 Research
 Open Access
Spectral efficiency of multiuser millimeter wave systems under single path with uniform rectangular arrays
 Weiqiang Tan^{1, 2},
 Shi Jin^{2}Email author,
 ChaoKai Wen^{3} and
 Tao Jiang^{4}
https://doi.org/10.1186/s1363801709664
© The Author(s) 2017
Received: 10 March 2017
Accepted: 18 October 2017
Published: 6 November 2017
Abstract
In this paper, we investigate the downlink achievable ergodic spectral efficiency (SE) of a singlecell multiuser millimeter wave system, in which a uniform rectangular array is used at the base station (BS) to serve multiple singleantenna users. We adopt a threedimensional channel model by considering both the azimuth and elevation dimensions under singlepath propagation. We derive the achievable ergodic SE for this system in with maximum ratio transmission precoding. This analytical expression enables the accurate and quantitative evaluation of the effect of the number of BS antennas, signaltonoise ratio (SNR), and the crosstalk (squared inner product between different steering vectors) which is a function of the angles of departure (AoD) of users and the interantenna spacing. Results show that the achievable ergodic SE logarithmically increases with the number of BS antennas and converges to a value in the high SNR regime. To improve the achievable ergodic SE, we also propose a user scheduling scheme based on feedback of users’ AoD information and obtain the maximum achievable ergodic SE. Furthermore, we consider a dense user scenario where every user’s AoD becomes nearly identical and then derive the system’s minimum achievable SE.
Keywords
 Millimeter wave communications
 Multiuser MIMO
 Maximum ratio transmission
 Spectral efficiency
 User scheduling
 Uniform rectangular array
1 Introduction
With the proliferation of smart wireless services such as highdefinition video streaming, cell broadcasting, and mobile TV, mobile data traffic is envisioned to grow 1000fold by 2020 [1]. To meet the predicted traffic demand, millimeter wave (mmWave) communications, which operates in the 30–300 GHz band, appear to be a promising candidate for next generation cellular systems that support multiple gigabitpersecond data rates [2]. Some recent results have demonstrated that the use of mmWave communications helps to achieve gigabytes per second (Gbps) data rates in both indoor [3] and outdoor wireless networks [4].
One salient feature of mmWave communications is the significant decrease of carrier wavelength, which allows a large number of antennas to be packed into a base station (BS) or an access point. The excessive use of transmit antennas, known as massive multipleinput multipleoutput (MIMO) technology [5, 6], provides substantial array gains to combat severe path loss and establishes reliable communication links. Furthermore, deployment of large antenna arrays at the BS enables efficient precoding for multiple data streams, thus improves the spectral efficiency and energy efficiency [7, 8]. To take advantage of the available space at the BS of a massive MIMO system, it is desirable to arrange the antenna elements as a uniform linear array (ULA), a cubic array, or a uniform rectangular array (URA)[9].
Most importantly, rectangular or cubic arrays enable not only horizontal beam pattern adaptation but also vertical beam pattern adaptation, that is, they enable threedimensional (3D) beamforming, which provides an additional degreeoffreedom for interference suppression [10]. Therefore, exploitation of 2D antenna array configurations such as uniform circular array (UCA) or URA in mmWave MIMO systems is desirable.
1.1 Related works
Several aspects of mmWave MIMO communications, including channel measurements [11–13], channel estimation [14–16], hybrid analog/digital precoding design [17–20], multiple amplifyandforward (AF) relaying networks [21–23], and multiplecell cooperative communication [24], have been investigated in prior works. In particular, various potential techniques for mmWave cellular systems were specified in [11] by considering mmWave channel characteristics. The measurements in [12, 13] demonstrated that mmWave channels have limited lightofsight (LoS) components and are related to the steering vectors which depend on the antenna array topologies of the transmitter and receiver. To enhance the beamforming gains and enable multiplexing of multiple data streams, hybrid precoding techniques were proposed in [19], where the processing was divided between the analog and digital domains. In [17], a hybrid precoding technique that requires only partial knowledge of mmWave channels was presented. The work in [19] proposed a lowcomplexity hybrid analog/digital scheme, which effectively relaxes the hardware constraints for downlink multiuser mmWave systems. A similar investigation was conducted in [14]. In [15], a compressed sensingbased channel estimation was proposed by exploiting the sparse nature of large mmWave MIMO channels. By leveraging the static nature of the angle of arrival and angle of departure (AoD), the work in [16] studied the performance of zeroforcing (ZF) beamforming with limited feedback. More recently, authors in [18] studied a multiuser MIMO downlink transmission scheme over Rician fading channels, where the BS exploits statistical channel state information (CSI). From these prior works, we notice that fully utilizing static or statistical information is one of the key requirements for massive mmWave MIMO systems.
