Optimal Energy Efficient Strategy for Cooperative Spectrum Sharing Networks

Green communications is an inevitable trend for future communication network design, especially for a cognitive radio network. Power allocation strategies are of crucial importance for green cognitive radio networks. However, energy-efficient power allocation strategies in green cognitive radio networks have not been fully studied. Energy efficiency maximization problems are analyzed in delay-insensitive cognitive radio, delay-sensitive cognitive radio, and simultaneously delay-insensitive and delay-sensitive cognitive radio, where a secondary user coexists with a primary user and the channels are fading. Using fractional programming and convex optimization techniques, energy-efficient optimal power allocation strategies are proposed subject to constraints on the average interference power, along with the peak/average transmit power. It is shown that the secondary user can achieve energy efficiency gains under the average transmit power constraint, in contrast to the peak transmit power constraint. Simulation results show that the fading of the channel between the primary user transmitter and the secondary user receiver and the fading of the channel between the secondary user transmitter and the primary user receiver are favorable to the secondary user with respect to the energy efficiency maximization of the secondary user, whereas the fading of the channel between the secondary user transmitter and the secondary user receiver is unfavorable to the secondary user.

The energy efficiency of space heaters is rated by Annual Fuel Utilization Efficiency (AFUE) governed by the Department of Energy in the United States which is a simple ratio of usable heat and fuel usage of a single heating device. It doesn't consider the overall performance of the heating system including not only the heating devices but also the characteristics of the building in different applications. The current AFUE method calculates only the energy efficiency which is thermodynamics first law efficiency. In this research, the systematic efficiency of a heating system rather than simple device efficiency has been defined and investigated. The systematic efficiency considers the overall efficiency of the whole heating system and it varies in the different applications even though with the same heating device. So it represents the performance of the system more precisely. Analytical models have been built to calculate both the systematic energy efficiency and exergy efficiency, and to evaluate the systematic energy and exergy efficiency of heating systems for direct fired and indirect fired heaters. Efficiency performances of the systems with these two types of heaters are compared. Sensitivities of input parameters for systematic energy efficiency are studied to show the impact towards systematic energy efficiency. Indoor carbon dioxide concentration level of direct fired heating system is also studied. In a case study, results show that systematic energy efficiency of indirect fired heating system is always constant at heater device efficiency which is 80\% while systematic energy efficiency of direct fired heating system varies from 40%-92% under different condition (heat loss coefficient, ambient temperature and air change requirement), indicating that simple device efficiency is not capable to evaluate the overall performance of heating system. New efficiency method such as systematic energy efficiency used in this research is needed to better describe the performance of the heating system. Results of indoor carbon dioxide level of direct fired heating system, from 1000 to 4500 PPM under different conditions, show that indoor air quality needs to be considered while using direct fired heating.

With the continuous growth of the wireless communication business, energy issues and environmental problems are becoming increasingly grim. Therefore, this paper investigates the energy efficient transmission scheme with cooperative sensing in cognitive radio networks, in which AND fusion rule is introduced to determine the presence of the primary user. It is proved that the energy efficiency is a quasi-concave function with sensing time when the number of cooperative users satisfies certain constraints. Aiming at maximizing the energy efficiency, the transmission power is selected at first, then a scheme of jointly optimizing sensing time, energy detector threshold and the number of cooperative users is proposed based on the related theory analysis. From the simulations, it can be found that the optimal sensing time is only about half of that consumed in single user sensing, and the proposed scheme has significant improvement in energy efficiency.

