An excellent routing protocol is important for wireless sensor network (WSN) construction and efficient data transmission. With the continuous expansion of the application scenarios and scopes of the Internet of Things, the existing WSN routing protocols are no longer suitable for the complex network structure and the huge demands of communications. Aiming at these issues of existing routing protocols, such as short network lifetime caused by high energy consumption and uneven distribution of surviving nodes, this article proposes an energy-saving clustering protocol based on adaptive Voronoi dividing, named energy-saving clustering by Voronoi adaptive dividing (ESCVAD) protocol. The innovation of ESCVAD protocol lies in the adaptive clustering algorithm based on Voronoi dividing and cluster head election optimization algorithm based on distance and energy comprehensive weighting. The advantage of proposed algorithms is effectively to balance the energy consumption between cluster head nodes and cluster member nodes. The simulation results show that, compared with the traditional routing protocols, such as low energy adaptive clustering hierarchy (LEACH) protocol and stable energy protocol (SEP), the proposed ESCVAD protocol can effectively reduce the clustering frequency and cluster head electing frequency, so as to reduce signaling interaction frequency, finally result in the energy consumption down and the network lifetime up. Among the six protocols for comparation, ESCVAD has the best network lifetime and energy efficiency.

The energy-harvesting cognitive radio (CR) network is proposed to improve the spectrum efficiency and energy efficiency. We focus on the optimization of sensing time and power allocation to maximize the throughput of the energy-harvesting CR network subject to the energy causality constraint and collision constraint. Based on the classification of operating regions, the optimization problem is divided into two sub-problems. Then, the efficient iterative Algorithm 1 and Algorithm 2 are proposed to solve sub-problem (A) and sub-problem (B), respectively. Numerical results show that a significant improvement in the throughput is achieved via joint optimization of sensing time and power allocation.

Increasing spectrum-efficiency (SE) as well as energy-efficiency (EE) has attracted much attention recently due to the fact that the future wireless networks need to address the issues of high throughput and low power consumption. However, the objective for optimizing SE sometimes conflicts with the one for optimizing EE, and the methods for improving EE may result in a decrease in SE. In this paper, we consider the SE-EE tradeoff for cognitive radio (CR) networks with co-operative spectrum sensing (CSS). First, we formulate the general problem, and analyze two special cases: the SE maximization problem and the EE maximization problem. The SE and EE are optimized separately via joint optimization of sensing duration and final decision threshold in CSS. Based on the solutions of the two special cases, the general problem for SE-EE tradeoff is solved. Then, we consider the tradeoff of SE and EE from two perspectives: (1) maximizing EE while satisfying SE requirement; and (2) maximizing SE while satisfying EE requirement. Efficient algorithms for sensing strategy design are proposed for each scenario. Finally, we demonstrate the effectiveness of the proposed sensing strategies and illustrate the tradeoff between SE and EE via simulations.

A link decision algorithm is proposed for underlay cognitive radio network over a Rayleigh fading channel. The authors assume that a diversity combining technique is available at the secondary receiver. Under the constraints on average interference power (AIC) at the primary receiver (PR) and peak transmit power at the secondary transmitter (ST), for a given bit error ratio (BER) target, the link decision problem is formulated as an optimisation problem. Then, Lagrange dual decomposition is applied to derive the optimal transmit power and the modulation level of the ST to obtain its achievable transmission rate. Based on the optimal transmit power and the modulation level, a practical link decision algorithm which requires integral modulation levels is also proposed. Achievable transmission rates and outage probabilities of the proposed algorithms for various power constraints, BER requirements and different number of diversity branches are analysed. Finally, the influence of the primary user's transmission activities (including transmit power and non‐active probability) on the transmission performance of the secondary link is also discussed. They compare all the aforementioned results with the case when peak interference power constraint (PIC) at the PR is employed instead of the AIC. Simulations reveal that the AIC is indeed superior over the PIC. All these numerical results validate the proposed algorithms and the analysis.

A hybrid spectrum access strategy which is different from traditional underlay or overlay strategy is proposed. In this strategy, the secondary users cooperatively sense the states of the primary users in the sharing spectrum band. Based on the sensing results and an additional requirement on the bit error rate, the secondary transmitter adapts its transmit power and modulation level. Specifically, if the primary user is detected to be inactive, the secondary transmitter allocates its power subject to a peak transmit power constraint. Else if the primary user is active, the average interference power constraint would also be imposed to protect the primary transmission. To demonstrate the superiority of the hybrid strategy, capacity of the secondary link with this strategy over Rayleigh fading channel is studied. It has been assumed that diversity combing technique is available at the secondary receiver. Perfect and imperfect estimations of the channel power gain from secondary transmitter to primary receiver are also considered. Then the expression of capacity of the secondary link is derived by using Lagrange dual decomposition method. Simulation results demonstrate that the capacity of the secondary link with hybrid strategy is significantly improved when compared with the traditional ones.

Spectrum sensing is one of the most important components of cognitive radio (CR) technology. In this paper, we investigate the design of the sensing time to minimize the secondary user (SU) transmission delay under the condition of sufficient protection to primary users (PUs). It has been proven that there exists one optimal sensing time which yields the minimum transmission delay. Then, a novel cooperative spectrum sensing (CSS) framework is proposed. In the novel CSS framework, one SU's reporting time is also used for other SUs' sensing. For time varying channels, the novel multi-slot CSS is derived. In order to minimize the transmission delay, some algorithms are derived to obtain the optimal fusion scheme. Computer simulations show that fundamental improvement of delay performance can be achieved by the optimal fusion scheme. In addition, the novel multi-slot CSS scheme shows a much lower transmission delay than CSS based on general frame structure.

The authors investigate the problem of blind multi‐user detection (MUD) for uplink of asynchronous direct‐sequence code‐division multiple access (DS‐CDMA) systems with hostile jamming. To cope with the lack of prior knowledge of spread signals, hostile jamming and channel state information, the authors propose a scheme based on blind source separation (BSS) to solve this problem. Dual receive antennas are used in the proposed scheme. By exploiting the structure of the two received signals, a BSS model with dependent sources is formed, and then blind hostile jamming cancellation and MUD are obtained by solving this BSS problem. The proposed scheme does not require to know any information about the hostile jamming, and can work well under multiple kinds of hostile jamming. Simulation results further demonstrate that if such proposed approach is used to cancel hostile jamming and make MUD, then the bit‐error‐rate performance can be substantially improved as compared with earlier detectors.

We propose a new blind separation approach based on the Generalized Generating Function (GGF) of observations for complex sources by generalizing the definition of generating function. A new core equation is obtained and an approximate joint diagonalization scheme is used to estimate the mixing matrix by diagonalizing the Hessian matrix of the second GGF of the observations. Simulation results show that the GGF approach has superior performance to the existing classical algorithms when the SNR of observations is low and the data block is short.