SingleRF MIMOOFDM system with beam switching antenna
 Illsoo Sohn^{1} and
 Donghyuk Gwak^{2}Email author
https://doi.org/10.1186/s136380160538z
© Sohn and Gwak. 2016
Received: 5 June 2015
Accepted: 26 January 2016
Published: 3 February 2016
Abstract
In this paper, we investigate the replica interference problem of a multiple input multiple output (MIMO) receiver with a beam switching antenna (BSA) within the orthogonal frequency division multiplexing (OFDM) framework. Our frequencydomain analysis has revealed the following important findings: (i) without coexisting system, replica interference in the system can be completely avoided as long as the beam pattern switching rate of the BSA receiver is an integer multiple of the product of the OFDM sampling rate and the number of receiving beam patterns and (ii) with coexisting systems, replica interference cannot always be avoided because coexisting systems may induce replicas in the operating frequency bands of the system. We present a replica interference criterion that depends on the coexisting status and users’ beam switching capabilities. Based on our findings, we propose various replica interference avoidance (RINA) strategies for different coexisting and cooperating network scenarios. In addition, the overall network operation principles of the proposed RINA strategy are presented. Simulation results verify that the proposed MIMOOFDM system with a BSA successfully provides both MIMO and OFDM benefits, thereby resolving replica interference issues.
Keywords
1 Introduction
In the last decade, multiple input multiple output (MIMO) techniques have been rigorously developed to dramatically increase wireless throughput and reliability [1–6]. Most of the recent wireless standards were initially designed in a MIMO framework. For example, the most popular recent commercial wireless systems such as 3GPPLTE/A [7–9] and IEEE802.11n/11ac [10, 11] adopt MIMO techniques as a key feature. In the ongoing 5G wireless system standardization, various MIMO evolutions are under discussion to maximize MIMO benefits, which include the use of mmWAVE [12–15] and 3DMIMO [16, 17]. In line with the technical evolution of MIMO, the number of antenna elements has been significantly increasing: four in LTE Rel8 and eight in LTE Rel10; a twodigit number is expected for 5G systems.
It has been first revealed in [25] that the beamsteering nature of a singleRF MIMO receiver induces replicas in the frequency domain. The induced replicas may interfere with the desired signals, which is the major drawback of this type of antenna. A more detailed analysis of the impacts of replicas in a singleRF MIMO receiver with a BSA is presented in [27]. Several restrictions on beam pattern switching rates to avoid replica interference have been presented. However, all of the aforementioned works in this field assume an idealized system model, e.g., a singlecarrier system and no coexisting systems. For practical applications of a singleRF MIMO receiver with a BSA, a more realistic system model is essential. In this paper, we investigate a singleRF MIMO receiver with a BSA assuming a practical wireless system. We consider a multicarrier system, specifically, an orthogonal frequency division multiplexing (OFDM) system, with coexisting systems in neighboring frequency bands. Replica interference scenarios in this case become very complicated, and a sophisticated control scheme is required to avoid them. We propose a practical singleRF MIMOOFDM system with a BSA that resolves replica interference problem. Compared to two most closely related works in [25, 27], this paper includes the following differential work and novelty. In contrast to continuously rotating antennas in [25], we investigate beam switching antennas, which improves implementation feasibility with the help of rapid advances in electrical switching devices, e.g., PIN diodes. In [27], only singlecarrier system model is considered for the simplicity of analysis while this paper extends the system model to capture multicarrier and coexisting system scenarios. Thus, this paper provides more practical results for realworld communication environments.

BSA for multicarrier systems: We generalize a singleRF MIMO receiver with a BSA within an OFDM framework. We have investigated the beam pattern switching rate of the BSA required to avoid replica interference. Our frequencydomain analysis has revealed that replica interference can be completely avoided if the beam pattern switching rate of the BSA receiver is an integer multiple of the product of the OFDM sampling rate and the number of receiving beam patterns. To the best of our knowledge, this has never been studied in the literature.

Coexistence strategies: We consider the practical case in which there are coexisting systems in the neighboring frequency bands. We have derived mathematical conditions under which coexisting systems interfere with each other by inducing replicas. Possible coexisting and cooperating scenarios are classified. Then, we propose the most appropriate replica interference avoidance (RINA) strategy for each case.

