- Open Access
On the achievable rates of multiple antenna broadcast channels with feedback-link capacity constraint
© Chen et al; licensee Springer. 2011
- Received: 26 October 2010
- Accepted: 23 June 2011
- Published: 23 June 2011
In this paper, we study a MIMO fading broadcast channel where each receiver has perfect channel state information while the channel state information at the transmitter is acquired by explicit channel feedback from each receiver through capacity-constrained feedback links. Two feedback schemes are considered, i.e., the analog and digital feedback. We analyze the achievable ergodic rates of zero-forcing dirty-paper coding (ZF-DPC), which is a nonlinear precoding scheme inherently superior to linear ZF beamforming. Closed-form lower and upper bounds on the achievable ergodic rates of ZF-DPC with Gaussian inputs and uniform power allocation are derived. Based on the closed-form rate bounds, sufficient and necessary conditions on the feedback channels to ensure nonzero and full downlink multiplexing gain are obtained. Specifically, for analog feedback in both AWGN and Rayleigh fading feedback channels, it is sufficient and necessary to scale the average feedback link SNR linearly with the downlink SNR in order to achieve the full multiplexing gain. While for the random vector quantization-based digital feedback with angle distortion measure in an error-free feedback link, it is sufficient and necessary to scale the number of feedback bits B per user as where M is the number of transmit antennas and is the average downlink SNR.
- Feedback-link capacity constraint
- limited feedback
- multiple antenna broadcast channel
- multiplexing gain
- multiuser MIMO
- zero-forcing dirty-paper coding (ZF-DPC)
The multiple antenna broadcast channels, also called multiple-input multiple-output (MIMO) downlink channels, have attracted great research interest for a number of years because of their spectral efficiency improvement and potential for commercial application in wireless systems. Initial research in this field has mainly focused on the information-theoretic aspect including capacity and downlink-uplink duality [1–4] and transmit precoding schemes [5–9]. These results are based on a common assumption that the transmitter in the downlink has access to perfect channel state information (CSI). It is well known that the multiplexing gain of a point-to-point MIMO channel is the minimum of the number of transmit and receive antennas even without CSIT . On the other hand, in a MIMO downlink with single-antenna receivers and i.i.d. channel fading statistics, in the case of no CSIT, user multiplexing is generally not possible and the multiplexing gain is reduced to unity . As a result, the role of the CSI at the transmitter (CSIT) is much more critical in MIMO downlink channels than that in point-to-point MIMO channels.
The acquisition of the CSI at the transmitter is an interesting and important issue. For time-division duplex (TDD) systems, we usually assume that the channel reciprocity between the downlink and uplink can be exploited and the transmitter in the downlink utilizes the pilot symbols transmitted in the uplink to estimate the downlink channel . The impact of the channel estimation error and pilot design on the performance of the MIMO downlink in TDD systems has been studied in [13–18]. For frequency-division duplex (FDD) systems, no channel reciprocity can be exploited, and thus it is necessary to introduce feedback links to convey the CSI acquired at the receivers in the downlink back to the transmitter.
There are generally two kinds of CSI feedback schemes applied for MIMO downlink channels in the literature. The first scheme is called the unquantized and uncoded CSI feedback or analog feedback (AF) in short, where each user estimates its downlink channel coefficients and transmits them explicitly on the feedback link using unquantized quadrature-amplitude modulation [12, 19–21]. The performance of the downlink linear zero-forcing beamforming (ZF-BF) scheme with AF was evaluated through simulations in , and analytical results were given later in  and . The second feedback scheme is called the vector quantized CSI feedback or digital feedback (DF) in short, where each user quantizes its downlink channel coefficients using some predetermined quantization codebooks and feeds back the bits representing the quantization index [20–27]. The MIMO broadcast channel with DF has been considered in [20, 21, 24–27]. In , a linear ZF-BF-based multiple-input single-output (MISO) system is firstly considered with random vector quantization (RVQ) limited feedback link, in which the closed-form expressions for expected SNR, outage probability, and bit error probability were derived. Then the vector quantization scheme based on the distortion measure of the angle between the codevector and the downlink channel vector was adopted in [20, 21, 25], and a closed-form expression of the lower and upper bound on the achievable rate of ZF-BF was derived. The results there also showed that the number of feedback bits per user must increase linearly with the logarithm of the downlink SNR to maintain the full multiplexing gain. Further, the authors in  pointed out that in the scenario where the number of users is larger than that of the transmit antennas, with simple user selection, having more users reduces feedback load per user for a target performance.
