CN115550121A - Novel UFMC system peak-to-average power ratio inhibition scheme - Google Patents
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Abstract
The invention discloses a novel UFMC system peak-to-average power ratio suppression scheme, and belongs to the technical field of wireless mobile communication. The scheme comprises the following steps: obtaining a signal sequence X o (k) Carrying out quadrature amplitude modulation, constructing an immune programming genetic algorithm, and searching to obtain an optimal phase rotation vector factor alpha (k); performing dot product, zero insertion, IFFT and FFT operations on alpha X (k) and frequency domain sample value X (k) to obtain time domain vector X b (n) mixing x b (n) a transit length of L f Filter f of b And (n) accumulating after processing to obtain a final signal s (n) of the transmitting end. The invention adopts clone operation, and effectively prevents the local optimum and avoids premature convergence by carrying out robust search in the whole solution space; and by adopting a population screening operation, the diversity of the population is further increased by reserving and continuously optimizing certain non-excellent particles, so that the aim of reducing the PAPR is fulfilled.
Description
Technical Field
The invention relates to the technical field of wireless mobile communication, in particular to a novel UFMC system peak-to-average power ratio suppression scheme.
Background
In the face of the internet era of immense change, requirements for communication technology are enhanced in a leap-forward manner, mass equipment is accessed, large-scale machine type communication is achieved, and requirements for shorter time delay, faster transmission speed and larger communication capacity are basic requirements for wireless communication application. The synchronization requirement of the Orthogonal Frequency Division Multiplexing (OFDM) technology is strict, and inherent system defect factors such as low spectrum utilization rate caused by guard intervals cannot meet the diversification requirement of modern people on wireless communication. The universal filter multi-carrier (UFMC) technology is a novel multi-carrier modulation technology, and has the advantages of enhanced mobile broadband, ultra-high reliability, low time delay, fragmented spectrum access, and the like. The method adopts filtering based on sub-bands, sub-carriers input by a system are divided into B sub-bands, each sub-band is respectively subjected to Discrete Fourier Transform (IDFT), and time domain signals of the sub-bands filtered by a filter are superposed to obtain a sending signal. The UFMC system has better sidelobe suppression effect, and has the advantages of high transmission rate, strong Frequency deviation (CFO) interference resistance, high Frequency spectrum phase ratio and the like. The UFMC system also integrates the advantages of the OFDM system and the FBMC system, continuously distributes sub-carriers, adapts and adjusts the length of the sub-band according to different requirements of a use scene, and can well meet the requirements of different services of daily life of people, so that the UFMC system is widely researched and concerned, and the UFMC is a good alternative waveform of a 5G system and is also a novel multi-carrier system for future mobile communication.
However, UFMC is still a multi-carrier modulation technique in nature, so it has a problem of peak-to-average power ratio (PAPR) as OFDM. The signal of the UFMC is based on the characteristic of sub-band filtering, and the UFMC system can make the peak value of a time domain signal larger due to the superposition of sub-carriers in a sending sub-band, so that a higher peak-to-average ratio is generated. The structural characteristics of the two systems are different, which shows that for the PAPR research of the OFDM system, the UFMC system has certain reference significance and cannot be completely applied to the UFMC system. Therefore, if the PAPR suppression scheme in the conventional OFDM is directly applied to the UFMC system, it is difficult to obtain a desired suppression effect. At the same time, the efficiency of the power amplifier is further reduced by too high PAPR value, the signal enters the nonlinear region of the high power amplifier, thereby causing nonlinear distortion, and the complexity of a/D and D/a conversion is increased. Therefore, it is necessary to adopt an effective method at the transmitting end to reduce the PAPR value to improve the performance of the UFMC system.
Aiming at the problem of high PAPR in the UFMC system, students in various countries around the world have proposed corresponding improvement schemes. The method mainly includes amplitude limiting, companding, selective mapping (SLM), partial Transfer Sequence (PTS), and the like, wherein the most common methods include the following types: 1. a signal distortion suppression technique for clipping a transmission signal by using nonlinearity before the signal enters the HPA; 2. limiting data sequence signals for transmission, and only selecting coding classes with smaller code elements and PAPRs for transmission; 3. and carrying out different scrambling processing on the input signals, and selecting the signal with the minimum PAPR as the probability class of the transmitted signals. However, although the principle is simple and intuitive, the method 1 inevitably causes distortion of signals; although the method 2 is a linear transformation process, the calculation complexity is very high when the number of subcarriers is large, and the information transmission rate is also greatly reduced; the SLM and PTS schemes in method 3 are also a signal non-distortion scheme, but it overcomes the problems of computation complexity and information transmission rate in method 2 to some extent, and thus has gained much attention.
