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The exploding increase of wireless communications combined with the existing inefficient usage of the licensed spectrum gives a strong impetus to the development and standardization of cognitive radio networking and communications. In this dissertation, a framework for Dynamic Spectrum Access (DSA) is first presented, which is the enabling technology for increasing the spectral efficiency of wireless communications. Based on that, Cognitive Radio (CR) can be developed as an enabling technology for supporting the DSA, which means that the wireless users are provided with enhanced capability for sensing the operating radio environment and for exploiting the network side information obtained from this sensing. The DSA concept means that the users of a wireless system are divided into a multi-tiered hierarchy with the primary users (PUs) entitled to protection and with cognitive radio capable secondary users (SUs). The improved spectrum efficiency is obtained by means of a medium access control protocol with knowledge about the statistical properties or available local information of the channels already occupied by PUs as well as knowledge about the interference tolerance within which the interference to PUs is kept to a given level. Related to this, emphasis is laid on the protocol capability to determine the efficiency of the secondary sharing of spectrum. Based on the type of available local information, the capacity of opportunistic communication is investigated for three models. These are: with dynamic, distributed channels information; with dynamic, parallel channels information; and under a dynamic sub-channels allocation scheme. The results indicate that this capacity is robust with reference to the uncertainty associated with localized sensing of distributed dynamic channels and with timely sensing of parallel dynamic channels. The extension to dynamic parallel sub-channels enables resource allocation to be carried out in sub-channels. The analytical results on the performance of sub-channel allocation indicate a robust traffic capacity in terms of blocking probability, drop-out probability and delay performance as function of PUs traffic loads.

The radio frequency (RF) spectrum is a scarce natural resource, currently regulated by government agencies. With the explosive emergence of wireless applications, the demands for the RF spectrum are constantly increasing. On the other hand, it has been reported that localised temporal and geographic spectrum utilisation efficiency is extremely low. Cognitive radio is an innovative technology designed to improve spectrum utilisation by exploiting those spectrum opportunities. This ability is dependent upon spectrum sensing, which is one of most critical components in a cognitive radio system. A significant challenge is to sense the whole RF spectrum at a particular physical location in a short observation time. Otherwise, performance degrades with longer observation times since the lagging response to spectrum holes implies low spectrum utilisation efficiency. Hence, developing an efficient wideband spectrum sensing technique is prime important. In this thesis, a multirate asynchronous sub-Nyquist sampling (MASS) system that employs multiple low-rate analog-to-digital converters (ADCs) is developed that implements wideband spectrum sensing. The key features of the MASS system are, 1) low implementation complexity, 2) energy-efficiency for sharing spectrum sensing data, and 3) robustness against the lack of time synchronisation. The conditions under which recovery of the full spectrum is unique are presented using compressive sensing (CS) analysis. The MASS system is applied to both centralised and distributed cognitive radio networks. When the spectra of the cognitive radio nodes have a common spectral support, using one low-rate ADC in each cognitive radio node can successfully recover the full spectrum. This is obtained by applying a hybrid matching pursuit (HMP) algorithm - a synthesis of distributed compressive sensing simultaneous orthogonal matching pursuit (DCS-SOMP) and compressive sampling matching pursuit (CoSaMP). Moreover, a multirate spectrum detection (MSD) system is introduced to detect the primary users from a small number of measurements without ever reconstructing the full spectrum. To achieve a better detection performance, a data fusion strategy is developed for combining sensing data from all cognitive radio nodes. Theoretical bounds on detection performance are derived for distributed cognitive radio nodes suffering from additive white Gaussian noise (AWGN), Rayleigh fading, and log-normal fading channels. In conclusion, MASS and MSD both have a low implementation complexity, high energy efficiency, good data compression capability, and are applicable to distributed cognitive radio networks.

We study a dynamic channel selection framework for cognitive radio networks (CRNs) which support both delay sensitive and best effort services. Unlike existing works in the literature, we consider the effect of heterogeneous radio frequency characteristics and heterogeneous primary user activities on channel selection in multi-channel CRNs. Optimal spectrum decision policies are obtained to achieve minimum delay using dynamic programming techniques, such as Markov decision process (MDP) and reinforcement learning, under different assumptions. To address the computational complexity issue in the MDP solutions, a myopic scheme is proposed based on the estimated packet sojourn time. / October 2016