Despite these existing body of literature, only few analytical results are available on the achievable ergodic spectral efficiency (SE) of multiuser mmWave MIMO systems, particularly for the 3D channel model. Owing to different propagation characteristics at such high frequencies, the coverage and rate trends differ drastically from the conventional cellular systems. In [25], the coverage and rate performance were studied for mmWave systems. The results of [25] showed that the achievable rate is sensitive to the density of BSs and the blockage distribution. In multiuser mmWave MIMO systems, linear precoding method such as ZF can perform almost as well as dirty paper coding, the multiuser mmWave MIMO system analysis of which is studied in [26]. However, the computation complexity due to the inverse of large dimensional matrix makes it inappropriate for realtime processing. In contrast, maximum ratio transmission (MRT) precoding is simple and exhibit near optimal performance (though worse than ZF) has received significant research interest [5]. Hence, studying the achievable ergodic SE of multiuser mmWave systems using MRT precoding is of special interest.
1.2 Summary of contributions

We present an exact closedform expression for the achievable ergodic SE by using MRT precoding under the assumption that the BS has perfect channel state information. We also present the achievable ergodic SE that is valid for massive numbers of antenna elements in the highSNR regime. We make use of these results to discuss the impact of the number of BS antennas, SNR, crosstalk, users’ AoD, and the interantenna spacing.

To improve the performance of MRT precoding, a user scheduling scheme is proposed by exploiting statistical users’ AoD information. We select a set of user group satisfying the orthogonal criterion and derive the corresponding maximum achievable SE. We also consider a dense user scenario and present an expression for the minimum achievable SE.
The remainder of this paper is organized as follows. Section 2 introduces the system model. Section 3 derives an exact closedform expression of the achievable ergodic SE for a finite number of users and antenna elements. Based on these analytical results, several practical insights are presented. Numerical and simulation results are also provided in Section 4, and we conclude the paper in Section 5.
Notation—Throughout the paper, matrices and vectors are expressed as upper and lower case boldface letters, respectively. Moveover, (·)^{ H } denotes the conjugate transpose; ∥·∥ and · represent the Euclidean norm and absolute value, respectively; \(\mathbb {E} \{\cdot \}\) and ⊗ represent the expectation operator and kronecker product, respectively; I _{ M } denotes an M×M identity matrix; e denotes the base of the natural logarithm; the notation x \(\sim {\mathcal {C}N}(0,1)\) means that x is the complex Gaussian random variable with mean zero and variance one; e is the EulerMascheroni constant; \({\text {Ei}\left ({ x} \right) =  \int _{x}^{\infty } {\frac {{{e^{ t}}}}{t}} {{dt}}}\) denotes the exponential integral function ([27], Eq. (8.211.1)); \({{E_{h}}\left (x \right) = \int _{1}^{\infty } {{t^{ h}}{e^{ xt}}dt}}\) is the exponential integral of order h([27], page xxxv), and \({\Gamma \left ({a,x} \right) = \int _{x}^{\infty } {{e^{ t}}} {t^{a  1}}dt}\) is the upper incomplete gamma function ([27], Eq. (8.350.2)).
2 System model
where ρ and g _{ k } (k=1,…,K) denote the average SNR and antenna array gain for user k, w _{ k }, and w _{ j } is unitnorm precoding vectors of user k and user j, respectively, s _{ k } and s _{ j } denote the transmit zeromean Gaussian symbols for user k and user j, respectively, n _{ k } \(\sim {\mathcal {C}N}(0,1)\) is the additive noise at the kth user, and \({\mathbf {h}_{k}} \in \mathbb {C}^{N_{t} \times 1 }\) is the mmWave channel vector from the BS to the kth user. We assume that the inputs must satisfy a transmit power constraint such that ∥w _{ j }∥=1 and \(\mathbb {E}\{s_{j}^{2}\} = 1\) for j=1,…,K and consider the equal power allocation scheme.
