The enhanced eigenvalue decomposition （EEVD） based channel estimation algorithm, which could solve the pilot contamination problem in massive multiple-input and multiple-output （Massive MIMO） channel estimation when the number of antennas at base stations （NABT） tends to infinity, is proposed in this paper. The algorithm is based on the close relationship between covariance matrix of received pilot signal and the channel fast fading coefficient matrix, i.e. the latter is the eigenvector matrix of the former when NABT tends to infinity. Therefore, we can get a set of normalized base vectors from the eigenvalue decomposition （EVD） of sample covariance matrix in practical Massive MIMO networks. By multiplying the received pilot signal with conjugate transpose of normalized base vector matrix, the channel matrix is projected to a lower dimensional matrix, and the intra-cell and inter-cell interference can be eliminated completely when NABT tends to infinity. Thus, we only need to estimate the lower dimensional projected matrix during the channel estimation. Simulation results show that the mean square error （MSE） performance of channel estimation is improved with approximately two orders of magnitude when the signal-to-noise ratio （SNR） is 40 dB, compared with EVD based channel estimation algorithm. And the signal-to-interference ratio （SIR） is improved greatly as well. The increment of SIR becomes larger and larger as SNR increasing.
Filter bank multi-carrier （FBMC） with offset quadrature amplitude modulation （OQAM） has been regarded as one of the candidates for next generation broadband wireless communication systems. Being a multi-carrier technique, FBMC suffers from the inherent drawback of high peak-to-average power ratio （PAPR）. And it has been turned out that conventional PAPR reduction schemes for orthogonal frequency division multiplexing （OFDM） systems are ineffective for FBMC-OQAM systems, due to the overlapping structure of FBMC-OQAM signals. In this paper, we propose an efficient PAPR reduction scheme based on a two-step optimization structure, named pretreated partial transmit sequence （P-PTS）. The first step uses a multiple overlapping symbols joint optimization scheme that the phase rotation sequences for current symbol is determined and optimized according to previous overlapped symbols. And in the second step, it employs a novel segment PAPR reduction scheme based on PTS technique. Simulation results indicate that the proposed P-PTS scheme can achieve better PAPR reduction performance than conventional methods with lower computational complexity and the complexity can be traded off more flexibly with PAPR reduction performance.
The maximum traffic intensity supported by a low earth orbit（LEO） mobile satellite system（MSS）（LEO-MSS） is important in practical application for providing satisfactory service. An analytical approach is proposed for determining the maximum traffic intensity of guaranteed handover（GH） scheme and channel complete sharing（CCS） scheme in LEO-MSS under quality of service（Qo S） constraints. By evaluating performance of these two schemes, the relationship between the traffic intensity and the Qo S constraints is established. The expressions of maximum traffic intensity of GH scheme and CCS scheme are deduced. Compared with the traditional method, the proposed analytical approach is more computationally efficient owing to the needlessness of the repeated iteration calculation. It also avoids the complex choice of the initial value of new call traffic intensity and its increment. Lastly, the accuracy and validity of the analysis approach have been verified by computer simulations.
Capacity analysis is a fundamental and essential work for evaluating the performance of cognitive wireless mesh network （CWMN） which is considered a promising option for the future network. Power control is an efficient way to avoid interference and improve capacity of wireless mesh networks. In this paper, a quantitative result of the per-node average throughput capacity of CWMN with power control is deduced for the first time, which is much helpful for understanding the limitations of CWMN. Firstly, under the large-scale channel fading model and protocol interference model, a closed-form expression for the maximum channel capacity of each node with power control is presented, under the constraint that the interference tolerated by the primary users （PUs） does not exceed a threshold. And then, with the deduced channel capacity result, the per-node average throughput capacity of CWMN is derived based on two regular topologies, i.e. square topology and triangle topology. The simulation results indicate that the capacity is effectively improved with power control, and affected by topology, tolerated interference threshold, the number of cognitive users （CUs） and primary users （PUs）.
The multi-radio multi-channel wireless mesh network （MRMC-WMN） draws general attention because of its excellent throughput performance, robustness and relative low cost. The closed interactions among power control （PC）, channel assignment （CA） and routing is contributed to the performance of multi-radio multi-channel wireless mesh networks （MRMC-WMNs）. However, the joint PC, CA and routing （JPCR） design, desired to achieve a global optimization, was poor addressed. The authors present a routing algorithm joint with PC and CA （JPCRA） to seek the routing, power and channel scheme for each flow, which can improve the fairness performance. Firstly, considering available channels and power levels, the routing metric, called minimum flow rate, is designed based on the physical interference and Shannon channel models. The JPCRA is presented based on the genetic algorithm （GA） with simulated annealing to maximize the minimum flow rate, an non-deterministic polynomial-time hard （NP-Hard） problem. Simulations show the JPCRA obtains better fairness among different flows and higher network throughput.
