DeepAI

# Channel-Statistics-Based Hybrid Precoding for Millimeter-Wave MIMO Systems With Dynamic Subarrays

This paper investigates the hybrid precoding design for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems with finite-alphabet inputs. The mmWave MIMO system employs partially-connected hybrid precoding architecture with dynamic subarrays, where each radio frequency (RF) chain is connected to a dynamic subset of antennas. We consider the design of analog and digital precoders utilizing statistical and/or mixed channel state information (CSI), which involve solving an extremely difficult problem in theory: First, designing the optimal partition of antennas over RF chains is a combinatorial optimization problem, whose optimal solution requires an exhaustive search over all antenna partitioning solutions; Second, the average mutual information under mmWave MIMO channels lacks closed-form expression and involves prohibitive computational burden; Third, the hybrid precoding problem with given partition of antennas is nonconvex with respect to the analog and digital precoders. To address these issues, this study first presents a simple criterion and the corresponding low complexity algorithm to design the optimal partition of antennas using statistical CSI. Then it derives the lower bound and its approximation for the average mutual information, in which the computational complexity is greatly reduced compared to calculating the average mutual information directly. In addition, it also shows that the lower bound with a constant shift offers a very accurate approximation to the average mutual information. This paper further proposes utilizing the lower bound approximation as a low-complexity and accurate alternative for developing a manifold-based gradient ascent algorithm to find near optimal analog and digital precoders. Several numerical results are provided to show that our proposed algorithm outperforms existing hybrid precoding algorithms.

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08/21/2018

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## I Introduction

Massive multiple-input multiple-output (MIMO) systems operating in the Millimeter wave (mmWave) band is a key technique candidate for future generation cellular systems to address the wireless spectrum crunch. It makes use of the frequency band from 30 GHz to 300 GHz, which provides a much wider bandwidth than current cellular systems operating in microwave bands. In addition, a short wavelength of radio signals in the mmWave band enables very large antenna arrays to be equipped at the transceivers, and this can provide significant increase of the spectral efficiency.

For mmWave MIMO systems, hybrid analog and digital precoding architectures have been proposed to achieve high spectral efficiency with low cost and power consumption. Extensive work has been devoted to designing hybrid precoding algorithms under perfect channel state information (CSI) and different constraints [1, 2, 3, 4, 5, 6, 7, 8]

. However, it is difficult to obtain the perfect CSI in mmWave MIMO systems. The reason is that the channel matrix measured at the baseband cannot be obtained directly because it is intertwined with the choice of analog precoders. Furthermore, conventional MIMO channel estimation is incapable of utilizing array gain in mmWave systems, and it leads to low signal-to-noise ratio (SNR). Therefore, the conventional channel estimation requires long training sequences to estimate mmWave MIMO channels, which is impractical due to fast variation of mmWave MIMO channels.

To address the challenge of training overhead, [9] proposed a hybrid precoding algorithm for single-user MIMO systems with partial knowledge of the CSI. For the multi-user MIMO scenario, [10] devised a mix-CSI-based hybrid precoding structure, where the analog precoding design is based on the slow-varying channel statistics, and the digital precoding design is based on the instantaneous CSI. Then the dimension of the effective channel matrix (instantaneous CSI) is greatly reduced. However, the work in [9] and [10] considered only the fully-connected hybrid architecture, which requires much more phase shifters compared to the partially-connected structure [11]. In the partially-connected structure, the antenna array is partitioned into a number of smaller disjoint subarrays, each of which is driven by a single radio frequency (RF) chain[12]. This structure is an extension of classic antenna selection methods, which allocate each RF chain to an antenna element [13]. In [14], the authors developed a successive interference cancellation based hybrid precoding for partially-connected structure with fixed subset of antennas. The partially-connected structure with dynamic subset of antennas is considered in [15], and a low complexity greedy algorithm is also proposed to design the best partitioning/grouping of antennas over the RF chains.

