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The substantial growth in mobile Internet and Inter-net of Things traffic has made system capacity enhancement the most important requirement for next-generation mobile communication systems.

Massive multiple-input, multiple-output (mMIMO) is a key technology for fulfilling the traffic requirements because of its strong potential to boost spectral efficiency (SE). However, the uplink and downlink of mMIMO usually operate in time-division duplex (TDD) or frequency-divi-sion duplex (FDD), which results in an insufficient utiliza-tion of time–frequency resources.

In this article, we present a smart composition utiliz-ing both mMIMO and in-band full-duplex (IBFD), called IBFD mMIMO, as a potential enabling technology for 5G and beyond. IBFD mMIMO can support multiple uplink and downlink users with the same time–frequency re-sources, and thus substantially enhances the SE. More-over, by exploiting the new degree of freedom provided by IBFD transmission, IBFD mMIMO can alleviate the complexity in the design of the base station (BS) due to

the large increase in the number of antennas. However, IBFD operation leads to high interference on the uplink and downlink when compared to that of the TDD/FDD system. To make the system feasible, we propose a new framework to mitigate the interference, based on state-of-the-art mMIMO precoding technologies.

Approaches to Boosting System CapacityIn [1], mobile data traffic is predicted to increase by approximately 1,000 times by 2020 and by more than 40,000 times by 2030. Key advances in mobile communi-cation systems are expected to meet the demands of this substantial increase in traffic.

The race to develop innovative fifth-generation (5G) solutions has begun worldwide. Recently, 4G Americas published a summary of global 5G initiatives that over-views 5G activities around the world [2]. Among these initiatives, 5G nonorthogonal waveforms for asynchro-nous signaling, funded by the European Union’s Sev-enth Framework Program, investigated nonorthogonal waveforms and other technology components for 5G development. The International Mobile Telecommuni-cations (IMT)-2020 (5G) Promotion Group, launched

Digital Object Identifier 10.1109/MVT.2018.2792198

Date of publication: 26 June 2018

A 5G-ENABLING TECHNOLOGY

Benefits, Feasibility, and Limitations of In-Band Full-Duplex mMIMO

Xiaochen Xia, Kui Xu, yurong Wang, and youyun Xu

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by the Ministry of Industry and Information Technol-ogy of China in 2013, aims to promote 5G research and facilitate global cooperation. Other countries, such as South Korea and Japan, have also embarked on devel-opment projects that focus on 5G system concepts, basic functions, and architectures.

Among these research programs, three approaches have been suggested to satisfy the data traffic require-ments of future mobile communication systems [3]: 1) SE enhancement through advanced air interface transmission technologies, 2) spectral extension, primarily by exploit-ing the millimeter-wave spectrum, and 3) network densi-fication by deploying massive infrastructures. We focus on increasing SE. According to [4], the SE of 5G systems is expected to reach 20~30 b/s/Hz, which outperforms the current long-term evolution (LTE) system—the 3rd Gen-eration Partnership Project (3GPP) LTE release 11—by a factor of ten and thus poses a significant challenge to it.

The major contributor for achieving this target is mMIMO [5], [6]. The benefits of mMIMO are twofold:

■ It increases the gain of conventional MIMO systems. With an N-antenna BS and K single-antenna users, we can achieve a multiplexing gain of K as long as .K N# By increasing both N and ,K we can obtain a consid-erable SE gain.

■ By coherent processing of signals over the large-scale antenna array, mMIMO can focus the signal energy to the direction of the desired users in the downlink and provide a considerable array gain in the uplink. As a result, the power consumption of both the BS and users can be substantially reduced.In TDD mMIMO [5]–[7], the multipath propagation

channels are reciprocal. This property is exploited at the BS to estimate the channel responses through uplink training. The training overhead scales linearly with the number of users and is independent of the number of BS antennas, which is acceptable in most typical scenarios [5]. Moreover, recent studies have shown that the train-ing overhead can be further reduced by exploiting users’ location information, which is useful for cases involving high user mobility [7].

In FDD mMIMO, there is no channel reciprocity. The downlink training and corresponding channel state infor-mation (CSI) feedback yield unacceptably high overhead, which poses a significant bottleneck on the achievable SE. One implementation of practical FDD mMIMO is called joint spatial division and multiplexing (JSDM ) [8], where the correlation between channels is exploited to reduce the training and feedback dimensions. Another scheme that enables FDD mMIMO is called beam-division multiple access [9], which eliminates the need for CSI at the transmitter and thus provides the strong potential for realizing mMIMO gain in FDD systems.

