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PAPER
Non-Orthogonal Multiple Access Using Intra-Beam Superposition Coding and Successive Interference Cancellation for Cellular MIMO Downlink∗ Kenichi HIGUCHI†a) and Yoshihisa KISHIYAMA†† , Members
SUMMARY We investigate non-orthogonal multiple access (NOMA) with a successive interference canceller (SIC) in the cellular multiple-input multiple-output (MIMO) downlink for systems beyond LTE-Advanced. Taking into account the overhead for the downlink reference signaling for channel estimation at the user terminal in the case of non-orthogonal multiuser multiplexing and the applicability of the SIC receiver in the MIMO downlink, we propose intra-beam superposition coding of a multiuser signal at the transmitter and the spatial filtering of inter-beam interference followed by the intra-beam SIC at the user terminal receiver. The intra-beam SIC cancels out the inter-user interference within a beam. Regarding the transmitter beamforming (precoding), in general, any kind of beamforming matrix determination criteria can be applied to the proposed NOMA method. In the paper, we assume open loop-type random beamforming, which is very efficient in terms of the amount of feedback information from the user terminal. Furthermore, we employ a weighted proportional fair (PF)-based resource (beam of each frequency block and power) allocation for the proposed method. Simulation results show that the proposed NOMA method using the intra-beam superposition coding and SIC simultaneously achieves better sum and cell-edge user throughput compared to orthogonal multiple access (OMA), which is widely used in 3.9 and 4G mobile communication systems. key words: non-orthogonal multiple access, successive interference cancellation, downlink, MIMO
1. Introduction In the 3rd generation mobile communication systems such as W-CDMA and cdma2000, non-orthogonal multiple access (NOMA) based on direct sequence-code division multiple access (DS-CDMA) is used. Orthogonal multiple access (OMA) based on orthogonal frequency division multiple access (OFDMA) or single carrier-frequency division multiple access (SC-FDMA) is adopted in the 3.9 and 4th generation mobile communication systems such as LTE [1] and LTE-Advanced [2], [3]. OMA was a reasonable choice for achieving good system-level throughput performance in packet-domain services using channel-aware time- and frequency-domain scheduling with simple single-user deManuscript received May 23, 2014. Manuscript revised March 25, 2015. † The author is with the Department of Electrical Engineering, Graduate School of Science and Technology, Tokyo University of Science, Noda-shi, 278-8510 Japan. †† The author is with NTT DOCOMO, INC., Yokosuka-shi, 2398536 Japan. ∗ The material in this paper was presented in part at the IEEE 78th Vehicular Technology Conference, Las Vegas, U.S.A., September 2013. a) E-mail:
[email protected] DOI: 10.1587/transcom.E98.B.1888
tection at the receiver. However, further enhancements in the system efficiency and quality of user experience (QoE), especially at the cell edge, are required in the future. To accommodate such demands, NOMA can again be a promising candidate as a wireless access scheme for systems beyond 4G. NOMA thoroughly utilizes the new approach of user multiplexing in the power-domain that was not sufficiently utilized in previous generations. To make NOMA promising, it should be used with advanced transmission/reception techniques such as dirty paper coding (DPC) or a successive interference canceller (SIC) [4]–[7], which is different from the 3rd generation mobile communication systems. We note that nonorthogonal user multiplexing using a simple spreading code as a channelization code assumed in the 3rd generation systems cannot fully utilize the potential gain of NOMA with a SIC. We assume that basic transmission signal generation is based on orthogonal frequency division multiplexing (OFDM), including discrete Fourier transform (DFT)spread OFDM [1], which is robust against multipath interference. The channelization is solely obtained through capacity-achieving channel codes such as the turbo code and low-density parity check (LDPC) code. This paper focuses on NOMA in the multiple-input multiple-output (MIMO) downlink. Here, we define NOMA in the MIMO downlink as the case when the number of multiplexed users per time/frequency block is greater than the number of base station (BS) transmitter antennas. A straightforward approach to achieve NOMA in the MIMO downlink may be to use non-orthogonal beamforming (precoding), in which the number of transmitter beams is greater than the number of BS transmitter antennas, as an extension of space division multiple access (SDMA). However, the increased inter-user (inter-beam) interference severely limits the throughput performance of this method [8]. In previous studies by our group, e.g., [9] and [10] and references therein, the potential gain of NOMA with the superposition coding and SIC was shown relative to OMA in a cellular system assuming a single-transmitter and multiplereceiver antenna configuration. NOMA with a SIC simultaneously improves the cell-edge user throughput and sum throughput compared to OMA in a cellular system where the channel conditions vary significantly among users due to the near-far effect. Thus, the SIC is very effective in reducing the negative impact of inter-user interference in NOMA
c 2015 The Institute of Electronics, Information and Communication Engineers Copyright
