Omar A. Nasr

In cooperative spectrum sensing, local sensing at different sensing nodes is done either using soft decisions or hard decisions. The hard decision-based sensing has the advantage of using only one bit to report the local decision. In the literature, the hard decisions are combined at the fusion center using AND, OR, or MAJORITY rules. Although the problem of finding the “optimal” fusion rule was addressed and solved for the soft decisions fusion, it was not solved in the hard-decisions sensing. The problem of calculating the local and global decision thresholds in hard decisions-based cooperative spectrum sensing is known for its mathematical intractability. Hence, previous studies relied on simple suboptimal counting rules for decision fusion in order to avoid the exhaustive numerical search required for obtaining the optimal thresholds. These simple rules are not globally optimal as they do not maximize the overall global detection probability by jointly selecting local and global thresholds. Instead, they try to maximize the detection probability for a specific global threshold. In this paper, a globally optimal decision fusion rule for Primary User signal detection based on the Neyman-Pearson (NP) criterion is derived. The algorithm is based on a novel representation for the global performance metrics in terms of the regularized incomplete beta function. Based on this mathematical representation, it is shown that the globally optimal NP hard decision fusion test can be put in the form of a conventional one dimensional convex optimization problem. A binary search for the global threshold can be applied yielding a complexity of O(log2(N)), where N represents the number of cooperating users. The logarithmic complexity is appreciated because we are concerned with dense networks, and thus N is expected to be large. The proposed optimal scheme outperforms conventional counting rules, such as the OR, AND, and MAJORITY rules. It is shown via simulations that, although the optimal rule tends to the simple OR rule when the number of cooperating secondary users is small, it offers significant SNR gain in dense cognitive radio networks with large number of cooperating users.

The two implementation choices for the baseband part of wireless radios are the application-specific platforms (e.g. application-specific integrated circuits (ASICs)) and the programmable processors (e.g. digital signal processors (DSPs)). An application-specific instruction-set processor (ASIP) is a customised processor that bridges the gap between the two platforms. In this work, a novel implementation of the signal processing part of an orthogonal frequency division multiplexing (OFDM) baseband processor using three ASIPs is presented. The ASIPs provide novel architectures for the symbol chain, including fast Fourier transform, channel estimation subsystem and synchronisation subsystem. This design provides a close to DSPs level of flexibility, making it suitable for supporting all the modes of a large number of OFDM standards. In the meantime, the system maintains a performance level comparable to ASICs. This is demonstrated by providing post-layout results for 0.13 μm Taiwan semiconductor manufacturing company complementary metal-oxide semiconductor technology.

Body Area Sensor Networks (BASNs) leverage wireless communication technologies to provide healthcare stakeholders with innovative tools and solutions that can revolutionize healthcare provisioning; BASNs thus promotes new ways to acquire, process, transport, and secure the raw and processed medical data to provide the scalability needed to cope with the increasing number of elderly and chronic disease patients requiring constant monitoring. However, the design and operation of BASNs is challenging, mainly due to the limited power source and small form factor of the sensor nodes. The main goal of this paper is to minimize the total energy consumption to prolong the lifetime of the wireless BASNs for healthcare applications. An Energy–Delay–Distortion cross-layer framework is proposed in order to ensure transmission quality for medical signals under limited power and computational resources. The proposed cross-layer framework spans the Application–MAC–Physical layers. The optimal encoding and transmission energy are computed to minimize the total energy consumption in a delay constrained wireless BASN. The proposed framework considers three scheduling techniques: TDMA, TDMA–Simultaneous Transmission and dynamic frequency allocation scheduling. The TDMA–ST scheme schedules the weakly interfering links to transmit simultaneously, and schedules the strongly interfering links to transmit at different time slots. The dynamic frequency allocation scheme allocates the time–frequency slots optimally based on the application’s requirements. Simulation results show that these proposed scheduling techniques offer significant energy savings, compared to the algorithms that ignore cross-layer optimization.

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