With the advent of new high data rate wireless applications, as well as growth of existing wireless services, demand for additional bandwidth is rapidly increasing. Existing spectrum allocation policies of the Federal Communications Commission (FCC) prohibits unlicensed access to licensed spectrum, constraining them instead to several heavily populated, interference-prone frequency bands, which causes spectrum scarcity. However, it has been shown by several spectrum measurement campaigns that the current licensed spectrum usage across time and frequency is inefficient. Therefore, a concept of unlicensed users temporarily ``borrowing" spectrum from incumbent license holders to improve the spectrum utilization, called ``spectrum pooling", which is based on dynamic spectrum access (DSA), is proposed. Cognitive radio is a communication paradigm that employs software-defined radio technology in order to perform DSA and offers versatile, powerful and portable wireless transceivers. Orthogonal frequency division multiplexing (OFDM) is a promising candidate for cognitive radio transmission. OFDM supports high data rates that are robust to channel impairments. In addition, some subcarriers can be deactivated which constitutes a non-contiguous OFDM (NC-OFDM) transmission. However, one of the biggest problems for OFDM transmission is high out-of-band (OOB) radiation, which is caused by sinc-type function representing the symbols during one time constant. Thus, high sidelobe may occur that will interfere with neighboring transmissions. This thesis presents two novel techniques for NC-OFDM sidelobe suppression. Another concern about cognitive radio systems is that the influence of frequency-selective fading channel. Consequently, this thesis also presents a combined approach employing power loading, bit allocation and sidelobe suppression for OFDM-based cognitive radio systems optimization.

认知无线电在处理频谱稀缺的问题上是一个非常有前途的解决方案。拥有多天线认知无线电的用戶通过发射波束成形技术可以和授权用在同一时刻同一频带共存，这样大大地增强了频谱效率。在实际系统中，最理想的情况是这些拥有多天线认知无线电用戶能够分布式地优化他们的发射波束形成向量以此达到系统的最优化。由于授权用戶受到的干扰是来自于所有认知无线电用戶的，为了实现分布式算法这些干扰必须被合理地规划以至于达到最优。也就是说，每个认知无线电用戶需要知道对授权用戶产生干扰的最佳约束上限。 / 从优化的角度处理这种解耦问题，最常用的方法是原始分解法和对偶分解法。然而这两种方法都需要用戶之间有大量的消息传递，这对于频谱效率来说是有害的。在对偶分解法中，指向授权用戶的耦合干扰被一协调者估测(通常是授权用戶本身)。协调者需要在每次迭代中更新和广播参数给认知无线电用戶。对于原始分解法，算法同样需要一协调者进行收集认知无线电用戶的目标函数信息以此计算每个用戶的最优干扰约束上限。协调者同样需要更新和广播大量消息给认知无线电用戶。这种大量的信息计算和传递在分布式系统中是不理想的，问题在认知无线电网络显得格外严重。因为授权用戶不希望担任这样的协调者除非他的计算参与降到最低。 / 在此论文中，我们提出了几种新型的基于认知无线电网络的分布式算法。目的是最小化授权用戶和认知无线电用戶的消息传递。通过研究半定规划中的最优分割法，我们指出不影响最优性条件下授权用戶和认知无线电用戶的大量消息传递是可以避免的。我们又提出了在多输入多数出认知无线电网络中一种基于对偶分解的鲁捧干扰控制。在此论文中提出的低消息传递算法大大地提高了多用戶多输入多数认知无线电网络的实用性。 / Cognitive radio (CR) is a promising solution to alleviate spectrum scarcity. In CR networks where mobile stations are equipped with multiple antennas, secondary users (SUs) can transmit at the same time as the primary users (PUs) by carefully controlling the interference through transmit beamforming, thus significantly enhancing the spectrum efficiency. In practical systems, it is desirable to have multiple SUs optimize their transmit beamforming vectors in a decentralized manner, and yet achieve an optimal system performance. In CR networks, the interference received by the PU is attributed to the transmission of all SUs. To facilitate distributed beamforming, the aggregate-interference constraint imposed by the PU must be decoupled, so that each individual SU knows the "fair share" of interference that is allowed to generate to the PU. / A commonly used technique for decoupling coupled constraintsin optimization problems is optimization decomposition, including dual and primal decompositions. Both the dual and primal decomposition methods require frequent message passing among users, which potentially offsets the spectrum benefit brought by cognitive radio techniques. Specifically, with dual decomposition, the aggregate interference generated to the PU must be measured by a coordinator,which is, naturally, the PU. The coordinator then updates and broadcasts the Lagrangian multiplier to all SUs. Likewise, the primal decomposition needs a coordinator, which can again be the PU, to gather the subgradient of the objective functions of each SUs for given interference partition. The coordinator then updates and broadcasts the permissible interference to all SUs. Whereas the large overhead incurred message computation and passing is undesirable in distributed systems, the problem is more acute in CR networks, because a typical PU would not be willing to take the coordinating role unless its involvement is minimized. / In this thesis, we propose several novel distributed optimization algorithms for CR networks with minimum message passing between the primary and secondary systems. By exploiting the theory of optimal partition (OP) for semi-definite programming (SDP), we show that most message passings between the primary and secondary systems can be eliminated without compromising the optimality of the solution. We also derive a robust interference control scheme based on the duality theory for MIMO CR network. The low message-passing distributed algorithms presented in this thesis greatly enhance the practicality of multiuser MIMO CR networks. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Yao, Leiyi. / Thesis (Ph.D.) Chinese University of Hong Kong, 2014. / Includes bibliographical references (leaves 114-123). / Abstracts also in Chinese.

