 Research
 Open Access
Energyefficient estimation of a MIMO channel
 Camila Muñoz^{1}Email author and
 Christian Oberli^{1}
https://doi.org/10.1186/168714992012353
© Muñoz and Oberli; licensee Springer. 2012
 Received: 21 June 2012
 Accepted: 23 October 2012
 Published: 26 November 2012
Abstract
Exploiting the benefits of multiple antenna technologies is strongly conditioned on knowledge of the wireless channel that affects the transmissions. To this end, various channel estimation algorithms have been proposed in the literature for multipleinput multipleoutput (MIMO) channels. These algorithms are typically studied from a perspective that does not consider constraints on the energy consumption of their implementation. This article proposes a methodology for evaluating the total energy consumption required for transmitting, receiving, and processing a preamble signal in order to produce a channel estimate in multiple antenna systems. The methodology is used for finding the training signals that minimize the energy consumption for attaining given mean square estimation error. We show that the energy required for processing the preamble signal by executing the estimation algorithms dominates the total energy consumed by the channel estimation process. Therefore, algorithm simplicity is a key factor for achieving energyefficient channel acquisition. We use our method for analyzing the widely used least squares and minimum mean square error (MSE) estimation algorithms and find that both have a similar energy consumption when the same MSE estimation is targeted.
Keywords
 MIMO channel estimation
 Energy efficiency
Introduction
Multipleinput multipleoutput (MIMO) communication techniques have been incorporated into different wireless systems due to their capability for allowing higher data rates (multiplexing gain) or for increasing link reliability (diversity gain). However, recent studies have shown that MIMO techniques can be used alternatively for reducing energy consumption in comparison to a singleinput singleoutput (SISO) link that attains the same data rate and link reliability. In[1], when the link distance is larger than a given threshold, data transmission using a 2×2 MIMO system with Alamouti space–time coding was shown to be more energyefficient than an equivalent SISO system. A detailed energy consumption model for an N×N singular value decompositionbased MIMO system is proposed in[2]. The model includes retransmission statistics and shows that for a given link distance and number of channels used exist a single optimal radiation power level at which the mean energy consumption required to transmit a bit correctly is minimized.
But the use of MIMO is strongly conditioned on knowing the wireless channel, which the above contributions assume perfectly known. This knowledge is typically obtained by transmitting a known training preamble that allows the receiver to estimate the channel by executing an estimation algorithm.
The design of training preambles for channel estimation has not yet been studied well in terms of energy efficiency. Typically, the design of the preamble signals focuses on minimizing the channel estimation error[3] or on maximizing the channel capacity under imperfect channel knowledge[4]. Furthermore, existing models of MIMO energy consumption as the ones in[1, 2] ignore the energy required for transmitting, receiving, and processing a preamble signal. In fact, MIMO channel estimation can be a significant part of the baseband processing energy consumption because the algorithms usually perform complex algebraic operations.
In this article, we present a method for comparing the energy efficiency of different channel estimation algorithms. We formulate an energy consumption model that allows to find the training signals that minimize the energy consumption of the algorithms given a mean square error (MSE) of estimation. Particularly, we study the minimum MSE (MMSE) and least squares (LS) channel estimation algorithms and optimize their respective preambles for minimum energy consumption at a given target MSE. We show that their optimal energy consumption difference is negligible.
The rest of the article is organized as follows: Section 2. describes the energy consumption model for channel estimation algorithms. Section 3. examines the LS and MMSE algorithms and details their energy consumption and MSE. Section 4. formulates and solves the optimization problem that allows to find the optimal training signals. Section 5. provides numerical results and Section 6. summarizes our conclusions.
1 Notation
x^{ H } denotes the conjugate transpose operation over x, ∥x∥ is the norm of vector x,$\mathbb{E}\{\xb7\}$ indicates expected value and I_{ M }is the M×M identity matrix. The superscript checkˇ denotes that the variable corresponds to a single branch of either the transmitter or receiver.
2 Energy consumption model
Our goal is to minimize the energy consumption required for producing a channel estimate with a given estimation quality. For this purpose, we formulate a model that includes the energy consumption of all the components involved in the channel estimation process.