Furthermore, most existing works on hybrid precoding assume Gaussian inputs, which are rarely realized in practice. It is well known that practical systems utilize finite-alphabet inputs, such as phase-shift keying (PSK) or quadrature amplitude modulation (QAM). Precoding designs under Gaussian inputs have been shown to be quite suboptimal for practical systems with finite-alphabet inputs [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27]. Recently, the authors in [28] presented a Broyden-Fletcher-Goldfarb-Shanno based hybrid precoding algorithm for mmWave MIMO systems with finite-alphabet inputs. The proposed algorithm utilizes both gradient and Hessian information, and simulation results showed that it outperforms existing hybrid precoding algorithms including [3, 5, 6, 8].

### I-a Contributions

In this paper, we investigate the hybrid precoding design for mmWave MIMO systems with finite-alphabet inputs under the following assumptions: 1) the system employs partially-connected hybrid precoding structure with dynamic subset of antennas; 2) the partition of antennas and analog precoder are designed based on statistical CSI, and the digital precoder is designed based on either statistical CSI or instantaneous CSI. We consider the statistical-CSI-based scenario and the mixed-CSI-based scenario, and the corresponding hybrid precoding problems under two scenarios have the same mathematical form. Then we propose a manifold-based gradient ascent algorithm to solve the hybrid precoding problem. The contributions of this paper are summarized as follows:

• We present a simple criterion to design the best partition of antennas using statistical CSI. The corresponding dynamic subarray design is a (nonconvex) combinatorial optimization problem, and we propose a low complexity algorithm to solve this problem.

• We derive a lower bound of the average mutual information for mmWave MIMO channels. The lower bound plus a constant shift serves as a very accurate approximation to the average mutual information, and its complexity is much lower than the original average mutual information. To further reduce the complexity, we also derive an accurate approximation of the proposed lower bound.

• We propose a manifold-based gradient ascent algorithm to design hybrid precoders. Simulation results show that 1) the proposed algorithm converges to a near globally optimal solution from arbitrary initial points; 2) the performance of mixed-CSI-based hybrid precoding is very close to that of instantaneous-CSI-based hybrid precoding. 3) the statistical-CSI-based hybrid precoding can achieve higher energy efficiency than the fully-connected hybird precoding.

### I-B Notations

The following notations are adopted throughout the paper: Boldface lowercase letters, boldface uppercase letters, and calligraphic letters are used to denote vectors, matrices and sets, respectively. The real and complex number fields are denoted by

and , respectively. The superscripts , and stand for transpose, conjugate, and conjugate transpose operations, respectively. is the trace of a matrix; denotes the Euclidean norm of a vector; represents the Frobenius norm of a matrix; represents the statistical expectation with respect to ; represents the -th element of ; and

denote an identity matrix and a zero matrix, respectively, with appropriate dimensions;

represents the Hadamard matrix product; represents the mutual information; and are the real and imaginary parts of a complex value; is used for the base two logarithm.

## Ii System and Channel Models

In this section, we present system and channel models for mmWave MIMO systems.

### Ii-a System Model

Consider a point-to-point mmWave MIMO system, where a transmitter with antennas sends data streams to a receiver with antennas. The number of RF chains at the transmitter is , which satisfies . We consider the hybrid precoding scheme, where data streams are first precoded using a digital precoder, and then shaped by an analog precoder. The received baseband signal can be written as

 y=HFBx+n (1)

where is the mmWave channel matrix; is the analog precoder; is the digital precoder; is the input data vector and is the independent and identically distributed (i.i.d.) complex Gaussian noise with zero-mean and covariance . To simplify our system model, we omit the analog and digital combiner, which can be designed similarly as the hybrid precoder.

In this paper, the analog precoder is implemented by a dynamic phase shifter subarray, where each RF chain is connected to a dynamic subset of transmit antennas. Let denote the collection of transmit antennas connected to th RF chain. We partition transmit antennas into subsets satisfying

 S={{Sj}Nrfj=1∣∣ ∣∣Nrf⋃j=1Sj={1,2,...,Nt},Sj∩Sk=∅,∀j≠k}. (2)

Since each RF chain can be connected to different number of antennas, the cardinalities of are different. In addition, if the th transmit antenna is connected to the th RF chain, i.e.,, the th entry of has unit modulus, otherwise it is zero. Therefore, the constraints on can be expressed by

 |Fij|=1Sj(i),∀(i,j) (3)

where is the indicator function:

