- Open Access
On massive MIMO performance with semi-orthogonal pilot-assisted channel estimation
© Zhang et al.; licensee Springer. 2014
- Received: 7 August 2014
- Accepted: 24 November 2014
- Published: 11 December 2014
With the rapidly increasing demand for high-speed data transmission and a growing number of terminals, massive multiple-input multiple-output (MIMO) has been shown promising to meet the challenges owing to its high spectrum efficiency. Although massive MIMO can efficiently improve the system performance, usage of orthogonal pilots and growing terminals causes large resource consumption especially when the coherence interval is short. This paper proposes a semi-orthogonal pilot design with simultaneous data and pilot transmission. In the proposed technique, we exploit the asymptotic channel orthogonality in massive MIMO systems, with which a successive interference cancellation (SIC)-based channel estimation is applied to mitigate the mutual interference between data and pilot. We derived the theoretical expressions of the achievable rates in massive MIMO systems with our proposed pilot design. Further discussion on performance verifies the superiority of our proposed pilot design for high or low signal-to-noise-ratios (SNRs) with any coherence interval length. And simulation results show that the proposed pilot design can achieve a significant performance improvement with reduced pilot resource consumption compared with the conventional orthogonal pilots.
- Massive MIMO
- Semi-orthogonal pilot design
- Interference cancellation
With a rapidly increasing demand for high data rates, as well as the growing number of serving users, massive multiple-input multiple-output (MIMO) is emerging as a promising technology to meet the challenge by providing a significant increment in reliability and data rate for wireless communications[1–3].
For MIMO systems, channel state information (CSI) is crucial for achieving multi-antenna gains. It becomes more challenging in massive MIMO systems due to numerous antennas at the base station (BS). Massive MIMO requires a large number of pilots if frequency-division duplexing (FDD) is used since the burden for downlink pilots is proportional to the number of BS antennas, while for time-division duplexing (TDD)[3, 4], uplink training is an effective method to obtain CSI by exploiting channel reciprocity. Generally, orthogonal pilot patterns are widely used for multi-channel estimation. It is well understood that the length of orthogonal pilots equals at least the number of users in a cell, which is in general much smaller than the number of massive BS antennas. However, even in TDD, the required resource for orthogonal pilots increases dramatically in a multi-cell massive MIMO system. Moreover, under the restriction of coherence interval duration and increasing user numbers, the same set of orthogonal pilots is reused for adjacent cells, thus pilot contamination[5–7] occurs in a muti-cell MIMO system. When the BS estimates the channel for a particular user, it may obtain a channel estimate contaminated by adjacent cell users that share the same pilot.
It has been revealed in[2, 8] that pilot contamination becomes a bottleneck that limits the performance benefits of massive MIMO. To solve this problem, recent studies[9–11] proposed various approaches tackling with pilot contamination. Although they tried to alleviate the pilot contamination between multiple cells, they still use orthogonal pilots in a single cell, which implies large pilot resource consumption, especially for short coherence interval.
Considering the pilot resource consumption as well as the importance of channel estimation’s accuracy, an efficient pilot design is essential for achieving full potential of massive MIMO systems. However, as far as we know, little attention has been paid to pilot design in a massive MIMO system. An exception studied the pilot sequence design which matters little about pilot resource consumption. Therefore, in this work, we study the problem of an efficient pilot design by exploiting the asymptotic channel orthogonality incorporated with successive interference cancellation (SIC) in massive MIMO systems.
The technical contributions of this work are summarized as follows: We present a novel pilot design with low resource consumption. In the proposed technique, we allow simultaneous data and pilot transmission and insert shifted pilot locations in slots, i.e., different users transmit pilots in different slots. It takes advantage of the asymptotic channel orthogonality for massive MIMO. Hence the mutual interference between data and pilot due to a semi-orthogonal pilot design can be mitigated by SIC. Numerical results show that the proposed pilot design outperforms the conventional orthogonal pilots. In particular, for low or high signal-to-noise-ratios (SNRs), we also theoretically prove the superiority of our proposed pilot design.
The paper is organized as follows. In Section 2, we describe the system model and transmission protocol of conventional massive MIMO systems. Section 3 addresses the transmission scheme of massive MIMO systems with the proposed pilot design. We analyze the achievable rates of both the uplink and downlink in Section 4. In Section 5, we deduce the asymptotic achievable rate as the SNR tends to infinity and zero. In Section 6, numerical results show that the proposed pilot design increases data transmission rates in various scenarios. Section 7 contains our conclusions.
We consider a cellular system composed of one BS with M antennas and K(K ≪ M) single-antenna users. Let ρ p , ρ u , and ρ d be the pilot SNR, the uplink SNR, and the downlink SNR, respectively. Denote as the channel vector between the BS and the k th user, where h k , the corresponding small-scale fading vector, is i.i.d and models the geometric attenuation as well as shadowing effects which is assumed to be constant and known a priori. We assume channel obeys reciprocity in TDD, i.e., the channel factors are the same for both the uplink and downlink, and h k remains constant during a coherence interval of length T.
