In a full-duplex multi-cognitive relay network supported by Simultaneous Wireless Information and Power Transfer （SWIPT）， in order to maximize energy-spectrum efficiency， the relay with the maximum energy harvesting was selected for decoding and forwarding， thus forming an energy-spectrum efficiency trade-off optimization problem. The problem was transformed into a convex optimization problem by variable transformation and concave-convex process optimization method. When the trade-off factor was 0， the optimization problem was equivalent to the optimization problem of maximizing the Spectrum Efficiency （SE）. When the trade-off factor was 1， the optimization problem was equivalent to the problem of minimizing the energy consumed by the system. In order to solve this optimization problem， an improved algorithm that could directly obtain the trade-off factor for maximizing Energy Efficiency （EE） was proposed， which was optimized by combining the source node transmit power and the power split factor. The proposed algorithm was divided into two steps. First， the power split factor was fixed， and the source node transmit power and trade-off factor that made the EE optimal were obtained. Then， the optimal source node transmit power was fixed， and the optimal power split factor was obtained by using the relationship between energy-spectrum efficiency and power split factor. Through simulation experimental results， it is found that the relay network with the maximum energy harvesting is better in EE and SE than the network composed of other relays. Compared with the method of only optimizing the transmit power， the proposed algorithm increases the EE by more than 63%， and increases the SE by more than 30%； its EE and SE are almost the same as the exhaustive method， and the proposed algorithm converges faster.

For relayed collaborative communications have weak signal of direct paths between the transmitter and the receiver and low Signal-to-Noise Ratio （SNR）， a Reconfigurable Intelligent Surface （RIS） assisted cooperative Index Modulation （IM） system of Decode-and-Forward （DF） relay （RIS-DF-IM） was proposed. In RIS-DF-IM， as smart Access Points （APs）， RISs were adopted as part of the transmitter at the source and relay nodes to perform phase compensation for the reflected channel to maximize the receiving antenna SNR according to the transmission information， and perform IM on multiple antennas of receivers of the relay and destination nodes to improve the spectral efficiency of the system. At the same time， the theoretical union bounds about the Bit Error Rate （BER） of the proposed dual-hop system were solved by using the Moment Generating Function （MGF） method. Besides， a Simplified Pre-greedy Maximum Likelihood （SPML） detector was proposed to reduce the computational complexity by decreasing the number of traversal antenna indexes and simplifying the Maximum Likelihood （ML） decoding criterion formula. Monte Carlo simulation results show that， when the number of RIS elements is 128 and the spatial modulation is adopted， the BER of RIS-DF-IM is about 10 lower than that of the cooperative spatial modulation system where RIS is not connected to the transmitter at the far end； and the BER is dramatically decreased by about 20 compared with the traditional precoded spatial modulation system. Although SPML detector has the BER increased by about 1.4 compared to the Maximum Likelihood （ML） detector， the computational complexity is reduced by a half， achieving an effective balance between BER and complexity.

In order to optimize the Energy Efficiency （EE） and Spectrum Efficiency （SE） of Decode-and-Forward （DF） full-duplex relay network， a trade-off method of EE and SE for DF full-duplex relay network was proposed. In full-duplex relay network， firstly， the EE of the network was optimized with the goal of improving the SE of the network. And the optimal power of the relay was obtained by combining the derivation and the Newton-Raphson method， then the Pareto optimal set of the objective function was given. Secondly， a trade-off factor was introduced through the weighted scalar method， a trade-off optimization function of EE and SE was constructed， and the multi-objective optimization problem of EE optimization and SE optimization was transformed into a single-objective energy-spectrum efficiency optimization problem by using normalization. At the same time， the performance of EE， SE and trade-off optimization under different trade-off factor was analyzed. Simulation results show that the SE and EE of the proposed method are higher at the same data transmission rate compared with the those of the full-duplex-optimal power method and the half-duplex-optimal relay-optimal power allocation method. By adjusting different trade-off factors， the optimal trade-off and the optimization of EE and SE can be achieved.

Aiming at the multi-dimensional resource allocation problem in the downlink heterogeneous cognitive radio Ultra-Dense Network （UDN）， an improved genetic algorithm was proposed to jointly optimize user association and resource allocation with the objective of maximizing the throughput of femtocell users. Firstly， preprocessing was performed before the algorithm running to initialize the user’s reachable base stations and available channels matrix. Secondly， symbol coding was used to encode the matching relationships between the user and the base stations as well as the user and the channels into a two-dimensional chromosome. Thirdly， dynamic choosing best for replication + roulette was used as the selection algorithm to speed up the convergence of the population. Finally， in order to avoid the algorithm from falling into the local optimum， the mutation operator of premature judgment was added in the mutation stage， so that the connection strategy of base station， user and channel was obtained with limited number of iterations. Experimental results show that when the numbers of base stations and channels are fixed， the proposed algorithm improves the total user throughput by 7.2% and improves the cognitive user throughput by 1.2% compared with the genetic algorithm of three-dimensional matching， and the computational complexity of the proposed algorithm is lower. The proposed algorithm reduces the search space of feasible solutions， and can effectively improve the total throughput of cognitive radio UDNs with lower complexity.

Aiming at the influence of the time consumption of the information transmission process and the channel estimation error on the energy efficiency of the network， a joint optimization method of full-duplex cognitive relay network based on nonlinear energy harvesting was proposed. The proposed method was based on the use of nonlinear energy harvesting in the relay and consideration the imperfect Channel State Information （CSI）. Firstly， the energy efficiency non-convex optimization problem was transformed into two convex sub-optimization problems to obtain the transmission power of the secondary user and the relay as well as the collected energy. Secondly， under the condition that the primary user interference threshold was guaranteed and the optimal transmission power was non-negative， the range of transmission channel capacity was obtained. Finally， the transmission power was substituted into the expression to obtain the time related objective function， and the Hessian matrix was used to prove that the objective function was a convex function， the optimal transmission time and power splitting factor were calculated， and the optimal solution of energy efficiency was obtained. Experimental results show that under the same conditions， the energy efficiency of the proposed joint optimization method is about 84.3% higher than that of only optimizing the transmission power. At the same time， it is verified that when the channel estimation error factor is 0.01， the energy efficiency of the network is reduced by about 1.9% by using the proposed method.