Virtual multiple-input-multiple-output (MIMO) systems using multiple antennas at the transmitter and a single antenna at each of the receivers have recently emerged as an alternative to point-to-point MIMO systems. This paper investigates the relationship between energy efficiency (EE) and spectral efficiency (SE) for a virtual-MIMO system that has one destination and one relay using compress-and-forward (CF) cooperation. To capture the cost of cooperation, the power allocation (between the transmitter and the relay) and the bandwidth allocation (between the data and cooperation channels) are studied. This paper derives a tight upper bound for the overall system EE as a function of SE, which exhibits good accuracy for a wide range of SE values. The EE upper bound is used to formulate an EE optimization problem. Given a target SE, the optimal power and bandwidth allocation can be derived such that the overall EE is maximized. Results indicate that the EE performance of virtual-MIMO is sensitive to many factors, including resource-allocation schemes and channel characteristics. When an out-of-band cooperation channel is considered, the performance of virtual-MIMO is close to that of the MIMO case in terms of EE. Considering a shared-band cooperation channel, virtual-MIMO with optimal power and bandwidth allocation is more energy efficient than the noncooperation case under most SE values.

The Device-to-Device (D2D) communication system is an important part of heterogeneous networks. It has great potential to improve spectrum efficiency, throughput and energy efficiency cooperation of multiple D2D users with the advantage of direct communication. When cooperating, D2D users expend extraordinary energy to relay data to other D2D users. Hence, the remaining energy of D2D users determines the life of the system. This paper proposes a cooperation scheme for multiple D2D users who reuse the orthogonal spectrum and are interested in the same data by aiming to solve the energy problem of D2D users. Considering both energy availability and the Signal to Noise Ratio (SNR) of each D2D user, the Kuhn-Munkres algorithm is introduced in the cooperation scheme to solve relay selection problems. Thus, the cooperation issue is transformed into a maximum weighted matching (MWM) problem. In order to enhance energy efficiency without the deterioration of Quality of Service (QoS), the link outage probability is derived according to the Shannon Equation by considering the data rate and delay. The simulation studies the relationships among the number of cooperative users, the length of shared data, the number of data packets and energy efficiency.

In a cognitive radio ad hoc network (CRAHN), the secondary user node has a restricted range for sensing other users within the network. To enhance the performance of nodes (secondary and primary users) in a CRAHN, the problem of limited channel sensing range and interference should be reduced which would enable the simultaneous transmission of primary and secondary users. In this paper, a cooperative MAC (medium access control) protocol is proposed—MLCP, which exploits the concept of multichannel network, for obtaining high system throughput. The main purpose of the proposed protocol is to introduce cooperation among secondary users, using the common control channel. The presence of the primary user is detected and the information is shared among neighbouring secondary users within the network proactively. This protocol is simulated using Network Simulator 2 and analysed with respect to system throughput, packet delivery ratio, and end-to-end delay experienced by primary users. The performance of the proposed protocol is also compared with existing cooperative MAC protocol and non-cooperative MAC protocol network scenario.

Cooperative communication with spectrum sharing has been proved to be an efficient way to reduce energy consumption. In this work, an optimal power allocation algorithm is proposed for mobile cooperative beamforming networks, where several secondary users (SUs) collaboratively act as relay nodes to assist the transmission of a primary user (PU) in reward of transmitting their own information. The optimal power allocation scheme is obtained by solving a convex optimization problem aiming at minimizing network energy consumption with guaranteed quality of service (QoS) for both PU and SUs. Simulation results show that with the consideration of overheads, circuit power, and energy costs for message sharing, a cooperative beamforming network augmented with our proposed power allocation algorithm performs better in scenarios of more SUs in terms of higher energy efficiency and less power consumption per unit throughput. Additionally, the proposed scheme outperforms the conventional best-relay selection approach in the aspects of power consumption per unit throughput, energy efficiency, and network power consumption. Furthermore, simulations also demonstrate that the derived cooperative beamforming algorithm exhibits superior performance in energy saving than conventional methods in high-speed scenarios.