While very promising, mMIMO encounters several problems in practical implementations. Despite the

previously identified difficulties, such as the require-ment of channel reciprocity and pilot contamination [10], a more serious challenge is that mMIMO’s uplink/downlink achievable rate scales logarithmically with the number of BS antennas instead of linearly [10]. To achieve the required SE, a drastic increase in the num-ber of BS antennas is required, which may lead to dis-ruptive BS design changes. For example, each antenna unit of the large-scale antenna array must be integrated with its own radio-frequency (RF) chain. Under this constraint, the exponentially increased hardware/en-ergy cost requires the design of the BS to be reformu-lated.

Here, we offer a novel mMIMO solution by utiliz-ing both large-scale antenna arrays and IBFD to poten-tially propel an easy evolution toward 5G and beyond. Benefiting from the maturity of self-interference (SI) cancellation technologies, IBFD has recently received considerable attention in wireless communication [11]. A wireless node with IBFD capability can transmit and re-ceive signals simultaneously at the same frequency band and thus can utilize the time–frequency resource more efficiently. IBFD has recently been considered a key tech-nology for 5G under the China IMT-2020 program [4]. The IBFD mMIMO system can further improve the system SE and reduce the design complexity of the BS. Further-more, the large-scale antenna array can be exploited to reduce the SI management complexity, as will be shown next. Therefore, IBFD mMIMO is a win–win proposition.

IBFD mMIMO

Basic ModelIn TDD and FDD mMIMO systems (i.e., half-duplex mMIMO systems), the uplink and downlink users must be allocated with orthogonal time slots or frequency bands, which results in insufficient utilization of time–frequency resources. As the BS is equipped with large-scale antenna arrays and has IBFD capability, it can combine the benefits of these two powerful technolo-gies and opens the possibility for higher SE. In this arti-cle, the SE is defined as the sum of the average uplink and downlink achievable rates per time–frequency resource.

Figure 1 illustrates the basic model of the IBFD mMIMO system. Because of the IBFD capability, the BS can serve multiple uplink users and downlink users simultaneous-ly at the same frequency band. On the particular time–frequency resource, the uplink users transmit signals to the BS, and the BS transmits signals to the downlink users. The received uplink signal at the BS is corrupted by the downlink transmit signals, i.e., the so-called SI. Meanwhile, the downlink users suffer from user-to-user interference (UI) from the uplink users. In the proposed model, we assume that the uplink and downlink users

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are half-duplex, based on a practical consideration that the implementation of IBFD is more challenging in small devices [11]. However, with the developments in antenna and RF circuit design, an all-IBFD network, i.e., one in which both the BS and users are IBFD, is possible and interesting to investigate.

To support IBFD transmission, the BS employs a sepa-rate antenna configuration, where two separate large-scale antenna arrays are used for transmission and reception. We note that there is another choice of shared antenna configuration that uses a single antenna array for both transmission and reception. However, under the current technologies, the shared configuration is difficult in the multiantenna system because of the significant crosstalk between antennas [10]. Therefore, we will not consider it here.

Potential BenefitsThe additional degree of freedom in the time–frequency dimension provided by IBFD transmission can improve the SE of a mMIMO system and alleviate the complexity in the design of the BS.

enhancing seThe most important advantage of IBFD mMIMO is its dis-tinct SE superiority over its half-duplex counterpart. Fun-damentally, the superiority comes from more efficient utilization of resources in the time–frequency dimension. To understand this advantage, let us compare the SEs of half-duplex and IBFD mMIMO. In the half-duplex system, the upl ink and downlink occupy or thogona l

time–frequency resources; thus, the SE of the half-duplex mMIMO can be expressed as R Ru u d dh h+ b/s/Hz, where Ru and Rd denote the uplink and downlink sum rates, respectively. The positive numbers uh and dh denote the fractions of the time–frequency resources allocated to the uplink and downlink transmissions and satisfy

.1u dh h+ = By contrast, if we can efficiently suppress SI and UI and render them negligible, the IBFD mMIMO can achieve the SE given by ,R Ru d+ since both the uplink and downlink receive the full resources.

enabling easy evolution toward 5g and beyondIn addition to providing SE gain, IBFD mMIMO has the poten-tial to reduce the complexity of the BS without decreasing the SE. For example, consider a system with K uplink users and K downlink users, and assume symmetric resource allocation for the half-duplex mMIMO system, i.e.,

./1 2u dh h == The system parameters are selected so that the uplink and downlink sum rates are roughly the same.

