Beamdomain hybrid timeswitching and powersplitting SWIPT in fullduplex massive MIMO system
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Abstract
In this paper, we consider the beamdomain hybrid timeswitching (TS) and powersplitting (PS) simultaneous wireless information and power transfer (SWIPT) protocol design in fullduplex (FD) massive multipleinput multipleoutput (MIMO) system, where the FD base station (BS) simultaneously serves a set of downlink halfduplex (HD) users (cellular users) and a set of fixed uplink HD users (fixed sensor nodes) which are uniformly distributed in its coverage area. In order to reduce the computational complexity, we investigate the beamdomain representation of massive MIMO channels based on the basis expansion model, and then the beamdomain SWIPT protocol which lies in intelligently scheduling the users and sensors based on the beamdomain distributions of their associated channels to mitigate SI and enhance transmission efficiency is designed for fullduplex massive MIMO system. The whole beamdomain hybrid TS and PS SWIPT protocol is divided into two phases based on the ideal of TS. The first phase is used for cellular users uplink training and sensor nodes energy harvesting as well as downlink training, wherein the cellular users transmit uplink pilots for uplink channel estimation at the BS, while the BS simultaneously transmits energy signals to the sensor nodes. Based on the idea of PS, the sensor nodes utilize the received energy signals for energy harvesting and downlink channel estimation. In the second phase, the BS forms the transmit beamformers for information transmission to the users as well as the receive beamformers for the sensors transmit their data to the BS simultaneously. By optimizing the TS ratio and transmit powers at the BS in two phases, the system achievable sum rate performance is maximized. Simulation results show the superiority of the proposed protocol on spectral efficiency compared with the existing massive MIMO SWIPT protocol.
Keywords
Fullduplex massive MIMO Beamdomain channel representation Simultaneous wireless information and power transfer Beamforming Achievable sum rate1 Introduction
In the existing fourth generation (4G) system, multipleinput multipleoutput (MIMO) technology has been used to improve the spectral efficiency (SE) performance. By deploying M antennas at transmitter, and N antennas at receiver, MIMO takes advantage of the spatial multiplexing gain of min(M,N) to improve the date rate. With the exponential growth of mobile Internet, the data traffic will increase by 1000 times beyond 2020. The existing 4G system cannot meet the increasing traffic demand of the future mobile communication, which put forward a great challenge to the fifth generation (5G) wireless communication systems [1].
As one of the key technologies in 5G, massive MIMO [1, 2], which uses largescale antenna arrays (with 100 or more antennas) to replace the currently adopted multiple antennas, has been proved to be able to greatly improve the SE of wireless communications system [3, 4, 5]. As the number of antennas in massive MIMO grows larger, the simple linear precoder and detector tend to optimize, and the noise as well as the uncorrelated interference can be effectively eliminated [1, 2]. What is more, the spatial resolution of massive MIMO is remarkably improved when compared with the conventional MIMO, and the signal beam can be focused within a narrow range; hence, various kinds of interference can be substantially suppressed [4, 6, 7, 8, 9, 10].
In the conventional halfduplex (HD) systems, i.e., timedivision duplex (TDD) systems or frequencydivision duplex (FDD) systems, the uplink and downlink are separated by orthogonal time or frequency resources, hence theoretically wastes half of the timefrequency resource [11]. As a potential SE enhancement technique in 5G systems, fullduplex (FD) wireless transmission scheme allows the transceiver to use the same frequency resource for simultaneous transmission and reception [12, 13, 14, 15, 16]. FD technology can potentially double the transmission capability of the wireless physical layer. In terms of practical FD implementation, the effect of selfinterference (SI) due to the coupling of own highpowered transmit signals to the receiver must be reduced [11]. Thanks to the promising analog/digital and spatial domain SI cancelation techniques that can achieve high transmitreceive isolation which have been reported in the recent literatures [11, 17], now, FD is becoming feasible in the near future.
1.1 Motivation and related work
In addition to the SE, energy efficiency (EE) also has gained wide research attention for the design of wireless networks [18, 19]. Energy constraints introduce an upper limit on the power of transmission and associated signal processing in wireless devices, especially for batterypowered equipments [20]. As a sustainable solution to maintain the lifetime of energy constrained wireless networks, energy harvesting (EH) technique has recently received significant attention [21, 22, 23]. EH technology can effectively solve the charging problem in batterypowered wireless networks, wherein the wireless nodes are inaccessible or a large number of wireless nodes are widely distributed.
Various sources of renewable energy, such as ambient heat, wind, solar, and vibration, can be used to power wireless communication devices [24]. However, one common problem of collecting energy from such sources is its random nature. Energy availability variations with location, time, or environmental conditions make resource allocation in such systems very challenging. Radio frequency (RF) signals radiated by ambient transmitters can be identified as a viable new inspiration for EH. Compared with conventional energy sources, RF signals can carry both information and energy simultaneously. Wireless devices collecting energy of RF signals to power the signal transmission are named as simultaneous wireless information and power transfer (SWIPT), which has recently attracted great attention for various wireless channels [25, 26, 27, 28]. Owing to its features, SWIPT can be used to remotely power a variety of applications such as wireless sensor networks, body area networks, wireless charging facilities, and future cellular networks. Recently, several important advances of SWIPT in the antenna and RF EH circuit designs, have greatly increased the feasibility of EH in practical wireless applications [29, 30, 31].
The SWIPT receiver was originally designed for simultaneous information decoding (ID) and EH from the same received signal [32, 33], which is found to be unrealistic in the later research [34]. Due to the nonideal EH circuit design, part of the RF signal energy will be lost during the ID process [35]. Hence, practical circuits used for EH are not able to decode the information carried by the signal directly. In the following SWIPT receiver designs, antennas with different functionalities are equipped for the information transceiver and the energy harvester, respectively. In [36], antennas with different power sensitivities are designed for receivers of different uses, i.e., − 20 to − 10 dBm for EH and − 60 dBm for ID.
According to the signal partition method for EH and ID, there are mainly two types of practical SWIPT receiver designs, namely the timeswitching (TS) and the powersplitting (PS) receiver architectures [18]. The TS receiver alternately switches between EH and ID according to the TS ratio [36]. The received RF signal is first sent to the EH receiver and then to the information receiver. In the PS receiver, the power of the incoming signal is split into two streams according to the PS ratio [26]. One portion of the received signal is sent to the EH receiver, and the remaining portion is sent to the information receiver.
