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Zero-forcing (or null-steering) precoding is a method of spatial signal processing by which a multiple antenna transmitter can null the multiuser interference in a multi-user MIMO wireless communication system.<ref>{{Cite conference |first1=Taesang |last1=Yoo |first2=Andrea J. |last2=Goldsmith |date=2005 |title=Optimality of zero-forcing beamforming with multiuser diversity |book-title=IEEE International Conference on Communications, 2005 |publisher=IEEE |volume=1 |pages=542–546 |doi=10.1109/ICC.2005.1494410 |isbn=978-0-7803-8938-0 |___location=Seoul, Korea (South)}}</ref> When the channel state information is perfectly known at the transmitter, the zero-forcing precoder is given by the pseudo-inverse of the channel matrix. Zero-forcing has been used in [[LTE (telecommunication)|LTE]] mobile networks.<ref>{{Cite journal |last1=Aslan |first1=Yanki |last2=Roederer |first2=Antoine |last3=Fonseca |first3=Nelson |last4=Angeletti |first4=Piero |last5=Yarovoy |first5=Alexander |date=Oct 2021 |title=Orthogonal Versus Zero-Forced Beamforming in Multibeam Antenna Systems: Review and Challenges for Future Wireless Networks |journal=IEEE Journal of Microwaves |volume=1 |issue=4 |pages=879–901 |doi=10.1109/JMW.2021.3109244 |doi-access=free |issn=2692-8388}}</ref>
==Mathematical description==▼
In a multiple antenna downlink system which comprises
:<math>y_k = \mathbf{h}_k^T \mathbf{x} + n_k, \quad k=1,2, \ldots, K</math>▼
If the transmitter knows the downlink [[channel state information]] (CSI) perfectly, ZF-precoding can achieve almost the system capacity when the number of users is large. On the other hand, with limited [[channel state information]] at the transmitter (CSIT) the performance of ZF-precoding decreases depending on the accuracy of CSIT. ZF-precoding requires the significant feedback overhead with respect to signal-to-noise-ratio (SNR) so as to achieve the full multiplexing gain.<ref name="Jindal_ZF">{{cite journal|author=N. Jindal|title=MIMO Broadcast Channels with Finite Rate Feedback|journal=IEEE Trans. Information Theory.|pages=5045–5059|volume=52|issue=11|date=Nov. 2006|doi=10.1109/TIT.2006.883550}}</ref> Inaccurate CSIT results in the significant throughput loss because of residual multiuser interferences. Multiuser interferences remain since they can not be nulled with beams generated by imperfect CSIT.▼
where <math>\mathbf{x} = \sum_{i=1}^K
▲==Mathematical description==
▲In a multiple antenna downlink system which comprises a <math>N_t</math> transmit antenna access point (AP) and <math>K</math> single receive antenna users, the received signal of user <math>k</math> is described as
The above signal model can be more compactly re-written as
▲:<math>y_k = \mathbf{h}_k^T\mathbf{x}+n_k, \quad k=1,2, \ldots, K</math>
:<math>
▲where <math>\mathbf{x} = \sum_{i=1}^K s_i P_i \mathbf{w}_i</math> is the <math>N_t \times 1</math> vector of transmitted symbols, <math>n_k</math> is the noise signal, <math>\mathbf{h}_k</math> is the <math>N_t \times 1</math> channel vector and <math>\mathbf{w}_i</math> is the <math>N_t \times 1</math> linear precoding vector. From the fact that each beam generated by ZF-precoding is orthogonal to all the other user channel vectors, one can rewrite the received signal as
▲:<math>y_k = \mathbf{h}_k^T \sum_{i=1}^K s_i P_i \mathbf{w}_i+n_k = \mathbf{h}_k^T s_k P_k \mathbf{w}_k +n_k, \quad k=1,2, \ldots, K</math>
where
:<math>\mathbf{y}</math>
:<math>\mathbf{W} = [\mathbf{w}_1, \ldots, \mathbf{w}_K]</math> is the <math>N_t \times K</math> precoding matrix,
:<math>\mathbf{D} = \mathrm{diag}(\sqrt{P_1}, \ldots, \sqrt{P_K})</math> is a <math>K \times K</math> diagonal power matrix, and
:<math>\mathbf{s} = [s_1, \ldots, s_K]^T</math> is the <math>K \times 1</math> transmit signal.