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on the system throughput. However, in the MIMO downlink, the broadcast channel is not degraded. Therefore, the superposition coding with a SIC is not optimal and DPC should be used to fully utilize the entire multiuser capacity region [7], [11]. However, DPC is very difficult to implement in practice and is very sensitive to delay in the feedback of the channel state information to the BS transmitter. Furthermore, in order to achieve the multiuser capacity region using DPC, userdependent beamforming must be employed. This results in increased overhead of the (orthogonal) reference signals dedicated to the respective users as the number of multiplexed users is increased beyond the number of transmitter antennas. This increase in overhead of the reference signal decreases the achievable throughput gain from the DPC in practice. To address the above problems, we propose a NOMA method using intra-beam superposition coding for the MIMO downlink. In the proposed method, the number of transmitter beams is restricted to the number of transmitter antennas (at maximum), which is the same as in LTEAdvanced assuming OMA. Within each of the beams, superposition coding of multiple user signals is applied (thus, intra-beam superposition coding). The transmitter in the proposed method can be seen as a combination of conventional SDMA and superposition coding. However, the proposed method achieves the following synergistic effects. First, using the proposed method, the number of reference signals is equal to the number of transmitter antennas, which is the same as in LTE-Advanced, irrespective of the number of non-orthogonally multiplexed users. Therefore, the reduction in the NOMA gain due to the increase in overhead of the reference signaling is avoided. Second, more effective non-orthogonal user multiplexing is achieved using both the space and power domains compared to the case with only SDMA. Furthermore, the proposed method can use the SIC to remove the inter-user interference. At the user terminal, the inter-beam interference is first suppressed by spatial filtering only by using multiple receiver antennas. After interbeam interference suppression, successive interference cancellation is processed against the spatially-filtered scalar received signal to remove the inter-user interference within a beam due to the superposition coding (thus, an intra-beam SIC). Since the channel among the non-orthogonally multiplexed users by superposition coding within a beam after the spatial filtering is degraded, we can apply the SIC, which is easier to implement and more robust against the channel variation compared to DPC, in the MIMO downlink. The proposed method also simplifies the resource allocation process since it can be performed for each beam independently, while the use of DPC forces joint optimization of the resource allocation processes among beams. In general, any kind of beamforming matrix determination criteria can be applied to the proposed NOMA method using intra-beam superposition coding and SIC. In the paper, we employ open loop-based random beamforming [12], [13] as an example. Random beamforming is ef-
fective in reducing the channel state information feedback. Furthermore, we employ a weighted proportional fair (PF)based resource (beam of each frequency block and power) allocation [14]–[18] for the proposed method. The remainder of the paper is organized as follows. Section 2 describes the proposed NOMA using the intrabeam superposition coding and SIC. Section 3 presents simulation results on the system-level throughput and a comparison to OMA. Finally, Sect. 4 concludes the paper. 2. 2.1
Proposed Method NOMA Using Intra-Beam Superposition Coding and SIC
We assume OFDM signaling with a cyclic prefix, although we consider non-orthogonal user multiplexing. Therefore, the inter-symbol interference and inter-carrier interference are perfectly eliminated assuming that the length of the cyclic prefix is sufficiently long so that it covers the entire multipath delay spread. There are F frequency blocks and the bandwidth of a frequency block is W Hz. The number of transmitter antennas at the BS is M. The number of receiver antennas at the user terminal is N. The number of users per cell is K. For simplicity, in the following, we describe the proposed method at some particular timefrequency block (resource block) f ( f = 1, . . . , F). For multiple time-frequency blocks, the same process is performed independently in principle. In this section, the time index, t, is omitted for simplicity. The BS performs MIMO transmission with B beams, where 1 ≤ B ≤ M. The M-dimensional b-th (b = 1, . . . , B) transmitter beamforming (precoding) vector at frequency block f is denoted as m f,b . We assume that the multiuser scheduler schedules a set of users, U f,b = {i( f , b, 1), i( f , b, 2), . . . , i( f , b, k( f , b))}, to beam b of frequency block f . Term i( f , b, u) indicates the u-th (u = 1, . . . , k( f , b)) user index scheduled at beam b of frequency block f , and k( f , b) (k( f , b) ≤ K) denotes the number of simultaneously scheduled users at beam b of frequency block f . At the BS transmitter, each i( f , b, u)-th user information bit sequence is independently channel coded and modulated. Term si( f,b,u), f,b denotes the coded modulation symbol of user i( f , b, u) at beam b of frequency block f . We assume E[|si( f,b,u), f,b |2 ] = 1. The allocated transmission power for user i( f , b, u) at beam b of frequency block f is denoted as pi( f,b,u), f,b . In the proposed method, si( f,b,u), f,b of all k( f , b) users is first superposition coded as intra-beam superposition coding and then multiplied by the transmitter beamforming vector, m f,b . Finally, by accumulating all B beam transmission signal vectors, the M-dimensional transmission signal vector, x f , at frequency block f is generated as xf =
B b=1
m f,b
k( f,b)
√
pi( f,b,u), f,b si( f,b,u), f,b .