The design of transceiver for MISO interference channel with EH is studied in [37], where the PS method is adopted and the transmit beamforming vectors as well as receive PS ratios are jointly optimized. The authors in [38, 39] consider a oneway singleantenna amplifyandforward (AF) SWIPT relay networks, where both the TS and PS protocols are studied. The joint transceiver designs for multiuser MISO relay systems with EH is studied in [40], where the received signal is divided into two parts for ID and EH based on the PS principle. Since the twoway relaying (TWR) system can further improve the SE, many works also studied the SWIPT protocols in the TWR scenario. The authors in [41] considered a twoway AF relaying system with SWIPT, where an EH relay node is used to help two source nodes exchange information. In [42], the authors consider the sumthroughput maximization problem in TWR network, where all the nodes are wirelesspowered. The authors in [43] consider a SWIPT AF TWR network, where two source nodes harvest energy from multiple relay nodes.
The above works on SWIPT relay network are studied in HD scenario, where devices can either transmit or receive on a single frequency band, but not simultaneously. Since FD can potentially double the wireless physical layer capacity, recently, the transceiver design problem in FD SWIPT systems has arised. In [44], the joint optimization problem of relay beamforming, the receiver PS ratio as well as the transmit power at the sources are investigated for SWIPT system with a FD MIMO AF relay to maximize the system achievable sum rate. The design of robust nonlinear transceivers for MIMO FD wirelesspowered relay networks is studied in [45], where the effects of imperfect channel state information (CSI) is took into consideration. The joint transceiver design for a FD cloud radio access network with SWIPT is considered in [46]. The joint transceiver designs for FD KPair MIMO interference channel with SWIPT is considered in [47]. Robust secure beamforming scheme for wirelesspowered FD MIMO systems with selfenergy recycling is studied in [48].
Despite the previous works on EH in FD system or MIMO system, a few works have been done on the SWIPT protocol design in cellular system with massive MIMO base station (BS). In [21], the joint FD selfbackhaul and EH protocol for small cell networks with massive MIMO is investigated. In [49], SWIPT protocol for threedimensional (3D) massive MIMO system is designed, where the matched filter (MF) precoder is adopted at the BS. In [50], the SWIPT protocol is designed for multipair TWR system with massive MIMO, where the linear precoders, i.e., zeroforcing (ZF) and maximal ratio combining (MRC), are adopted at the relay. A lowcomplexity SWIPT scheme with retrodirective maximum ratio transmission (MRT) beamforming is studied in [51], where all energy receivers (ERs) send a common beacon signal simultaneously to the energy transmitter (ET) in the uplink and the ET simply conjugates and amplifies its received sumsignal and transmits to all ERs in the downlink for SWIPT. For SWIPT system with massive MIMO, instantaneous full dimensional CSI is needed to perform linear precoding. To obtain full dimensional CSI, the uplink training overhead scales linearly with the number of user equipments (UE) in TDD system, the downlink training overhead scales linearly with the number of antennas in FDD system, and the corresponding CSI feedback yield an unacceptably high overhead, and therefore poses a significant bottleneck on the achievable SE [52].
1.2 Contributions
In this paper, we consider the beamdomain hybrid TS and PS SWIPT protocol design in FD massive MIMO system, where the FD BS simultaneously serves a set of downlink HD users (cellular users) and a set of fixed uplink HD users (fixed sensor nodes) which are uniformly distributed in its coverage area.

We investigate the beamdomain (BD) representation of massive MIMO channels based on the basis expansion model (BEM). In this way, the full dimensional channel can be compressed into BD effective channel (BDEC), as a result, the computational and implementation complexity can be greatly reduced and the SE performance can be improved.

The BD hybrid TS and PS SWIPT protocol which lies in intelligently scheduling the users and sensors based on the BD distributions of their associated BDECs to mitigate SI and enhance SE performance is designed for FD massive MIMO system. Specifically, the whole BD hybrid TS and PS SWIPT protocol is divided into two phases based on the ideal of TS. Phase I is used for cellular users uplink training and sensor nodes EH as well as downlink training. During this phase, the cellular users transmit pilots for uplink UEBS channel estimation at the BS, while the BS simultaneously transmits energy signals to the sensor nodes. Based on the idea of PS, the sensor nodes utilize the received energy signals for EH and downlink SensorBS channel estimation. In phase II, the BS forms the transmit beams for information transmission to the users as well as the receive beams for the sensors transmit their data to the BS simultaneously.

By optimizing the TS ratio and transmit powers at the BS in two phases, the system achievable sum rate performance is maximized. Simulation results shown the superiority of the proposed protocol on spectral efficiency compared with the conventional HD massive MIMO SWIPT protocol.
The rest of the paper is organized as follows. The system model is described in Section 2. Sections 3 and 4 consider the beamdomain channel representation and proposed BD massive MIMO FD SWIPT transmission scheme, respectively. Section 5 consider the practical beamdomain user and sensor grouping problem. Section 6 derives the system achievable sum rate and the optimum TS ratio as well as transmit powers at the BS in two phases. Section 7 presents the simulation results. Section 8 draws the conclusions.
Notations: In this paper, \(\mathbb {E}(\cdot)\) denotes the expectation. \(\mathbf {A}^{\{B_{1}, B_{2}\}}\phantom {\dot {i}\!}\) denotes the submatrix of A by keeping its rows indexed by set B_{1} and columns indexed by set B_{2}. A^{{B,:}} and A^{{:,B}} denote the submatrix of A by keeping its rows and columns indexed by set B, respectively. δ(·) denotes the dirac delta function. (·)^{ T }, (·)^{∗}, (·)^{ H }, ·, ∥·∥, and tr(·) denote transpose, conjugate, conjugate transpose, determinant, Frobenius norm, and trace of a matrix, respectively.