2.1 Channel model
where β _{ k } and β _{ k,l } are the complex gain of LoS path and multipath components for the kth user, respectively, i.e., β _{ k } (β _{ k,l })\(\sim {\mathcal {C}N}(0,1)\), L denotes the number of multipath components, ϕ _{ k } and θ _{ k } are the azimuth and elevation AoD of the kth user, respectively, and v(θ _{ k },ϕ _{ k }) is the steering vector with respects to antenna array configuration.
are the steering vectors in the horizontal and vertical directions, respectively. Herein, k _{0}=2π/λ is the number of waves and λ is the carrier wavelength; N _{ x } and N _{ y } are the numbers of antenna elements placed in the horizontal and vertical dimensions, respectively; d _{ y } and d _{ x } denote the interantenna spacing in the horizontal and vertical directions, respectively, which has a linear relationship with λ i.e., d _{ x }=d _{ y }=η λ with η being an any positive real number. Accordingly, the total number of BS antennas N _{ t } is equal to N _{ x }×N _{ y }. In the following subsection, we present the definition of antenna array gain.
2.2 Antenna array gain
where ϕ _{3dB} and θ _{3dB} represent the horizontal and vertical halfpower beamwidth, respectively, A _{ m } represents the maximum attenuation of the array antennas, and θ _{tilt} denotes the antenna tilting angle which is allowed to be adjusted within the given interval.
From (9), we observe that the antenna gain g _{ k } depends on the maximum antenna gain, the antenna tilting angle, the azimuth and elevation AoD of the kth user, and the horizontal and vertical halfpower beamwidth, respectively. In practice, once the system configuration is complete, it means the maximum antenna gain, the antenna tilting angle, and the horizontal and vertical halfpower beamwidth is fixed. For the AoD information which includes the azimuth and elevation of the kth user, changes very slowly and remains constant over a coherence time intervals. Compared with the complex gain of LoS path, the AoD information is treated as statistical CSI and assumed to be known. We show the achievable ergodic SE of system in the following subsection.
2.3 Achievable ergodic SE
To maximize the total achievable ergodic SE in (12) is identical to maximize the achievable ergodic SE of the kth user in (11) since users are independent of each other. It is worth pointing out that the achievable ergodic SE depends on the choice of the transmit beamforming vector. To maximize the achievable ergodic SE in (11), the optimization aims to find the beamforming vectors w _{ i } (i=1,…,K) such that the w _{ k } must simultaneously maximize the numerator and minimize the denominator of (11). Among different choices, achievable rate achieving nonlinear precoders are known to involve high computational complexity. We show the following lemma that presents the optimal beamforming vector maximizing R _{sum} when the number of BS antennas grows without bound.
Lemma 1
Proof
which converges to zero when the number of BS antennas grows without bound and the AoDs of users are distinct (i.e. ϕ _{ k }≠ϕ _{ j } and θ _{ k }≠θ _{ j }),^{2} which leads to δ _{ x }≠0 and δ _{ y }≠0. Thus, the above criterion in (15) is satisfied. □
From Lemma 1, it is evident that the optimal beamforming vector \({\mathbf {w}_{k}^{\text {opt}}}\) is obtained by adopting MRT precoding. This is because when the number of BS antennas grows without bound, the channel steering vectors between different users become asymptotically orthogonal to each other. This implies that the interference between different users is effectively suppressed. Then, the achievable SE attains its maximum value.
where \({c = {1} /{{{\left \ {{\mathbf {h}_{k}}} \right \}}} = {1}/ {\sqrt {{{ {{\beta _{k}}} }N_{t}}}} }\) denotes the normalized factor of the precoding vector while the expectation in (19) is across all channel realizations of the complex gain β _{ k } and steering vector v _{ k }. We assume the azimuth and elevation angles of users are known a priori information because they can be obtained in practice via feedback.^{3}
3 Achievable SE analysis
In this section, we derive a new exact closedform expression of the achievable ergodic SE for an arbitrary number of BS antennas and investigate its behavior in the highSNR regime. Based on the analytical expression, we also evaluate the maximum achievable ergodic SE by a user scheduling method and the system’s minimum achievable ergodic SE in a dense user scenario.
3.1 Achievable SE analysis
In this subsection, we focus on deriving closedform expressions of the achievable ergodic SE by analyzing the SINR in (19). The following theorem is useful to calculate on the achievable ergodic SE when a URA configuration is employed at the BS.
Theorem 1
where the steering vector v _{ k } is defined in (4). Note that when the steering vector v _{ k } equals to the itself v _{ k }, we have a _{ k }=N _{ t }.
Proof
Subsequently, by substituting (31) and (32) into (22), followed by some basic algebraic manipulations, we arrive at the desired result. □
In Theorem 1, we observe that the closedform expression for the achievable ergodic SE is a function of the average SNR the number of users and crosstalk coefficients, which depends on the number of BS antennas, interantenna spacing and the azimuth and elevation AoDs. This makes it very different to understand the impact when the number of BS antennas is increased. To gain further insights, we have the following result.
