Energy-efficient resource allocation is considered in sensing-based spectrum sharing for cooperative cognitive radio networks (CCRNs). The secondary user first listens to the spectrum allocated to the primary user (PU) to detect the PU state and then initiates data transmission with two power levels based on the sensing decision (e.g., idle or busy). Under this model, the optimization problem of maximizing energy efficiency (EE) is formulated over the transmission power and sensing time subject to some practical limitations, such as the individual power constraint for secondary source and relay, the quality of service (QoS) for the secondary system, and effective protection for the PU. Given the complexity of this problem, two simplified versions (i.e., perfect and imperfect sensing cases) are studied in this paper. We transform the considered problem in fractional form into an equivalent optimization problem in subtractive form. Then, for perfect sensing, the Lagrange dual decomposition and iterative algorithm are applied to acquire the optimal power allocation policy; for imperfect sensing, an exhaustive search and iterative algorithm are proposed to obtain the optimal sensing time and corresponding power allocation strategy. Finally, numerical results show that the energy-efficient design greatly improves EE compared with the conventional spectrum-efficient design.

In this paper, a high performance current latch sense amplifier (CLSA) with vertical MOSFET is proposed, and its performances are investigated. The proposed CLSA with the vertical MOSFET realizes a 11% faster sensing time with about 3% smaller current consumption relative to the conventional CLSA with the planar MOSFET. Moreover, the proposed CLSA with the vertical MOSFET achieves an 1.11 dB increased voltage gain G(f) relative to the conventional CLSA with the planar MOSFET. Furthermore, the proposed CLSA realizes up to about 1.7% larger yield than the conventional CLSA, and its circuit area is 42% smaller than the conventional CLSA.

A sensing efficiency optimization scheme based on two-stage spectrum sensing that maximizes the achievable throughput of the secondary network and minimizes the average sensing time is proposed in this paper. A selection method for the threshold is proposed and proved to ensure optimal sensing performance. An effective iterative algorithm is presented to solve the constructed efficiency optimization problem.

This letter considers a multiple-channel cognitive radio network (CRN) which can simultaneously sense multiple narrowband channels at a time. Taking the maximization of the CRN's overall throughput as the design objective, the optimization problem of jointly designing sensing time, sensing thresholds and transmission power allocation is formulated under the total power constraint of the CRN and the average interference constraint of the primary network. An iterative algorithm is proposed to obtain the locally optimal values for these parameters. Finally, numerical results show that significant overall throughput gain is achieved through the joint design.

A spectrum sensing scheme for cognitive radio that includes coarse and fine sensing stages based on cyclostationarity is proposed in this paper. The cyclostationary feature detection (CFD) based on a single cyclic frequency (SCF) is used in the coarse sensing stage and that based on multiple cyclic frequencies (MCF) is employed in the fine sensing stage. Whether the fine sensing stage is performed or not is decided by comparing the statistic constructed in the coarse sensing stage with two thresholds. Theoretical analyses and simulation results show that the proposed sensing scheme has superior sensing performance and needs shorter sensing time.