2 System model
We consider framebased transmissions over Rayleigh fading channels. The length of one frame is fixed to T seconds, which is assumed to be less than the coherence interval of the channel. We use \(\mathbf {h}_{k_{u}}\in \mathbb {C}^{N \times 1}\) and \(\mathbf {h}_{k_{d}}\in \mathbb {C}^{N \times 1}\) to denote the uplink UEBS channel vector from the user k_{ u } to the receive antenna array of BS and the downlink UEBS channel vector from the transmit antenna array of BS to the user k_{ d }. Let \(\mathbf {h}^{e}_{k_{u}}\in \mathbb {C}^{N \times 1}\) and \(\mathbf {h}^{e}_{k_{d}}\in \mathbb {C}^{N \times 1}\) denote the uplink sensorBS channel vector from the sensor node k_{ d } to the receive antenna array of the BS and the downlink sensorBS channel vector from the transmit antenna array of the BS to the sensor node k_{ d }. We use \(\mathbf {H}_{SI}\in \mathbb {C}^{N \times N}\) to denote the SI channel matrix from the transmit antenna array to the receive antenna array of the BS.
Note that the channel model between the user and the BS is the same as the channel model between the sensor and the BS; in the following sections, we only introduce the uplink and downlink channel model between the user and the BS.
2.1 Uplink/downlink channel model
where ξ∈{u,d}, \(\mathbf {a}\left (\theta \right) = \left [ 1,\exp \left ({  j\frac {{2\pi d\sin \left (\theta \right)}}{\lambda }} \right), \cdots, \exp \left ({  j\frac {{2\pi d\left ({N  1} \right)\sin \left (\theta \right)}}{\lambda }} \right) \right ]^{T}\) is the array response vector with d and λ denoting the antenna spacing and carrier wavelength, respectively. \(r_{k_{\xi },i}(\theta)\) denotes the complexvalued response gain of uplink/downlink channel.
2.2 SI channel model
where r_{SI,i}(θ_{ R },θ_{ T }) denotes the complexvalued response gain.
where S_{ω,i}(·),ω∈{k_{ u },k_{ d },SI}, represents the product of the largescale fading and channel power angle spectrum (PAS).
we obtain the rayclusterbased spatial channel model, which is usually used for millimeter wave massive MIMO channel [55].
3 Beamdomain channel representation
In order to reduce the number of dimensions of the channel to be estimated as well as the amount of feedback, we resort to the BEM [56]. Using the BEM, the BD channel representation can be obtained by projecting the channel vector (matrix) on common bases. In this way, the channel can be compressed in the BD under certain selected basis spaces and the channel dimension required to be estimated can be reduced greatly. Moreover, by exploiting the BD structure of SI channel, it is possible to eliminate the SI without using the instantaneous SI channel knowledge and hence realize efficient SWIPT transmission.
where \(B_{k_{\xi }}\) is the selected active beam set (ABS) of uplink/downlink which contains the indexes of beams with nonnegligible BD channel gains. All the elements selected in the set \(B_{k_{\xi }}\) is related to the DOA/DOD, and the detailed method for determining all the elements in the set of \(B_{k_{\xi }}\) will be described in the following section. \(\mathbf {F}^{\{B_{k_{\xi }}\}}\) is the corresponding active beam spaces, whose columns are consisted of the beams in \(B_{k_{\xi }}\). The reduceddimension BD channel vector \(\widetilde {\mathbf {h}}_{{k_{\xi }}}^{\left \{ {{B_{{k_{\xi }}}}} \right \}} \in {\mathbb {C}^{\left  {{B_{{k_{\xi }}}}} \right  \times 1}}\) is called the BDEC. Note that (7) holds with equality as N→∞.
where ϖ<1 is the threshold.
In the same way, we can decompose the SI channel in the BD form, that is \({\tilde {\mathbf {H}}_{SI}} = {\mathbf {F}^{H}}{\mathbf {H}_{SI}}\mathbf {F}\). Since the average BD SI channel gain has nonnegligible value when the beam angle of receive side f_{ m } lies in \( \cup _{i = 1}^{{M_{SI}}}\left ({\sin \theta _{R,i}^{\min }  \varepsilon,\sin \theta _{R,i}^{\max } + \varepsilon } \right)\), and the beam angle of transmit side f_{ n } lies in \( \cup _{i = 1}^{{M_{SI}}}\left ({\sin \theta _{T,i}^{\min }  \varepsilon,\sin \theta _{T,i}^{\max } + \varepsilon } \right)\), where ε approaches to zero in the large N regime. Hence, the average BD SI channel gain \(\mathbb {E}\left [ {{{\left  {{{\left [ {{\tilde {\mathbf {H}}_{SI}}} \right ]}_{m,n}}} \right }^{2}}} \right ] = \mathbb {E}\left [ {{{\left  {\mathbf {f}_{m}^{H}{\mathbf {H}_{SI}}{\mathbf {f}_{n}}} \right }^{2}}} \right ]\).x
4 BD hybrid TS and PS SWIPT protocol
In this section, we propose a BD scheme to realize SWIPT in the cellular system with FD massive MIMO BS. In order to accomplish the channel estimation, the transmit and receive antenna arrays of the BS are assumed to be shared antennas, i.e., the transmit and receive RF chains share the same antenna [10]. In the framebased BD massive MIMO FD SWIPT system, each frame is divided into two phases, phase I: estimation and EH phase and phase II: BD FD transmission phase, as shown in Fig. 1.
In the phase II of time period (1−α)T, the BS obtains the downlink UEBS CSI based on channel reciprocity and then forms the beams for information transmission to the users. Moreover, the sensors feedback the uplink sensorBS CSI to the BS and transmit their data during the interval (1−α)T using the harvested energy. Specifically, the sensors transmit signal \(\sqrt {\beta \eta }y_{t}^{S}\) to the BS, where η<1 denotes the energy conversion efficiency. Note that the resource consumption for CSI feedback of sensors are omitted in this paper.