Theorem 2
where a _{ j } was defined in (21).
Proof
Finally, substituting (39) and (40) into (35) concludes the proof. □
Based on Theorem 2, we observe that in highSNR regime, the achievable ergodic SE is a function of the number of BS antennas, the number of users and the crosstalk, which converges to a constant. The reason is both the signal power and the interference power increase as the SNR. This implies that the performance of the system with MRT precoding can severely deteriorate in a high SNR regime. More importantly, from Theorems 1 and 2, we observe the involvement of the crosstalk coefficients. According to the definition of a _{ j } in (21), we see that the crosstalk coefficient mainly depends on the particular antenna array configuration at the BS. Once the BS deployment is completed, the crosstalk coefficient a _{ j } can be easily acquired. In addition, other antenna array configuration such as UCA or ULA can be easily applied to Theorems 1 and 2 by plugging the corresponding crosstalk coefficients. The corresponding crosstalk coefficients a _{ j } for these antenna arrangements can be found in [32, 36].^{4}
According to (42), we see that the effect of the crosstalk coefficient on the achievable ergodic SE is difficult to derive for the general case. Alternatively, we first observe that the normalized crosstalk coefficient depends on the number of BS antennas in the horizontal and vertical planes, interantenna spacing in the corresponding horizontal and vertical planes, and users’ AoD. In the following, we present the effect of users’ AoD and interantenna spacing on the normalized crosstalk coefficient.
In order to improve the achievable ergodic SE, we now propose a user scheduling scheme by exploiting azimuth and elevation AoDs of users. The work in [36] has demonstrated that perfect orthogonality between different user channel vectors does exist for ULA and URA configurations under LoS propagation conditions. Herein, we only consider the URA configuration in the paper since ULA configuration is a special of the URA configuration.
3.2 Maximum and minimum achievable SE

The scheduler acquires AoD information of the users and according to the acquired AoD information, the users satisfying the orthogonal criterion are selected as the scheduled group. The specific process is that the AoD information is firstly estimated at mobile stations by using the estimated algorithm (such as multiple signal classification (MUSIC), estimation of signal parameters via rotational invariance techniques (ESPRIT), and subspace algorithms [10, 39]) and then fed back to BS via feedback of downlink channel. In closedloop frequency division duplex (FDD) MIMO system, downlink AoD information is usually fed back to BS in forms of codebook or channel quality indicator (CQI)[40, 41].

According to (42), if the steering vectors satisfy the orthogonal criterion for given an arbitrary finite number of BS antennas, then$$ \cos (N_{x} k_{0} d_{x} \delta_{x})=1 ~\textmd{or} / \textmd{and}~ \cos(N_{y} k_{0}d_{y}\delta_{y})=1. $$(43)As a result, we have the following conditions for δ _{ x } and δ _{ y }, respectively$$ {\frac{{{N_{x}} d_{x}}{\delta_{x}}}{\lambda} } = n_{x} ~\textmd{or} / \textmd{and}~ {\frac{{{N_{y}} d_{y}{\delta_{y}}}}{\lambda} } = n_{y}, $$(44)where n _{ x } and n _{ y } are any positive numbers. From (43), we obtain the conditions for the orthogonal criterion as follows:$$ {{N_{x}} \eta} \left(\sin {\theta_{k}}\cos {\phi_{k}}  \sin {\theta_{j}}\cos {\phi_{j}}\right)=n_{x}, $$(45)or/and$$ {{N_{y}} \eta} \left(\sin {\theta_{k}}\sin {\phi_{k}}  \sin {\theta_{j}}\sin {\phi_{j}}\right)=n_{y}. $$(46)
where η is defined in (4).

The BS transmits data streams to selected users with equal power allocation.
Remark 1
The conditions in (45) and (46) guarantee that the users in the selected set \(\mathcal {S}\) are mutually orthogonal. Combined with the user scheduling, the MRT precoding is able to obtain a similar performance to the ZF precoding for the reason that the selected user interferences have been completely canceled. Finally, note that our proposed scheduling method focuses on maximizing the achievable ergodic SE; however, in doing so, the fairness among the users is not guaranteed.
We now analyze the asymptotic performance of the proposed scheduling method.
Proposition 1
Proof
By applying the identity E _{1}(x)=−Ei(−x), we complete the proof. □
Remark 2
From Proposition 1, we observe that the achievable ergodic SE of MRT precoding with the user scheduling method is identical to that of the ZF precoding [ 42 ]. This is because the MRT transmission scheme with the scheduling criterion in Proposition 1 facilitates interuser interference cancelation. In addition, compared to the ZF precoding, the MRT precoding enjoys a much lower computational complexity and does not involve matrix inverse calculations, whereas the user scheduling scheme described above emphasizes the importance of selecting users for multiuser mmWave MIMO systems.