In this setup, the SE scaling laws for the half-duplex and IBFD mMIMO systems are given in [12] and shown in Figure 2. In the figure, t denotes the average received sig-nal-to-noise ratio when there are only one uplink user and one downlink user in the system and the BS is equipped with a single antenna. Note that these results are valid for the situation with a very large number of RF chains at the BS, which is exactly the case in mMIMO systems. To achieve the same SE, the required number of RF chains for an IBFD mMIMO system is proportional to the square root of that for the half-duplex system. This directly re-veals a significant reduction in hardware complexity and

energy consumption.

Fundamental Design Requirements: Mitigation of Two Types of InterferenceIn reality, IBFD transmission causes two types of interference, i.e., SI and UI, which greatly limit the potential benefits of the IBFD mMIMO sys-tem.

requirement for si cancellationGiven that the intended received signal over the air can be more than a million times weaker than the SI signal because of path loss and fad-ing [10], it is impossible to detect the intended signal without careful SI suppression. In an IBFD MIMO system with small-scale antenna arrays, the state-of-the-art SI can-cellation design relies on a combi-nation of various passive and active

SI

UI

IBFD mMIMO BS

Separate AntennaConfiguration

Uplink

Uplink Users Downlink Users

Downlink

Figure 1 an illustration of the ibFD mmimo system.

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cancellation technologies to achieve a satisfactory can-cellation level [11].

Passive cancellation, which suppresses the SI signal before it enters the received RF chain circuits by jointly using path loss, cross-polarization, and antenna direc-tionality, is commonly the first step in the SI cancellation scheme [14]. Active cancellation is applied to address the residual SI, where analog-domain SI cancellation suppresses the SI before it enters the analog-to-digital converter (ADC) with a cancellation control circuit, and where digital-domain SI cancellation is performed after the ADC by subtracting the estimated SI directly in the baseband.

However, the fundamental requirement for high-di-mension instantaneous SI channel information in active cancellation makes these schemes challenging when ap-plied in the IBFD mMIMO system, because the SI chan-nel is an NN TR # matrix, where NR and NT denote the numbers of receive and transmit antennas of the BS. The learning of the SI channel requires an impractically long pilot sequence when using the conventional pilot-based channel estimation scheme. Additionally, the cancella-tion control circuit employed in the active cancellation for each RF chain significantly increases the hardware complexity and is not suitable for large-scale use.

requirement for ui controlWhen multiple uplink users and downlink users are active simultaneously at the same frequency band, the resultant network suffers from increased UI from uplink users to downlink users. Hubermavn and Le-Ngoc showed that multiuser precoding is an efficient approach for UI control [14]. However, the challenge is that the number of simultaneous served users is expect-ed to increase dozens of times in the IBFD mMIMO sys-tem. In this case, the cooperation between users to obtain the UI channels (required for multiuser precod-ing) becomes quite difficult. Moreover, the dramatically increased computational complexity with respect to the numbers of BS antennas and users in the multiuser pre-coding scheme limits its application in the IBFD mMIMO system [14].

Making the IBFD mMIMO Feasible: A New Framework for Interference MitigationWhen the numbers of BS antennas and users grow over-whelmingly large, the current interference mitigation schemes previously discussed suffer from several chal-lenges, such as the impractical requirement for high-dimension channel knowledge and extremely high hardware and computational complexities. Therefore, redesign of the interference mitigation scheme consider-ing the new features is necessary when combining IBFD transmission and mMIMO.

In this section, we propose a new interference miti-gation framework for the IBFD mMIMO system. The key idea of the framework lies in 1) using passive self-inter-ference cancellation and hybrid precoding sequentially in the physical layer to suppress the SI and 2) performing joint user scheduling and power control in the multiple-access layer, based on the output signal-to-interference-plus-noise ratio (SINR) of the precoder to mitigate UI and optimize the achievable SE. The framework needs only slow time-varying channel information and requires no change to the transmit/receive RF chain circuits. Thus, it is simple to implement in the large-scale antenna system.