4.1 Training design
Let \({\Phi _{u}} \in {{\mathbb {C}}^{{\tau _{u}} \times \mathop {\max }\limits _{{g_{u}} \in {G_{u}}} {K_{{g_{u}}}}}}\) be the orthogonal pilot sequence set for uplink UEBS training, where τ_{ u } denotes the length of pilot sequence which satisfies \({\tau _{u}} \ge \mathop {\max }\limits _{{g_{u}} \in {G_{u}}} {K_{{g_{u}}}}\), g_{ u } denotes the user group, \(K_{{g_{u}}}\) is the number of users in the group g_{ u }, and G_{ u } denotes the set of user groups. The pilot sequences allocated for group g_{ u } can be given by \({\Phi _{{g_{u}}}} = \Phi _{u}^{\left \{ {:,1,:{K_{{g_{u}}}}} \right \}}\) and \({\left  {{{\left [ {{\Phi _{{g_{u}}}}} \right ]}_{i,j}}} \right ^{2}} = {p_{u}}\), where p_{ u } denotes the power of each uplink pilot symbol. In this paper, we consider the fixed sensors, hence it is reasonable to assume that the downlink sensors group \(g^{e}_{d}\) and the active beam sets \(B_{g^{e}_{d}}\) are known. Let \(B_{g^{e}_{d}}=b^{e}_{d}\) and \({\Phi _{g_{d}}^{\mathrm {E}}} \in {{\mathbb {C}}^{{\tau _{d}}\times b^{e}_{d}}}\) be the orthogonal energy sequence set, where τ_{ d } denotes the length of energy sequence which satisfies \({\tau _{d}} \ge b^{e}_{d}\). Let \(K_{{g^{e}_{d}}}\) denotes the number of sensors in the group \(g_{d}^{e}\), and \(g_{d}^{e}\) denotes the set of downlink sensor groups.
where \(\mathbf {H}_{g_{d}^{e}}^{} = \left [ {\mathbf {h}_{g_{d}^{e},1}^{}, \cdots,\mathbf {h}_{g_{d}^{e},{K_{g_{d}^{e}}}}^{}} \right ]\) and \({\mathbf {N}_{g_{d}^{e}}}\) denotes the AWGN with variance σ.
4.2 UEBS BDEC estimation
where p_{ u } denotes the power of each pilot symbol transmitted by users. The second term of righthand sides of (16) indicates the pilot contamination due to the use of the same pilot sequence set over all the user groups. The third term is the SI due to the simultaneous uplink training and downlink energy transmission.
where \(\mathbf {G}_{{\theta _{R}},{\theta _{T}}}^{\left \{ {{B_{{g_{u}}}}}, {B_{{g'}_{d}^{e}}} \right \}} \buildrel \Delta \over = {\left ({{\mathbf {F}^{\left \{ {{B_{{g_{u}}}}} \right \}}}} \right)^{H}}\mathbf {a}\left ({{\theta _{R}}} \right){\mathbf {a}^{H}}\left ({{\theta _{T}}} \right) \mathbf {F}^{\{B_{{g'}_{d}^{e}}\}}\) and meanwhile \({\Psi _{k}} = {{\left ({\dot {\Phi }_{{g_{d}}}^{\mathrm {E}}} \right)}^{T}}\Phi _{u}^{*}{\mathbf {e}_{k}}\mathbf {e}_{k}^{H}\Phi _{u}^{T}{{\left ({\dot {\Phi }_{{g_{d}}}^{\mathrm {E}}} \right)}^{\ast }}\). The BStousers channel CSI \(\widetilde {\mathbf {h}}_{{g_{d,k}},LM}^{\left \{ {{B_{{g_{d}}}}} \right \}}\) is obtained by using the reciprocity properties of the wireless channel.
4.3 Energy transfer for sensors
where \({{h}}_{{k_{u}},g^{e}_{d,k}}\in \mathbb {C}^{1\times 1}\) denotes the interuser interference (IUI) channel between the k_{ u }th user and the \(g^{e}_{d,k}\)th sensor node, \(\Phi _{{k_{u}}}\) is one row of Φ_{ u } and denotes the pilot sequence transmit by user k_{ u }, and \({\mathbf {n}_{{g^{e}_{d,k}}}}\sim \mathcal {CN}(0,\sigma ^{2})\) denotes the AWGN at the \(g^{e}_{d,k}\)th sensor node.
where ∥·∥ denotes the Euclidean norm of a vector, 0<η<1 denotes the energy conversion efficiency. \({\hat {p}^{\mathrm {H}}_{{g^{e}_{d,k}}}}= p_{g_{d}^{e}} q_{g_{d,k}^{e}}\), where \( q_{g_{d,k}^{e}}=\eta \beta {\left \ {\tilde {\mathbf {h}}_{g_{d,k}^{e}}^{\left \{ {{B_{g_{d}^{e}}}} \right \}}} \right \^{2}}\). It is worth noting that for a fixed sensor node, the downlink sensor grouping and the ABS remain unchanged. Therefore, when the instantaneous channel information is not available, effective energy transmission can be achieved only by using the information of the ABS, regardless of how the energy sequences are allocated.
The uplink sensorstoBS channel CSI \(\tilde {\mathbf {h}}_{g_{u,k}^{e},LM}^{\left \{ {{B_{g_{u}^{e}}}} \right \}}\) is obtained by using the reciprocity properties of the wireless channel.
4.4 Beamdomain FD information transmission
The BS uses the estimated BDEC to perform beamforming to transmit information to the K_{ d } users. At the same time, it receives data from the K_{ u } sensors.
4.4.1 BStoUE downlink transmission
where \({\mathbf {n}_{{g_{d}}}}\) denotes the AWGN vector with variance σ, \({\mathbf {x}_{{g_{d}}}} \in {\mathbb {C}^{N \times 1}}\) denotes the precoded transit signal of the BS, \({\mathbf {H}_{{g_{d}}}} = \left [ {{\mathbf {h}_{{g_{d,1}}}}, \cdots,{\mathbf {h}_{{g_{d,{K_{{g_{d}}}}}}}}} \right ]\) denotes the channel matrix from the user group g_{ d } to the BS, \({\mathbf {H}_{{K_{u}},{g_{d}}}} \in {\mathbb {C}^{{K_{u}} \times {K_{{g_{d}}}}}}\) denotes the channel matrix from K_{ u } sensors to the user group g_{ d }, and \({\mathbf {x}_{u,{K_{u}}}}\in \mathbb {C}^{K_{u} \times 1}\) denotes the transmit signal vector of K_{ u } sensors.
where \({\Upsilon _{{g_{d}}}}\) is a diagonal normalized matrix with \({\left [ {{\Upsilon _{{g_{d}}}}} \right ]_{l,l}}\) can be expressed as \({\left [ {{\Upsilon _{{g_{d}}}}} \right ]_{l,l}} = \mathbf {e}_{l}^{H}{\left ({{{\left ({\tilde {\mathbf {H}}_{{g_{d,LM}}}^{\left \{ {{B_{{g_{d}}}}} \right \}}} \right)}^{H}}\tilde {\mathbf {H}}_{{g_{d,LM}}}^{\left \{ {{B_{{g_{d}}}}} \right \}}} \right)^{ 1}}\mathbf {e}_{l}^{}\).
