C-RAN with Hybrid RF/FSO Fronthaul Links:Joint Optimization of RF Time Allocation and Fronthaul Compression

# C-RAN with Hybrid RF/FSO Fronthaul Links: Joint Optimization of RF Time Allocation and Fronthaul Compression

Marzieh Najafi, Vahid Jamali, Derrick Wing Kwan Ng, and Robert Schober
Friedrich-Alexander University of Erlangen-Nuremberg, Germany
University of New South Wales, Sydney, Australia
This paper has been presented in part at IEEE Globecom 2017 [1].

\EndPreamble

## I Introduction

Cloud radio access network (C-RAN) is a novel cellular architecture whereby the baseband signal processing is moved from the base stations (BSs) to a cloud-computing based central unit (CU) [2, 3, 4, 5]. In a C-RAN, the BSs operate as remote radio units (RUs) that relay the mobile users’ (MUs’) data to the CU via fronthaul links. The CU jointly processes the MUs’ data which enables the exploitation of distributed multiple-input multiple-output (MIMO) multiplexing gains. However, conveying the signals received at the RUs to the CU via the fronthaul links is a major challenge as this may require a huge fronthaul capacity, e.g. on the order of Gbits/sec [3]. Reviews of recent advances in fronthaul-constrained C-RANs are provided in [3, 4, 6].

To fully exploit the fronthaul capacity, we employ quantization at the RUs. To this end, we consider three different quantization schemes, namely per-antenna vector quantization (AVQ), per-RU vector quantization (RVQ), and distributed source coding (DSC). In contrast to AVQ, RVQ exploits the correlations between the signals received at the different antennas of a given RU, whereas DSC also takes advantage of the correlations among the signals received at different RUs [13, 14]. In addition, for the CU, we consider two different receivers, namely a linear minimum mean square error (MMSE) receiver and the optimal successive interference cancellation (SIC) receiver. The considered quantization and detection schemes offer different trade-offs between complexity and performance. Thereby, the performance improvements of DSC over RVQ and of RVQ over AVQ come at the expense of an increased complexity. Similarly, the SIC receiver generally outperforms the linear MMSE receiver at the cost of a higher computational complexity. In practice, one can select a suitable pair of quantization and detection schemes given the affordable complexity at the RU and CU.

The goal of this paper is to jointly optimize the quantization noise covariance matrices at the RUs and the RF time allocated to the RF multiple-access and fronthaul links for rate region maximization. In order to maximize the achievable rate region, for each pair of the adopted quantization and detection schemes, we first formulate a weighted sum rate maximization problem for optimization of the RF time allocation and the RU quantization noise covariance matrices. Then, we develop a unified representation for the resulting optimization problems valid for all considered quantization and detection schemes. Since the obtained unified optimization problem is non-convex and difficult to solve, we transform it into a bi-convex problem, i.e., a problem that is convex in each optimization variable assuming the other variables are fixed. Exploiting this property, we develop an efficient algorithm based on golden section search (GSS) and alternating convex optimization (ACO) to obtain a suboptimal solution. Moreover, we analyze the asymptotic computational complexities of the proposed algorithm and the considered quantization and detection schemes as functions of the numbers of MUs, RUs, and RU antennas.

• We propose to employ hybrid RF/FSO systems for wireless fronthauling of C-RANs and to adaptively optimize the RF transmission time allocated to the multiple-access and fronthaul links. To the best of the authors’ knowledge, this system architecture has not been considered in the literature before.

• We investigate several quantization schemes for use at the RUs and two different detection schemes for use at the CU in order to strike a balance between complexity and performance. Moreover, we formulate a unified weighted sum rate maximization problem which is valid for any pair of the considered quantization and detection schemes and derive an efficient suboptimal solution.

• Our results provide several interesting insights for system design. In particular, we show that the proposed RF time allocation and fronthaul compression policies can achieve a significant performance gain compared to pure FSO fronthauling, especially under adverse weather conditions. Furthermore, under such unfavorable conditions, applying an efficient quantization scheme at the RUs is crucial for the overall performance since the limited fronthaul capacity has to be used as effectively as possible. In contrast, under good atmospheric conditions, applying simple AVQ yields a performance close to that achieved with DSC.