(1)
u=1
The transmission power allocation constraint is represented
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as k( f,b)
B
pi( f,b,u), f,b = pb ,
u=1
pb = ptotal ,
(2)
b=1
where pb is the transmission power of beam b and ptotal is the total transmission power per frequency block. We assume that pb and ptotal are identical for all frequency blocks in the paper. The set of pi( f,b,u), f,b at beam b of frequency block f is denoted as P f,b . The N-dimensional received signal vector of user i( f , b, u) at frequency block f , yi( f,b,u), f , is represented as yi( f,b,u), f = Hi( f,b,u), f x f + wi( f,b,u), f = Hi( f,b,u), f
B
k( f,b )
m f,b
√
pi( f,b ,u ), f,b si( f,b ,u ),b
u =1
b =1
+ wi( f,b,u), f ,
(3)
where Hi( f,b,u), f is the N × M-dimensional channel matrix between the BS and user i( f , b, u) at frequency block f and wi( f,b,u), f denotes the receiver noise plus inter-cell interference vector at frequency block f . In the proposed method, the user terminal first performs spatial filtering to suppress the inter-beam interference. Assuming that user i( f , b, u) uses the N-dimensional spatial filtering vector, vi( f,b,u), f,b , to receive beam b of frequency block f , the scalar signal after the spatial filtering, zi( f,b,u), f,b , is represented as zi( f,b,u), f,b = vi(Hf,b,u), f,b yi( f,b,u), f = vi(Hf,b,u), f,b Hi( f,b,u), f m f,b
k( f,b)
√
pi( f,b,u ), f,b si( f,b,u ), f,b
u =1 k( f,b ) B √ H +vi( f,b,u), f,b Hi( f,b,u), f m f,b pi( f,b ,u ), f,b si( f,b ,u ), f,b u =1 b =1 b b H +vi( f,b,u), f,b wi( f,b,u), f , (4)
In the paper, we assume that vi( f,b,u), f,b is calculated based on the minimum mean squared error (MMSE) criteria. The second and third terms of (4) are the inter-beam interference and receiver noise plus inter-cell interference observed at the spatial filtering output, respectively. By normalizing the aggregated power of the inter-beam interference and receiver noise plus inter-cell interference to be one, (4) can be rewritten as zi( f,b,u), f,b =
(6) Thus, among users to which beam b of frequency block f is allocated, the channel after the spatial filtering is a degraded SISO channel, and the equivalent normalized channel gain of user i( f , b, u) becomes gi( f,b,u), f,b . We note that when B > N, the inter-beam interference needs to be partially suppressed via appropriate beamforming. The proposed method can use any kind of beamforming control strategy. With closed loop-based beamforming, this can be done by using the set of beamforming vectors, which can be seen as (quasi) orthogonal at the user terminal receiver. In the case with open loop-based beamforming such as random beamforming, which we assume in the paper as an example, this can be indirectly accomplished by selecting users with a high signal-to-interference-plus-noise ratio (SINR) for a given set of beamforming vectors in the scheduling process. Therefore, the proposed method works even in the case where B > N similar to the other multiuser MIMO transmissions. We apply the intra-beam SIC to signal zi( f,b,u), f,b in order to remove the inter-user interference within a beam. Similar to the SISO downlink [5], the optimal order of decoding is in the order of the increasing equivalent normalized channel gain, gi( f,b,u), f,b . Based on this order, any user can correctly decode the signals of other users whose decoding order comes before that user for the purpose of interference cancellation. Thus, user i( f , b, u) can remove the interuser interference from user i( f , b, u ) whose gi( f,b,u ), f,b is lower than gi( f,b,u), f,b . As a result, the instantaneous throughput of user k (k = 1, . . . , K) at beam b of frequency block f assuming that the scheduler schedules user set U f,b with allocated transmission power set P f,b is represented as R f,b (k|U f,b ⎛ , P f,b ) = ⎞ ⎧ ⎜⎜⎜ ⎟⎟⎟ ⎪ ⎪ gk, f,b pk, f,b ⎪ ⎟⎠ , k ∈ U f,b . ⎪ ⎨W log2 ⎜⎜⎝1 + gk, f,b p j, f,b +1 ⎟ ⎪ j∈U f,b ,gk, f,b 1) achieves better throughput than OMA for the entire region of the cumulative distribution. This is because the user throughput of OMA is severely limited by the orthogonal bandwidth allocation, which reduces the bandwidth for the respective users. The proposed NOMA allows for wider bandwidth usage of all users irrespective of the channel conditions. Allocating high power to the power-limited cell-edge users associated with the SIC process, which is applied to the bandwidth-limited cellinterior users, enhances the throughput of the users under a wide range of channel conditions. The impact of the transmission bandwidth limitation on OMA is especially clear in the high cumulative distribution probability region, where the users are under bandwidth-limited conditions. The gain by further increasing Nmax from two to four is relatively small. This indicates that it is sufficient to multiplex nonorthogonally a moderate number of users to obtain the most from the potential gain using the proposed NOMA. It should