4.4.2 SensortoBS uplink transmission
The problem in (32) is in generalized Rayleigh quotient form. It is well known that the maximum value in (32) is obtained when \(\mathbf {w}_{g_{u,k}^{e}}^{}\) equals to the generalized eigenvector corresponding to the maximum generalized eigenvalue.
5 Beamdomain user and sensor grouping

Criterion 1: The sensors or users with the same ABS are collected in the same sensor/user group. The ABSs of different sensor/user groups are nonoverlapping with certain guard interval \(\mho \). Let \(B_{g_{u}}\) and \(B_{g_{u}'}\) be the ABSs of two arbitrary user groups g_{ u } and gu′, and \(B_{g_{u}}\) and \(B_{g_{u}'}\) should satisfy \(\mathcal {D}(B_{g_{u}},B_{g_{u}'})\geq \mho \), as shown in Fig. 3. Let \(B_{g_{d}}\) and \(B_{g_{d}'}\) be the ABSs of two arbitrary sensor groups g_{ d } and gd′, and \(B_{g_{d}}\) and \(B_{g_{d}'}\) should satisfy \(\mathcal {D}(B_{g_{d}},B_{g_{d}'})\geq \mho \).

Criterion 2: The complete ABS of all the user groups or sensor groups is nonoverlapping with the receive ABS or transmit ABS of the SI channel under certain guard interval \(\mho \). Let G_{ u } and G_{ d } be the complete set of sensor groups and user groups, respectively. The ABSs \(B_{g_{u}}\) and \(B_{g_{d}}\) satisfy \(\mathcal {D}(\bigcup _{g_{u}\in G_{u}}B_{g_{u}}, B_{SI,R})> \mho \) or \(\mathcal {D}(\bigcup _{g_{d}\in G_{d}}B_{g_{d}}, B_{SI,T})> \mho \).
In the practical implementation, the users and sensors with different ABSs must be partitioned so that the above two conditions are satisfied as close as possible, as shown in Fig. 3. For all the users or sensors, the ABS of the SI channel at the receive side B_{ SI,R } and transmit side B_{ SI,T } should be took into consideration when carry out grouping. In the following, we proposed a BD user and sensor grouping scheme, which consists of the following two steps.
Step 1: Remove the ABS of the SI channel at the receive side B_{ SI,R } and transmit side B_{ SI,T } from the set of all the beams Γ with a certain guard interval \(\mho \).
Step2: Remove all the beams that satisfy the adjacent \(\mho \) beams including itself which are not occupied by any user or sensor. We obtain serval beam sets, each of which contains several continuous beams occupied by different users or sensors. For each beam set with several continuous beams, the corresponding users or sensors form a user and sensor group.
In this way, the selfinterference at the BS can be mitigated effectively based on the statistical CSI of SI channel, that is, the ABS of SI channel, rather than the instantaneous CSI of SI channel. Note that users and sensors should be grouped independently. Meanwhile, to mitigate the interference between the uplink and downlink, the user group or sensor group with overlap beams cannot be allocated with the same frequency band for transmission.
6 Achievable rate analysis and optimization
We carry out the achievable rate analysis and optimization in this section. In this paper, the part of the time used for channel estimation at the BS and energy harvesting αT can be determined by αT=τ_{ d }≥τ_{ u }. It is worth noting that when αT≥τ_{ u }, more time is allocated to the sensors for EH which as a result will increase the harvested energy and hence increase the transmit power of sensors. But for the users, less time is allocated for transmitting and hence reduce the SE.
6.1 Downlink achievable rate
6.1.1 Downlink achievable rate with perfect CSI
6.1.2 Downlink achievable rate with imperfect CSI
6.2 Uplink achievable rate
6.2.1 Uplink achievable rate with perfect CSI
when \({{\ddot {\mathbf {\Xi }}_{g_{u,k}^{e}}}}\) is completely eliminated, and hence, we have \({{\vartheta _{g_{u,k}^{e}}}}=0\).
6.2.2 Uplink achievable rate with imperfect CSI
6.3 Optimization of the achievable sum rate
In particular system, the assumption of having perfect instantaneous CSI is idealistic due to the fact that the CSI at the BS, users, and sensors are obtained by estimation or feedback. Hence, the CSI is subject to estimation, feedback, delay, and quantization errors. In this subsection, we consider the problem of optimizing the system achievable sum rate with imperfect CSI.
where \({K_{g}^{E}}\) denotes the number of sensor groups. \(P^{\mathrm {E}}_{d}\) and P_{ d } denote the maximum transmit power constraint at the BS in phase I and phase II, respectively. \({\mathcal {P}} = \left \{ {{p_{{g_{d,1}}}}, \cdots,{p_{{g_{d,{K_{d}}}}}},{p_{g_{1}^{e}}}, \cdots,{p_{g_{K_{g}^{E}}^{e}}}} \right \}\).
6.3.1 Optimize \(\mathcal {P}\) for fixed α
The SE optimization problem in (48) becomes a standard GP and can be solved by the Algorithm 1.
6.3.2 Optimize α for fixed \(\mathcal {P}\)
6.3.3 Joint optimization of α and \(\mathcal {P}\)
The joint optimal α and \(\mathcal {P}\) can be obtained by finding the optimum \(\mathcal {P}\) for each α and then selecting the found \(\mathcal {P}\) and α that maximize the problem in (46). Hence, a onedimensional search over α is needed. We can conclude that the required onedimensional search can be limited to a small region of α by exploiting the structure of the problem (46) and the properties obtained in (54)–(56). Hence, the computational complexity for solving the joint optimization problem can be greatly reduced. The proposed solution to jointly optimize α and \(\mathcal {P}\) is summarized in Algorithm 2.
7 Simulation
In this section, the performance of BD hybrid TS and PS SWIPT protocol in FD massive MIMO system is evaluated using the 3GPP LTE simulation model for macrocell environment [62]. The center frequency is 2.4 GHz and system bandwidth is set to 20 MHz. Thermal noise density is set to − 174 dBm/Hz. The channel coherent time is 200 symbol times. The path loss between BS and users/sensors are modeled as 2.7 + 42.8 log10(R) (dB), and the path loss between users/sensors are modeled as 55.78 + 40 log10(R) (dB), where R denotes the distance. The number of scattering clusters is set to M_{ u } = M_{ d } = 1. It is assumed that the passive SIC scheme for infrastructure nodes proposed in [63] has been employed at the BS. In such scheme, the suppression is from two parts, namely (1) the path loss introduced by the 20 m separation between transmit and receive antenna arrays and (2) an additional cancelation of 45 dB provided by techniques, such as radio frequency absorber material and crosspolarization. No other active SIC scheme is used in this paper.