 1Sj(i)={1ifi∈Sj0otherwise. (4)

The transmitted signal is restricted by a total power constraint :

 Ex∥FBx∥2=tr(BHFHFB)≤P. (5)

To decouple and in coupled power constraint (5), we consider the following change of variables:

 ¯F=F(FHF)−12 (6) ¯B=(FHF)12B. (7)

Then the power constraint in (5) becomes

 B={¯B∣∣tr(¯BH¯B)≤P} (8)

and the constraints on can be expressed by

 F={¯F∣∣∣∣∣¯Fij∣∣=|Sj|−121Sj(i),∀(i,j)}. (9)

Furthermore, by plugging and into the system model in (1), we have

 y=H¯F¯Bx+n. (10)

Combining (8) and (10), we observe that and can be regarded as the effective channel and precoder for typical MIMO Gaussian channels, respectively. Since there exists a one-to-one mapping between and , we will focus on designing the effective analog and digital precoders throughout the rest of this paper.

### Ii-B Channel Model

The mmWave MIMO channel is characterized by a standard multi-path model[29, ch. 7.3.2]:

 H=√NrNtLL∑ℓ=1γℓa(θr,ℓ)a(θt,ℓ)H (11)

where denotes the number of physical propagation paths between the transmitter and the receiver; represents the complex gain of the th propagation path; We assume that

are i.i.d. complex Gaussian distributed with zero-mean and unit-variance;

and represent the receive and transmit array steering vectors, with and being the angles of arrival (AOA) and the angles of departure (AOD), respectively. In this paper, the transmitter and receiver adopt uniform linear arrays, whose array steering vector is given by

 a(θ)=1√N[1,e−j2πλdsinθ,...,e−j2πλd(N−1)sinθ]T (12)

where is the number of antenna element, is the wavelength of the carrier frequency and is the antenna spacing.

The channel model in (11) can be rewritten more compactly as

 H=√NrNtLArΓAHt (13)

where ; and are stacked array steering vectors of AOA and AOD respectively, given by

 Ar=[a(θr,1),...,a(θr,L)] (14) At=[a(θt,1),...,a(θt,L)]. (15)

This work assumes that the small scale fading varies rapidly while the variation of angle information and is slow[30]. Since the angle information changes slowly, we further assume that the transmitter can obtain statistical CSI through feedback, i.e., the transmitter knows and .

## Iii Problem Formulation

For mmWave MIMO systems, it may not be practical to obtain the instantaneous CSI by conventional channel estimation techniques because 1) the channel matrix measured in the baseband depends on the choice of analog precoder; 2) the training blocks may be prohibitively long due to the large bandwidth and low signal-to-noise ratio (SNR). To mitigate this difficulty, we propose new formulations in which analog and/or digital precoders are designed under statistical CSI.

### Iii-a Statistical-CSI-Based Formulation

We assume that the transmitter has the knowledge of statistical CSI, including , and the distribution of . Then we design the analog and digital precoder to maximize the average mutual information. Suppose each entry of the input data vector

is uniformly distributed from a given constellation set with cardinality

. The average mutual information between and is given by

 EHI(x;y|H) (16)

where is the instantaneous mutual information between and [20]

 I(x;y|H)=logK−1KK∑m=1En{logK∑k=1exp(−dmk)}. (17)

Here is a constant number, and , with and being two possible data vectors taken from . The average mutual information maximization problem can then be formulated as

 maximize{Sj}∈SR({Sj}) (18)

where is the maximum average mutual information with given partition of subsets, i.e.,

 R({Sj})=maximize¯F∈F,¯B∈BEHI(x;y|H). (19)

Problem (18) is a combinatorial optimization problem for which finding the optimal solution requires an exhaustive search over all nonempty in . The total number of combinations is known as Stirling number of the second kind [31] and is given by

 |S|=1Nrf!Nrf∑k=0(−1)Nrf−k(Ntk)kNt. (20)

Then we can rewrite problem (18) as

 maximizeℓ∈{1,...,|S|}R({Sj,ℓ}) (21)

where represents the th given partition of subsets belonging to .