However, the aforementioned literatures [20, 21, 25] both focus on the linear ZF-BF scheme, which is not asymptotically optimal compared with nonlinear schemes, such as zero-forcing dirty-paper coding (ZF-DPC) . So, it is necessary to investigate the limiting performance for MIMO downlink channels with limited digital feedback link. In , the authors analyzed both the linear ZF-BF and nonlinear zero-forcing dirty-paper coding (ZF-DPC) and derived loose upper bounds of the achievable rates with limited feedback. But different from the distortion measure of the angle in [20, 21, 25], another vector quantization approach based on the distortion measure of mean-square error (MSE) between the codevector and the downlink channel vector was adopted in . Simultaneously, the exact lower bounds of the achievable rates with limited feedback for ZF-DPC are not given in .
In this paper, we consider both analog and digital feedback schemes and study the achievable rates of a MIMO broadcast channel with these two feedback schemes, respectively. Different from [21, 25] focusing on the ZF-BF, the ZF-DPC is analyzed in our work which is inherently superior to the ZF-BF due to its nonlinear interference precancelation characteristic and is asymptotically optimal  as . Specially, for DF, we adopt the vector quantization distortion measure of the angle between the codevector and the downlink channel vector, and perform RVQ [20, 21, 25] for analytical convenience. Our main contributions and key findings in this paper are as follows:
A comprehensive analysis of the achievable rates of ZF-DPC with either analog or digital feedback is presented, and closed-form lower and upper bounds on the achievable rates are derived. For fixed feedback-link capacity constraint, the downlink achievable rates of ZF-DPC are bounded as the downlink SNR tends to infinity, which indicates that the downlink multiplexing gain with fixed feedback-link capacity constraint is zero.
In order to achieve full downlink multiplexing gain, it is sufficient and necessary to scale the average feedback link SNR linearly with the downlink SNR for AF in both AWGN and Rayleigh fading feedback channels. While for DF in an error-free feedback link, it is sufficient and necessary to scale the feedback bits per user as where M is the number of transmit antennas and is the average downlink SNR.
We note that although the ZF-DPC with DF has been considered in , our work also differs from it in several aspects. First, a different distortion measure for channel vector quantization is applied in our work compared to that in  as stated earlier. Actually, for RVQ-based DF, the angle distortion measure in [20, 21, 25] seems more reasonable than the MSE distortion measure in , which will be discussed in this paper. Second, a more thorough analysis about the downlink achievable rates (including upper and lower bounds) and multiplexing gain is presented in this paper than that in  (only upper bounds are given), covering both AF and DF.
The remainder of this paper is organized as follows. We give a brief introduction to the ZF-DPC with perfect CSIT in Section 2. Comprehensive analysis of achievable rates and multiplexing gain for both AF and DF are presented in Sections 3 and 4, respectively. A rough comparison of AF and DF is also given in Section 4. Finally, conclusions and discussions for future work are given in Section 5.
Throughout the paper, the symbols (·) T , (·)* and (·) H represent matrix transposition, complex conjugate and Hermitian, respectively. [·] m, n denotes the element in the m th row and the n th column of a matrix. ||·|| represents the Euclidean norm of a vector. |·| and ∠(·) denote the magnitude and the phase angle of a complex number, respectively. represents expectation operator. Var(·) is the variance of a random variable. denotes a circularly symmetric complex Gaussian random variable with mean of a and variance of b.
where and v = [v1v2 ⋯ v k ]T.