SI Suppression Using Large-Scale Antenna ArraysAs in the traditional SI cancellation scheme, we employ passive cancellation as the first line of defense against SI. The advantage of passive cancellation is that it can be applied in an SI channel-unware environment [13]. Thus, it is suitable for the situation where the SI channel is difficult to obtain. In practical implementation, the transmit and receive antenna arrays of the BS can be deployed on opposite sides of a building at a distance of tens of meters, which further facilitates the implementa-tion of passive cancellation, since more path loss can be exploited.

In general, passive SI cancellation alone cannot pro-vide sufficient cancellation in the cellular system. For example, based on the LTE BS-to-user path loss model for macrocells [15], the signal power received from the cell edge user is approximately 118 dB lower than that of the SI signal when the cell radius is 0.5 km. With the most powerful passive cancellation scheme known to date, which can deliver at most 90 dB of SI suppression [13], the received signal-to-interference ratio is still −28 dB in the worst case. In this article, we propose a hybrid

SE Scaling Law (IBFD)

2 X K log (1+N × ρ)

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K log (1+N × ρ)

NHDNIBFD

Number of Transmit/Receive RF Chains (N )

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ctra

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NHDNIBFD ∝

Figure 2 the se scaling laws of half-duplex and ibFD mmimo sys-tems. note that the number of transmit/receive antennas is equal to the number of transmit/receive rF chains.

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precoding scheme to mitigate this gap by exploiting the potential of large-scale antenna arrays. In the hybrid scheme, we split the MIMO precoder at the BS into a sta-tistical precoder followed by a multiuser precoder.

The aim of the statistical precoder is to suppress the residual SI by using the direction of arrival (DOA) and direction of departure (DOD) information of the SI chan-nel. The DOA/DOD changes slowly when compared with the instantaneous CSI [8]. Therefore, it can be readily ob-tained at the BS via long-term estimation. In the practical system, the BS is commonly elevated at a relatively high altitude, e.g., on the top of a high building or a dedicated tower, so there are few surrounding scatters. The poor scattering environment and high spatial resolution of large-scale antenna arrays make the SI channel exhibit specific sparsity in the DOA/DOD domain.

As a concrete example, Figure 3 shows contour plots of the average normalized SI channel gain in the DOA/DOD domain, considering the physical channel model with multiple scattering clusters in [15]. Let HSI be the SI channel matrix. The figure is obtained by projecting the SI channel matrix onto the space spanned by array response vectors with DOA/DOD in [–90°, 90°] and then calculating the average gain (denoted by ( , )G R Ti i ) of the projected channel, i.e.,

( , ) ,G E a H aR T RH

SI T2

i i i i= ^ ^h h8 B (1)

where a Ri^ h and a Ti^ h denote the array response vec-tors [16] with DOA Ri and DOD ,Ti respectively. Figure 3 shows that the power of the SI channel is concentrated

on the small DOA/DOD regions corresponding to the propagation paths of the SI signal, i.e., the dark-brown regions in the figure [e.g., the small region around the DOA of 37° and DOD of 55° in Figure 3(a)]. Based on this property, the statistical precoder is designed to project the useful signal onto the subspace with the weakest SI channel gain in the DOA/DOD domain, i.e., the dark blue areas in Figure 3. In this way, the SI power is greatly reduced.

The multiuser precoder is employed to realize mul-tiuser communications based on the estimations of the uplink and downlink channels, which is the same as the conventional MIMO precoder. The precoding matrix can be selected from the class of linear precoders. The wide-ly investigated schemes, such as matched filter precoder and zero-forcing precoder, are promising candidates. The detailed algorithm to compute the precoding matri-ces can be found in [17].

Joint User Scheduling and Power Control for UI MitigationTo reduce the effect of UI and maximize the SE, the uplink and downlink users must be scheduled intelligent-ly, while optimizing the transmit powers of the BS and users. A natural and efficient UI mitigation approach is to separate the uplink and downlink users as far as possi-ble. We utilize cell sectorization to achieve this goal. As shown in Figure 4(a), on the particular time–frequency resource, we schedule the uplink and downlink users in two opposite 120° sectors, which ensures that an uplink user and downlink user with a small separation distance

DO

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Figure 3 the contour plots of the average normalized si channel gain in the Doa/DoD domain. the angle spread of each scattering cluster is 5°. the numbers of transmit and receive antennas are both 128. (a) two scattering clusters with DoDs {–23°, 55°} and Doas {–34°, 37°}. (b) three scattering clusters with DoDs {–27°, 15°, 61°} and Doas {–31°, 24°, 43°}.