The conventional pilot design in massive MIMO systems utilizing orthogonal pilots can prevent pilot contamination within one cell and obtain relatively accurate channel estimates. However, the required pilot overhead is for each user in a cell, which is too large especially when T is small and K is large in massive MIMO systems. In the next section, we will propose a possible pilot design which can keep a balance between the efficiency of data transmission and performance of the system.
In this section, we propose a semi-orthogonal pilot design with shifted locations, which reduces the pilot overhead while guaranteeing the system performance due to the merit of asymptotic channel orthogonality in massive MIMO systems.
y k [i]
Received signal at the BS when the
k th user transmits pilot
w k [i]
Unit variance AWGN when the
k thuser transmits pilot
The k th user’s pilot
The t th user’s uplink data signal when
the k th user transmits pilot
q t [i]
The t th user’s uplink data signal
after the channel estimation period
s t [i]
The t th user’s downlink data signal
Remark 1. Note that the pilot overhead for each user in the i th(i > 1) coherence interval of the proposed pilot design is from Figure2. And especially when T is small. As for the first coherence interval of the proposed pilot design, the pilot overhead is, which is also smaller than of conventional orthogonal pilots.
Due to the difference between the communications in the first and the i th(i > 1) coherence intervals, the uplink and downlink data transmissions as well as the channel estimation are elaborated in detail in the following subsections. Before the elaboration, we first show notational definitions in Table1, where parameter i represents the i th coherence interval. Besides, we replace g k with g k [i] to signify the channel vector between the BS and the k th user.
3.1 Communication in the first coherence interval
3.1.1 Uplink of the first user
Generally, the channel can be decomposed as. From the properties of MMSE estimation,, is the independent estimation error, where and.
Once the BS gets the first user’s channel estimate, the first user starts uplink data transmission. By exploiting the merit of massive MIMO, simultaneous pilot and data transmission of other users has little impact on the first user’s uplink data detection.
where we divide both the denominators and numerators in by M and apply Lemma 1, because is independent of g t (t ≠ 1),, and w k  from (1) and (2) according to the nature of MMSE estimation. Note that in a massive MIMO system, we assume that M is large enough to meet Lemma 1.
Lemma 1. Let p and q are two mutually independent L × 1 vectors whose elements are i.i.d(0,1) random variables. Thenand, where ‘’ denotes almost sure convergence.
3.1.2 Uplink of the kth(k>1) user
As for the other users in the first coherence interval, the main difference lies in the channel estimation period.
From (14) and (15), the uplink data for the k th user in the first coherence interval can also be precisely detected in a massive MIMO system with the proposed pilot design.
3.1.3 Downlink of all users
where we divide both the denominator and numerator of the last term in by and it vanishes as M → ∞. Equation (18) shows that the downlink data can also be accurately detected.
3.2 Communication in the i th(i > 1) coherence interval
Note that, different from the first coherence interval, all users show the same transmission pattern in the i th(i > 1) coherence interval as shown in Figure2. Hence, without loss of generality, we take the k th user for instance. Its channel estimation is contaminated by all the other users’ uplink data. As for the uplink and downlink data transmissions, they are similar to the procedures elaborated for the first coherence interval in the above subsection and we omit the detailed description due to space limitation. To be concise, we here briefly introduce the processing procedure of channel estimation in the following.
which uses the known channel estimate from the present coherence interval when 1 ≤ t ≤ k - 1 and uses that from the previous one when k ≤ t ≤ K. Hence (20) is separately expressed based on two conditions.
Here, we assume that the variation of channel vectors during a coherence interval is ignorable. Any two channel vectors between the same user and BS can be treated as approximately equal if they locate within a distance of T. Take the channel vector from the second user to BS for example. Let g A and g B respectively denote the channel vectors at time slots ‘A’ and ‘B’ in Figure2. Since the distance between ‘A’ and ‘B’ is no larger than T, it is reasonable to approximately treat g B as g A , i.e., g B ≈ g A . Therefore, as shown in (20) for the k ≤ t ≤ K case, the BS utilizes the estimate of g A to detect the second user’s uplink data at time slot ‘B’. This is different from the uplink data detection in (14), which is expressed based on only one condition. As for the following channel estimation process, it is performed in the similar way to that in Section 3.1. In this way, the k ≤ t ≤ K case for (20) establishes the main difference of the analysis for the i th(i > 1) coherence interval from that of Section 3.1.
Equation (20) can also be extended similarly to the form in (4) and then we arrive at the conclusion that.
Equation (26) is the unified form of for any i th(i ≥ 1) coherence interval by comparing (13) with (26). Analysis in the next section shows that will be useful in the performance analysis of our proposed pilot design.
Given the processing procedure elaborated in the above section, we are now ready to conduct theoretical performance analysis of our proposed scheme. From Figure2, the proposed pilot design saves more resource for data transmission compared with the conventional orthogonal one when K grows large along with M. However, due to the semi-orthogonal pilot pattern, SIC-based channel estimation is adopted, leading to a larger estimation error than the conventional one. In this section, performance analysis is presented to show explicitly the potential benefits that can be achieved by our proposed pilot design.
where γ k is the associated signal-to-interference-noise-ratio (SINR); S, I, and N stand for the power of signal, interference, and noise, respectively. Next the achievable rate is calculated by evaluating the power of these items term by term.