Although (21) provides a theoretically possible way for solving problem (18), its computational complexity is prohibitive even for a small number of transmit antennas and RF chains. For example, when and , is equal to , which implies that we need to solve problem (19) over ten million times to obtain the optimal analog and digital precoder.

We propose a new formulation to reduce the computational complexity of problem (18). Recall that represent positions of nonzero entries in , and the role of is to reshape the effective channel matrix . Therefore, we design and the corresponding such that the average effective channel gain is maximized. The dynamic subarray design problem can then be formulated as

 maximize¯F∈F,{Sj}∈SEHtr(¯FHHHH¯F). (22)

We solve problem (22) to obtain its optimal solutions, denoted by and . Then we solve problem (19) with given to obtain the optimally effective analog and digital precoders . Note that since is not obtained by maximizing the average mutual information, we do not use it directly as the optimally effective analog precoder. However, the solution serves as a good initial point for solving problem (19). Therefore, we first design a low complexity algorithm to solve problem (22), and then design an effective algorithm to solve the hybrid precoding problem (19) with given .

### Iii-B Mixed-CSI-Based Formulation

The basic idea of mixed CSI based formulation is to design the analog precoder based on statistical CSI, and then estimate the reduced-dimensional effective channel matrix , where is the optimally effective analog precoder based on statistical CSI. After that, the transmitter utilizes the instantaneous effective channel matrix to design effective digital precoder , and this is a typical MIMO precoding problem. In this case, the burden of channel estimation is greatly reduced because the dimension of is much smaller than that of .

Given the instantaneous effective channel matrix , the digital precoding problem can be expressed by

 C(H¯F)=maximize¯B∈BI(x;y|H) (23)

where is the maximum mutual information under the given effective channel matrix . Then the mixed-CSI-based hybrid precoding problem can be formulated as

 maximize¯F∈F,{Sj}∈SEHC(H¯F). (24)

Problem (24) is intractable because it is prohibitive to compute the objective function . In order to estimate at a given point , we need to solve the nonconvex problem (23) thousands of times for randomly generated channel matrix . To mitigate this difficulty, we replace by a computationally efficient bound. Invoke Jensen’s inequality, can be lower bounded by

 EHC(H¯F)≥maximize¯B∈BEHI(x;y|H). (25)

Replacing by its lower bound, problem (24) is approximated as

 maximize¯F∈F,{Sj}∈S,¯B∈BEHI(x;y|H) (26)

which is exactly the same as problem (18). Then we can use the same procedure to solve this problem, i.e., we first solve problem (22) to obtain , and then solve problem (19) with given to obtain the optimally effective analog precoder. Note that although the statistical-CSI-based formulation and the mixed-CSI-based formulation solve the same optimization problem, there is an important difference between them. The optimization variable in the mixed-CSI-based formulation is just an auxiliary variable made for analog precoder design. After obtaining the optimally effective analog precoder, the real digital precoder should be obtained by solving problem (23).

## Iv Dynamic Subarray Design

In this section, we propose a low complexity algorithm to solve problem (22). Note that the objective function in problem (22) can be rewritten as

 EH tr(¯FHHHH¯F) =NrNtLEΓtr(¯FHAtΓHAHrArΓAHt¯F) =NrNtLtr(¯FHAtAHt¯F) (27)

where the second equality in equation (IV) holds because . Plugging into equation (28), we obtain the following problem

 maximizeF,{Sj} tr[(FHF)−12FHAtAHtF(FHF)−12] (28) subjectto |Fij|=1Sj(i),∀(i,j) {Sj}∈S.

It is difficult to solve problem (28) directly because the feasible set of problem (28) is characterized by and . To address this issue, the following proposition rewrites the feasible set as explicit constraints of .

###### Proposition 1

The feasible set of problem (28) can be expressed by

 |Fij|∈{0,1},∀(i,j)∥Fi∙∥0=1,∀i (29)

where denotes the th row of , and represents the total number of nonzero elements in a vector.

###### Proof:

See Appendix.