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will not be scheduled on the same time–frequency resource.

In the worst case, where the uplink and downlink us-ers are both located on the boundaries of the sectors, as shown in Figure 4(a), the UI channel between the uplink and downlink is still much weaker than the useful chan-nel. For example, when the distance between the BS and users is 300 m, the interference channel between two boundary users is 20 dB weaker than the useful channel, according to the 3GPP LTE BS-to-user and user-to-user path loss models [15]. On the other hand, to cover the whole cell evenly, we can schedule the users in rotated sectors, as shown in Figure 4(b) and (c), using orthogo-nal time–frequency resources.

After user scheduling, an SE optimization problem is solved to find the optimal transmit powers of the BS and uplink users. One important property of the IBFD mMIMO system is that the instantaneous uplink/down-link SINR converges to its deterministic equivalent ap-proximation, which is related to only slow, time-varying parameters, e.g., the large-scale fading of channels [5]. Thus, the real, instantaneous SINR required in the op-timization problem can be replaced by its deterministic equivalent approximation to reduce the overhead and computational complexity.

Simulation EvaluationIn this section, we evaluate the SE of IBFD mMIMO with the proposed interference mitigation framework using the 3GPP LTE simulation model for a macrocell environ-ment [15]. We consider a single cell with a 0.5-km radius and users randomly distributed in the two 120° sectors, as shown in Figure 4(a). The distance between the BS and each user is randomly distributed in [450, 500] m. The number of uplink users is six, as is the number of d o w n l i n k u s e r s . T h e b a n d w i d t h o f

the IBFD mMIMO system is set to 20 MHz. More detailed simulation parameters are summarized in Table 1. As the first SI suppression step, we employ the passive cancella-tion scheme for infrastructure nodes proposed in [13]. In this scheme, the suppression is from two parts, i.e., 1) the path loss introduced by the 15-m separation between the transmit and receive antenna arrays and 2) an addi-tional cancellation of 25 dB provided by techniques such as the RF absorber material and cross-polarization.

To obtain the figure, the uplink and downlink sum rates are first computed according to the procedure in [17] for 200 random channel realizations. Then, the SE is obtained by adding the uplink and downlink sum rates and averaging the results over all of the channel real-izations. To demonstrate the superiority of IBFD mMIMO with the proposed interference mitigation framework, we also provide the performances of the half-duplex mMIMO system [6], IBFD mMIMO system with linear ze-ro-forcing precoder [18] (where the SI and UI are treated as noise), and the perfect IBFD mMIMO (the sum of the uplink and downlink capacities without SI and UI).

Figure 5 shows that the SE of the IBFD mMIMO with the proposed interference mitigation framework ap-proaches the sum of the uplink and downlink capacities as the number of BS antennas increases. Meanwhile, the IBFD mMIMO achieves 1.74 times and 1.92 times SE gains over the half-duplex mMIMO when the number of trans-mit/receive RF chains is 100 and 200, respectively. With-out downlink channel reciprocity, the training overhead of the linear precoding IBFD mMIMO system in [18] in-creases linearly with N. As a result, the SE performance of the IBFD mMIMO system with linear precoding [18] is even lower than that of the half-duplex mMIMO method [6].

In terms of complexity reduction, 64 RF chains are suf-ficient for the IBFD mMIMO system with the proposed

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Figure 4 an illustration of cell sectorization. on the particular time–frequency resource, the uplink and downlink users are scheduled in two opposite 120° sectors to reduce ui.

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interference mitigation framework to realize a ten times SE gain over the LTE system. By contrast, 175 antennas are required for half-duplex mMIMO [6]. For the linear precod-ing IBFD mMIMO system in [18], more than 250 RF chains are required to obtain the SE of 30 b/s/Hz. Therefore, the complexity in the design of the BS can be greatly alleviated

by using the IBFD mMIMO with the proposed interference mitigation framework.