4.1 Downlink analysis
where the signal, interference, and noise terms are marked with S, I, and N, and is assumed to be accurately estimated at the k th user.
Besides, exploits Lemma 2 in the following and utilizes the fact that.
Lemma 2. Let x and y are two mutually independent L × 1 vectors whose elements are i.i.d (0, σ2) random variables. Then E[|x H x|2]=(L2 + L)σ4 and E[|x H y|2] = L σ4.
Proof.: It can be directly obtained by correlating the vectors in the element-wise way.
where is based on the independency between g k [i] and (k ≠ j) and the fact that the variance of g j [i] is β j I M .
4.2 Uplink analysis
There are three kinds of uplink transmission patterns in massive MIMO systems with the proposed pilot design. The first kind of uplink communication contains only uplink data, i.e., case ① in Figure2, while the other two kinds of uplink communications, i.e., cases ② and ③ in Figure2, cover both the uplink data and pilot. Here we take the first kind of uplink communication as an example since the respective analysis of the other two kinds of uplink communications are similar.
4.3 Performance evaluation
Remark 2. By comparing (43) with (45), the additional uplink data transmission, i.e., cases ② and ③ in Figure2, has an influential positive effect on the uplink achievable rate of the proposed pilot design for a small T. Though at a cost of channel estimation accuracy, however, by exploiting the merit of massive MIMO, the proposed pilot design can achieve a better tradeoff between pilot resource consumption and channel estimation accuracy. It outperforms the conventional one in many scenarios.
Remark 3. Note that from the elaboration in Section 3, compared with the conventional orthogonal pilot design, the main difference of our proposed one lies in the subtraction of data interference from the received signal at the BS, which can be easily completed without much additional complexity.
For a better view of the system performance comparison, and are introduced to respectively stand for the system overall achievable rates of the proposed pilot design and the conventional one.
In this section, we will look at the performance at asymptotically low and high pilot and data SNRs. For simplicity, assume that and are fixed. Hence, ρ u → 0 and ρ d → 0 as ρ p → 0, and it is likewise as ρ p → ∞. We can use ρ to stand for ρ p , ρ u , and ρ d when considering asymptotically low and high SNRs. Furthermore, the SNR of our proposed pilot design is defined as to provide an equal overall system power, where ρ op denotes the SNR of the conventional orthogonal one.
5.1 High SNR analysis
The similar manipulations to (49) is applied to and in (41) and (42), respectively, and it achieves that.
Therefore, based on (51), it is easily seen that. Define and, then we have.
Some remarks on the high SNR analysis show the priority of our proposed pilot design over the conventional one.
5.2 Low SNR analysis
where the last term tends to infinity at asymptotically low SNR. Compared with λop,k, it arrives at the result that, which is also applied to and in (41) and (42). Define and, then we have.
Define and, then obviously, we have.
Fortunately, based on the above analysis, we are able to achieve Theorem 1 characterizing the advantage of our proposed pilot design.
Theorem 1. The proposed pilot design outperforms the conventional one for both low and high SNRs.
The conclusion in Theorem 1 is independent of coherence interval length T and number of concerned coherence intervals N c . It provides a superior pilot design for scenarios of small or large noise and interference.
In this section, we present some numerical results about the performance of the proposed pilot design. The system tested here consists of K = 5 users within the same distance from the BS. Without loss of generality, assume that the large-scale fading coefficients β k are all 1. In practice, the users can be scheduled according to their channel conditions. If it is not specified, the number of BS antennas is set to be 128, and ρ u = ρ d = ρ p .
Concerning the high SNR analysis in both Theorem 1 and Figures3 and6, this is due to the consideration that massive MIMO applies not only for future wireless communication systems but also for current long term evolution/long term evolution-advanced (LTE/LTE-A) systems. For systems like LTE/LTE-A, the operation region in terms of SNR varies widely, for example, from -3 dB to 30 dB. In particular, for users locating in the proximity of BS, they experience a relatively high quality of SNR. Moreover, users are more likely to experience high SNR transmissions especially for the emerging small cell deployment with reduced cell sizes. The combination of small cells and massive MIMO could lead to a high SNR scenario. In Theorem 1, the high SNR analysis validates the application of our proposed pilot design for these scenarios. Finally, the analysis for both low and high SNRs presents a complete performance comparison between two pilot designs.
This paper proposes a semi-orthogonal pilot design using SIC in a TDD massive MIMO system, which makes full use of the asymptotic channel orthogonality. The performance of the proposed pilot design is elaborated both theoretically and numerically. Simulation results show that the proposed pilot design outperforms the conventional orthogonal pilots. And particularly for low or high SNRs with any coherence interval length, the superiority of our proposed pilot design is theoretically proven.
Part of this paper will be presented at the IEEE Globecom, Austin, USA, Dec. 2014. This work was supported by the 973 Program under 2013CB329203, the NSFC under 61471114 and 61223001, and the Important National Science & Technology Specific Projects 2012ZX03001038 and 2013ZX03003016.
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