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In this paper, we introduce a new spatial spectrum-sharing strategy for massive multiple-input multiple-output (MIMO) cognitive radio (CR) systems. Different from the conventional MIMO CR system, CR terminals can be discriminated by their angular information with the help of high spatial resolution of massive antennas at CR base station (CBS). Moreover, the discrete Fourier transform can be applied to efficiently obtain such angular information thanks to the massive antennas, again. We then formulate a 2-D spatial basis expansion model to represent the uplink/downlink channels of CRs with reduced parameter dimensions, which immediately alleviates the general headaches of massive MIMO systems, such as uplink pilot contamination and downlink training overhead. Moreover, we present a full-space coverage concept by employing two CBSs at the adjacent sides of each cell, which diminishes the sheltering effect from the primary radio. We also design two greedy CR scheduling algorithms for the dual CBSs to improve the spectral efficiency and enhance the scheduling probability of CRs. Since the proposed strategy exploits angular information and since the angle reciprocity holds for two frequency carriers with moderate distance, the proposed strategy is applied for both time division duplex and frequency division duplex systems. [ABSTRACT FROM PUBLISHER]

In this paper, an optimal beamforming and power allocation for the sensing-based spectrum sharing (SBSS) that employs a soft decision on the state of the primary user is proposed. Different from the existing soft-decision-based SBSS that employs a single antenna and adjusts only transmit power, in the proposed scheme, the secondary transmitter employs multiple transmit antennas and controls the beamforming direction and the transmit power. We design the optimal beamforming and power allocation under the constraints of total transmit power and interference power, where both average and instantaneous power constraints are considered. Simulation results demonstrate that the proposed technique can significantly improve the average throughput of the secondary user compared with the existing methods. The proposed beamforming for the soft-decision-based SBSS is a promising scheme, particularly for cognitive femto base stations (BSs) to boost up the spectral efficiency while managing the interference to the macro BSs. [ABSTRACT FROM AUTHOR]

Cognitive Radio (CR) capitalizes advances in signal processing and radio technology and is capable of reconfiguring RF and switching to desired frequency bands. It is a frequency-agile data communication device that is vastly more powerful than recently proposed multi-channel multi-radio (MC-MR) technology. In this paper, we investigate the important problem of multi-hop networking with CR nodes. For such a network, each node has a pool of frequency bands (typically of unequal size) that can be used for communication. The potential difference in the bandwidth among the available frequency bands prompts the need to further divide these bands into sub-bands for optimal spectrum sharing. We characterize the behavior and constraints for such a multi-hop CR network from multiple layers, including modeling of spectrum sharing and sub-band division, scheduling and interference constraints, and flow routing. We develop a mathematical formulation with the objective of minimizing the required network-wide radio spectrum resource for a set of user sessions. Since the formulated model is a mixed-integer non-linear program (MINLP), which is NP-hard in general, we develop a lower bound for the objective by relaxing the integer variables and using a linearization technique. Subsequently, we design a near-optimal algorithm to solve this MINLP problem. This algorithm is based on a novel sequential fixing procedure, where the integer variables are determined iteratively via a sequence of linear programs. Simulation results show that solutions obtained by this algorithm are very close to the lower bounds obtained via the proposed relaxation, thus suggesting that the solution produced by the algorithm is near-optimal. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) and energy-efficient design have emerged as two promising techniques to achieve high spectrum efficiency (SE) and energy efficiency (EE), respectively. In this paper, we study energy-efficient opportunistic spectrum access strategies for an orthogonal frequency division multiplexing (OFDM)-based CR network with multiple secondary users (SUs). Both worst EE and average EE are considered and optimized for different emphases and application scenarios. Since the original optimization issues belong to nonconvex integer combinatorial fractional program and are essentially NP-hard for an optimal solution, we use continuous and convex relaxation to modify the problems for somewhat better mathematical tractability. For the relaxed worst-EE-based spectrum access problem, we first demonstrate the joint quasiconcavity of EE on subchannel and power allocation matrices and then develop a framework to find the optimal solution based on efficient root finding and convex optimization. We also develop a low-complexity alternative for suboptimal solution. The relaxed average-EE-based spectrum access problem is still NP-hard and may have many local optima. We first transform the problem into an equivalent form and introduce a general concave envelope based branch-and-bound (B&B) approach to find the global optimal solution. We then exploit the underlying properties of the energy-efficient transmission to speed up the convergence of the B&B approach. Besides, we develop a low-complexity heuristic approach to find a suboptimal solution. Simulation results show that the energy-efficient spectrum access strategies significantly boost EE compared with the conventional spectral-efficient spectrum access ones while the low-complexity suboptimal approaches can well balance the performance and complexity. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new blind spectrum sensing method based on the polarization characteristic of the received signal, which is completely represented by the orientation of a polarization vector. We first discuss a spectrum sensing model based on polarization vectors' orientation. Then we develop the directional statistics of polarization vectors that contain both the signal and noise or noise only. The distinctive difference between the two statistics can be used to decide whether the primary signal exists or not. Based on this, by using the well-known generalized likelihood ratio test (GLRT) paradigm, a new polarization sensing algorithm GLRT-polarization vector (GLRT-PV) is proposed. By applying directional statistics, we derive closed-form expressions for the probability of false alarm and the probability of detection under both dual-polarized additive white Gaussian noise (AWGN) and Rayleigh-fading channels. Our numerical simulation and experimental results show that the proposed method exhibits better performance than other existing methods in the case of unknown primary transmitter polarization and/or presence of noise power uncertainty. [ABSTRACT FROM PUBLISHER]