At present, most of PAPR suppression schemes in UFMC proposed at home and abroad are improved on the basis of the traditional OFDM suppression scheme, and the PAPR suppression scheme integrated with an artificial intelligence algorithm is rarely available.
Disclosure of Invention
Aiming at the problems, on the basis of an SLM (selective mapping) scheme based on the current genetic algorithm, the invention further improves the capability of the genetic algorithm for searching the global optimal rotation vector factor and effectively overcomes the problem of population degradation of the genetic algorithm, thereby achieving the purpose of further reducing the PAPR (peak-to-average power ratio) of the UFMC system.
A new UFMC system peak-to-average ratio suppression scheme, comprising the steps of:
(1) Obtaining a signal sequence: a sequence generator at a transmitting end transmits a group of subcarrier frequency domain sequence groups X of N sequences o (k) And is recorded as: x o (k)＝[X o (0),X o (1),...,X o (N-1)],0≤k≤N-1；
(2) Signal modulation: to X o (k) Quadrature amplitude modulation is performed, and the sequence X (k) after modulation is expressed as: x (k) = [ X (0), X (1),.., X (N-1)]；
(3) Constructing an immune programming genetic algorithm, and searching to obtain an optimal phase rotation vector factor alpha (k);
(4) Performing dot multiplication on the solved optimal phase rotation vector factor alpha (k) and a frequency domain sample value X (k) to obtain an output sequence X μ (k)：
X μ (k)＝<X(k)·α * (k)>＝[X(0)α * (0),X(1)α * (1),...,X(N-1)α * (N-1)]
Wherein < x > represents a point multiplication between two vectors;
(5) At X μ (k) Respectively inserting (L-1) N/2 zeros to prevent generation of spurious signals, obtaining X o μ (p)：
(6) To X o μ (p) performing an IFFT operation to obtain x o μ (n)：
(7) For x o μ (n) performing SLM algorithm processing, and selecting a sequence with the minimum PAPR value as an output sequence, and recording as x (n);
(8) FFT operation is carried out on X (n) to obtain a frequency domain signal X o * (k) (ii) a Will be provided withX o * (k) The subband signal X is obtained after division into B subbands b * (k) (B is more than or equal to 1 and less than or equal to B) and is recorded as:
wherein k is the number of subcarriers, b is the number of subbands;
(9) To X b * (k) Performing IFFT operation to obtain time domain vector x b (n)：
(10) X is to be b (n) a transit length of L f Filter f of b And (n) accumulating after processing to obtain a final signal s (n) of the transmitting end:
where denotes convolution.
Further, the immune planning genetic algorithm in step 3 comprises:
(3.1) carrying out initialization operation aiming at the initial population A (k) in the genetic algorithm to obtain the phase rotation vector factor alpha (t) (k) And its fitness value function fit (alpha) (t) (k) In which: t is the number of iterations;
(3.2) for the phase rotation factor α (t) (k) According to its fitness value function fit (alpha) (t) (k) In ascending order of size) to yield:
A(k)＝{A 1 (t),A 2 (t),…,A i (t),…,A N (t)},1≤i≤N；
(3.3) sorting the particles A i (k) Replication q i (k) Then, cloning operation is performed to obtain Y i (k)；
(3.4) against population Y after cloning operation i (k) Carry out cross weightingGroup (iv) to obtain Z i (k)；
(3.5) against Z after Cross-recombination i (k) Performing population screening to obtain A i ′(k)；
(3.6) judging whether the maximum iteration number is reached, if not, returning to the step (3.1); otherwise, calculating the optimal phase rotation factor alpha (k) at the moment:
the IFFT {. Is used for representing inverse fast Fourier transform, the argmin {. Is used for representing the independent variable value corresponding to the function when the function obtains the minimum value, and L is an oversampling factor.