Overall network operations: Based on the proposed coexistence strategies, we present the overall network operation principles for a singleRF MIMOOFDM system with a BSA. The distinguishing feature of the proposed system is that the beam switching capabilities of user devices are initially reported to the BS. The scheduler at the BS uses this information for user and subcarrier allocation. Then, the scheduling information (including selected users, subcarrier, and the corresponding beam pattern switching rates) is broadcasted.
The remainder of this paper is organized as follows. Our system model is described in Section 2. Frequencydomain analysis of replica interference in singleRF MIMOOFDM systems is presented in Section 3. In Section 4, various strategies for replica interference avoidance are proposed and compared. The overall network operation of the proposed singleRF MIMOOFDM system with a BSA is explained in Section 5. Simulation results are presented in Section 6. Finally, conclusions are drawn in Section 7.
Notation.

N _{T}: total number of transmitting beam patterns

M _{R}: total number of switching beam patterns of user

N _{U}: total number of users

N _{S}: total number of OFDM subcarriers

N _{B}: total number of coexisting systems

T _{OFDM}: OFDM symbol duration

f _{S}: OFDM sampling frequency

T _{S}: OFDM sample duration

Ω: oversampling factor of a BSA

f _{SW}: beam pattern switching rate of a BSA

n: transmitting beam patter index, m=1,2,…,N _{T}

m: receiving beam pattern index, n=1,2,…,M _{R}

u: user index, u=1,2,…,N _{U}

k: subcarrier index, k=1,2,…,N _{S}

\(\mathcal {B}\): set of coexisting system subbands

\(\mathcal {U}\): set of all users

\(\mathcal {S}\): set of selected users

Φ: set of all subcarriers

Ψ: set of selected subcarriers

\(\mathcal {W}\): set of selected beam pattern switching rates
2 System model
In practical wireless service scenarios, one or more systems may coexist in the neighboring frequency bands. The operating frequency bands of the coexisting systems are set to be mutually exclusive, assuming there is no outofband interference between them. The coexisting systems can be secondary carriers of the same network operator, carriers of other network operators, or completely different radio access systems, e.g., nonOFDM systems. Cooperating scenarios may vary depending on coexisting status, which will be specified in Section 4.
3 Replica interference in a singleRF MIMOOFDM system
In this section, we investigate replica interference in a singleRF MIMOOFDM system with a BSA. First, we preform frequencydomain analysis. Then, we reveal the required beam pattern switching rate to avoid replica interference, both with and without coexisting systems.
3.1 Frequencydomain analysis
3.2 Selfreplica interference
Using frequencydomain analysis, we have revealed the exact positions of the induced replicas. Here, we assume that there are no coexisting systems. According to (9), each subband is composed of a summation of the received signals at different receiving beam patterns. Since (a) and (b) simply change phases and amplitudes, the bandwidth of each subband remains the same with the transmitted signal, i.e., f _{S}. From (9), the spacing between the adjacent replicas is Ω f _{S}. Accordingly, the required condition under which the replicas do not overlap and interfere with each other is Ω f _{S}≥f _{S}. Note that Ω is the oversampling factor of a BSA and is an integer greater than or equal to one (Ω≥1). This analysis confirms that the required condition above is always satisfied for this definition of Ω.
Remark.
In a singleRF MIMOOFDM receiver with a BSA, replica interference can be completely avoided if the beam pattern switching rate of the BSA receiver is an integer multiple of the product of the OFDM sampling rate and the number of receiving beam patterns. In other words, there will be no performance degradation due to selfreplica interference.
3.3 Replica interference from coexisting systems
The amount of replica interference experienced by the reference system varies depending on the total number of coexisting systems and their relative distributions in the frequency domain. In the next section, we will develop sophisticated strategies for replica interference avoidance. These strategies are designed for use in different coexisting and cooperating scenarios.
3.4 Sum capacity with the presence of replica interference