Limitations and Future Research Opportunities

CSI AcquisitionThe uplink/downlink channel estimation is essential for the precoder design of IBFD mMIMO systems. Conventionally, this task can be completed using an approach similar to that in TDD systems, where the CSI acquisition for down-link users relies on channel reciprocity [18]. In particular, to obtain the downlink channel, the downlink users send a set of orthogonal pilot sequences to the transmit antenna array of the BS. The received pilot signals are processed through receive RF chains, and the channel estimates are obtained using the traditional minimum mean-square error (MMSE) estimator at the baseband. However, as shown in Figure 6, since IBFD mMIMO uses separate antenna arrays for transmission and reception, an additional RF switch unit is needed to connect the transmit antenna array to the receive RF chains. The RF switch available with current technologies is far from ideal. The most significant draw-back is that its performance is limited by the transfer atten-uation, which must be compensated by more sensitive low-noise amplifiers at the receive RF chains [19].

To avoid the use of an RF switch, the channel estima-tion methods in FDD systems can be employed, where the

BS transmits pilot sequences and the downlink users estimate and feed the estimates back to the BS. In this case, an advanced training scheme should be studied to reduce the training and feedback dimensions. One possible solution is to borrow the ideas of JSDM [8]. As shown in [8], the chan-nel vectors of users with nonover-lapping DOA/DOD regions approach orthogonal when the number of BS antennas grows large. Thus, the us-ers can be allocated with the same pi-lot sequence. By using this property and scheduling the users properly, it is possible to reduce the consump-tion of training resources. Moreover, since the BS has IBFD capability, the IBFD training scheme, where the up-link and downlink training phases take the same time–frequency re-source, can also be studied to further improve the training efficiency.

IBFD mMIMO in a Multicell EnvironmentThe application of IBFD mMIMO in a multicell environment will provide

Table 1 The simulation setup.

Parameters Value

bandwidth 20 mhz

number of users uplink: six

Downlink: six

maximum transmit power bs: 16.5 dbm

user: 11.7 dbm

path loss (bs to user) path loss = 2.7 + 42.8log10(R) db

R: Distance in meters

path loss (user to user) path loss = 40log10(R) + 175.78 db

R: Distance in kilometers

thermal noise density –174 dbm/hz

radius of cell 0.5 km

Distance between bs and user

randomly selected in [450, 500] m

0

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1.74x SE Gain overHalf-Duplex System

10x SE Gainover LTE System(30 b/s/Hz)

1.92x SE Gainover Half-DuplexSystem

Spe

ctra

l Effi

cien

cy (

b/s/

Hz)

Number of Transmit/Receive RF Chains (N )

Perfect IBFD mMIMO (No SI and UI)IBFD mMIMO (Proposed Framework)IBFD mMIMO (Linear Precoding [19]) Half-Duplex mMIMO [6]

50 100 150 200 250

N = 64 N = 175

Figure 5 a comparison of ses in ibFD mmimo and half-duplex mmimo. the small-scale fad-ing of channels is modeled using the physical channel model. then, uplink/downlink channels are obtained using the conventional mmse estimator.

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new benefits and result in several research challenges. On the one hand, the introduction of IBFD trans-mission has the potential to signifi-cantly improve the overall SE of the system. On the other hand, each transmission in a multicell IBFD mMIMO system experiences more interference—including SI, UI from both within the cell and neighbor-ing cells, and BS-to-BS interfer-ence—than that in the single-cell situation. To realize the gain of IBFD mMIMO, efficient multicell interfer-ence mitigation technologies from different aspects, which may include user scheduling, power con-trol, and multiuser precoding with limited interference channel knowl-edge, should be studied.

Challenges of Dynamic Uplink/Downlink Data TrafficWith the rapid growth of online video streaming and social network services, future wireless data traffic will present time-varying properties [1]. In particular, the ratio between uplink and downlink traffic may change dynamically with time and user location. The performance gain of IBFD mMIMO decreases in scenarios with asymmetric uplink/downlink traffic because there is less demand for supporting simulta-neous uplink and downlink communications. In this case, an opportunistic IBFD/half-duplex mMIMO scheme may be a more efficient and flexible solution. In such a scheme, the core problem is a joint user and dynamic IBFD/half-duplex mode selection mechanism subject to the uplink and downlink traffic requirements, which is a nonconvex problem in most cases. Finding a global opti-mum is computationally difficult; thus, a suboptimal heu-ristic approach should be developed.

