Systems, methods, and devices to enable range extension for wireless communication in sub-gigahertz bands are described herein. In some aspects, portions of packets are repeated in time and/or space when transmitting the packet in order to increase the chance that the packet can be decoded. The repetition of portions of the packet may be based, in part, on the channel characteristics of the channel over which the packet is sent. In some aspects, portions of packets are transmitted over a smaller bandwidth with increased power per frequency range.

Systems and methods for range extension of wireless communication in sub-gigahertz bands

The invention relates to a single-target multi-production line scheduling control method based on a particle swarm optimization (PSO)-genetic algorithm (GA) mixing optimization algorithm. The method comprises the following steps that: step one, selectable production lines of different workpieces, corresponded preparation time for processing of a first batch of workpieces, die changing time of thedifferent workpieces on the selectable production lines, processing time of the different workpieces on the selectable production lines; a single objective function and a processing batching strategyare input; step two, random coding is employed and operating parameters are initialized; step three, a population P(t) is initialized; step four, the P(t) is segmented into two subpopulations: A and B subpopulations, wherein the two subpopulations have equal particle sizes; step five, the number of times of iteration is set; step six, PSO operation based on SA local improvement is carried out on a particle swarm P1(t) of the A population; step seven, GA operation based on the SA local improvement is carried out on a particle swarm P2(t) of the B population; step eight, information sharing is carried out on the A population and the B population; and step nine, it is determined whether a terminal condition is satisfied; if so, an optimal individual is output. According to the invention, scheduling optimization is realized.

Single-target multi-production line scheduling control method based on PSO-GA mixing optimization algorithm

Particle swarm optimization algorithm based cooperative spectrum sensing optimization method

The invention discloses a channel ambiguity removing method in the MIMO signal blind detection process. The method is characterized by including the first step of dividing a channel matrix of an MIMO system into channels of Mt SIMO systems, the second step of intercepting received signals of each SIMO system, the third step of estimating the channels of each SIMO system through a subspace blind channel estimation algorithm based on second-order statistics according to the intercepted received signals, the fourth step of estimating the obtained channels and the received signals of each SIMO system and balancing out a training sequence of each transmitting antenna through a zero-forcing mode, and the fifth step of utilizing the training sequence obtained through balancing in a first frame and the intercepted signals to carry out channel estimation on subsequent data blocks, enabling the channels of the Mt SIMO systems to form the channel matrix of the MIMO system and obtaining the channel estimation value of the MIMO system. The channel ambiguity removing method has the advantages of being fewer in calculated quantity and low in complexity, eliminating sorting ambiguity, reducing normalization root-mean-square errors and the error rate of the system and improving the reliability of a non-cooperative communication system.

Channel ambiguity removing method in MIMO signal blind detection process

The invention discloses a signal detection and estimation method based on a multi-dimensional characteristic neural network, and relates to the technical fields of artificial neural networks and direct sequence spread spectrum (DSSS) communication. According to the method, the presence of a DSSS signal is detected by using a correlation function accumulation method, and the estimation of a PN (Pseudo-Noise) code is finished, namely, a direct spread signal spread spectrum code is detected under the situation of low signal-to-noise ratio based on a multi-dimensional characteristic neural network method. The method comprises the following steps: extracting the multi-dimensional characteristics of a signal (comprising the period of the PN code, the width of a code element, the starting and stopping moments of the synchronization of the PN code and an information code, and the like); periodically segmenting the signal; inputting the segmented signals into the neural network in batches for training; and training and converging the segmented signals through the neural network to detect a spread spectrum sequence. Through adoption of the method, a signal can be detected from noise and a PN sequence of the signal can be estimated in non-cooperative communication through an artificial neural network; and moreover, the network can quickly converge to a sequence or a reverse sequence thereof under the situation of low signal-to-noise ratio.

Signal detection and estimation method based on multi-dimensional characteristic neural network

This invention discloses a relative channel estimation method of MIMO based on self-adaptive training sequence. The process is that, the receptor determines the best length of the training sequence according to relative known information of the channel and transfers the value of the length and relative information of the channel through feedback link to the transmitter. The transmitter uses this feedback information to compute the optimal training sequence correspondent to current state of the channel according to the training sequence expression St=UD1/2tU*t designed by this model and transfers this optimal training sequence to the receptor through the forward link. Then the training cycle begins and the transmitter launches the training sequence to wireless channel. The receptor estimates the channel parameters of current time according to the known training sequence and the receipted signal in the training cycle and by using the minimum mean square error estimation criteria. The training sequence designed by this invention can make self-adaptive adjustment according to the needs of the actual system and the change of relative information of the channel. The invention has advantages of high estimation performance and strong robustness, thus can be used in wireless communication system with muti-antenna MIMO.