Fig. 3
Cumulative distribution of user throughput.
be noted that the overhead required for the transmission of a downlink reference signal for the proposed NOMA using intra-beam superposition coding and SIC is the same as that for OMA irrespective of the Nmax value. In the following, the user throughput value at the cumulative probability of 5% is denoted as the cell-edge user throughput [22]. Figures 4(a) and 4(b) show the sum user throughput (Rsum ) and cell-edge user throughput (Redge ) as a function of the number of users per cell, K. We tested the cases with Nmax of one and two and evaluate M = B of one and two. From Fig. 4(a), assuming the same M (= B), the proposed NOMA using the Nmax of two significantly increases Rsum compared to OMA. Interestingly, the Rsum of NOMA with a SIC assuming the M of one is very similar to that of OMA with the M of two. This implies that NOMA with a SIC has an effect similar to spatial multiplexing using random beamforming and achieves a competitive level of performance to OMA by using a smaller number of BS transmitter antennas. It may also be noted that as M (= B) and Nmax increase, the required number of K for obtaining sufficiently saturated Rsum is increased. This is because as M and Nmax increase,
Fig. 4
Rsum and Redge as a function of K.
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can achieve a high user throughput with the SIC. Since the remaining large fraction of BS transmission power can be used by the other users, scheduling these users under very good channel conditions does not significantly degrade the throughput of the other users in NOMA and this kind of resource allocation is effectively implemented using PF-based resource allocation. When the α of 1.0 is assumed, the proposed NOMA achieves approximately 1.5 and 1.2-fold gains in Rsum and Redge , respectively, compared to OMA which is assumed in LTE-Advanced. When α is increased to 1.5 for the proposed NOMA method, the proposed method increases the throughput gain at the cell-edge to approximately 1.6 fold at the cost of a moderate reduction in Rsum (however, it is still higher than that of OMA with the α of 1.0), since as α is increased, more radio resources tend to be allocated to the cell-edge users. Fig. 5
User throughput gain.
we need more candidate users in order to select an appropriate user set to be scheduled to achieve a sufficient multiuser diversity gain. From Fig. 4(b), we also see a clear gain in Redge by increasing Nmax from one to two similar to that for Rsum . Thus, the proposed NOMA using intra-beam superposition coding and SIC is effective in simultaneously improving the system efficiency, e.g., Rsum , and cell-edge user experience, e.g., Redge . Figure 5 shows the user throughput gain by using the proposed NOMA assuming the Nmax of two relative to OMA for the respective user coverage positions (0 indicates the cell edge and 1 indicates the vicinity of the BS). The values of M and B are set to two. Term K is set to 30. In OMA, the α of 1.0 is assumed. For the proposed NOMA, the α of 1.0 and 1.5 are tested. The Rsum values of OMA with the α of 1.0 and NOMA with the α of 1.0 and 1.5 are approximately 31, 47, and 40 Mb/s, respectively. Figure 5 shows that a user throughput gain of greater than one by using the proposed NOMA using intra-beam superposition coding and SIC is achieved for the entire region of the user coverage position. With PF-based resource allocation (thus α of 1), the log-sum of the user throughput is maximized. To achieve this, the throughput of users experiencing poor channel conditions should be increased to some extent since the logarithm of the user throughput quickly decreases as the user throughput decreases. The proposed NOMA method effectively achieves this by using the power-domain user multiplexing. Therefore, the user throughput gain in the region of the lower user coverage position, in other words, the celledge user throughput gain, is especially significant, which means improvement in the user fairness. This will be very beneficial in accommodating future traffic demands. Meanwhile, the users in the region over 0.8 of the user coverage position experience very good channel conditions. Therefore, even with a very small power assignment, these users
4.