In this paper, we focus on the case K = M. If K < M, there will be a loss of multiplexing gain. The case K > M will introduce multi-user diversity gain and we will leave it for future work.
We first give a brief introduction of ZF-DPC under perfect CSIT in this section.
where g ij = [G] i, j and d i , the i th entry of d, is the output of dirty-paper coding for user i treating the term as the ∑j < ig ij d j noncausally known interference signal.
where is the exponential integral function of order n.
In this section, we consider the analog feedback (AF) scheme, where each user estimates its downlink channel coefficients and transmits them explicitly on the feedback link without any quantization or coding. In order to focus on the impact of feedback link capacity constraint, we assume perfect CSI at each user's receiver (CSIR), and no feedback delay, i.e., the downlink CSI is fed back instantaneously in the same block as the subsequent downlink data transmission. For ease of analysis, we also impose two restrictions on the transmission strategy: (1) the total transmit power is equally allocated to the users and (2) independent Gaussian encoding is applied for each user at the transmitter side.
In order to compare the impact of different feedback channels for AF scheme, we first consider the AWGN feedback channels from Sections 3.1 to 3.4, then extend the analysis to the Rayleigh fading channels in Section 3.5.
Analog feedback in AWGN feedback channels
where denotes the downlink channel gain from the m th transmit antenna of the BS to user i, the 1 × β fb M vector with i.i.d. entries each distributed as denotes the additive white Gaussian noise on the feedback channel and SNR fb represents the average transmit power (and also the average SNR in the feedback channel).
Moreover, and δi, mare independent from each other.
The vector quantization scheme using the distortion measure of MSE in  leads to the same statistics of the channel error as the AF scheme introduced above, so it is equivalent to the AF scheme. Therefore, the following analysis framework developed for AF can be readily applied to the case studied in .
Lower bound on the achievable rate of ZF-DPC with AF in AWGN feedback channels
where and [Δ]i, m= δi, m. Obviously, and Δ are mutually independent.
where and Δ i is the i th row of Δ.
We have the following theorem that gives a lower bound on the achievable ergodic rate of ZF-DPC under AF.
With uniform power allocation among the M users and independent Gaussian encoding , d i and d j (i ≠ j) are independent of each other. So x i and s i are mutually independent, but n i is no longer Gaussian and is not independent of x i , so we cannot directly apply the result of dirty-paper coding in  to derive the capacity of this channel.
where the first "≥" follows from the fact that the entropy is larger than the conditional entropy, and the second "≥" follows from the fact that a Gaussian random variable has the largest differential entropy when the mean and variance of a random variable are given.
The above inequality shows the lower bound on the achievable rate of user i under given . In the following paragraph, we derive closed-form expression for the lower bound on the achievable ergodic rate under fading downlink channel.
Thus, we have completed the proof.
Upper bound on the achievable rate of ZF-DPC with AF in AWGN feedback channels
An upper bound of the achievable rate is derived by assuming a genie who can provide the encoders at the BS and the decoders at the users with some extra information. This upper bound is referred to as the genie-aided upper-bound.
where is the i th column of , and . Assume there is a genie who knows the values of and and tells these values to the encoder and decoder for user i, then with i.i.d. channel inputs , n i is Gaussian distributed with zero mean and variance and is independent of x i . Hence the channel for user i in Equation 25 will be recognized as a standard dirty-paper channel and its capacity is log2 (1 + Var(x i )/Var(n i )) . Finally the downlink achievable ergodic rate can be upper bounded by the genie-aided upper bound as given in the following theorem.
It is difficult to derive a closed-form expression for the right-hand side (RHS) in Equation 26, so we use Monte Carlo simulations to obtain this upper bound.
where γ is the Euler-Mascheroni constant  and .