This paper studies the challenge of efficient resource allocation in wireless networks by investigating the potential benefits of cognitive radio networks (CRN) and non‐orthogonal multiple access (NOMA) techniques. Specifically, the purpose of this paper is to explore user pairing and power allocation in a multiuser cooperative CRN‐NOMA network. To achieve the goal, a novel user pairing algorithm with reinforcement learning (RL) is proposed to adaptively select the optimal group for pairing according to the factors such as channel gain, distances, and angles. A user group is consisted of a primary user (PU) and up to three secondary users (SUs). The key findings of this paper highlight the effectiveness of RL in optimizing power allocation while considering various constraints. As a result, significant improvements in network throughput and user fairness are achieved. Compared with the classical power allocation algorithms (i.e., equal power allocation, proportional power allocation, iterative power allocation, and iterative water‐filling power allocation), the proposed power allocation based on RL algorithm and iterative water‐filling power allocation algorithm have better performance in terms of throughput, fairness, spectrum efficiency, and energy efficiency. In addition, compared with the iterative water‐filling power algorithm, the proposed algorithm improves the throughput by 31.71% and obtains twice the spectrum efficiency and energy efficiency. The simulation results consistently show that the proposed method has advantages in enhancing PU capacity. Further advancements in this field of research are encouraged based on the promising outcomes of this study.

The performance of cooperative spectrum sensing in cognitive radio (CR) networks depends on the sensing mode, the sensing time and the number of cooperative users. In order to improve the sensing performance and reduce the interference to the primary user (PU), a periodic cooperative spectrum sensing model based on weight fusion is proposed in this paper. Moreover, the sensing period, the sensing time and the searching time are optimized, respectively. Firstly the sensing period is optimized to improve the spectrum utilization and reduce the interference, then the joint optimization algorithm of the local sensing time and the number of cooperative users, is proposed to obtain the optimal sensing time for improving the throughput of the cognitive radio user (CRU) during each period, and finally the water-filling principle is applied to optimize the searching time in order to make the CRU find an idle channel within the shortest time. The simulation results show that compared with the previous algorithms, the optimal sensing period can improve the spectrum utilization of the CRU and decrease the interference to the PU significantly, the optimal sensing time can make the CRU achieve the largest throughput, and the optimal searching time can make the CRU find an idle channel with the least time.

ABSTRACTThere are many studies in cognitive radio networks to find the best transmission time for secondary users (SUs) to maximize transmission or energy efficiency. Increasing sensing duration leads to higher sensing accuracy and less interference. However, it decreases SUs' transmission time and increases energy consumptions for spectrum sensing. Conversely, longer transmission duration results in more opportunities for SUs to access the channel and reasonably higher interference with primary user (PU) due to the higher probability of PU returns. This paper presents an analytical framework based on the renewal process for SU spectrum access. Next, the amount of the interference between PU and SU that is due to the PU returns is derived analytically. The probability of the collision and the transmission efficiency are formulated to obtain the optimum transmission time.

We investigate the achievable sum rate and energy efficiency of zero-forcing precoded downlink massive multipleinput multiple-output systems in Ricean fading channels. A simple and accurate approximation of the average sum rate is presented, which is valid for a system with arbitrary rank channel means. Based on this expression, the optimal power allocation strategy maximizing the average sum rate is derived. Moreover, considering a general power consumption model, the energy efficiency of the system with rank-1 channel means is characterized. Specifically, the impact of key system parameters, such as the number of users N, the number of BS antennas M, Ricean factor K and the signal-to-noise ratio (SNR) ρ are studied, and closed-form expressions for the optimal ρ and M maximizing the energy efficiency are derived. Our findings show that the optimal power allocation scheme follows the water filling principle, and it can substantially enhance the average sum rate in the presence of strong line-of-sight effect in the low SNR regime. In addition, we demonstrate that the Ricean factor K has significant impact on the optimal values of M, N and ρ.