where \(\mathcal {S}\) is the set of the scheduled users among the entire user set \(\mathcal {U}\), i.e., \(\mathcal {S} \subset \mathcal {U}\).
4 Replica interference avoidance strategy
Assuming no coexisting systems, the frequencydomain analysis in Section 3.2 have revealed that replica interference can be completely avoided if the beam pattern switching rate of the BSA receiver is an integer multiple of the product of the OFDM sampling rate and the number of receiving beam patterns. However, this does not hold with coexisting systems. As shown in Fig. 4, oversampling rate Ω decides the spacing between adjacent replicas. Any possible overlaps of replicas from different coexisting systems result in performance degradation. This performance degradation depends on the choice of beam pattern switching rate f _{SW}=Ω M _{R} f _{S}. In this section, we consider three possible coexisting and cooperating scenarios in which replica interference degrades the system performance and propose the bestsuit replica interference avoidance (RINA) strategy for each.
4.1 Maximumcapability RINA
In some coexisting cases, the system does not have any information regarding the coexisting status such as the number of neighboring systems and their distributions in the frequency bands. This situation occurs when the coexisting systems adopt different radio access technologies or no cooperating protocols are available. When there is no cooperating information from the coexisting systems, the best strategy for a singleRF MIMOOFDM receiver with a BSA is to increase the beam pattern switching rate of the BSA to its maximum, which is referred to as maximumcapability RINA (MAXRINA). The MAXRINA strategy maximizes the spacing between replicas in the frequency domain, reducing the probability that the replicas of different systems overlap.
4.2 Static RINA
We consider a limited cooperating case in which the system has partial knowledge of other coexisting systems. It is assumed that the system knows the number of coexisting systems that induce replica interference and degrade the system performance of each other. The simple solution in this case is that the coexisting systems use exclusive radio resources, i.e., subcarriers. The entire set of subcarriers is partitioned into multiple subgroups; each coexisting system uses only one of the subgroups not used by others. This restriction prevents any system from inducing replicas in subcarriers that are used by other coexisting systems. Because the partitioning and subgroup assignments need to be updated only when the coexisting status changes, this strategy is referred to as static RINA (SRINA).
4.3 Dynamic RINA
We consider a more aggressive cooperating case in which the system has full knowledge of all other coexisting systems. Full knowledge implies that the system knows not only the exact distribution of the coexisting systems in the frequency domain but also their scheduling information, including which subcarriers are currently selected for transmissions. This cooperating information can be exchanged through the backhaul. In recent advanced wireless systems, the backhaul is typically implemented with optical fibers. Hence, the impacts of latency and backhaul capacity are assumed minimal [33, 34]. Based on the cooperating information, the system can determine how each subcarrier is influenced by replica interference. Some subcarriers may be corrupted by the replicas of multiple coexisting systems. Only users with the high beam pattern switching capability can avoid the replica interference if they are allocated those subcarriers. On the contrary, some subcarriers may be corrupted by replicas of a small number of coexisting systems. Hence, most of the users (even those with low beam pattern switching capability) can avoid replica interference. Based on the observations above, the key principle of the proposed dynamic RINA (DRINA) strategy is that the users are allocated subcarriers such that they can completely avoid replica interference using their own beam pattern switching capabilities. The proposed DRINA algorithm is listed in Algorithm 1.