This thesis investigates different aspects of spectrum sensing in cognitive radio (CR) technology. First a probabilistic inference approach is presented which models the decision fusion in cooperative sensing as a probabilistic inference problem on a factor graph. This approach allows for modeling and accommodating the uncertainties and correlations in the cooperative sensing system. A constraint in the cognitive radios is the lack of knowledge about the primary signal and channel gain statistics at the secondary users. Therefore, a practical composite hypothesis approach is proposed which does not require any prior knowledge or estimates of these unknown parameters. Detection delay is an important performance measure in spectrum sensing. Quickest detection aiming to minimize detection delay has been studied in other contexts, and we apply it here to spectrum sensing. To combat the destructive channel conditions such as fading, various cooperative schemes based on the cumulative sum (CUSUM) algorithm are considered in this thesis. Furthermore, cooperative quickest sensing with imperfectly known parameters is investigated and a new solution is derived, which does not require any parameter estimation or iterative algorithm. In cognitive radios, there is a fundamental trade-off between the achievable throughput by the CRs and the level of protection for the primary user. In this thesis, this trade-off is formulated for the quickest sensing-based CRs. By throughput analysis, it is shown that for the same protection level to the primary user, the quickest sensing approach results in significantly higher average throughput compared to that of the conventional block sensing approach.

This thesis investigates different aspects of spectrum sensing in cognitive radio (CR) technology. First a probabilistic inference approach is presented which models the decision fusion in cooperative sensing as a probabilistic inference problem on a factor graph. This approach allows for modeling and accommodating the uncertainties and correlations in the cooperative sensing system. A constraint in the cognitive radios is the lack of knowledge about the primary signal and channel gain statistics at the secondary users. Therefore, a practical composite hypothesis approach is proposed which does not require any prior knowledge or estimates of these unknown parameters. Detection delay is an important performance measure in spectrum sensing. Quickest detection aiming to minimize detection delay has been studied in other contexts, and we apply it here to spectrum sensing. To combat the destructive channel conditions such as fading, various cooperative schemes based on the cumulative sum (CUSUM) algorithm are considered in this thesis. Furthermore, cooperative quickest sensing with imperfectly known parameters is investigated and a new solution is derived, which does not require any parameter estimation or iterative algorithm. In cognitive radios, there is a fundamental trade-off between the achievable throughput by the CRs and the level of protection for the primary user. In this thesis, this trade-off is formulated for the quickest sensing-based CRs. By throughput analysis, it is shown that for the same protection level to the primary user, the quickest sensing approach results in significantly higher average throughput compared to that of the conventional block sensing approach.

In a CR network, the maximum SUs throughput is desired generally. In this thesis, We investigate and formulate the problem of the secondary users¡¦ throughput maximization in cognitive radio systems under channel capacity constrain. By using KKT theorem, an objec- tive function is developed to obtain an optimal solution for the SU throughput maximization problem. An numerical example is also presented for illustration. The most important results revealed in the example show that the maximum SU throughput is achieved by cooperating an optimal number of SU pairs instead of cooperating all the SU pairs.

Cognitive Radio (CR) is considered to be a novel approach to improve the underutilization of precious radio resources by exploiting the unused licensed spectrum in dynamically changing environments. Designing efficient resource allocation algorithms for dynamic spectrum sharing and for power allocation in OFDM-CR networks is still a challenging problem. In this thesis, we specifically deal with these two problems. Dynamic spectrum sharing for the unlicensed secondary users (SU)s with device coordination could minimize the wastage of the spectrum. But this is a feasible approach only if the network considers the fairness criterion. We study the dynamic spectrum sharing problem for device coordinated cognitive radio networks with respect to fairness. We propose a simple modified proportional fair algorithm for a dynamic spectrum sharing scenario with two constraints, time and utility. Utility is measured by the amount of data processed and time is measured as the duration of a slot. This algorithm could result in variable or fixed length time slots. We will discuss the several controls possible on the algorithm and the possible extension of this algorithm for multicarrier OFDM based CR systems. Traditional water-filling algorithm is inefficient for OFDM-CR networks due to the interaction with primary users (PU)s. We consider reliability/availability of subcarriers or primary user activity for power allocation. We model this aspect mathematically with a risk-return model by defining a general rate loss function. We then propose optimal and suboptimal algorithms to allocate power under a fixed power budget for such a system with linear rate loss. These algorithms as we will see allocate more power to more reliable subcarriers in a water-filling fashion with different water levels. We compare the performance of these algorithms for our model with respect to water-filling solutions. Simulations show that suboptimal schemes perform closer to optimal scheme although they could be implemented with same complexity as water-filling algorithm. We discuss the linearity of loss function and guidelines to choose its coefficients by obtaining upper bounds on them. Finally we extend this model for interference-limited OFDM-CR systems.