In this paper, we assume that the BS can obtain the perfect DOA and DOD information when users access the system. In the following simulations, we consider the fixed sensors, hence it is reasonable to assume that the BS can obtain the perfect DOA and DOD information of sensors. We first consider a scenario where the sensors and users gather perfectly in three groups and the DOA/DOD regions of users in each group are identical. We assume that each group contains 5 uplink/downlink users. The DOA/DOD regions of three user groups are [− 45°,− 35°], [12°,22°] and [42°,52°], respectively. Since we assume M_{ u } = 1, the DOA region of uplink group is [a,b], that is \(\left [ {\theta _{{g_{u,k},1}}^{\min },\theta _{{g_{u,k},1}}^{\max }} \right ] = \left [ {a,b} \right ]\). Similarly, the DOA/DOD regions of three sensor groups are [− 42°,− 32°], [16°,26°] and [39°,49°], respectively. The DOA and DOD regions of SI channel between the transmit antenna array and receive antenna array are set to [− 12°,− 22°], [56°,66°] and [− 56°,− 46°], [27°,37°], respectively. The resulting ABSs for all the groups satisfy the user and sensor grouping criteria.
8 Conclusions
In this paper, we propose a BD hybrid TS and PS SWIPT protocol for FD massive MIMO system. In order to reduce the pilot resource cost used for channel estimation, we resort BEM to represent massive MIMO channel in the BD. Due to the spatial sparsity of massive MIMO channel, we can compress the massive MIMO channel in the BD under certain selected basis spaces and the channel dimension required to be estimated can be greatly reduced. The hybrid TS and PS SWIPT protocol which lies in intelligently scheduling the users and sensors based on the distributions of their associated BD ABSs to mitigate SI and enhance transmission efficiency is designed. The whole BD hybrid TS and PS SWIPT protocol is divided into two phases based on the ideal of TS. The first phase is used for cellular users uplink training and sensor nodes energy harvesting as well as downlink training, wherein the cellular users transmit uplink pilots for uplink channel estimation at the BS, while the BS simultaneously transmits energy signals to the sensor nodes. Based on the idea of PS, the sensor nodes utilize the received energy signals for energy harvesting and downlink channel estimation. In the second phase, the BS forms the transmit beamformers for information transmission to the users as well as the receive beamformers for the sensors transmit their data to the BS simultaneously. By optimizing the TS ratio and transmit powers at the BS in two phases, the system achievable sum rate performance is maximized. Simulation results shown the superiority of the proposed protocol on SE compared with the existing massive MIMO SWIPT protocol.
Notes
Acknowledgements
The authors would like to thank the reviewers for their careful readings and valuable comments.
Funding
This work is supported by the Jiangsu Province Natural Science Foundation under grant BK20160079, National Natural Science Foundation of China (no. 61671472).
Authors’ contributions
KX proposed the main idea and is the main writer of this paper. ZS, YW, and XX assisted in the simulations and analysis. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
 1.TL Marzetta, Noncooperative cellular wireless with unlimited numbers of BS antennas. IEEE Trans. Wirel. Commun. 9(11), 3590–3600 (2010).CrossRefGoogle Scholar
 2.F Rusek, D Persson, BK Lau, EG Larsson, TL Marzetta, O Edfors, F Tufvesson, Scaling up MIMO: opportunities and challenges with very large arrays. IEEE Signal Process. Mag. 30(1), 40–60 (2013).CrossRefGoogle Scholar
 3.J Hoydis, S Brink, M Debbah, Massive MIMO in UL/DL of cellular networks: how many antennas do we need? IEEE J. Sel. Areas Commun. 31:, 160–171 (2013).CrossRefGoogle Scholar
 4.EG Larsson, F Tufvesson, O Edfors, TL Marzetta, Massive MIMO for next generation wireless systems. IEEE Commun. Mag. 52(2), 186–195 (2014).CrossRefGoogle Scholar
 5.F Boccardi, RW Heath Jr, A Lozano, TL Marzetta, P Popovski, Five disruptive technology directions for 5G. IEEE Commun. Mag. 52(2), 74–80 (2014).CrossRefGoogle Scholar
 6.PWC Chan, ES Lo, RR Wang, EKS Au, VKN Lau, RS Cheng, WH Mow, RD Murch, KB Letaief, The evolution path of 4G networks: FDD or TDD? IEEE Commun. Mag. 44(12), 42–50 (2006).CrossRefGoogle Scholar
 7.A Adhikary, J Nam, JY Ahn, G Caire, Joint spatial division and multiplexing: the largescale array regime. IEEE Trans. Info. Theory. 59(10), 6441–6463 (2013).MathSciNetCrossRefMATHGoogle Scholar
 8.C Sun, X Gao, S Jin, M Matthaiou, Z Ding, C Xiao, Beam division multiple access transmission for massive MIMO communications. IEEE Trans. Commun. 63(6), 2170–2184 (2015).CrossRefGoogle Scholar
 9.A Liu, V Lau, Phase only RF precoding for massive MIMO systems with limited RF chains. IEEE Trans. Signal Process. 62(17), 4505–4515 (2014).MathSciNetCrossRefGoogle Scholar
 10.D Kim, G Lee, Y Sung, Twostage beamformer design for massive MIMO downlink by trace quotient formulation. IEEE Trans. Commun. 63(6), 2200–2211 (2015).CrossRefGoogle Scholar
 11.A Sabharwal, P Schniter, D Guo, DW Bliss, S Rangarajan, R Wichman, Inband fullduplex wireless: challenges and opportunities. IEEE J.Sel. Areas Commun. 32(9), 1637–1652 (2014).CrossRefGoogle Scholar