The rest of this paper is organized as follows. In Section II, we provide the system and channel models. In Section III, we present the compression and detection strategies for the considered uplink C-RAN. In Section IV, the rate region maximization problem is formulated and an adaptive algorithm for RF time allocation and fronthaul compression is developed. In Section V, the complexities of the proposed algorithms and quantization and detection schemes are analyzed. Simulation results are provided in Section VI, and conclusions are drawn in Section VII.

Notations: Boldface lower-case and upper-case letters denote vectors and matrices, respectively. The superscripts , , and denote the transpose, Hermitian transpose, and matrix inverse operators, respectively; and denote the expectation and matrix trace operators, respectively, and denotes the determinant of a matrix or the cardinality of a set. indicates that matrix is positive semidefinite; represents the -dimentional identity matrix. Set denotes the complement of set . Moreover, , , and denote the sets of positive real, real, and complex numbers, respectively. We use to denote a block diagonal matrix formed by matrices . Moreover, is defined as , denotes the natural logarithm, is the big-O notation, and  is used to indicate that  is a random complex Gaussian vector with mean vector  and covariance matrix .

## Ii System and Channel Models

In this section, we present the system model and the channel models for the multiple-access and fronthaul links.

### Ii-B Channel Model

In the following, we provide the channel models for the multiple-access and fronthaul links.

For the RF multiple-access links, we assume a standard fading additive white Gaussian noise (AWGN) channel. All MUs transmit simultaneously using the same frequency band. The signal received at RU is denoted by and is given by

 ym=Hmx+nm,∀m∈M, (1)

where is the vector containing the signals transmitted by all MUs and is the signal transmitted by MU. We assume , i.e., the signals transmitted by different MUs are independent; and is the transmit power of MU. In addition, is the noise vector at RU. The elements of , i.e., the noise samples at each antenna, are modelled as mutually independent zero-mean complex AWGN with variance . Moreover, denotes the channel matrix corresponding to the RF multiple-access links from the MUs to RU.

The fronthaul links are hybrid RF/FSO. For the FSO links, the aperture transmitter of each RU is directed towards the corresponding PD at the CU. We assume an intensity modulation direct detection (IM/DD) FSO system. Particularly, after removing the ambient background light intensity, the signal intensity detected at the -th PD of the CU is denoted by and modelled as [15]

 ~ym=gm~xm+~nm,∀m∈M, (2)

where is the symbol transmitted by RU, is the zero-mean real-valued additive white Gaussian shot noise with variance caused by ambient light at the CU, and denotes the FSO channel gain from RU to the CU’s -th PD. Moreover, we assume an average power constraint . The capacity of the IM/DD FSO link from RU to the CU’s -th PD is not known. Nevertheless, in [16, Eq. 26], the following rate has been shown to be achievable

 Cfsom=12Wfsolog2⎛⎝1+e~P2mg2m2πδ2⎞⎠% bits/sec, (3)

where is the bandwidth of the FSO signal.

We assume that the multiple-access and fronthaul links utilize the same RF resources. However, since the RUs are HD nodes w.r.t. the RF links, the RF multiple-access and fronthaul links cannot be used at the same time. Therefore, we adopt a time division duplex (TDD) protocol. In particular, we divide each RF transmission time slot into fractions such that holds, where in fraction of each time slot, the RF multiple-access links are active and in , fraction, the RF fronthaul link from RU to the CU is active. The RF signal of RU received at the CU is denoted by and given by

 ¯ym=Fm¯xm+¯nm,∀m∈M, (4)

where is the vector of signals transmitted over the antennas of RU. We assume that holds, where is the fixed transmit power of RU over the RF fronthaul link. denotes the noise vector at the CU. The noise at each antenna of the CU, i.e., each element of , is modelled as zero-mean complex AWGN with variance . Moreover, denotes the channel matrix of the RF fronthaul link from RU to the CU. The capacity of the RF fronthaul link between RU and the CU is obtained via optimal waterfilling as [15]

