate with low transmission powers and confines their area of coverage. In an effort to reach remote destinations, underlay cognitive sources make use of relaying techniques. Selecting the best relay among those who are ready to cooperate is different in underlay settings than traditional non-cognitive networks. In this paper, we present a relay selection scheme which uses the quotient of the relay link signal to noise ratio (SNR) and the interference generated from the relay to the primary user to choose the best relay. The proposed scheme optimizes this quotient in a way to maximize the relay link SNR above a certain value whereas the interference is kept below a defined threshold. We derive closed expressions for the outage probability and bit error probability of the system incorporating this scheme. Simulation results confirm the validity of the analytical results and reveal that the relay selection in cognitive environment is feasible in low SNR regions.

Energy efficient cooperation in underlay RFID cognitive networks for a water smart home.

Shrinking water resources all over the world and increasing costs of water consumption have prompted water users and distribution companies to come up with water conserving strategies. We have proposed an energy-efficient smart water monitoring application in [1], using low power RFIDs. In the home environment, there exist many primary interferences within a room, such as cell-phones, Bluetooth devices, TV signals, cordless phones and WiFi devices. In order to reduce the interference from our proposed RFID network for these primary devices, we have proposed a cooperating underlay RFID cognitive network for our smart application on water. These underlay RFIDs should strictly adhere to the interference thresholds to work in parallel with the primary wireless devices [2]. This work is an extension of our previous ventures proposed in [2,3], and we enhanced the previous efforts by introducing a new system model and RFIDs. Our proposed scheme is mutually energy efficient and maximizes the signal-to-noise ratio (SNR) for the RFID link, while keeping the interference levels for the primary network below a certain threshold. A closed form expression for the probability density function (pdf) of the SNR at the destination reader/writer and outage probability are derived. Analytical results are verified through simulations. It is also shown that in comparison to non-cognitive selective cooperation, this scheme performs better in the low SNR region for cognitive networks. Moreover, the hidden Markov model’s (HMM) multi-level variant hierarchical hidden Markov model (HHMM) approach is used for pattern recognition and event detection for the data received for this system [4]. Using this model, a feedback and decision algorithm is also developed. This approach has been applied to simulated water pressure data from RFID motes, which were embedded in metallic water pipes.

Performance analysis of selective cooperation with fixed gain relays in Nakagami-m channels

Abstract Selecting the best relay using the maximum signal to noise ratio (SNR) among all the relays ready to cooperate saves system resources and utilizes the available bandwidth more efficiently compared to the regular all-relay cooperation. In this paper, we analyze the performance of the best relay selection scheme with fixed gain relays operating in Nakagami- m channels. We first derive the probability density function (PDF) of upper bounded end-to-end SNR of the relay link. Using this PDF, we derive some key performance parameters for the system including average bit error probability and average channel capacity. The analytical results are verified through Monte Carlo simulations.

Relay selection in underlay cognitive networks with fixed transmission power nodes

Underlay cognitive networks operate simultaneously with primary networks satisfying stringent interference constraints, which reduces their transmission power and coverage area. To reach remote destinations, secondary sources use relaying techniques. Selecting the best relay among the available ones is a well known technique. Recently, selective cooperation is investigated in cognitive networks where the secondary nodes can adapt their transmission power to always satisfy the interference threshold. In this paper, we investigate a situation where the secondary nodes have a fixed transmission power and may violate the interference threshold. We present two relay selection schemes; the first one excludes the relays not satisfying the interference constraint and then picks up a relay from the remaining ones that can provide the maximum signal-to-noise ratio (SNR). The other scheme uses a quotient of the relay link SNR and the interference from the relay to the primary user and optimizes it to maximise the relay link SNR. We derive closed form expressions for outage probability, bit error rate, channel capacity and diversity of the system for both schemes by using tight approximations. We also study mutual effects of interference. Simulation results confirm the analytical results and reveal that the relay selection is feasible at low SNRs. Copyright © 2013 John Wiley & Sons, Ltd.

Performance analysis of best relay selection scheme for amplify-and-forward cooperative networks in identical Nakagami-m channels

In cooperative communication networks, the use of multiple relays between the source and the destination was proposed to increase the diversity gain. Since the source and all the relays must transmit on orthogonal channels, multiple relay cooperation is considered inefficient in terms of channel resources and bandwidth utilization. To overcome this problem, the concept of best relay selection was recently proposed. In this paper, we analyze the performance of the best relay selection scheme for a cooperative network with multiple relays operating in amplify-and-forward (AF) mode over identical Nakagami-m channels using exact source-relay-destination signal to noise ratio (SNR) expression. We derive accurate closed form expressions for various system parameters including probability density function (pdf) of end-to-end SNR, average output SNR, average probability of bit error and average channel capacity. The analytical results are verified through extensive simulations. It is shown that the best relay selection scheme performs better than the regular all relay cooperation.

A Diversity Compression and Combining Technique Based on Channel Shortening for Cooperative Networks

The cooperative relaying process with multiple relays needs proper coordination among the communicating and the relaying nodes. This coordination and the required capabilities may not be available in some wireless systems where the nodes are equipped with very basic communication hardware. We consider a scenario where the source node transmits its signal to the destination through multiple relays in an uncoordinated fashion. The destination captures the multiple copies of the transmitted signal through a Rake receiver. We analyze a situation where the number of Rake fingers N is less than that of the relaying nodes L. In this case, the receiver can combine N strongest signals out of L. The remaining signals will be lost and act as interference to the desired signal components. To tackle this problem, we develop a novel signal combining technique based on channel shortening principles. This technique proposes a processing block before the Rake reception which compresses the energy of L signal components over N branches while keeping the noise level at its minimum. The proposed scheme saves the system resources and makes the received signal compatible to the available hardware. Simulation results show that it outperforms the selection combining scheme.