Further, step 3.1 comprises:
(3.1.1) population mapping operation: mapping the N particles in the initial population a (k) into the form of the phase rotation vector factor to obtain:
A(k)→α (t) (k)＝[α (t) (0),α (t) (1),...,α (t) (N-1)],1≤t≤T
α (t) (k)∈{-j,-1,j,1}→{(0,0),(0,1),(1,0),(1,1)}
(3.1.2) rotating the vector factor α according to the phase (t) (k) And calculating the fitness value of the product to obtain:
further, step 3.3 comprises:
for A i (k) Carrying out cloning operation:
wherein,
denotes cloning operation, E i Is an elementQ is all 1 i (k) Vector of dimension, q i (k) Can be expressed as:
n is the clone scale, int {. Cndot } is the minimum integer greater than · s;
the population Y (k) after cloning operations can be expressed as:
Y(k)＝{Y 1 (k),Y 2 (k),…,Y i (k),…,Y n (k)}
wherein, Y i (k)＝{A i1 (k),…,A ij (k),…,A iqi (k)}，1≤j≤q i And has A ij (k)＝A i (k)。
Further, step 3.4 includes:
(3.4.1) mixing Y i (k) Binary multipoint crossing is performed as follows:
wherein S1 and S2 respectively represent particle structures before crossing, and P1 and P2 respectively represent particle structures after crossing;
(3.4.2) converting Y i (k) By cross-recombination and mutation operation T g C {. The }, yield:
further, step 3.5 comprises:
(3.5.1) clone selection:
to Z i (k) Performing clone selection operation, and selecting excellent individuals to form new population A i (k+1)＝T s C {Z i (k) At the same time, reserve Z i (k) Some of non-excellent individuals B i (k) For continued evolution, note
Comprises the following steps:
B i (k)＝{z i (k)|max(fit(z i (k)))}
wherein, B i (k) Substituted A i (k) Is formed as A i Probability of (k + 1) being P s It can be expressed as:
wherein α (α > 0) is a parameter reflecting population diversity;
(3.5.2) introduce the clone deletion operator:
suppose A i (k) Through recombination variation, the gene is evolved into A i ' (k) comparing the magnitude of the fitness function of the two, and if:
fit(A' i (k))<fit(A i (k))
then use A i (k) To replace A i ′(k)。
The invention has the beneficial effects that:
(1) And adopting clone operation to improve the capability of searching the globally optimal rotation vector factor. The cloning operation copies the particles according to their fitness values i Secondly, the genetic algorithm can further increase the probability of excellent particles of iterative cycle on the basis of the existing excellence, and the PAPR inhibition effect is further improved by effectively preventing the situation of falling into local optimum and avoiding premature convergence through carrying out robust search in the whole solution space;
(2) The problem of population degradation is overcome by a population screening operation. By adopting a probability selection method adopted by clone selection in population screening operation, the diversity of the population is further increased by reserving and continuously optimizing some non-excellent particles, so that the genetic algorithm can be effectively prevented from falling into local optimum, the global convergence speed can be increased, and the aim of further reducing the PAPR is fulfilled.
Drawings
Fig. 1 is a schematic structural diagram of a time domain sequence X (k) obtained by acquiring a signal sequence according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a dot product of the optimal phase rotation factor α X (k) and the frequency domain sample X (k) according to an embodiment of the present invention.
Fig. 3 is a flowchart illustrating operation of a new peak-to-average power ratio suppression scheme for UFMC systems according to an embodiment of the present invention.
Detailed Description
The invention provides a new specific implementation mode of genetic algorithm-based peak-to-average ratio suppression in a UFMC system, wherein the whole scheme totally comprises the following 14 steps (the working flow chart is shown in FIG. 3), which is described in detail below with reference to the accompanying drawings:
step S1: obtaining a signal sequence: a sequence generator at a transmitting end transmits a group of subcarrier frequency domain sequence groups X of N sequences o (k) (the structure is shown in FIG. 1), which is expressed as:
X o (k)＝[X o (0),X o (1),…,X o (N-1)],0≤k≤N-1。
step S2: signal modulation: to X o (k) Carrying out QAM modulation mapping to obtain:
X(k)＝[X(0),X(1),...,X(N-1)]。
and step S3: constructing an immune programming genetic algorithm, and searching to obtain an optimal phase rotation vector factor alpha (k);
and initializing operation of the immune programming genetic algorithm. Setting maximum iteration number T and initialized adaptive parameter K 1 、K 2 、K 3 、K 4 Which comprises the following steps:
step S3.1: initializing an initial population A (k) in a genetic algorithm to obtain a phase rotation vector factor alpha (t) (k) And its fitness value function fit (alpha) (t) (k))；
And (3) performing population mapping operation: mapping the N particles in the initial population a (k) into the form of a binary phase rotation factor, resulting in:
A(k)→α (t) (k)＝[α (t) (0),α (t) (1),...,α (t) (N-1)]∈{-j,-1,j,1}→{(0,0),(0,1),(1,0),(1,1)},1≤t≤T
according to the phase rotation factor alpha (t) (k) And calculating the fitness value of the product to obtain:
step S3.2: first pair phase rotation factor alpha (t) (k) According to its fitness value function fit (alpha) (t) (k) In ascending order of size) to yield:
A(k)＝{A 1 (t),A 2 (t),…,A i (t),…,A N (t)},1≤i≤N。
step S3.3: then the sorted particles A are i (k) Replication q i (k) Q is i (k) Can be expressed as:
wherein N is the clone scale and Int {. Cndot } is the smallest integer greater than.