 (5)

where is the bandwidth of the RF signal. In (5), is the -th singular value of and is the water level which is chosen to satisfy the power constraint as the solution of the following equation

 min{N,L}∑j=1[μ−ϱ2χ2m,j]+=¯Pm. (6)

## Iii Compression, Transmission, and Detection Strategies

In this section, we first present the three considered compression schemes for the RUs, then we introduce the transmission strategy over the fronthaul links, and finally, we explain the two considered detection schemes for the CU. The block diagram of the considered uplink C-RAN is depicted in Fig. 2.

### Iii-a Fronthaul Compression

We assume that the RUs employ compress-and-forward relaying and quantize their received signals. Let and denote the vectors of received and quantized signals at all RUs where is the quantized version of RU’s signal. In particular, the RUs quantize their received imphase/quadrature (I/Q) samples with a sampling rate of and forward the compressed signals to the CU through hybrid RF/FSO fronthaul links. Assuming the Gaussian quantization test channel, is given by [7]

 ^y=y+z, (7)

where is the quantization noise and is the joint distortion matrix at the RUs. The mean square distortion between the received I/Q sample vector and the corresponding quantized vector is given by the main diagonal entries of . In this paper, we consider three commonly used quantization schemes, namely AVQ, RVQ, and DSC, which offer different trade-offs between complexity and performance.

#### Iii-A1 Per-Antenna Vector Quantization

One simple way of compressing the received signals at the RUs is to independently quantize the I/Q samples at each antenna of the RUs. In this case, is a diagonal matrix. Let be the distortion at the -th antenna of RU, i.e., , where and denote the quantized and the original versions of the signal received at the -th antenna of RU, respectively. Therefore, is obtained as . Moreover, based on the lossy source coding theorem [17], the output rate of the quantizers at RU, denoted by , has to meet the following constraint in order to guarantee a maximum distortion of

 rm≥α0fs∑∀nI(ym,n;^ym,n),∀m∈M, (8)

where is given by

 I(ym,n;^ym,n)=log2hTm,nΣh∗m,n+dm,n+σ2dm,n. (9)

Here, is a vector containing the channel coefficients between the MUs and the -th antenna of RU.

#### Iii-A2 Per-RU Vector Quantization

A higher compression rate can be achieved if each RU exploits the correlation between the signals it receives at different antennas and jointly quantizes the I/Q samples of the antennas. In this case, is a block diagonal matrix with matrix entries , where , i.e., the covariance matrix of at RU, is in general non-diagonal. Moreover, according to the lossy source coding theorem [17], the output rate of the quantizer at RU has to satisfy the following constraint if the original signal is to be recovered with maximum distortion

 rm≥α0fsI(ym;^ym),∀m∈M, (10)

where is given by

 I(ym;^ym)=log2∣∣HmΣHHm+Dm+σ2IN∣∣|Dm|. (11)

#### Iii-A3 Distributed Source Coding

Since the received signals at different RUs are statistically dependent, the RUs can exploit this dependence via DSC. It has been shown that multiple isolated sources, i.e., the RUs here, can compress data using DSC as efficiently as if they knew each others’ received signals and jointly quantized them [17, 18, 19]. In DSC, each RU quantizes its received signal based on the conditional statistics of (or the correlation model between) the RUs’ signals and not based on the actual signals received at the other RUs [19]. Let denote the joint distortion matrix of the signals received at all RUs. Based on the lossy source coding theorem [17], for DSC, the output rates of the quantizers at the RUs have to meet the following constraints

 ∑∀m∈Srm≥α0fsI(y(S);^y(S)|^y(Sc)),∀S⊆M, (12)

where in vectors and , we collect all and for , respectively, and is a non-empty subset of . Moreover, is given by

 (13)

where matrices and contain all and , respectively, whose indices belong to set .