A detailed description of the proposed DRINA algorithm follows. The algorithm consists of two phases. In the first phase, the algorithm checks all subcarriers in Φ for the possible existence of replicas. Then, for each subcarrier k, the minimum required beam pattern switching rate \(\bar {\Omega }_{k}\) required to avoid replicas on that subcarrier is determined. In the second phase, users are allocated subcarriers. The allocation starts with the user with the highest beam pattern switching capability Ω _{ u }. Among the candidate subcarriers in which the user can avoid replica interference Δ, the user is allocated one that requires the maximum beam pattern switching rate. The determined user u ^{⋆}, subcarrier k ^{⋆}, and corresponding beam pattern switching rate \(\bar {\Omega }_{k^{\star }}\) are added to the selected user set \(\mathcal {S}\), Ψ, and \(\mathcal {W}\), respectively. This process is repeated until all users or all subcarriers has been allocated.
5 Overall network operations
6 Simulation results
In this section, we compare the performances of different RINA strategies. The number of transmitting beam patterns is set to N _{T}=4. The number of receiving beam patterns is set to M _{R}=4. Rayleigh flat fading is considered for beamspacedomain MIMO channels. The number of OFDM subcarrier is 128. The coexisting systems are assumed to be located in consecutive frequency bands. The cell loadings of the coexisting systems are set to 0.9. We consider different maximum beam pattern switching capabilities for different users. The maximum beam pattern switching capability of each user is randomly chosen from the integer set [1,2,…,Ω _{max}] with equal probability. The sum rate is averaged over 1000 independent realizations of user channels.
7 Conclusions
In this paper, we propose a singleRF MIMOOFDM system with a BSA. We have solved the replica interference problem of a MIMO receiver with a BSA, which has, until now, been the primary impediment to practical implementations. We have presented two important findings. (i) Without any coexisting systems, replica interference in the system can be completely avoided if the beam pattern switching rate of a user is an integer multiple of the product of the OFDM sampling rate and the number of receiving beam patterns. (ii) With coexisting systems, replica interference in the system cannot always be avoided and the criterion as to whether each user experiences replica interference depends on the coexisting status and users’ beam switching capabilities. We have considered three practical coexistence scenarios that are commonly encountered in commercial cellular systems. Three different RINA strategies are proposed for the scenarios. In addition, detailed network operation principles that adopt the proposed RINA algorithm have been described within the framework of commercial LTE systems. Future research directions will include channel estimation mechanisms, the extension to a multipath channel model, and a more sophisticated investigation of beam switching patterns.
Declarations
Acknowledgements
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF2015R1D1A1A01057100). This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. R010115244, Development of 5G Mobile Communication Technologies for HyperConnected Smart Services).
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
References
 A Goldsmith, SA Jafar, N Jindal, S Vishwanath, Capacity limits of MIMO channels. IEEE J. Sel. Areas Commun. 21(5), 684–702 (2003).View ArticleGoogle Scholar
 G Caire, S Shamai, On the achievable throughput of a multiantenna Gaussian broadcast channel. IEEE Trans. Inf. Theory. 49(7), 1691–1706 (2003).View ArticleMathSciNetMATHGoogle Scholar
 P Viswanath, D Tse, Sum capacity of the vector Gaussian broadcast channel and uplinkdownlink duality. IEEE Trans. Inf. Theory. 49(8), 1912–1921 (2003).View ArticleMathSciNetMATHGoogle Scholar
 N Jindal, A Goldsmith, Dirtypaper coding versus TDMA for MIMO broadcast channels. IEEE Trans. Inf. Theory. 51(5), 1783–1794 (2005).View ArticleMathSciNetMATHGoogle Scholar
 I Sohn, JG Andrews, KB Lee, MIMO broadcast channels with spatial heterogeneity. IEEE Trans. Wirel. Commun. 9(8), 2449–2454 (2010).View ArticleGoogle Scholar
 I Sohn, JG Andrews, Approaching largesystem limits faster in multiuser MIMO with adaptive channel feedback adjustments. IEEE Commun. Lett. 14:, 1125–1127 (2010).View ArticleGoogle Scholar
 BA Bjerke, LTEadvanced and the evolution of LTE deployments. IEEE Wirel. Commun. 18(5), 4–5 (2011).View ArticleGoogle Scholar
 Nokia Networks, LTE Release 12 and Beyond, 1–15 (2015). http://www.nsn.com.
 H Holma, A Toskala, LTE for UMTS: Evolution to LTEAdvanced, 2nd Edition (Wiley, 111 River Street Hoboken, NJ 070305774, 2011).View ArticleGoogle Scholar