The proof of the corollary is in Appendix 1. Although this upper bound is quite loose, it does predict the ceiling effect on the achievable rate with fixed feedback-link capacity.
Achievable downlink multiplexing gain with AF in AWGN feedback channels
From Corollary 1, it is obvious that the downlink multiplexing gain with fixed feedback-link capacity is zero. In order to maintain a nonzero multiplexing gain, the feedback channel quality should improve at some rate as the downlink SNR increases, which is given in detail in the following theorem:
Theorem 3. For AF and AWGN feedback channels, and β fb SNR fb scales as , then a sufficient and necessary condition for achieving the multiplexing gain of M (0 < b0 < 1) is that b = b0; a sufficient and necessary condition for achieving the full multiplexing gain of M is that b ≥ 1. Moreover, for b > 1, the asymptotic rate gap between the achievable rate of ZF-DPC with perfect CSIT and that under AF is zero as the downlink SNR goes to infinity.
Achievable rates and multiplexing gain with AF in Rayleigh fading feedback channels
In this subsection, we will further consider the effects of Rayleigh fading feedback channels to the achievable rates and multiplexing gain with AF. From Equation 11, we notice that D i is the function of the feedback channel . If the feedback channel is a fading channel, then D i will become a random variable and thus the lower bound we have obtained in Equation 15 is also random. So we need to take the mean of the RHS of the inequality in Equation 15 with respect to to get the new lower bound for the fading feedback channel case.
First, we introduce a lemma to help us derive the lower bound.
Lemma 1. f(x) = e x E n (x) (n ≥ 1) is a convex and monotonically decreasing function.
The proof of this lemma is in Appendix 3. Then we have the following closed-form lower bound on the downlink achievable ergodic rate in the Rayleigh fading feedback channels.
where . The second "≥" in Equation 30 follows from Lemma 1 and the Jensen inequality for convex functions.
This finishes the proof.
The upper bound of the achievable ergodic rate with fading feedback channels can also be derived from Equation 26 as the following corollary, and simulations are still needed to calculate the upper bound:
where γ is the Euler-Mascheroni constant.
The proof is similar to that of Corollary 2 and thus omitted due to the page limit. From this corollary, we also have the observation for the fading feedback channel that when the downlink SNR goes to infinity while keeping the parameters of the feedback channel constant, there is also a ceiling effect on the achievable ergodic rate of ZF-DPC.
From Corollary 2 we can see that the upper bound also tends to a constant. So the multiplexing gain is zero, which is the same as the AWGN feedback channel case. In order to maintain a multiplexing gain of M, the SNR of the feedback channel should scale with the downlink SNR, as shown in the following corollary:
Corollary 3. For AF and i.i.d. Rayleigh fading feedback channel, let β fb SNR fb scales as , a, b > 0, then if b ≥ 1, the multiplexing gain of the downlink will maintain as M;
if b < 1, the multiplexing gain of at least bM can be achieved. Moreover, for b > 1, the asymptotic rate gap between the achievable rate of ZF-DPC with perfect CSIT and that under AF is zero as the downlink SNR goes to infinity.
The proof is quite similar to that of Theorem 3 and thus omitted here for brevity. We also notice that the results are the same as those for AWGN feedback channels, so no more simulation results are given here.
We now consider digital feedback (DF), where the downlink CSI are estimated and quantized into several bits using a vector quantization codebook at each user and the quantization bits are fed back to the BS. The feedback channel is assumed to be capacity-constrained and error-free, i.e., as long as the number of feedback bits does not exceed the feedback-link capacity in terms of the maximum feedback bits per fading block, the feedback transmission will be error-free . We also assume perfect CSIR and no feedback delay as in Section 3. Moreover, the same restrictions are imposed on the transmission strategy as in Section 3.
The downlink channel vector h i of user i can be expressed as , where is the amplitude of h i and is the direction of h i. Under the assumption that the entries of h i are i.i.d. , we have and is uniformly distributed on the M dimensional complex unit sphere . Moreover, λ i and are independent of each other .