 1.Startup energy consumption, ${\mathit{\u0190}}_{\text{ST}}$We assume that the transmitter and receiver are by default in a lowpower consumption (sleep) mode. Hence, they must be brought online before they can communicate the preamble. We denote as ${\mathit{\u0190}}_{\text{ST}}$ the energy required to startup the transceivers, which is dominated by the stabilization of the frequency synthesizer [5]. If this component consumes a power P _{syn} has a settling time T _{tr}and is shared among all branches (either transmitting or receiving), then the startup energy of two frequency synthesizers can be expressed as${\mathit{\u0190}}_{\text{ST}}=2{P}_{\text{syn}}{T}_{\text{tr}}.$(1)
 2.Energy consumption of the transmitter electronics, ${\mathit{\u0190}}_{\text{el,tx}}$It represents the energy consumption of the digitaltoanalog converters (DAC), filters, and mixers of the transmitter. These components consume energy for each transmitted preamble symbol. We define a binary variable s _{(n,t)}that indicates if transmitter branch n transmits a preamble symbol during symbol time t, with n=1,…,N _{ t } and t=1,…,N _{ p }. Thus, the total energy consumed by these components is given by${\mathit{\u0190}}_{\text{el,tx}}={T}_{s}\left({\stackrel{\u030c}{P}}_{\text{el,tx}}\sum _{n=1}^{{N}_{t}}\sum _{t=1}^{{N}_{p}}{s}_{(n,t)}+{P}_{\text{syn}}{N}_{p}\right),$(2)
 3.Energy consumption due to electromagnetic radiation, ${\mathit{\u0190}}_{\text{PA}}$Each preamble symbol is broadcast from a transmitting antenna with a transmission power ${\stackrel{\u030c}{P}}_{\text{tx}}$ provided by the respective power amplifier (PA). The PA’s power consumption is modeled by${\stackrel{\u030c}{P}}_{\text{PA}}=\frac{\xi}{\eta}{\stackrel{\u030c}{P}}_{\text{tx}},$(3)
 4.Energy consumption of the receiver electronics, ${\mathit{\u0190}}_{\text{el,rx}}$It represents the energy consumption of the components that remain energized during the reception time of the preamble, which is equal to N _{ p } T _{ s }. Thus,${\mathit{\u0190}}_{\text{el,rx}}={N}_{p}{T}_{s}\left({\stackrel{\u030c}{P}}_{\text{el,rx}}{N}_{r}+{P}_{\text{syn}}\right),$(5)
 5.Energy consumption due to channel estimation, ${\mathit{\u0190}}_{\text{estim}}$Every time a packet is received, the channel estimation engine performs K different kinds of arithmetic operations, each of which has an energy consumption ${\mathit{\u0190}}_{k}$, with k=1,…,K and is performed n _{ k } times during the execution of the entire channel estimation algorithm. Thus,${\mathit{\u0190}}_{\text{estim}}=\sum _{k=1}^{K}{n}_{k}{\mathit{\u0190}}_{k}.$(6)
We now turn our attention to the estimation problem, focusing on the number of arithmetic operations required by various common channel estimation algorithms.
3 Channel estimation algorithms
In this section, we characterize the LS and MMSE channel estimation algorithms by their complexity of implementation and associated MSE performance. This requires to formulate a signal model that describes the communication of the preamble and to determine the arithmetic operations that each algorithm performs.
where A d^{ α } represents the path loss, with d the link distance, α the path loss exponent, and A a parameter that depends on the transmitter and receiver antenna gains and the transmission wavelength (A may include shadow fading)[8]. p_{ j } is the preamble sequence transmitted by the j th branch. h^{ j }is the j th column of H and its elements represent the small scale fading of the MIMO channel. We assume that the wireless channel is static and flat fading. V_{ j }is a matrix of independent complex Gaussian random variables with zero mean and variance${\sigma}_{n}^{2}$, representing additive white Gaussian noise. The variance${\sigma}_{n}^{2}$ depends on the transmission bandwidth W , on the receiver noise figure, N_{ f }, and on the link margin M_{ l }[1].
In the following, we analyze the energy consumption and MSE of the LS and MMSE algorithms.
3.1 LS algorithm
As${\mathbf{p}}_{j}^{H}/\parallel {\mathbf{p}}_{j}{\parallel}^{2}$ is known a priori, the estimation only requires the product between the${N}_{r}\times \frac{{N}_{p}}{{N}_{t}}$ matrix S_{ j } and an$\frac{{N}_{p}}{{N}_{t}}\times 1$ vector. This takes$\frac{{N}_{r}{N}_{p}}{{N}_{t}}$ complex products and${N}_{r}\left(\frac{{N}_{p}}{{N}_{t}}1\right)$ complex sums each time a column of H is estimated. Standard implementations of these complex operations require four real products and two real sums for each complex product, and two real sums for each complex sum[10]. Then, performing the estimation (13) for N_{ t } columns of H requires 2N_{ r }(2N_{ p }−N_{ t }) real additions and 4N_{ r }N_{ p }real multiplications.
which uses the fact that each column of H is estimated with$\frac{{N}_{p}}{{N}_{t}}$ equal power pilot symbols.