Conclusion
Aiming at further enhancement of the system efficiency and cell-edge user experience for the systems beyond LTEAdvanced, this paper proposed a NOMA method with a SIC in the cellular MIMO downlink. The first feature of the proposed method is the use of intra-beam superposition coding and intra-beam SIC for non-orthogonal multiuser multiplexing within a beam. This brings about the effective use of the receiver SIC and avoids increasing the overhead of the downlink orthogonal reference signals dedicated to the respective users when the number of multiplexed users is increased beyond the number of transmitter antennas. In general, any kind of beamforming matrix determination criteria can be applied to the proposed NOMA method. We assumed open loop-type random beamforming in the paper, which is very efficient in terms of the amount of feedback information from the user terminal. Furthermore, we employed weighted PF-based resource allocation for the proposed method. From the simulation results, we show that the proposed NOMA using the intra-beam superposition coding and SIC simultaneously achieves better sum and cell-edge user throughput compared to OMA which is assumed in the 3.9 and 4th generation mobile communication systems such as LTE and LTE-Advanced. To verify the effectiveness of the proposed NOMA method, a detailed performance evaluation is needed assuming a realistic channel code and QAM data modulation. The design of realistic control signaling is also an important issue. These issues are left for future study. References [1] 3GPP TS36.300, Evolved Universal Terrestrial Radio Access (EUTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Overall description. [2] 3GPP TR36.913 (V8.0.0), “3GPP; TSG RAN; Requirements for further advancements for E-UTRA (LTE-Advanced),” June 2008. [3] 3GPP TR36.814 (V9.0.0), “Further advancements for E-UTRA physical layer aspects,” March 2010.
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[4] G. Caire and S. Shamai, “On the achievable throughput of a multiantenna Gaussian broadcast channel,” IEEE Trans. Inf. Theory, vol.49, no.7, pp.1691–1706, July 2003. [5] P. Viswanath and D.N.C. Tse, “Sum capacity of the vector Gaussian broadcast channel and uplink-downlink duality,” IEEE Trans. Inf. Theory, vol.49, no.8, pp.1912–1921, Aug. 2003. [6] N. Jindal, S. Vishwanath, and A. Goldsmith, “On the duality of Gaussian multiple-access and broadcast channels,” IEEE Trans. Inf. Theory, vol.50, no.5, pp.768–783, May 2004. [7] H. Weingarten, Y. Steinberg, and S.S. Shamai, “The capacity region of the Gaussian multiple-input multiple-output broadcast channel,” IEEE Trans. Inf. Theory, vol.52, no.9, pp.3936–3964, Sept. 2006. [8] M. Xia, Y.-C. Wu, and S. A¨ıssa, “Non-orthogonal opportunistic beamforming: Performance analysis and implementation,” IEEE Trans. Wireless Commun., vol.11, no.4, pp.1424–1433, April 2012. [9] N. Otao, Y. Kishiyama, and K. Higuchi, “Performance of non-orthogonal multiple access with SIC in cellular downlink using proportional fair-based resource allocation,” IEICE Trans. Commun., vol.E98-B, no.2, pp.344–351, Feb. 2015. [10] Y. Saito, Y. Kishiyama, A. Benjebbour, T. Nakamura, A. Li, and K. Higuchi, “Non-orthogonal multiple access (NOMA) for cellular future radio access,” Proc. 2013 IEEE 77th Vehicular Technology Conference (VTC Spring), pp.1–5, 2013. [11] M. Sharif and B. Hassibi, “A comparison of time-sharing, DPC, and beamforming for MIMO broadcast channels with many users,” IEEE Trans. Commun., vol.55, no.1, pp.11–15, Jan. 2007. [12] P. Viswanath, D.N.C. Tse, and R. Laroia, “Opportunistic beamforming using dumb antennas,” IEEE Trans. Inf. Theory, vol.48, no.6, pp.1277–1294, June 2002. [13] M. Sharif and B. Hassibi, “On the capacity of MIMO broadcast channels with partial side information,” IEEE Trans. Inf. Theory, vol.51, no.2, pp.506–522, Feb. 2005. [14] F. Kelly, “Charging and rate control for elastic traffic,” Eur. Trans. Telecommun., vol.8, no.1, pp.33–37, 1997. [15] A. Jalali, R. Padovani, and R. Pankaj, “Data throughput of CDMA-HDR a high efficiency-high data rate personal communication wireless system,” Proc. VTC2000-Spring. 