### Iii-B Fronthaul Transmission

Due to the limited available RF spectrum, we assume that both the multiple-access and the fronthaul RF links utilize the same RF resource. Hence, the advantages of employing a hybrid RF/FSO system for fronthauling come at the expense of a bandwidth reduction for the multiple-access links. In the following, we propose an adaptive protocol which divides the available RF transmission time between the multiple-access and the fronthaul links in a TDD manner222Although TDD based time sharing of RF resources between multiple-access and fronthaul links is in general suboptimal, we adopt this approach here to avoid interlink interference, which facilitates a simple transceiver design.. Recall that fraction of each RF time slot is allocated to the fronthaul link from RU to the CU. Hence, a fraction of of each RF time slot is available for the multiple-access links. For future reference, we define , where . In order to recover reliably at the CU, based on the channel coding theorem [17, 18], the rate at the output of the quantizers at RU has to meet the following constraint

 rm≤Cfsom+αmCrfm,∀m∈M, (14)

where and are given in (3) and (5), respectively.

### Iii-C Detection at the CU

For detection of the signals transmitted by the MUs at the CU, we consider two detection schemes, namely a suboptimal linear detector and an optimal detector. The linear detector requires lower computational complexity compared to the optimal detector at the expense of a loss in performance. In the following, we explain the detectors employed at the CU in detail. For future reference, let denote the channel vector corresponding to the RF multiple-access links from MU to all RUs.

#### Iii-C1 Linear Detector

We assume that is a linear filter applied at the CU in order to recover MU’s signal . The corresponding output signal of the filter is given by . Let denote the signal-to-interference-plus-noise ratio (SINR) at the output of the filter for MU which is given by

 γk=PkmHkhkhHkmkmHk(∑Kj≠kPjhjhHj+D+σ2IMN)mk. (15)

The linear filter that maximizes , denoted by , and the maximum SINR can be obtained using the Rayleigh quotient inequality [20] as

 msinrk =⎛⎝K∑j≠kPjhjhHj+D+σ2IMN⎞⎠−1hkand (16) γoptk =PkhHk⎛⎝K∑j≠kPjhjhHj+D+σ2IMN⎞⎠−1hk, (17)

respectively. As can be observed, (17) is identical to the SINR at the output of an MMSE detector [21]. Hence, the optimal linear detector that maximizes the MUs’ SINRs is the well-known linear MMSE detector. Therefore, for the Gaussian RF access channel in (1) and the Gaussian quantization test channel in (7), the transmission rate of MU employing linear MMSE detection at the CU is obtained as [7]

 Rk(α0,D)=α0WrfI(xk;(msinrk)H^y)=α0Wrflog2(1+γoptk). (18)

Using Sylvester’s determinant theorem [22], (18) can be written as follows

 Rk(α0,D)=α0Wrflog2∣∣∑Kj=1PjhjhHj+D+σ2IMN∣∣∣∣∑Kj≠kPjhjhHj+D+σ2IMN∣∣. (19)

#### Iii-C2 Optimal Detector

For the multiple access channel (MAC), SIC is capacity-achieving. Assume an arbitrary decoding order of , and suppose the MUs are indexed according to the decoding order. The SIC decoder first decodes the signal of MU, subtracts it from the combined signal, , and continues until all MUs’ signals are decoded. Therefore, the signal based on which the message of MU is decoded is given by . For the Gaussian RF MAC in (1) and the Gaussian quantization test channel in (7), the transmission rate of MU employing SIC at the CU is obtained as [7]

 (20)
###### Remark 1

Employing SIC to achieve (20) is equivalent to applying MMSE decision-feedback equalization to the signal received at the CU. This is due to the fact that for Gaussian inputs and the AWGN channel, the MMSE filter is information lossless [23, Proposition 5.13]. In other words, holds, where is the MMSE filter after removing MU’s signal .