According to Proposition 1, we rewrite problem (28) as

 maximizeF tr[(FHF)−12FHAtAHtF(FHF)−12] (30) subjectto |Fij|∈{0,1},∀(i,j) ∥Fi∙∥0=1,∀i.

Problem (30) is still intractable due to nonconvex discrete constraints and . Therefore, we first drop the constraints and consider the unconstrained problem

 maximizeFtr[(FHF)−12FHAtAHtF(FHF)−12]. (31)

Problem (31

) is a generalized eigenvalue problem, and its optimal solution is given by

[15]

 F=UAR (32)

where is the left singular vectors of corresponding to the largest singular values, and

is an arbitrary unitary matrix. Note that when

, the remaining left singular vectors in can be chosen arbitrarily as long as satisfies .

In general, if there exists a unitary matrix such that the unconstrained optimal solution satisfies (29), then is the globally optimal solution of problem (30). However, such may not exist and thus we use to find a nearby feasible solution. Specifically, consider the following optimization problem

 minimizeF,R∈U ∥F−UAR∥2F (33) subjectto |Fij|∈{0,1},∀(i,j) ∥Fi∙∥0=1,∀i

where denotes the set of unitary matrices. Since the optimization variables and are separate, we adopt the alternating minimization approach to solve problem (33).

Given , the optimal of problem (33) has a simple closed form solution. Let , then the optimal of problem (33) can be expressed by

 (34)

Given , problem (33) is reduced to an orthogonal procrustes problem

 minimizeR∈U∥F−UAR∥2F. (35)

Let the singular value decomposition of

be

 Z=FHUA=UZΣZVHZ (36)

where is a unitary matrix with left singular vectors, is a diagonal matrix with singular values arranged in decreasing order, and is another unitary matrix with right singular vectors. Then the optimal solution of problem (35) is given by [32]

 R=VZUHZ. (37)

Combining (34) and (37), we propose a simple alternating minimization algorithm to solve problem (33) and obtain the corresponding near optimal partition of subsets . The details of this algorithm is summarized in Algorithm 1.

We conclude this section with several remarks on Algorithm 1:

• The convergence of Algorithm 1 is guaranteed because the objective function is bounded, and it is decreasing in each iteration.

• Since problem (33) is a nonconvex problem, the solution obtained by Algorithm 1 depends on the initial unitary matrix . Therefore, we can run Algorithm 1 several times with different initial , and then choose the solution corresponding to the largest .

• When is determined, the corresponding is given by

 S⋆j={i∣∣∣∣[¯F⋆init]ij∣∣≠0},j=1,...,Nrf.

## V Hybrid Precoding With Finite-Alphabet Inputs

In this section, we first derive the lower bound for the average mutual information , and then propose an effective algorithm to design analog and digital precoders.

### V-a Lower Bound For Average Mutual Information

It is difficult to compute and optimize the average constellation-constrained mutual information directly because both and its gradient have no closed form expressions. To estimate as well as its gradient, we need to use Monte Carlo method and/or numerical integral, whose computational complexity are prohibitively high.

This difficulty can be partially mitigated by the following proposition, which provides the lower bound of in closed form.

###### Proposition 2

The average constellation-constrained mutual information of mmWave MIMO channels can be lower bounded by

 L(¯F,¯B)=logK −Nr(1ln2−1)−1KK∑m=1 logK∑k=1det[I+(AHrAr)T∘Wmk]−1 (38)

where

 Wmk=NrNt2σ2LAHt¯F¯B(xm−xk)(xm−xk)H¯BH¯FHAt. (39)
###### Proof:

See Appendix.

The computational complexity of the lower bound is still very high because it needs to calculate the determinant times. For example, when we adopt 16QAM modulation () and the number of data streams is 4, is equal to . To further reduce the complexity, we notice that the receive steering vectors are asymptotically orthogonal to each other when the number of receive antennas approaches infinity, i.e., . Based on this observation, we derive a low complexity approximation of in the following proposition.

###### Proposition 3

The lower bound can be approximated by

 LA(¯F,¯B)=logK −Nr(1ln2−1)−1KK∑m=1 logK∑k=1L∏ℓ=1(1+NrNt2σ2L|βmkℓ|2)−1 (40)

where , with being the th column of . In addition, the limit of is as approaches infinity.