Step S3.4: against population Y after cloning i (k) Performing a cross-recombination operation comprising:
will Y i (k) Binary multipoint crossing is performed as follows:
will Y i (k) By cross-recombination and mutation operation T g C {. The }, yield:
step S3.5: for Z after cross recombination i (k) Performing population screening operations, wherein the operations comprise:
clone selection operation T g C {. The }: from Z i (k)Select excellent individuals to form a new population A i (k+1)＝T s C {Z i (k) Get into the next iteration cycle while keeping Z i (k) Some non-excellent individuals in order to continue evolution, are scored as
Comprises the following steps:
B i (k)＝{z i (k)|max(fit(z i (k)))}
wherein, B i (k) Substituted A i (k) Is formed as A i The probability of (k + 1) is Ps, which can be expressed as:
wherein α (α > 0) is a parameter reflecting population diversity.
Introducing a clone deletion operator: suppose A i (k) Through recombination variation, the gene is evolved into A i ' (k) comparing the fitness of both, if satisfied
fit(A' i (k))<fit(A i (k))
Then use A i (k) To replace A i ′(k)。
Step S3.6: judging whether T is more than T, if so, adding 1 to T and returning to the step S3.1; otherwise, calculating the optimal phase rotation factor at the moment:
the IFFT {. Is used for representing inverse fast Fourier transform, the argmin {. Is used for representing the independent variable value corresponding to the function when the function obtains the minimum value, and L is an oversampling factor.
And step S4: performing dot multiplication on the optimal phase rotation factor alpha (k) solved by the CSA-IGA algorithm and the frequency domain sample value X (k) to obtain:
X μ (k)＝<X(k)·α * (k)>＝[X(0)α * (0),X(1)α * (1),…,X(N-1)α * (N-1)]。
step S5: zero filling operation: at X μ (k) (L-1) N/2 zeros were inserted on both sides, respectively, to obtain:
step S6: to X o μ (p) performing an IFFT operation to obtain:
step S7: for x o μ And (n) performing SLM algorithm processing, and selecting a sequence with the minimum PAPR value as an output sequence, which is marked as x (n).
Step S8: obtaining a frequency domain signal X after FFT operation is carried out on X (n) o * (k) (ii) a Simultaneously X is converted for UFMC system o * (k) The subband signal X is obtained after division into B subbands b * (k) (B is more than or equal to 1 and less than or equal to B) and is recorded as:
where k is the number of subcarriers and b is the number of subbands.
Step S9: to X b * (k) Performing an IFFT operation to obtain:
step S10: x is to be b (n) a transit length of L f Filter f of b (n) after treatment, accumulating to obtain:
where denotes convolution.
Claims (6)
1. A novel UFMC system peak-to-average ratio inhibition scheme is characterized by comprising the following steps:
(1) Obtaining a signal sequence:
a sequence generator at a transmitting end transmits a group of subcarrier frequency domain sequence groups X of N sequences o (k) And is recorded as: x o (k)＝[X o (0),X o (1),...,X o (N-1)],0≤k≤N-1；
(2) Signal modulation:
to X o (k) Quadrature amplitude modulation is performed, and a sequence X (k) after modulation is expressed as: x (k) = [ X (0), X (1),.., X (N-1)]；
(3) Constructing an immune programming genetic algorithm, and searching to obtain an optimal phase rotation vector factor alpha (k);
(4) Performing dot multiplication on the solved optimal phase rotation vector factor alpha (k) and a frequency domain sample value X (k) to obtain an output sequence X μ (k)：
X μ (k)＝<X(k)·α * (k)>＝[X(0)α * (0),X(1)α * (1),…,X(N-1)α * (N-1)]
Wherein < x > represents a point multiplication between two vectors;
(5) At X μ (k) Are respectively inserted with (L-1) N/2 zeros to prevent the generation of false signals, to obtain
(6) To X o μ (p) performing an IFFT operation to obtain x o μ (n)：
(7) For x o μ (n) performing SLM algorithm processing, and selecting a sequence with the minimum PAPR value as an output sequence, and recording as x (n);
(8) FFT operation is carried out on X (n) to obtain a frequency domain signal X o * (k) (ii) a Mixing X o * (k) The subband signal X is obtained after division into B subbands b * (k) (B is more than or equal to 1 and less than or equal to B) and is recorded as:
wherein k is the number of subcarriers, b is the number of subbands;
(9) To X b * (k) Performing IFFT operation to obtain time domain vector x b (n)：
(10) X is to be b (n) a transit length of L f Filter f of b And (n) accumulating after processing to obtain a final signal s (n) of the transmitting end:
where denotes convolution.