 12.D Kim, H Lee, D Hong, A survey of inband fullduplex transmission: from the perspective of PHY and MAC layers. IEEE Commun. Surv. Tutor. 17(4), 2017–2046 (2015). Fourthquarter.CrossRefGoogle Scholar
 13.Z Zhang, X Chai, K Long, AV Vasilakos, L Hanzo, Full duplex techniques for 5G networks: selfinterference cancellation, protocol design, and relay selection. IEEE Commun. Mag. 53(5), 128–137 (2015).CrossRefGoogle Scholar
 14.Y Liao, K Bian, L Song, Z Han, Fullduplex MAC protocol design and analysis. IEEE Commun. Lett. 19(7), 1185–1188 (2015).CrossRefGoogle Scholar
 15.U Ugurlu, T Riihonen, R Wichman, Optimized inband fullduplex MIMO relay under singlestream transmission. IEEE Trans. Veh. Technol. 65(1), 155–168 (2016).CrossRefGoogle Scholar
 16.Y Jang, K Min, S Park, S Choi, in 2015 IEEE International Conference on Communications (ICC). Spatial resource utilization to maximize uplink spectral efficiency in fullduplex massive MIMO (London, 2015), pp. 1583–1588.Google Scholar
 17.HA Suraweera, I Krikidis, G Zheng, C Yuen, PJ Smith, Lowcomplexity endtoend performance optimization in MIMO fullduplex relay systems. IEEE Trans. Wirel. Commun. 13(2), 913–927 (2014).CrossRefGoogle Scholar
 18.Z Liu, W Du, D Sun, Energy and spectral efficiency tradeoff for massive MIMO systems with transmit antenna selection. IEEE Trans. Veh. Technol. 66(5), 4453–4457 (2017).Google Scholar
 19.Y Li, P Fan, A Leukhin, Liu L, On the spectral and energy efficiency of fullduplex smallcell wireless systems with massive MIMO. IEEE Trans. Veh. Technol. 66(3), 2339–2353 (2017).CrossRefGoogle Scholar
 20.S Sudevalayam, P Kulkarni, Energy harvesting sensor nodes: survey and implications. IEEE Commun. Surv. Tutor. 13(3), 443–46 (2011).CrossRefGoogle Scholar
 21.L Chen, FR Yu, H Ji, B Rong, X Li, VCM Leung, Green fullduplex selfbackhaul and energy harvesting small cell networks with massive MIMO. IEEE J. Sel. Areas Commun. 34(12), 3709–3724 (2016).CrossRefGoogle Scholar
 22.DK Nguyen, DNK Jayakody, S Chatzinotas, JS Thompson, J Li, in IEEE Access, vol. 5. Wireless energy harvesting assisted twoway cognitive relay networks: protocol design and performance analysis, (2017), pp. 21447–21460.Google Scholar
 23.W Wang, R Wang, H Mehrpouyan, N Zhao, G Zhang, in IEEE Access, vol. 5. Beamforming for simultaneous wireless information and power transfer in twoway relay channels, (2017), pp. 9235–9250.Google Scholar
 24.MA Marsan, G Bucalo, AD Caro, M Meo, Y Zhang, in 2013 IEEE International Conf. on Communications Workshops (ICC). Towards zero grid electricity networking: powering BSs with renewable energy sources (Budapest, 2013), pp. 596–601.Google Scholar
 25.H Liu, KJ Kim, KS Kwak, HV Poor, QoSconstrained relay control for fullduplex relaying with SWIPT. IEEE Trans. Wireless Commun. 16(5), 2936–2949 (2017).CrossRefGoogle Scholar
 26.G Pan, H Lei, Y Yuan, Z Ding, Performance analysis and optimization for SWIPT wireless sensor networks. IEEE Trans. Commun. 65(5), 2291–2302 (2017).CrossRefGoogle Scholar
 27.F Zhou, Z Li, J Cheng, Q Li, J Si, Robust ANaided beamforming and power splitting design for secure MISO cognitive radio with SWIPT. IEEE Trans. Wirel. Commun. 16(4), 2450–2464 (2017).CrossRefGoogle Scholar
 28.AA Lu, X Gao, YR Zheng, C Xiao, in IEEE Transactions on Communications, vol. 65, no. 7. Linear precoder design for SWIPT in MIMO broadcasting systems with discrete input signals: manifold optimization approach, (2017), pp. 2877–2888.Google Scholar
 29.YH Suh, K Chang, A highefficiency dualfrequency rectenna for 2.45and 5.8GHz wireless power transmission. IEEE Trans. Microw. Theory Tech. 50(7), 1784–1789 (2002).CrossRefGoogle Scholar
 30.JR Smith, Wirelessly powered sensor networks and computational RFID (SpringerVerlag New York, New York, 2013).CrossRefGoogle Scholar
 31.K Huang, V Lau, Enabling wireless power transfer in cellular networks: architecture, modeling and deployment. IEEE Trans. Wirel. Commun. 13(2), 902–912 (2014).CrossRefGoogle Scholar
 32.LR Varshney, in Proc. IEEE International Symposium on Information Theory (ISIT). 2008 Transporting information and energy simultaneously (Toronto, 2008), pp. 1612–1616.Google Scholar
 33.P Grover, A Sahai, in 2010 IEEE International Symposium on Information Theory. Shannon meets Tesla: wireless information and power transfer (Austin, 2010), pp. 2363–2367.Google Scholar
 34.X Zhou, R Zhang, CK Ho, Wireless information and power transfer: architecture design and rateenergy tradeoff. IEEE Trans. Commun. 61(11), 4754–4767 (2013).CrossRefGoogle Scholar
 35.X Lu, P Wang, D Niyato, DI Kim, Z Han, Wireless networks with RF energy harvesting: a contemporary survey. IEEE Commun. Surv. Tutor. 17(2), 757–789 (2015).CrossRefGoogle Scholar
 36.K Huang, X Zhou, Cutting the last wires for mobile communications by microwave power transfer. IEEE Commun. Mag. 53(6), 86–93 (2015).MathSciNetCrossRefGoogle Scholar
 37.MM Zhao, Y Cai, Q Shi, B Champagne, MJ Zhao, Robust transceiver design for MISO interference channel with energy harvesting. IEEE Trans. Signal Process. 64(17), 4618–4633 (2016).MathSciNetCrossRefGoogle Scholar
 38.I Krikidis, S Timotheou, S Sasaki, RF energy transfer for cooperative networks: data relaying or energy harvesting? IEEE Commun. Lett. 16(11), 1772–1775 (2012).CrossRefGoogle Scholar