 TA Levanen, J Pirskanen, T Koskela, J Talvitie, M Valkama, Radio interface evolution towards 5G and enhanced local area communications. IEEE Access. 2:, 1005–1029 (2014).View ArticleGoogle Scholar
 E Perahia, R Stacey, Next Generation Wireless LANs: 802.11n and 802.11ac, 2nd ed. (Cambridge Univ. Press, New York, NY, USA, 2013).View ArticleGoogle Scholar
 Z Pi, F Khan, An introduction to millimeterwave mobile broadband systems. IEEE Commun. Mag. 49(6), 101–107 (2011).View ArticleGoogle Scholar
 TS Rappaport, S Sun, R Mayzus, H Zhao, Y Azar, K Wang, GN Wong, JK Schulz, M Samimi, F Gutierrez, Millimeter wave mobile communications for 5G cellular: it will work!IEEE Access. 1:, 335–349 (2013).View ArticleGoogle Scholar
 J Qiao, X Shen, JW Mark, Y He, MACLayer concurrent beamforming protocol for indoor millimeterwave networks. IEEE Trans. Veh. Technol. 64(1), 327–338 (2015).View ArticleGoogle Scholar
 B Li, Z Zhou, W Zou, X Sun, G Du, On the efficient beamforming training for 60 GHz wireless personal area networks. IEEE Trans. Wirel. Commun. 12(2), 504–515 (2013).View ArticleGoogle Scholar
 Y Kim, H Ji, H Lee, J Lee, BL Ng, J Zhang, Evolution beyond LTEadvanced with full dimension MIMO. IEEE Int. Conf. Commun. Workshops (ICC), Budapest, 9–13 (2013).Google Scholar
 YH Nam, BL Ng, K Sayana, Y Li, J Zhang, Y Kim, J Lee, Fulldimension MIMO (FDMIMO) for next generation cellular technology. IEEE Commun. Mag. 51(6), 172–179 (2013).View ArticleGoogle Scholar
 A Sayeed, N Behdad, Continuous aperture phased MIMO: basic theory and applications. IEEE Int. Conf. Communication, Control, and Computing Allerton, IL, USA, 1196–1203 (Sept. 29–Oct. 1 2010).Google Scholar
 A Sayeed, Deconstructing multiantenna fading channels. IEEE Trans. Signal Process. 50:, 2563–2579 (2002).View ArticleGoogle Scholar
 A Kalis, A Kanatas, C Papadias, A novel approach to MIMO transmission using a single RF front end. IEEE J. Sel. Areas Commun. 26:, 972–980 (2008).View ArticleGoogle Scholar
 M Wennstrom, T Svantesson, An antenna solution for MIMO channels: the switched parasitic antenna. IEEE Int. Symp. Personal, Indoor and Mobile Radio Communications San Diego, CA, USA. 1:, A159–163 (Sep. 2001).Google Scholar
 K Gyoda, T Ohira, Design of electronically steerable passive array radiator (ESPAR) antennas. IEEE Int. Symp. Antennas and Propagation Society Salt Lake City, UT, USA. 2:, 922–925 (July 16–21, 2000).Google Scholar
 MA Sedaghat, RR Mueller, G Fischer, A novel singleRF transmitter for massive MIMO. 18th International ITG Workshop on Smart Antennas (WSA) Erlangen, 1–8 (12–13 Mar. 2014).Google Scholar
 Z Jin, JH Lim, TY Yun, Smallsize and highisolation MIMO antenna for WLAN. ETRI Journal. 34(1), 114–117 (2012).View ArticleGoogle Scholar
 R Bains, R Muller, Using parasitic elements for implementing the rotating antenna for MIMO receivers. IEEE Trans. Wireless Commun. 7(11), 4522–4533 (2008).View ArticleGoogle Scholar
 M Yoshida, K Sakaguchi, K Araki, Single frontend MIMO architecture with parasitic antenna elements. IEICE Trans. Commun. E95B(3), 882–888 (2012).View ArticleGoogle Scholar
 D Gwak, I Sohn, SH Lee, Analysis of singleRF MIMO receiver with beam switching antenna. ETRI Journal. 3:, 647–656 (2015).View ArticleGoogle Scholar
 V Barousis, AG Kanatas, N Skentos, A Kalis, Pattern diveristy for single RF user terminals in multiuser environments. IEEE Commun. Lett. 14(2), 151–153 (Feb. 2010).Google Scholar
 V Barousis, AG Kanatas, A Kalis, J PerruisseauCarrier, Reconfigurable parasitic antennas for compact mobile terminals in multiuser wireless systems. EURASIP J. Wirel. Commun. Netw, 30 (2012). doi:10.1186/16871499201230, Published: 3 Feb. 2012.
 S Shelley, J Costantine, CG Christodoulou, DE Anagnostou, JC Lyke, FPGAcontrolled switchreconfigured antenna. IEEE Antennas Wireless Propag. Lett. 9:, 355–96358 (2010).View ArticleGoogle Scholar
 CG Christodoulou, Y Tawk, SA Lane, SR Erwin, Reconfigurable antennas for wireless and space applications. Proc. IEEE. 100(7), 2250–2261 (2012).View ArticleGoogle Scholar
 VI Barousis, AG Kanatas, A Kalis, Beamspacedomain analysis of singleRF frontend MIMO systems. IEEE Trans. Veh. Technol. 60(3), 1195–1199 (2011).View ArticleGoogle Scholar
 3GPP TSGRAN WG1, Orange Telefonica, Backhaul modelling for CoMP, R1111174 (Feb. 2011).Google Scholar
 3GPP TSGRAN WG3, TSG RAN WG3, Reply LS to R3070527/R1071242 on Backhaul (X2 interface) Delay, R3070689 (Mar. 2007).Google Scholar