The problem of cognitive access of parallel channels occupied by primary users is considered. The transmissions of primary users are modeled as independent continuous-time Markov processes. A secondary cognitive user employs a slotted transmission format and a periodic sensing strategy such that it decides if and where to transmit according to its sensing outcomes. The objective of the cognitive user is to maximize its throughput while satisfying collision constrains imposed by the primary users. Three access policies are analyzed. The optimal access policy is obtained based on a formulation of constrained Markov decision processes. A simple suboptimal memoryless policy is obtained by the use of instantaneous sensing outcome. As an upper bound of the optimal policy, a memoryless policy assuming the access of full channel states is also considered. For a symmetric system, we show that the simple memoryless policy is asymptotically optimal when the collision constraint is tight by proving that the performances of all three policies converge to the same value.

Cognitive radio methodologies have the potential to dramatically increase the throughput of wireless systems. Herein, control strategies which enable the superposition in time and frequency of primary and secondary user transmissions are explored in contrast to more traditional sensing approaches which only allow the secondary user to transmit when the primary user is idle. In this paper, the optimal transmission policy for the secondary user when the primary user adopts a retransmission-based error control scheme is investigated. The policy aims to maximize the secondary users' throughput, with a constraint on the throughput loss and failure probability of the primary user. Due to the constraint, the optimal policy is randomized, and determines how often the secondary user transmits according to the retransmission state of the packet being served by the primary user. The resulting optimal strategy of the secondary user is proven to have a unique structure. In particular, the optimal throughput is achieved by the secondary user by concentrating its transmission, and thus its interference to the primary user, in the first transmissions of a primary user packet. The rather simple framework considered in this paper highlights two fundamental aspects of cognitive networks that have not been covered so far: 1) the networking mechanisms implemented by the primary users (error control by means of retransmissions in the considered model) react to secondary users' activity; 2) if networking mechanisms are considered, then their state must be taken into account when optimizing secondary users' strategy, i.e., a strategy based on a binary active/idle perception of the primary users' state is suboptimal.

In cooperative cognitive radio networks (CCRN), secondary users (SUs) relay data for primary users (PUs) for the redounding spectrum opportunity from PUs. Furthermore, financial and environmental considerations make the green communication as a new trend in the optimization of CCRN. In this paper, a new power and time allocation scheme is proposed for green CCRN. The problem of minimizing the overall energy consumption under service quality constraints is modeled. And a joint power and time allocation strategy is proposed for the problem. Extensive simulation results demonstrate that the proposed scheme significantly outperforms the existing single-SU cooperative scheme on energy saving.

The sixth generation (6G) is expected to be a crucial technology that will enable the pervasive and seamless connectivity of a large count of Internet-of-Things (IoT) devices. The need to alleviate the worry of lowering energy consumption, i.e., green communication, is fueled by the extraordinarily fast data rate, low end-to-end delay, and high mobility of IoT devices. Sensor-enabled IoT relies heavily on green communication. Energy is at the centre of the smart IoT application that allows the sensors to function. The sensors' ability to work efficiently is hampered by rapid energy loss. To prevent the quicker loss of energy from sensors in the IoT, an energy-efficient system is necessary. Since heuristic approaches are not suited for such issues and may turn into NP-hard problems in the case of dense area sensor networks, meta-heuristic approaches are most successful in solving these kinds of problems with near-optimal solutions. Extending the longevity as well as stability of WSNs via energy efficiency is a difficult task. Clustering has increased energy efficiency by selecting Cluster Heads (CHs), but its application is yet difficult. The areas where CHs are desirable are initially sought in traditional CH selection algorithms. This paper plans to design and develop a new framework of Green IoT communication via a new, improved meta-heuristic model. Here, a novel energy-aware objective function is derived based on the constraints like “distance, delay, energy, transmission energy, reception energy, and data aggregation energy”. The Adaptive Random Variable-based Tunicate Swarm Algorithm (ARV-TSA) is adopted for performing the novel green communication in IoT. The experimentation is performed for the parametric data from Texas for “temperature sensor, humidity sensor, Thermistor, ultrasonic sensor with minimum voltage, maximum voltage, minimum current, maximum current etc”. The simulation findings have demonstrated a substantial increase in energy conservation and network stability period improvement when compared to identical methods.