Then the B = log2N quantization bits are fed back to the BS.
We note that Equation 34 is actually based on the distortion measure of the angle between the codevector and the downlink channel vector, which is equivalent to (2) in  and (51) in . It is obviously different from the distortion measure of MSE adopted in . We also find out that the MSE distortion measure in  is similar to the distortion measure (Equation 12) used in AF in our work; therefore, the analysis based on MSE distortion measure in  can be easily incorporated into our AF analysis framework.
Define and , then we introduce two lemmas that are useful for further discussion.
Lemma 3. : θ i is uniformly distributed in the interval (-π, π] and independent from ν i .
In the next subsection, we will find that the information of θ i is necessary for phase compensation at user i's receiver. Therefore, we need to store the value of θ i at user i's receiver. Notice that the norm information of the channel vectors is not conveyed to the BS.
Lower bound on the achievable rate of ZF-DPC with DF
where is a diagonal matrix, and .
where is a diagonal matrix, has the same statistics as v.
We first give three lemmas useful for deriving the lower bound of the achievable rate of ZF-DPC under DF.
Lemma 4. .
where N = 2 B , and .
Lemma 6. f(x) = e x E n (x) (n ≥ 1) is a monotonically decreasing function.
The proofs of these three lemmas are in Appendices 4-6, respectively. Then we have the following theorem on the lower bound of the achievable ergodic rate of ZF-DPC under DF.
where ψ(x) is the Euler psi function  and is given in Lemma 5.
With Gaussian inputs and uniform power allocation, , then .
where the second "≥" follows from the Jensen inequality of the concave function.
where ψ(x) is the Euler psi function .
where the closed-form expression of has been obtained in Lemma 5.
and thus, the same lower bound remains no matter how many bits are used to quantize . Therefore, the information of θ i and phase compensation plays an important role in the DF scheme, which is different from the case in  where no phase compensation is needed.
Upper bound on the achievable rate of ZF-DPC with DF
The upper bound on the achievable rate of ZF-DPC with DF can be obtained in a similar way as in Section 3.3.
where is the i th column of , and . We also assume there is a genie who knows the values of λ i , and , then following the methodology in Section 3.3, we can see that the channel for user i is also recognized as a standard dirty-paper channel. Therefore, the downlink achievable ergodic rate can be upper bounded by the genie-aided upper bound as shown below:
Simulations are also necessary to calculate the upper bound given in Theorem 6.
From Theorem 6, we also derive a closed-form upper bound for the achievable rate with DF as shown below.
The proof is similar to those of Corollaries 1 and 2, and thus omitted due to the page limit. Although this upper bound is quite loose, it does also predict the ceiling effect on the achievable rate with fixed feedback bits per user.
Achievable downlink multiplexing gain with DF
The multiplexing gain of the downlink with DF and fixed feedback bits per user is zero due to the ceiling effect. In order to maintain nonzero multiplexing gain, the feedback bits per user should scale with the downlink SNR. With Theorem 5 and Corollary 4, we can derive the following sufficient and necessary conditions on the scaling to ensure nonzero and full multiplexing gain:
A sufficient and necessary condition for achieving the downlink multiplexing gain of α 0 M (0 < α 0 < 1) is that α = α 0.
A sufficient and necessary condition for achieving the full downlink multiplexing gain of M is that α ≥ 1.
Comparison of AF and DF
In order to compare DF with AF, we need to relate the number of feedback bits B per user with SNR fb and the number of channel uses in the feedback link, or equivalently β fb . In this paper, we make an idealistic assumption that the AWGN feedback link can operate error-free at its capacity, i.e., it can reliably transmit log2 (1 + SNRfb) bits per channel use. This assumption describes the maximum possible number of bits that can ever be conveyed correctly through the AWGN feedback channel. In , the authors have pointed out that for fair comparison, β fb M feedback channel uses in AF should correspond to β fb (M - 1) feedback channel uses in DF, since no channel norm information is fed back in DF and a system using DF could use one feedback channel symbol to transmit the norm information. Thus, the number of feedback bits per user in the AWGN feedback channel is B = β fb (M - 1) log2 (1 + SNRfb).