## Iv Rate Region Maximization

In this section, we first formulate the rate region maximization problems for the considered quantization and detection schemes, and then represent them in a unified form. Since the resulting unified problem is non-convex, we transform the problem to a bi-convex problem which enables the derivation of an effective suboptimal solution. Based on this solution, we propose an adaptive protocol for joint optimization of the fronthaul compression and the RF time allocation.

### Iv-a Problem Formulation

Let be the vector of the output rates of the quantizers at the RUs. Moreover, let , where is the weight representing the priority associated with MU. Without loss of generality, we assume that , and . Then, the rate region maximization problem can be formulated as the following weighted sum rate maximization problem

 maximizeα∈A,D⪰0,r K∑k=1μkRk(α0,D) subjectto C1:rsatisfies(???)or(???)or(???), C2:rsatisfies(???),

where is the transmission rate of MU given in (19) and (20) for the MMSE and SIC receivers at the CU, respectively. Moreover, constraints (8), (10), and (12) in are the source coding constraints for AVQ, RVQ, and DSC, respectively, and (14) in represents the channel capacity constraint.

To fully characterize the rate region, we have to solve (IV-A) for all possible . Note that for a given , the optimal SIC order is obtained by decoding the MUs with smaller weights first [8]. Thereby, without loss of generality, we assume resulting in the decoding order in the following.

Next, we provide a unified representation of (IV-A) which enables us to solve this problem for all considered quantization and detection schemes efficiently. To this end, we first define some auxiliary variables. Let denote a subset of , where is the index set of all antennas at the RUs. Moreover, we define as the index set corresponding to all antennas of RU, where is a non-empty subset of . Furthermore, we define for any given set as the pseudo identity matrix of size whose entry in the -th row and -th column is equal to if the -th element of set is equal to , otherwise it is equal to zero. Based on the definition of , we use to select the -th entries of and . For instance, for and , holds.

Using the above notations, in the following proposition, we present (IV-A) for all adopted quantization and detection schemes in a unified manner.

###### Proposition 1

The achievable rate region maximization problem (IV-A) can be formulated in unified form for the considered quantization and detection schemes as follows

 maximizeα∈A,D,r α_0 W^rf∑_k=1^K μ_klog_2—Vk+D——Wk+D— (22) subjectto C1: ∑_m∈Sr_m ≥α_0 f_s log_2—C(S)+I(TS)D(I(TS))T+σ2I—S—N——I(TS)D(I(TS))T—, ∀S ∈¯S, C2: r_m≤C_m^fso+α_mC_m^rf, ∀m ∈M, C3: I(T)D(I(T))^T⪰0  and I(T)D(I(T^c))^T=0_—T—×—T^c—, ∀T ∈¯T,

where is the complement set of w.r.t. . Furthermore, , , and are constant matrices and and are constant sets defined as follows

 Vk Wk = {∑j≠kKPjhjhjH+σ2IMN,MMSE∑j¿kKPjhjhjH+σ2IMN,SIC C(S) ={diag{H(S)Σ(H(S))H},AVQH(S)Σ(H(S))H,RVQ and DSC ¯S = {{∀S⊂M∣—S—=1},AVQ and RVQ{∀S∣S⊆M},DSC
 ¯T=⎧⎪⎨⎪⎩{∀T⊂NT∣|T|=1},AVQ{∀TS∣S∈M},RVQ{∀T⊆NT∣|T|=MN},DSC.%
###### Proof:

The proof is provided in Appendix A. \qed

Note that optimization problem (22) is jointly non-convex in . Hence, a brute-force search may be needed for finding the globally optimal solution which entails a prohibitively high computational complexity. In addition, we note that given , (22) is non-convex w.r.t. since the objective function of the maximization problem is convex instead of concave. Therefore, in the following, we reformulate the objective function of (22) such that the problem becomes bi-convex, i.e., a problem that is convex in each optimization variable assuming the other variables are fixed. This facilitates the application of ACO for finding a suboptimal solution to (22). To further reduce complexity, we transform constraint such that the optimization vector is replaced by scalar optimization variable . The optimal can then be found with a simple one dimensional search.