Method for estimating MIMO related channel based on self-adaptive training sequence

This invention discloses a small wave packet multi-carrier amplifying-frequency system based on neural net balancer and its controlling method. The system comprises a transmitting end composed of a quad-rature modulator, a copier, an inverse small wave packet conversion with an amplifying frequency code inserted before; a receiving end composed of multi-radial neural net balance, discrete small wave packet conversion, summer, a maximum likelihood tester and quad-rature modulator; after the conversion, inserting amplifying frequency code WH and sub-belt gain factor. The system owes a control switch to control its dislocation to realize using LMS method to renewing weight of multiradial neural net balancer and the communication of the sender and receiver, including channel balance, amplifying frequency, small wave packet multi-carrier adjusting, modulation and ML testing decision.

Wavelet packet multi-carrier spread-spectrum system and control method based on neural net equalizer

The invention discloses a detection method of a communication system with the space-time encoding in vertical division, which detects the communication system with the space-time encoding in vertical division by using an improved optimization algorithm of particle clusters, computes a fitness function of the optimization algorithm of particle clusters by using an ML detection formula that F(s) equals II r minus Hs II , while s is (s1, s2,..., sM) and belongs to the Omega space, and achieves the goal of acquiring the detection performance which is close to that of the ML method with low complexity. The implementation of the detection method comprises the following steps: first step, the signal received by a baseband and a channel estimation value are input into a detector, algorithm parameters are set, and the speed and the position of the particles are initialized; second step, according to the detection formula, the fitness of the particles is computed, and an individual extreme value and a global extreme value are initialized; third step, the speed and the position of the particles are updated; fourth step, a variation operation is implemented on a position vector; fifth step, the individual extreme value and the global extreme value are updated; sixth step, the method determines whether a largest iteration number is reached, if yes, the method outputs the global extreme value which is the final detection value; if not, the method turns to the third step. The algorithm used by the invention has simplicity, low computation complexity, quicker computation speed and higher computation precision.

A detecting method on vertical layered time space code message system

Considering multiple input/ output communication system (MIMO) as composed of multiple sub single input/ output systems (SISO), the invention resolves issue of estimating parameters of MIMO system as estimating parameters of SISO system with interference. The disclosed method includes steps: approach of iteration interference cancellation is adopted for estimating parameters of SISO system with interference, i.e. after channel estimation and frequency deviation estimation, interference estimation is carried out first; then, using the approach of iteration interference cancellation carries out estimation for channel gain and frequency deviation, according to presetting number, iteration is carried out repeatedly so as to obtain final estimated value of channel gain and frequency deviation. Comparing the existed methods, the disclosed method possesses features of low complexity, high transmission efficiency, and low cost.

Interactive parameter estimation method of multi-input and multi-output system

The transmitting end (TE) in system includes encoder, interlacing device (ID), parallel to serial converter (PSC), modulator, and time space convertor (TSC). The receiving end (RE) includes soft input/output detector, decoder, ID, de-ID, PSC, TSC, and reverse TSC. Being time-space mapped to different antennae, unitive encoded sequence at TE is transmitted out after independent encoding is carried out again. Unitive encoder (UE) and branch encoder (BE) are cascaded by using similar Turbo encoding mode. After being tested by the soft output, the signal received by RE is de-multiplexed into prior info of code word for each BE and prior info of check bit for UE. After soft decoding for each branch sequence, BE outputs additional info of info bit, which is as prior info of info bit for UE. Soft decoding is carried out for the said prior info to output final judgment. Features are: low error rate, flexible encoding. The invention is applicable to MIMO comm system in high speed.

Jiandong Li

Papers

Method for estimating MIMO related channel based on self-adaptive training sequence

Wavelet packet multi-carrier spread-spectrum system and control method based on neural net equalizer

A detecting method on vertical layered time space code message system

Interactive parameter estimation method of multi-input and multi-output system

BLAST transceiving system