###### Proof:

See Appendix.

The accuracy and computational complexity of the lower bound and its approximation will be shown in Fig. 1 and Table 1 in the simulation result section.

### V-B Hybrid Precoding Design

In this section, we solve the hybrid precoding problem (19) with given obtained by Algorithm 1. First, by replacing the average mutual information with the approximated lower bound , problem (19) can be approximated as

 maximize¯F,¯B LA(¯F,¯B) (41) subjectto ∣∣¯Fij∣∣=∣∣S⋆j∣∣−121Sj(i),∀(i,j) tr(¯BH¯B)≤P.

Note that the constraint implies that only the phase of nonzero can be changed. Therefore, instead of using as the optimization variable, it is more convenient to optimize the phase of nonzero entries in . Define the phase matrix as

 Φij=∠¯Fij1S⋆j(i),∀(i,j) (42)

where represents the phase of . Then can be expressed as

 ¯Fij=∣∣S⋆j∣∣−12exp(ȷΦij)1S⋆j(i),∀(i,j). (43)

Using as the optimization variable and defining a new function , problem (41) can be rewritten as

 maximizeΦ,¯B R(Φ,¯B) (44) subjectto tr(¯BH¯B)=P.

Here we express the power constraint as because is monotonically increasing with respect to . Then we provide the gradient of in the following proposition, which forms the foundation for solving problem (44).

###### Proposition 4

The gradient of with respect to and are given by

 ∇¯BR(Φ,¯B)=L∑ℓ=1¯FHa(θt,ℓ)a(θt,ℓ)H¯F¯BEℓ ∇ΦR(Φ,¯B)=2L∑ℓ=1I[¯FHa(θt,ℓ)a(θt,ℓ)H¯F¯BEℓ¯BH∘¯F∗] (45)

where

 Eℓ=1ln(2)⋅K∑m,kζmkℓ(xm−xk)(xm−xk)H (46)

with

 ζmkℓ= (2σ2LNrNt+|βmkℓ|2)−1⋅L∏ℓ=1(1+NrNt2σ2L|βmkℓ|2)−1⋅ [K∑k=1L∏ℓ=1(1+NrNt2σ2L|βmkℓ|2)−1]−1.
###### Proof:

See Appendix.

We propose a manifold-based gradient ascent algorithm to optimize and simultaneously using the gradient information. At the th iteration, the algorithm updates the current solution to by the following rules

 Φk+1=Φk+ρk∇ΦR(Φk,¯Bk) ¯Bk+1=Proj[¯Bk+ρkgrad¯BR(Φk,¯Bk)] (47)

where is the stepsize, , and is the gradient of on the following (sphere) manifold

 M={¯B∣∣tr(¯BH¯B)=P}. (48)

Based on the definition, can be computed by projecting onto the tangent space at , where is given by

 (49)

Then can be expressed by

 grad¯BR(Φ,¯B)=argmax¯X∈T¯BM∥∥¯X−∇¯BR∥∥2F. (50)

Using the standard Lagrangian multiplier method, the closed form solution of problem (50) is given by

 grad¯BR(Φ,¯B)=∇¯BR(Φ,¯B)−Rtr[(∇¯BR)H¯B]P¯B. (51)

After obtaining the ascent direction, we need to determine the stepsize such that the objective function is increasing in each iteration. We propose a modified backtracking line search method, which is usually more efficient than the classic backtracking line search [33]. The main idea is to use as the initial guess of , and then either increases or decreases it to find the largest such that

 f(ρk)≜ R(Φk+1,¯Bk+1)−R(Φk,¯Bk)−ρkβga (∥∇ΦR(Φk,¯Bk)∥2F+∥grad¯BR(Φk,¯Bk)∥2F)≥0

where is a constant to control the stepsize. Specifically, the stepsize is set as

 ρk=⎧⎨⎩2K1−1⋅ρk−1iff(ρk−1)≥0(12)K2⋅ρk−1iff(ρk−1)<0 (52)

where is the smallest integer such that , and is the smallest integer such that . The details of our proposed manifold-based gradient ascent algorithm is summarized in Algorithm 2.