### Iv-B Problem Transformation

In this section, we present a transformation of the constraints and the objective function which allows us to efficiently tackle non-convex optimization problem (22).

#### Iv-B1 Constraint Transformation

Given , the problem is linear w.r.t. and hence can theoretically be handled by ACO. However, the ACO algorithm fails to converge to an efficient suboptimal solution due to the following reason. For a given in the -th iteration, the optimal is found such that constraint holds with equality. Therefore, in the next iteration, given , the optimal returns the same solution as in the previous iteration, i.e., . In other words, the ACO algorithm will be trapped at the initial point . To overcome this challenge, we reformulate the constraints in terms of such that the reformulated problem does not exhibit the aforementioned issue. The following lemma provides an equivalent representation of the channel capacity constraint in (22) which is useful for handling .

###### Lemma 1

Constraint in (22) can be written in the following equivalent form

 ˜C2:∑∀m∈SrmGm(S)≤(1−α0)G(S)+∑∀m∈SGm(S)Cfsom,∀S⊆M, (23)

where , , and denotes a non-empty subset of .

###### Proof:

The proof is provided in Appendix B. \qed

###### Remark 2

The equivalence of constraint in (23) and in (22) is analogous to the equivalence of the capacity region of the MAC and the rate region achieved via time-sharing and SIC, see [18, Chapter 15]. The advantage of Lemma 1 is that the -dimensional optimization variable in in (22) reduces to the one-dimensional optimization variable in in (23). This comes at the expense of increasing the number of constraints from to .

#### Iv-B2 Golden Section Search (GSS)

Now, since variable is bounded in the interval , the optimal can be obtained via a full search assuming the optimal have been already obtained for any given . Note that since is one-dimensional and bounded, a full search is feasible; however, the optimal can be found even more efficiently as described in the following. In particular, assuming that the weighted sum rate is a unimodal function w.r.t. , i.e., a function that has only one optimal point in a given bounded interval [24], the efficient GSS algorithm can be used to find the optimal . Please refer to Appendix C for a detailed discussion of the unimodality of the weighted sum rate and the proof for the special case of . For the general case, a rigorous proof for the unimodality of the weighted sum rate is cumbersome. Nevertheless, our simulation studies in Section VI suggest that the weighted sum rate is in general unimodal w.r.t. . Furthermore, in the following, an intuitive justification for this property is provided. In particular, increasing affects the weighted sum rate in two aspects, namely the RF multiple-access time interval increases and the distortion caused by the quantization also increases as the RF fronthaul time interval decreases. In other words, by increasing , the weighted sum rate first increases owing to the increasing RF multiple-access time interval, but ultimately decreases due to the decreasing fronthaul capacity and the resulting larger distortion. We note that the required number of iterations, denoted by , for finding with an accuracy of is for the GSS and for the full search [24]. Hence, we employ the GSS to find the optimal . In the following, we assume is fixed and find the corresponding optimal .

#### Iv-B3 Objective Function Transformation

The problem in (22) is non-convex in since the objective function of the maximization problem is convex in (instead of concave). We use the following lemma to convexify the objective function of (22) w.r.t. .

###### Lemma 2 ([25])

For any matrix which satisfies , the following equation holds

 log2|X−1|=maxY⪰0log2|Y|−1ln(2)Tr(YX)+Jln(2), (24)

where the optimal solution of the right-hand side of (24) is given by .

Defining and , where is a new auxiliary optimization matrix, and applying Lemma 2 to the objective function of (22) and replacing the original constraint with the equivalent constraint from Lemma 1, we reformulate optimization problem (22) as follows

 maximizeα0∈[0,1],D,r,Bk⪰0,∀k T= α_0 W^rf∑_k=1^K μ_klog_2—V_k+D—+μ_klog_2—B_k—-μkln(2)Tr(B_k(W_k+D)) subjectto C1: ∑_m∈Sr_m ≥α_0 f_s log_2—C(S)+I(TS)D(I(TS))T+σ2I—S—N——I(TS)D(I(TS))T—, ∀S ∈¯S, ˜C2: ∑_∀m∈S r_m G_m(S)≤(1-α_0)G(S)+∑_∀m∈SG_m(S)C_m^fso,   ∀S ⊆M, C3: I(T)D(I(T))^T⪰0  and I(T)D(I(T^c))^T=0_—T—×—T^c—,   ∀T ∈¯T.