Corollary 1
Proof
By making the number of BS antennas and the SNR grow without bound, and performing by some basic algebraic manipulations, we prove the result. □
From Corollary 1, we see that in the high SNR regime, MRT precoding with orthogonal user scheduling not only reduces the consumption of transmit power but also ensures a high achievable ergodic SE. More importantly, the MRT scheme with user scheduling only needs a small number of channel feedback bits to perform nearideal interference cancelation because the steering vectors become deterministic. These observations clearly reveal the effectiveness of user scheduling.
We now focus on a dense user deployment. In a dense user scenario, e.g., conference hall, railway station, airplane, or subway entrances, many devices could be active within close proximity [44]. When users are colocated, the users’ AoDs shall become nearly identical. This extreme case introduces very high interuser interference. In the following proposition, we analyze this specific case and evaluate the minimum achievable ergodic SE of the system.
Proposition 2
Proof
By applying the identity E _{ h }(x)=x ^{ h−1} Γ(1−h,x), we complete the proof. □
From Proposition 2, we draw an interesting conclusion that the \( R_{k}^{\text {min}}\) is function of the SNR, the number of BS antennas and users. \(R_{k}^{\text {min}}\) decreases as the number of users increases. The reason is that E _{ K }(·) is a monotonically decreasing function of K, and contributes toward increasing the interuser interference. Therefore, increasing number of users in a dense user scenario cannot benefit the achievable SE. This observation is different from the case when the number of BS antennas is increased. Clearly, if we do not perform user scheduling, then the achievable SE tends to zero. The following corollary presents the impact of the SNR and the number of BS antennas on the downlink achievable SE.
Corollary 2
Proof
Corollary 2 showcases that the fixed the number of users, \(R_{k}^{\text {min}}\) converges to constant as the number of BS antennas grows without bound. This because the colocated users brings high interuser interference and degrades the performance. Therefore, improving the number of BS antennas and high SNR regime in this scenario cannot contribute to the achievable SE.
4 Numerical results
The azimuth and elevation angle of the users (K=8)
U _{1}  U _{2}  U _{3}  U _{4}  U _{5}  U _{6}  U _{7}  U _{8}  

ϕ  0.9681  2.1770  3.0080  1.7151  1.0428  0.7703  0.1790  1.2570 
θ  − 1.5008  0.6122  − 0.4638  0.2146  0.5428  − 0.7163  0.4769  1.0956 
5 Conclusions
This paper investigated the achievable ergodic SE of the downlink of singlecell multiuser mmWave systems, where the BS is equipped with a large number of transmit antennas and service multiple singleantenna users. An exact analytical expression for the achievable ergodic SE was derived. Results showed that the total achievable SE converges to a saturation value in the high SNR regime and increase remarkably in the large antenna regime. For finite number of antennas at the BS, we designed a user scheduling scheme based on users’ AoD information, and then derived the corresponding maximum achievable ergodic SE. Under this scheduling scheme, the total achievable SE with MRT precoding can be substantially improved. Furthermore, we presented the minimum achievable ergodic SE of a system based on a dense user case.
6 Endnotes
^{1} Note that although a uniform circular array configuration can also achieve 3D beamforming, channel steering vectors between different users do not achieve orthogonality with a large number of antennas [36].
^{2} Note that for randomly distributed users in the circularshaped cell, the condition ϕ _{ k }≠ϕ _{ j } holds with probability one.
^{3} The AoDs of the azimuth and elevation angles of the users can be obtained by the BS, and can be treated as constants over a long period [10].
where δ= cosϕ _{ k }− cosϕ _{ j }.
Declarations
Acknowledgements
We would like to thank the anonymous reviewers for their insightful comments on the paper, as these comments led us to an improvement of the work.
Funding
This work was supported by the National Natural Science Foundation of China under Grant 61531011, the International Science and Technology Cooperation Program of China under Grant 2014DFT10300, and the Guangzhou university project under Grant 27000503123.
Authors’ contributions
SJ conceived and designed the idea. WT performed the experiments and analyzed the data. CW and TJ gave valuable suggestions on the structuring of the paper and assisted in the revising and proofreading. All authors read and agreed the manuscript.
Competing interests
The authors declare that they have no competing interests.
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Authors’ Affiliations
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