We present a new approach for enhancing spectrum efficiency by means of multisystem spectrum channelization. The proposed approach incorporates impact between spectrum channelization and exploiting channel state information at transmitter (CSIT). We create disparities in accuracy of CSIT for different systems through frame offsets between the systems such that the time segments of the best CSIT accuracy for different systems are disjoint. We assign more spectrum resources to each system during its duration of best CSIT accuracy. This yields systematic and periodic changing of spectrum boundaries (in contrast to the fixed boundaries of the existing approach) while providing better CSIT accuracy level and larger degrees of freedom in resource allocation and hence higher spectrum efficiency for each system. We formulate the given collaborative multisystem channelization problem, derive the analytical performance metrics (postscheduling average data rate under time-varying CSIT accuracy for several schedulers), and develop the optimally channelized resource maps of the systems. In the basic form, the proposed channelization is designed offline once. We also generalize it into adaptive channelization to accommodate disparities of traffic demands for different systems. Analytical and simulation results corroborate substantial performance gains of the proposed scheme over the existing approach.

Interference alignment (IA) is known to significantly increase sum-throughput at high SNR in the presence of multiple interfering nodes, however, the reliability of IA is little known, which is the subject of this paper. We study the error performance of IA and compare it with conventional orthogonal transmission schemes. Since most IA algorithms require extensive channel state information (CSI), we also investigate the impact of CSI imperfection (uncertainty) on the error performance. Our results show that under identical rates, IA attains a better error performance than the orthogonal scheme for practical signal to noise ratio (SNR) values but is more sensitive to CSI uncertainty. We design bit loading algorithms that significantly improve error performance of the existing IA schemes. Furthermore, we propose an adaptive transmission scheme that not only considerably reduces error probability, but also produces robustness to CSI uncertainty.

To find an efficient and valid alternative of polysomnography (PSG), this paper investigates real-time sleep apnea and hypopnea syndrome (SAHS) detection based on electrocardiograph (ECG) and saturation of peripheral oxygen (SpO(2)) signals, individually and in combination. We include ten machine-learning algorithms in our classification experiment. It is shown that our proposed SpO (2) features outperform the ECG features in terms of diagnostic ability. More importantly, we propose classifier combination to further enhance the classification performance by harnessing the complementary information provided by individual classifiers. With our selected SpO(2) and ECG features, the classifier combination using AdaBoost with Decision Stump, Bagging with REPTree, and either kNN or Decision Table achieves sensitivity, specificity, and accuracy all around 82% for a minute-based real-time SAHS detection over 25 sleep-disordered-breathing suspects' full overnight recordings.

Interference control is crucial in cognitive radio networks and the existing related works focus on quasi-static channels. The expansion of wireless services and applications in everyday life has necessitated support of higher mobility, as evident from the emerging wireless standards. This results in time-varying fading channels within a transmission frame for which the interference power control schemes and power allocation strategies have not been developed yet. This paper proposes several interference control and power allocation strategies with different criteria for underlay cognitive radios in time-varying frequency-selective fading channels and extend the transmission schemes to multiple secondary systems. The transmission schemes are evaluated in terms of interference statistics at the primary receiver and the ergodic capacity of the secondary user link. Simulation results over the wide Doppler range show how the proposed schemes with different criteria shape the interference at the primary receiver and the capacity of the secondary user. The results also illustrate better interference control capability of the proposed approaches over the existing scheme.

Carrier frequency offset (CFO) causes spectrum misalignment of transmitter and receiver filters. This misalignment leads to energy loss and distortion of received signal, resulting in performance degradation of the whole system. However, these issues are often overlooked by existing works. This paper presents an exact signal model for OFDM that takes into account the CFO-induced spectral misalignment and related aliasing effects. Incorporating these practical issues, we also propose a new preamble structure and a new CFO compensation approach to mitigate their negative effects. Theoretical analysis and simulation results substantiate the importance of the exact signal model and the advantage of the proposed CFO compensation scheme.

We consider the effective degrees of freedom (DoF) achieved by interference alignment when channel state information (CSI) is acquired by training and feedback. For a flat block- fading K × K interference channel with power P per transmitter and M antennas per node, we show that interference alignment achieves higher DoF than orthogonal transmission (e.g., TDMA) only if the channel coherence time is large and the capacity of feedback link is at least as Θ(log P). Under this condition, to maximize the effective DoF, each receiver needs to feed back CSI via (M2-1)log P bits per coherence interval; smaller growth rate of feedback bits will decrease the effective DoF. We also show that in the presence of training and feedback cost, K = 3 achieves the optimal DoF for a broad range of channel characteristics; with larger number of user pairs, the DoF falls short of its optimum and beyond a certain point becomes a decreasing function of K.