 39.AA Nasir, X Zhou, S Durrani, RA Kennedy, Relaying protocols for wireless energy harvesting and information processing. IEEE Trans. Wirel. Commun. 12(7), 3622–3636 (2013).CrossRefGoogle Scholar
 40.Y Cai, MM Zhao, Q Shi, B Champagne, MJ Zhao, Joint transceiver design algorithms for multiuser MISO relay systems with energy harvesting. IEEE Trans. Commun. 64(10), 4147–4164 (2016).Google Scholar
 41.Z Chen, B Xia, H Liu, in 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP). Wireless information and power transfer in twoway amplifyandforward relaying channels (Atlanta, 2014), pp. 168–172.Google Scholar
 42.K Tutuncuoglu, B Varan, A Yener, Throughput maximization for twoway relay channels with energy harvesting nodes: the impact of relaying strategies. IEEE Trans. Commun. 63(6), 2081–2093 (2015).CrossRefGoogle Scholar
 43.D Li, C Shen, Z Qiu, in 2013 IEEE International Conference on Communications (ICC). Twoway relay beamforming for sumrate maximization and energy harvesting (Budapest, 2013), pp. 3155–3120.Google Scholar
 44.AA Okandeji, MRA Khandaker, KK Wong, Z Zheng, in 2016 IEEE Globecom Workshops (GC Wkshps). Joint transmit power and relay twoway beamforming optimization for energyharvesting fullduplex communications (Washington, 2016), pp. 1–6.Google Scholar
 45.L Zhang, Y Cai, M Zhao, B Champagne, L Hanzo, 16. Nonlinear MIMO transceivers improve wirelesspowered and selfinterferenceaided relaying, (2017), pp. 6953–6966.Google Scholar
 46.MM Zhao, Q Shi, Y Cai, MJ Zhao, Joint transceiver design for fullduplex cloud radio access networks with SWIPT. IEEE Trans. Wirel. Commun. 16(9), 5644–5658 (2017).CrossRefGoogle Scholar
 47.MM Zhao, Y Cai, Q Shi, M Hong, B Champagne, Joint transceiver designs for fullduplex K pair MIMO interference channel with SWIPT. IEEE Trans. Commun. 65(2), 890–905 (2017).CrossRefGoogle Scholar
 48.W Wu, B Wang, Y Zeng, H Zhang, Z Yang, Z Deng, Robust secure beamforming for wireless powered fullduplex systems with selfenergy recycling. IEEE Trans.Veh. Technol. 66(11) (10055–10069).Google Scholar
 49.L Fan, H Zhang, Y Huang, L Yang, Exploiting BS sntenna tilt for SWIPT in 3D massive MIMO systems. IEEE Wirel. Commun. Lett. 6(5), 666–669 (2017).CrossRefGoogle Scholar
 50.X Wang, J Liu, C Zhai, in IEEE Transactions on Wireless Communications, vol. 16, no. 11. Wireless power transfer based multipair twoway relaying with massive antennas, (2017), pp. 7672–7684.Google Scholar
 51.S Lee, Y Zeng, R Zhang, in IEEE Wireless Communications Letters, vol. PP, no. 99. Retrodirective multiuser wireless power transfer with massive MIMO, (2017), pp. 1–1.Google Scholar
 52.X Xia, K Xu, D Zhang, Y Xu, Y Wang, Beamdomain fullduplex massive MIMO: realizing cotime cofrequency uplink and downlink transmission in the cellular system. IEEE Trans. Veh. Technol. 66(10), 8845–8862 (2017).CrossRefGoogle Scholar
 53.3GPP TR 25.996, Universal mobile telecommunications system (UMTS): spatial channel model for multiple input multiple output (MIMO) simulations, v.12.0.0 (2012). www.3gpp.org. Accessed 07 Feb 2018.
 54.KI Pedersen, PE Mogensen, BH Fleury, A stochastic model of the temporal and azimuthal dispersion seen at the base station in outdoor propagation environments. IEEE Trans.Veh. Technol. 49(2), 437–447 (2000).CrossRefGoogle Scholar
 55.J Singh, S Ramakrishna, On the feasibility of codebookbased beamforming in millimeter wave systems with multiple antenna arrays. IEEE Trans.Wirel. Commun. 14(5), 2670–2683 (2015).CrossRefGoogle Scholar
 56.GB Giannakis, C Tepedelenlioglu, Basis expansion models and diversity techniques for blind identification and equalization of timevarying channels. Proc. IEEE. 86(10), 1969–1986 (1998).CrossRefGoogle Scholar
 57.D Nguyen, LN Tran, et al., Precoding for full duplex multiuser MIMO systems: spectral and energy efficiency maximization. IEEE Trans. Signal Process. 61(16), 4038–4050 (2013).MathSciNetCrossRefGoogle Scholar
 58.B Hassibi, BM Hochwald, How much training is needed in multipleantenna wireless links? IEEE Trans. Inf. Theory. 49(4), 951–963 (2003).CrossRefMATHGoogle Scholar
 59.Hager WW, Updating the inverse of a matrix. SIAM Rev. 31:, 221–239 (1989).MathSciNetCrossRefGoogle Scholar
 60.S Boyd, L Vandenberghe, Convex Optimization (Cambridge University Press, University Printing House, Cambridge, 2004).CrossRefMATHGoogle Scholar
 61.PC Weeraddana, M Codreanu, M Latvaaho, A Ephremides, Resource allocation for crosslayer utility maximization in wireless networks. IEEE Trans. Veh. Technol. 60(6), 2790–2809 (2011).CrossRefMATHGoogle Scholar
 62.3GPP TR 36.828, Further enhancements to LTE time division duplex (TDD) for downlinkuplink (DLUL) interference management and traffic adaptation, v.11.0.0 (2012). www.3gpp.org. Accessed 07 Feb 2018.
 63.E Everett, A Sahai, A Sabharwal, Passive selfinterference suppression for fullduplex infrastructure nodes. IEEE Trans. Wirel. Commun. 24(2), 680–694 (2014).CrossRefGoogle Scholar
 64.HQ Ngo, HA Suraweera, M Matthaiou, EG Larsson, Multipair fullduplex relaying with massive arrays and linear processing. IEEE J. Sel. Areas Commun. 32(9), 1721–1737 (2014).CrossRefGoogle Scholar
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