We have investigated the performance of ZF-DPC in the multiuser MIMO downlink of a FDD system where the CSIT is obtained through capacity-constrained feedback channels. Two CSI feedback schemes, i.e., the analog and digital feedback schemes, are considered in our work. Closed-form expressions for lower and upper bounds on the achievable ergodic rates of ZF-DPC with Gaussian inputs and uniform power allocation are derived. Based on the closed-form rate bounds, sufficient and necessary conditions on the feedback channels to ensure nonzero and full downlink multiplexing gain are obtained. Our primary results show that for AF in both AWGN and Rayleigh fading feedback channels, it is both sufficient and necessary to scale the average feedback link SNR linearly with the downlink SNR in order to achieve full downlink multiplexing gain. While for the RVQ-based DF with angle distortion measure in an error-free feedback link, it is both sufficient and necessary to scale the feedback bits per user as where M is the number of transmit antennas and is the average downlink SNR.
We also mention that there are several issues not considered in our current work. In this paper, we have assumed perfect CSI at the users' receivers. In a practical system, however, there are always channel estimation errors due to finite number of training symbols, which will further degrade the performance of ZF-DPC. The impact of the feedback delay of the downlink CSI on the achievable rates is also not considered, which could be significant when the downlink channel is fast fading. For DF scheme, we apply the RVQ for quantization of the channel vector in order to make the analysis easier. Generalization to arbitrary vector quantization codebooks is an interesting issue and we expect the same conclusions could be drawn. In the analysis of DF scheme, we made an optimistic assumption that the AWGN feedback link can operate error-free at its capacity. This assumption can be removed by considering practical feedback transmission schemes, such as the uncoded QAM modulation discussed in . Throughout the paper, we have assumed that the number of users is equal to the number of transmit antennas. We conjecture that when the number of users is larger than that of transmit antennas and we properly design the user selection scheme, the feedback link quality (average feedback SNR for AF and the number of feedback bits for DF) per user could be less stringent while keeping the same performance. Finally, the analysis of the achievable ergodic rates are carried out with the restrictions of Gaussian inputs and uniform power allocation. Determining whether Gaussian input is optimal and the optimal power allocation scheme under imperfect CSIT is a challenging problem.
where γ is the Euler-Mascheroni constant . Substituting Equations A-2, A-3 and A-4 into Equation A-1, we arrive at the conclusion.
Then we can find out that if 0 < b0 < 1 and β i ~ O(N0/P) if b0 ≥ 1.
Let , a, b > 0. From Equations B-6, B-7 and B-11, we have min(b, 1) ≤ b0 ≤ b. So for the case 0 < b0 < 1, we have b ≤ b0 ≤ b and thus b = b0; while for the case b0 = 1, we have b ≥ 1. ■
And thus f'(x) < 0 which indicates that f(x) = e x E n (x) is a monotonically decreasing function.
Since f"(x) > 0, we can conclude that f(x) is convex. ■
Since we use RVQ, has the same distribution as , i.e., . Therefore, , i.e., the entries of are i.i.d. . Note that is the QR decomposition of where is a lower triangular matrix and is a unitary matrix. Since is the i th diagonal element of , then from  we can conclude that .
Thus, we have completed the proof. ■
So f'(x) < 0 which indicates that f(x) = e x E n (x) is a monotonically decreasing function.
This work was supported by China's 863 Project-No. 2009AA011501, National Natural Science Foundation of China-No. 60832008, China's National S&T Major Project-No. 2008ZX03003-004, Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT), National Science and Technology Pillar Program No. 2008BAH30B09 and National Basic Research Program of China No. 2007CB310608.
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