Mohamed M. Abdallah

Beamforming Algorithms for Information Relaying in Wireless Sensor Networks

We develop beamforming algorithms for information relaying over shared slowly nonselective fading channels in wireless sensor networks. We assume that, prior to beamforming their received data to a destination, the relays preprocess them by either data amplifying or decoding. The beamforming weights are broadcasted by the destination to the relays and are formed based on the individual relay-destination channel coefficients and an m-bit description of the quality of each source-relay channel. For both relay data-preprocessing models, we present methods for optimizing the m-bit quantizer employed at each relay for encoding its source-relay channel quality level, and for choosing the beamforming weights at the destination, so as optimize the destination uncoded bit error rates. As our simulations and analysis reveal, a coarse single-bit description of each source-relay channel coefficient at the destination may suffice, as it results in only a small increase in uncoded bit error rates with respect to the case where full knowledge of the source-relay channel coefficients are exploited at the destination.

Multivariate analysis for probabilistic WLAN location determination systems

WLAN location determination systems are gaining increasing attention due to the value they add to wireless networks. In this paper, we present a multivariate analysis technique for enhancing the performance of WLAN location determination systems by taking the correlation between samples from the same access point into account. We show that the autocorrelation between consecutive samples from the same access point can be as high as 0.9. Giving a sequence of correlated signal strength samples from an access point, the technique estimates the user location based on the calculated probability of this sequence from the multivariate distribution. We use a linear autoregressive model to derive the multivariate distribution function for the correlated samples. Using analytical analysis, we show that the proposed technique provides better location accuracy over previous techniques especially for the highly correlated samples in a typical WLAN environment. Implementation of the technique in the Horus WLAN location determination system shows that the average system accuracy is increased by more than 64%. This significant enhancement in the accuracy of WLAN location determination systems helps increase the set of context-aware applications implemented on top of these systems.

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.

Revisiting active cancellation carriers for shaping the spectrum of OFDM-based Cognitive Radios

Orthogonal frequency division multiplexing (OFDM) is widely used in wireless communication systems, and it is a candidate for Cognitive Radios (CR). Shaping the spectrum of OFDM-based CR is needed to reduce the out-of-band radiations and reduce the interference delivered to the licensed user (LU). In this paper, shaping the spectrum using windowing and active cancellation carriers is discussed. It is shown that the use of windowing alone for large band is a better choice. The window choice in case of large number of subcarriers spanned by a LU is investigated and it is shown that the Hanning window outperforms the raised cosine window by 7dB. Computational complexity analysis is introduced and it shows a huge advantage by using windowing alone for large frequency bands and multiple bands vis-à-vis cancellation carriers. Simulations show that such technique offers no effect on the bit error rate performance.

Efficient FPGA Implementation of MIMO Decoder for Mobile WiMAX System

In this paper, we present a FPGA prototyping of the MIMO Decoder for the IEEE 802.16e WiMAX mobile systems. The IEEE 802.16e standard supports three types of MIMO space time codes (STC), referred to in the standard by matrix A, B, and C, that achieve different levels of throughput and diversity depending on the quality of the MIMO channels. In particular, the STC matrix A achieves full diversity by employing the Alomuti coding, while the STC matrix B achieves full rate by employing spatial multiplexing and the STC matrix C achieves full rate and diversity by employing the Golden code. In this paper, we present a FPGA architecture of MIMO decoder based on the fixed sphere decoder (FSD) algorithm that achieves close-to ML BER performance with a reduced computational complexity and fixed throughput. We show how a single FSD can be used to decode the different STC by adaptively processing the received signal according to the STC type prior to be fed to the FSD. The FPGA design is incorporated with a QR decomposition of the channel matrix. The proposed FSD achieves fixed and high throughput required for the WiMAX systems. The FPGA implementation is incorporated with a MATLAB simulation model of an FUSC OFDMA-based WiMAX 2x2 MIMO system to validate the hardware design.

A survey on energy trading in smart grid

As the distributed energy generation and storage technologies are becoming economically viable, energy trading is gradually becoming a profit making option for end-users. This trend is further supported by the regulators and the policy makers as it aids the efficiency of power grid operations, reduces power generation cost and the Green House Gas (GHG) emissions. To that end, in this paper we provide an overview of distributed energy trading concepts in smart grid. First, we identify the motivation and the desired outcomes of energy trading framework. Then we present the enabling technologies that are required to generate, store, and communicate with the trading agencies. Finally, we survey on the existing literature and present an array of mathematical frameworks employed.

Capacity Planning Frameworks for Electric Vehicle Charging Stations With Multiclass Customers

In order to foster electric vehicle (EV) adoption, there is a strong need for designing and developing charging stations that can accommodate different customer classes distinguished by their charging preferences, needs, and technologies. By growing such charging station networks, the power grid becomes more congested and, therefore, controlling charging requests should be carefully aligned with the available resources. This paper focuses on an EV charging network equipped with different charging technologies and proposes two frameworks. In the first framework, appropriate for large networks, the EV population is expected to constitute a sizable portion of the light duty fleets. This necessitates controlling the EV charging operations to prevent potential grid failures and distribute the resources efficiently. This framework leverages pricing dynamics in order to control the EV customer request rates and to provide a charging service with the best level of quality of service (QoS). The second framework, on the other hand, is more appropriate for smaller networks, in which the objective is to compute the minimum amount of resources required to provide certain levels of QoS to each class. The results show that the proposed frameworks ensure grid reliability and lead to significant savings in capacity planning.

Sequential signal encoding and estimation for distributed sensor networks

We develop algorithms for sequential signal encoding from sensor measurements, and for signal estimation via fusion of channel-corrupted versions of these encodings. For signals described by state space models, we present optimized sequential binary-valued encodings constructed via threshold-controlled scalar quantization of a running Kalman filter signal estimate from the sensor measurements. We also develop methods for robust fusion from observations of these encodings corrupted by binary symmetric channels.

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.

Cognitive Relaying in Wireless Sensor Networks: Performance Analysis and Optimization

The anticipated increase in the density of the deployed wireless sensor networks calls for spectrum sharing through unlicensed access to licensed spectrum. The key technology for spectrum sharing in this scenario is cognitive radio networks. Cognitive relaying scenarios, where a cognitive (unlicensed) user provides relaying services to a licensed (primary) user, have been proposed before as a method to increase chances of spectrum white spaces. In this paper, we develop a wireless sensor network framework containing a cognitive user (sensor node), with delay sensitive data and limited power budget. The cognitive user offers relaying capability to the primary traffic when the primary connection fails to deliver. The cognitive user utilizes a scheduling mechanism that grants priority to relayed traffic over its own traffic. In our framework, the cognitive user is allowed to control the volume of the relayed traffic through an admission control parameter. The objective of the sensor node (cognitive user) is to minimize its traffic delay, subject to certain power budget allowed for relaying the primary traffic. Our key contributions in this work are the development of the aforementioned framework, the establishment of a mathematical formalization for this problem, the derivation of mathematical expressions for average power consumption and average packet delay, and the solution for the developed optimization problem by finding a value for the admission control parameter that minimizes delay while satisfying power budget constraints.