Recently, a cooperative cognitive radio network (CCRN) and multiple-input multiple-output (MIMO) cognitive radio (CR) network have been proposed and investigated individually as promising paradigms for CR networks. Herein, we consider applying the MIMO techniques to a CCRN in order to achieve increased spectral efficiency. Assuming that the system comprises a single-input single-output (SISO) primary user (PU) pair and a multi-input single-output (MISO) secondary user (SU) pair, we propose jointly optimizing the beamforming vector and power allocation for the SU transmitter in order to maximize the rate for the SU while meeting the rate requirement for the PU. As the problem is nontrivial, we circumvent it using the uplink and downlink duality and equality of the left and right eigenvalues. Further, we introduce an iterative algorithm with a proof of convergence. From the numerical results, the proposed algorithm converges in a small number of iterations and outperforms the zero-forcing (ZF) beamforming algorithm while providing an upper bound for the CCRN-MIMO performance. Furthermore, the application of the proposed algorithm is not limited to CCRN-MIMO; it can also be extended to the conventional relay MIMO and/or MU-MIMO with a quality-of-service (QoS) requirement. [ABSTRACT FROM AUTHOR]

We propose continuous power allocation strategies for secondary users (SUs) based on sensing the primary user (PU) channels in a multiband cognitive radio (CR) network. Unlike the conventional sensing-based spectrum sharing, where there are two transmit power levels corresponding to whether the PU is sensed present or not, in the proposed strategy, the power levels are continuous functions of the sensing statistics, and optimized with respect to the achievable rate of the SU. The continuous power allocation function is parameterized by some channel parameters of the PU and SU, and we treat the cases of perfect and quantized channel state information (CSI) separately, where the former provides an upper bound on the achievable rate with full channel information; and the latter constitutes an efficient practical power allocation method for the SU with statistic/partial channel information. The power control process consists of two phases: in the first phase, the SU listens to the multiple bands licensed to the PU and obtains the sensing statistics, e.g., the received signal energies on these bands; in the second phase, the SU adjusts its transmit power levels on these bands based on the sensing results. Optimal power allocation schemes are derived to maximize the achievable rate at the SU under several possible combinations of the peak/average transmit power constraints at the SU and the peak/average interference power constraints at the PU. Simulation results demonstrate that the proposed strategies can significantly improve the achievable throughput of the SU compared to the conventional methods. [ABSTRACT FROM AUTHOR]

Cognitive ultra-wideband (UWB) radio is proposed to exploit the advantages and the unique features of enhancing UWB wireless technologies by utilizing cognitive radio functionality. In order to achieve the cognitive UWB radio, multiple pulse waveforms adaptation has been investigated for producing the expected spectral notches while matching with the Federal Communications Commission (FCC) spectral mask. Then the specifically designed pulse waveform with adaptive spectral notches is tested by high-speed digital processing unit for implementation estimation. To verify the pulse distortion effects of UWB antenna in the cognitive UWB radio, the generalized pulse waveform is further transferred through an actual UWB antenna. Furthermore, a pulse waveform predistortion scheme is considered before antenna transmission, so as to compensate the pulse distortion effects. [ABSTRACT FROM AUTHOR]

This paper proposes a new Nash bargaining solution (NBS) based cooperative game-theoretic scheduling framework for joint channel and power allocation in orthogonal frequency division multiple access cognitive radio (CR) systems. Our objectives are to maximize the overall throughput of the CR system with the protection of primary users' transmission, while guaranteeing each CR user's minimum rate requirement and the proportional fairness and efficient power distribution among CR users. Using time-sharing variable transformation, we introduce a novel method that involves Lambert-W function properties and obtain closed-form analytical solutions. A low-complexity algorithm is also developed which does not require iterative processes as usual to search the optimal solution numerically. Simulation results demonstrate that our optimal policies outperform the existing maximal rate, fixed assignment and max-min fairness, while achieving the 99.985% in average of the optimal capacity. [ABSTRACT FROM PUBLISHER]