A ''zero-forcing precoder'' is defined as a precoder where <math>\mathbf{w}_i</math> intended for user <math>i</math> is orthogonal to every channel vector <math>\mathbf{h}_j</math> associated with users <math>j</math> where <math>j \neq i</math>. That is,
=== Quantify the feedback amount ===▼
:<math>\mathbf{w}_i \perp \mathbf{h}_j \quad \mathrm{if} \quad i \neq j.</math>
Thus the interference caused by the signal meant for one user is effectively nullified for rest of the users via zero-forcing precoder.
From the fact that each beam generated by zero-forcing precoder is orthogonal to all the other user channel vectors, one can rewrite the received signal as
:<math>y_k = \mathbf{h}_k^T \sum_{i=1}^K \sqrt{P_i} s_i \mathbf{w}_i + n_k = \mathbf{h}_k^T \mathbf{w}_k \sqrt{P_k} s_k + n_k, \quad k=1,2, \ldots, K</math>
The orthogonality condition can be expressed in matrix form as
:<math>\mathbf{H}^T \mathbf{W} = \mathbf{Q}</math>
where <math>\mathbf{Q}</math> is some <math>K \times K</math> diagonal matrix. Typically, <math>\mathbf{Q}</math> is selected to be an identity matrix. This makes <math>\mathbf{W}</math> the right [[Moore–Penrose inverse|Moore-Penrose pseudo-inverse]] of <math>\mathbf{H}^T</math> given by
:<math>\mathbf{W} = \left( \mathbf{H}^T \right)^+ = \mathbf{H} (\mathbf{H}^T \mathbf{H})^{-1}</math>
Given this zero-forcing precoder design, the received signal at each user is decoupled from each other as
:<math>y_k = \sqrt{P_k} s_k + n_k, \quad k=1,2, \ldots, K.</math>
Quantify the amount of the feedback resource required to maintain at least a given throughput performance gap between zero-forcing with perfect feedback and with limited feedback, i.e.,
:<math>\Delta R = R_{ZF} - R_{FB} \leq \log_2 g</math> .
Jindal showed that the required feedback bits of a [[Spatial Correlation|spatially uncorrelated]] channel should be scaled according to SNR of the downlink channel, which is given by:<ref name="Jindal_ZF" />
:<math> B = (M-1) \log_2 \rho_{b,m} - (M-1) \log_2 (g-1) </math>
where ''M'' is the number of transmit antennas and <math>\rho_{b,m}</math> is the SNR of the downlink channel.
To feed back ''B'' bits though the uplink channel, the throughput performance of the uplink channel should be larger than or equal to 'B'
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:<math> b_{FB} \log_2(1+\rho_{FB}) \geq B </math>
where <math>b = \Omega_{FB} T_{FB}</math> is the feedback resource consisted
:<math> b_{FB} \geq \frac{B}{\log_2(1+\rho_{FB})} = \frac{(M-1) \log_2 \rho_{b,m} - (M-1) \log_2 (g-1)}{\log_2(1+\rho_{FB})} </math>.
Note that differently from the feedback bits case, the required feedback resource is a function of both downlink and uplink channel conditions. It is reasonable to include the uplink channel status in the calculation of the feedback resource since the uplink channel status determines the capacity, i.e., bits/second per unit frequency band (Hz), of the feedback link.
:<math> b_{FB,min}^* = \lim_{\rho_{FB} \to \infty } \frac{(M-1) \log_2 \rho_{b,m} - (M-1) \log_2 (g-1)}{\log_2(1+\rho_{FB})} = M - 1</math>.
It follows from the above equation that the feedback resource (<math>b_{FB}</math>) is not necessary to scale according to SNR of the downlink channel, which is almost contradict to the case of the feedback bits. One, hence, sees that the whole systematic analysis can reverse the facts resulted from each reductioned situation.
==Performance==
▲If the transmitter knows the downlink [[channel state information]] (CSI) perfectly, ZF-precoding can achieve almost the system capacity when the number of users is large. On the other hand, with limited [[channel state information]] at the transmitter (CSIT) the performance of ZF-precoding decreases depending on the accuracy of CSIT. ZF-precoding requires the significant feedback overhead with respect to signal-to-noise-ratio (SNR) so as to achieve the full multiplexing gain.<ref name="Jindal_ZF">{{cite journal |
==See also==
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