Although, for a given , optimization problem (IV-B3) is still jointly non-convex in , the problem is convex w.r.t. the individual variables. This allows the use of ACO to find a suboptimal solution of the problem in terms of for any given . In particular, given , the problem is convex w.r.t. and can be solved using standard semi-definite programming (SDP) solvers [26]. Moreover, given , the problem is convex w.r.t. and has the following optimal closed-form solution based on Lemma 2

 B∗k=(Wk+D)−1. (26)

In the following subsection, we propose a nested loop algorithm which exploits Lemma 1 and Lemma 2.

### Iv-C Proposed Algorithm

We propose an algorithm consisting of an outer loop, i.e., Algorithm 1, to find based on GSS, and an inner loop, i.e., Algorithm 2, to find for a given based on ACO, respectively. In the following, we describe the outer and inner loops in detail.

Outer Loop (Algortihm 1): In this loop, we employ the iterative GSS algorithm to maximize the weighted sum rate w.r.t. . Suppose is the search interval in a given iteration. In each iteration, the GSS algorithm requires the evaluation of the weighted sum rate at the intermediate points using the inner loop, i.e., Algorithm 2. Then, it compares the values of the objective function at and denoted by and , respectively, and updates the search interval for the next iteration as follows [24]

 Search Interval={[αmin0,α(2)0]ifT(1)≥T(2),ifT(1)

where are obtained as

 (α(1)0,α(2)0)=(αmin0+ρΔα,αmax0−ρΔα). (28)

Here, and with the so-called golden ratio  [24]. The GSS algorithm is summarized in Algorithm 1 for the problem considered in this paper. In Algorithm 1, is a small positive number which is used in line 10 to terminate the iteration if the desired accuracy of the GSS algorithm is achieved.

Inner Loop (Algortihm 2): For the inner loop, is provided by the outer loop. Here, we employ ACO to find a stationary point of the problem w.r.t. , , and for the fixed . The proposed ACO method is concisely given in Algorithm 2 where is a small positive number and is the maximum number of iterations that are used in the termination condition in line 7. In the next section, we provide a modified ACO (M-ACO) method, which is easier to implement in popular numerical solvers such as CVX and also included in Algorithm 2 in blue italic font.

Once is known, the optimal RF time allocation variable for fronthaul link RU-CU, , is obtained as

 α∗m=r∗m−CfsomCrfm,∀m∈M. (29)

### Iv-D Implementation in CVX

Although for given , , and , optimization problem (IV-B3) is convex in , its solution using popular numerical solvers, such as CVX [26], can be challenging. More specifically, the implementation of line 5 of Algorithm 2 in CVX is not directly possible since the current version of CVX, i.e., CVX 2.1, does not have a built-in convex function that can directly handle the right-hand side of in (IV-B3) [26]. In the following, we propose a transformation of denoted by to address this issue. Defining and , where is a new auxiliary optimization matrix, and using (24) in Lemma 2, the following inequality holds for the right-hand side of in (IV-B3)

 log2∣∣C(S)+I(TS)D(I(TS))T+σ2I|S|N∣∣|I(TS)D(I(TS))T| ≤−log2|A(S)|+1ln(2)Tr(A(S)(C(S)+I(TS)D(I(TS))T+σ2I|S|N)) −|S|Nln(2)−log2∣∣I(TS)D(I(TS))T∣∣≜Rub,∀A(S)⪰0. (30)

In (IV-D), equality holds if

 (31)

Substituting (IV-D) into in (IV-B3), we obtain as follows

 ˜C1:∑m∈Srm≥α0fsRub, (32)

which is convex in if is fixed and vice versa. We note that the feasible set of w.r.t. is identical to that of w.r.t. . In fact, since for any (IV-D) holds, the feasible set of cannot be larger than that of . On the other hand, since contains as a special case, the feasible set of cannot be smaller than that of either. Hence, the feasible sets of and are identical. Therefore, we can rewrite problem (IV-B3) in the following equivalent form

 maximizeα0∈[0,1],D,rBk⪰0,∀k,A(S)⪰0,∀S T subjectto ˜C1,˜C2,andC3,

where , , and are given in (IV-B3) and is given in (32).