2. The novel UFMC system peak-to-average ratio suppression scheme of claim 1, wherein said immunoplaning genetic algorithm in step 3 comprises:
(3.1) carrying out initialization operation aiming at the initial population A (k) in the genetic algorithm to obtain the phase rotation vector factor alpha (t) (k) And its fitness value function fit (alpha) (t) (k) Whereinsaid: t is the number of iterations;
(3.2) for the phase rotation factor α (t) (k) Push buttonAccording to its fitness value function fit (alpha) (t) (k) In ascending order of size) to yield:
A(k)＝{A 1 (t),A 2 (t),…,A i (t),…,A N (t)},1≤i≤N；
(3.3) sorting the particles A i (k) Replication q i (k) Then, cloning operation is performed to obtain Y i (k)；
(3.4) against population Y after cloning operation i (k) Performing cross recombination to obtain Z i (k)；
(3.5) against Z after Cross-recombination i (k) Performing population screening to obtain A i ′(k)；
(3.6) judging whether the maximum iteration number is reached, if not, returning to the step (3.1); otherwise, calculating the optimal phase rotation factor alpha (k) at the moment:
the IFFT {. Is used for representing inverse fast Fourier transform, the argmin {. Is used for representing the independent variable value corresponding to the function when the function obtains the minimum value, and L is an oversampling factor.
3. A new UFMC system peak-to-average ratio suppression scheme, as claimed in claim 2, wherein step 3.1 comprises:
(3.1.1) population mapping operation: mapping the N particles in the initial population a (k) into the form of the phase rotation vector factor, resulting in:
A(k)→α (t) (k)＝[α (t) (0),α (t) (1),…,α (t) (N-1)],1≤t≤T
α (t) (k)∈{-j,-1,j,1}→{(0,0),(0,1),(1,0),(1,1)}
(3.1.2) rotating the vector factor α according to the phase (t) (k) And calculating the fitness value of the product to obtain:
4. a new UFMC system peak-to-average ratio suppression scheme, as claimed in claim 2, wherein step 3.3 comprises:
for A i (k) Carrying out cloning operation:
Y i (k)＝T c C {A i (k)}＝E i ×A i (k)
wherein, T c C {. Represents a cloning operation, E i Q for all elements 1 i (k) Dimensional column vector, q i (k) Can be expressed as:
n is the clone scale, int {. Cndot } is the minimum integer greater than · s;
the population Y (k) after cloning operations can be expressed as:
Y(k)＝{Y 1 (k),Y 2 (k),…,Y i (k),…,Y n (k)}
wherein, Y i (k)＝{A i1 (k),…,A ij (k),…,A iqi (k)}，1≤j≤q i And has A ij (k)＝A i (k)。
5. A new UFMC system peak-to-average ratio suppression scheme as claimed in claim 2, wherein step 3.4 comprises:
(3.4.1) mixing Y i (k) A binary multi-point crossover is performed as follows:
S1:
P1:
S2:
P2:
wherein S1 and S2 respectively represent a particle structure before crossing, and P1 and P2 respectively represent a particle structure after crossing;
(3.4.2) converting Y i (k) By cross-recombination and mutation operation T g C {. The }, yield:
6. a new UFMC system peak-to-average ratio suppression scheme, as claimed in claim 2, wherein step 3.5 comprises:
(3.5.1) clone selection:
to Z is paired i (k) Performing clone selection operation, and selecting excellent individuals to form new population A i (k+1)＝T s C {Z i (k) At the same time, retaining Z i (k) Some of non-excellent individuals B i (k) For continued evolution, note
Comprises the following steps:
B i (k)＝{z i (k)|max(fit(z i (k)))}
wherein, B i (k) Substituted A i (k) Is formed as A i The probability of (k + 1) is P s It can be expressed as:
(3.5.2) introduce the clone deletion operator:
suppose A i (k) Evolved to A after recombinant mutation i ' (k) comparing the magnitude of the fitness function of the two, and if:
fit(A i '(k))<fit(A i (k))
then use A i (k) To replace A i ′(k)。
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