ConclusionsThis article gives an overall understanding of the poten-tial benefits, feasibility, and challenges of IBFD mMIMO cellular systems. IBFD mMIMO is a win–win proposition. On the one hand, the additional degree of freedom pro-vided by IBFD transmission can nearly double the SE of the mMIMO system and substantially reduce the hard-ware complexity of the BS. On the other hand, the high spatial resolution of large-scale antenna arrays decreas-es the difficulty of the SI cancellation required for IBFD

transmission. Because of these advantages, we suggest the IBFD mMIMO system as a key technology to enable the easy evolution toward future 5G and beyond. Howev-er, several limitations remain for the practical applica-tion of IBFD mMIMO, such as channel acquisition, implementation in multicell systems, and challenges with asymmetric uplink/downlink traffic, which provide future research opportunities.

AcknowledgmentsThis work was supported by the National Natural Science Foundation of China (grant 61671472), the Jiangsu Prov-ince Natural Science Foundation (grant BK20160079), and the Major Research Plan through the National Natural Science Foundation of China (grant 91438115).

Author InformationXiaochen Xia (tjuxiaochen@gmail.com) received his B.S. degree in electronic science and technology from Tianjin University, China, in 2010 and his M.S. degree in communication and information systems from the Army

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Figure 6 Downlink training using channel reciprocity: (a) data transmission phase; and (b) downlink training phase. tx: transmitter; rx: receiver.

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Engineering University of the People’s Liberation Army (PLA), Nanjing, China, in 2013. He is currently working toward his Ph.D. degree in communications engineer-ing at the Army Engineering University of the PLA. His research interests include relaying network, full-duplex communication, network coding, and multiple-input, multiple-output techniques. He received the 2013 Excel-lent Master’s Degree Dissertation Award of Jiangsu Prov-ince, China.

Kui Xu (lgdxxukui@139.com) received his B.S. de-gree in wireless communications, his M.S. degree in communication and information systems, and his Ph.D. degree in software-defined radio from the People’s Lib-eration Army (PLA) University of Science and Technol-ogy, Nanjing, China, in 2004 and 2009, respectively. He is currently an associate professor with the College of Communications Engineering, Army Engineering Uni-versity of the PLA, Nanjing, China. Since 2013, he has been a postdoctoral fellow with the Army Engineering University of the PLA. He has authored approximately 50 papers in refereed journals and conference proceedings and holds five patents in China. He is currently serving on the Technical Program Committee of the 2014 IEEE International Conference on Wireless Communications and Signal Processing. He received the Union Radio-Sci-entifique Internationale Young Scientists Award in 2014, and the 2010 Ten Excellent Doctor’s Degree Dissertations Award of the PLA University of Science and Technology. He is a Member of the IEEE.

Yurong Wang (wyrbitzj@sina.com) received her B.S. degree in computer science and technology from Beijing Institute of Technology, China, in 2013 and her M.S. de-gree in communication and information systems from the Army Engineering University of the People’s Libera-tion Army, Nanjing, China. She is now working toward her Ph.D. degree in communications engineering, Army Engineering University of the People’s Liberation Army, Nanjing, China. Her research interests include full-duplex communication, massive multiple-input, multiple-output systems, and broadband wireless communications.

Youyun Xu (lgdxxyy@139.com) received his B.S. de-gree in communication engineering form the Air Force Institute of Telecommunication Engineering, Xi’an, China, his M.S. degree in communication and information systems from the Nanjing Institute of Communication, China, and his Ph.D. degree in information and communication engi-neering from Shanghai Jiao Tong University (SJTU), China, in 1988, 1993, and 1999, respectively. He is currently a pro-fessor with the National Engineering Research Center of Communication and Network Technologies, Nanjing Uni-versity of Posts and Telecommunications, China. He is also a part-time professor with the Institute of Wireless Commu-nication Technology, SJTU. He has more than 20 years of professional experience in teaching and research in com-munication theory and engineering. His current research

interests focus on new-generation wireless mobile com-munication systems (International Mobile Telecommunica-tions advanced and related), advanced channel coding and modulation techniques, multiuser information theory and radio resource management, wireless sensor networks, and cognitive radio networks. He is a senior member of the Chinese Institute of Electronics and a Senior Member of the IEEE.

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