Based on (IV-D), an M-ACO algorithm w.r.t. can be developed. The corresponding changes are provided in blue italic font in Algorithm 2.

###### Remark 3

In summary, we note that the global optimal solutions of the three non-convex optimization problems (22), (IV-B3), and (IV-D) lead to identical values for the weighted sum rate. However, unlike original problem (22), problem (IV-B3) can be solved suboptimally using ACO, and problem (IV-D) can be solved suboptimally using ACO via CVX. The obtained suboptimal solutions are local optima of original problem (22) because of the convergence properties of ACO, see e.g. [27, 28]. Therefore, we use (IV-D) to generate the simulation results shown in Section VI.

## V Complexity Analysis

In this section, we first analyze the computational complexity of Algorithms 1 and 2. Subsequently, we characterize the complexity of the quantization and detection schemes adopted in this paper for online transmission.

### V-a Algorithms 1 and 2

In the complexity analysis presented below, we only consider the operations that entail the highest computational complexity. In particular, calculating the matrix inversion in Step 4 and solving the SDP problem in Step 5 of Algorithm 2 dominate the overall complexity of both Algorithms 1 and 2 and are discussed in the following.

Step 4: The computational complexity of inverting a matrix of size is [29, 30]. The ACO in Algorithm 2 includes a matrix inversion for finding for MUs which entails a complexity of . For M-ACO, matrix inversions are needed to find both and . The complexity of computing depends on the type of quantization employed at the RUs, cf. (31), and is for AVQ since is diagonal, for RVQ since is block diagonal, and for DSC since is a general matrix and has to be computed for . Therefore, the overall complexity of computing and in Step 4 of M-ACO is for AVQ and RVQ and for DSC.

Step 5: The computational complexity required per iteration for solving an SDP with a numerical convex program solver is given by [31, 27], where and denote the number of semidefinite cone constraints and the dimension of the semidefinite cone, respectively. Moreover, the number of iterations needed to solve the SDP problem with an accuracy of is on the order of iterations [31, 27]. This leads to a complexity order of . For AVQ, we do not have semidefinite cones; hence the complexity of Step 4 is higher than the complexity of Step 5. Moreover, for RVQ, we have and . Hence, the complexity of solving one SDP is . For DSC, and , and the corresponding complexity for solving one SDP is .

Overall: The overall complexity of the proposed algorithms is limited by the complexity of Steps 4 and 5 of Algorithm 2. Moreover, Steps 4 and 5 are repeated at most times for Algorithm 2 to converge and times for Algorithm 1 to converge. Therefore, the overall complexity order of the proposed algorithm is given by

 ⎧⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪⎨⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪⎩O(KM3N3nmaxlog1ϵ)ACO/M-ACO algorithm with AVQ,O((KM3N3+MN3.5)nmaxlog1ϵ)ACO/M-ACO algorithm with RVQ,O((KM3N3+M3.5N3.5)nmaxlog1ϵ)ACO algorithm with DSC,O(((K+2M)M3N3+M3.5N3.5)nmaxlog1ϵ)M-ACO algorithm with DSC. (34)

As can be seen from (34), the complexities of both ACO and M-ACO grow linearly w.r.t. the number of MUs, , for all considered quantization and detection schemes. However, the growth speed of the complexity of ACO/M-ACO in terms of the number of RUs, , and their number of antennas, , depends on the adopted quantization scheme. For instance, the complexity of ACO/M-ACO in terms of the number of antennas, , for AVQ is cubic while it is on the order of for RVQ and DSC.