2000 IEEE 51st Vehicular Technology Conference (Cat. No.00CH37026), pp.1854–1858, 2000. [16] J. Mo and J. Walrand, “Fair end-to-end window-based congestion control,” IEEE/ACM Trans. Netw., vol.8, no.5, pp.556–567, Oct. 2000. [17] R. Agrawal, A. Bedekar, R.J. La, and V. Subramanian, “Class and channel condition based weighted proportional fair scheduler,” Teletraffic Engineering in the Internet Era, Proceedings of the International Teletraffic Congress — ITC-I7, Teletraffic Science and Engineering, vol.4, pp.553–567, Elsevier, 2001. [18] A. Sang, X. Wang, M. Madihian, and R.D. Gitlin, “A flexible downlink scheduling scheme in cellular packet data systems,” IEEE Trans. Wireless Commun., vol.5, no.3, pp.568–577, March 2006. [19] R. Hashimoto, A. Benjebbour, and K. Higuchi, “A study on closedloop beamforming matrix control method in non-orthogonal access with intra-beam superposition coding and SIC for cellular downlink,” IEICE Technical Report, RCS2013-299, Jan. 2014 (in Japanese). [20] M. Kobayashi and G. Caire, “An iterative water-filling algorithm for maximum weighted sum-rate of Gaussian MIMO-BC,” IEEE J. Sel. Areas. Commun., vol.24, no.8, pp.1640–1646, Aug. 2006. [21] N. Nonaka, A. Benjebbour, and K. Higuchi, “System-level throughput of NOMA using intra-beam superposition coding and SIC in MIMO downlink when channel estimation error exists,” Proc. 2014 IEEE International Conference on Communication Systems (ICCS), pp.202–206, 2014. [22] 3GPP, TR 25.814 (V7.0.0), “Physical layer aspects for Evolved UTRA,” June 2006.
Kenichi Higuchi received the B.E. degree from Waseda University, Tokyo, Japan, in 1994, and received the Dr.Eng. degree from Tohoku University, Sendai, Japan in 2002. In 1994, he joined NTT Mobile Communications Network, Inc. (now, NTT DOCOMO, INC.). While with NTT DOCOMO, INC., he was engaged in the research and standardization of wireless access technologies for wideband DS-CDMA mobile radio, HSPA, LTE, and broadband wireless packet access technologies for systems beyond IMT-2000. In 2007, he joined the faculty of the Tokyo University of Science and currently holds the position of Associate Professor. His current research interests are in the areas of wireless technologies and mobile communication systems, including advanced multiple access, radio resource allocation, inter-cell interference coordination, multiple-antenna transmission techniques, signal processing such as interference cancellation and turbo equalization, and issues related to heterogeneous networks using small cells. He was a co-recipient of the Best Paper Award of the International Symposium on Wireless Personal Multimedia Communications in 2004 and 2007, a recipient of the Young Researcher’s Award from the IEICE in 2003, the 5th YRP Award in 2007, the Prime Minister Invention Prize in 2010, and the Invention Prize of Commissioner of the Japan Patent Office in 2015. He is a member of the IEICE and IEEE.
Yoshihisa Kishiyama received his B.E., M.E., and Dr. Eng. degrees from Hokkaido University, Sapporo, Japan in 1998, 2000, and 2010, respectively. In 2000, he joined NTT DOCOMO, INC. He has been involved in research and development for 4G/5G mobile broadband technologies and physical layer standardization in 3GPP. He is currently a Senior Research Engineer of 5G Laboratory in NTT DOCOMO, INC. His current research interests include 5G radio access technologies such as massive MIMO/beamforming, non-orthogonal multiple access (NOMA), and so on. He was a recipient of the International Telecommunication Union (ITU) Association of Japan Award in 2012.