3.2 MMSE algorithm
Number of instructions required by the MMSE estimator
Operation  Number of instructions 

Sum  n_{sum}=4N_{ r }N_{ p } + 9N_{ t } 
Product  n_{prod}=4N_{ r }N_{ p } + 4N_{ r }N_{ t } + 14N_{ t } 
Division  n_{div}=2N_{ t } 
where c_{sum}, c_{prod}, and c_{div} describe the number of cycles required for performing a sum, product, and division, respectively.
Constants of the energy model (19)
LS  MMSE  

k _{1}  $2{P}_{\text{syn}}{T}_{\text{tr}}2\frac{{V}_{\text{dd}}{I}_{o}}{{f}_{\text{APU}}}{N}_{r}{N}_{t}{c}_{\text{sum}}$  $2{P}_{\text{syn}}{T}_{\text{tr}}+\frac{{V}_{\text{dd}}{I}_{o}}{{f}_{\text{APU}}}{N}_{t}\left[9{c}_{\text{sum}}+(4{N}_{r}+14){c}_{\text{prod}}+2{c}_{\text{div}}\right]$ 
k _{2}  ${T}_{s}({\stackrel{\u030c}{P}}_{\text{el,tx}}+{N}_{r}{\stackrel{\u030c}{P}}_{\text{el,rx}}+2{P}_{\text{syn}})+\frac{4{V}_{\text{dd}}{I}_{o}{N}_{r}}{{f}_{\text{APU}}}\left({c}_{\text{sum}}+{c}_{\text{prod}}\right)$  ${T}_{s}({\stackrel{\u030c}{P}}_{\text{el,tx}}+{N}_{r}{\stackrel{\u030c}{P}}_{\text{el,rx}}+2{P}_{\text{syn}})+\frac{4{V}_{\text{dd}}{I}_{o}{N}_{r}}{{f}_{\text{APU}}}({c}_{\text{sum}}+{c}_{\text{prod}})$ 
k _{3}  ${T}_{s}\frac{\xi}{\eta}$  ${T}_{s}\frac{\xi}{\eta}$ 
k _{4}  $\frac{A{d}^{\alpha}{\sigma}_{n}^{2}{N}_{r}{N}_{t}^{2}}{{\epsilon}_{\text{max}}}$  $\frac{A{d}^{\alpha}{\sigma}_{n}^{2}{N}_{r}}{{\epsilon}_{\text{max}}}({N}_{t}^{2}{\epsilon}_{\text{max}})$ 
4 Minimization of the channel estimation energy consumption
In this section, we formulate and solve the optimization problem of minimizing the total energy consumption required for carrying out the LS and MMSE channel estimation algorithms as a function of the number of pilot symbols N_{ p } and of the transmission power P_{tx}. Expression (19) is the objective function of the minimization problem.
We assume the following
A1: The transceivers have N_{ r }=N_{ t }=N antennas and P_{syn}, T_{tr},${\stackrel{\u030c}{P}}_{\text{el,tx}}$,${\stackrel{\u030c}{P}}_{\text{el,rx}}$, V_{dd}, I_{ o }, f_{APU}, c_{sum}, c_{prod}, c_{div}, η, and W are known parameters.
A2: The path loss parameter A, link distance d, and path loss exponent α are given and the matrix H is an uncorrelated flat fading MIMO Rayleigh channel with${\mathbf{R}}_{H}=\mathbb{E}\left\{{\mathbf{H}}^{H}\mathbf{H}\right\}=N{\mathbf{I}}_{N}$.
In addition, we consider the following restrictions:
R1: In order to ensure a given estimation error, expressions (15) and (18) are upperbounded by ε_{max}.
R2: The number of pilot symbols N_{ p }should be equal or greater than N, so that at least one pilot symbol is transmitted by each antenna.
R3: Transmission power is constant P_{tx}(thus, ξ=1) and limited to P_{max}.
where k_{1}through k_{4}are given in Table2. This optimization problem has a quadratic objective function with restrictions forming a convex domain. It is to be noted that (20a) is to be solved as an integer optimization problem, because$\frac{{N}_{p}}{N}\in \mathbb{N}$ must be satisfied. We do this by first solving (20a) by means of Lagrange multipliers[11] in its continuous variable form (see Appendix) and then analyze the integer solution requirement.
The optimal values of the number of pilot symbols${N}_{p}^{\ast}$ and transmission power${P}_{\text{tx}}^{\ast}$ depend on constant k_{4}:

If N P_{max}≥k_{4}, then the constraints (20b) and (20c) are active. Therefore,${N}_{p}^{\ast}=N$(21a)

If N P_{max}≤k_{4}, then the constraints (20b) and (20d) are active for the noninteger optimization problem, so that P_{tx}=P_{max} and${N}_{p}=\frac{{k}_{4}}{{P}_{\text{max}}}$. By imposing the integer constraint over N_{ p }, we find${N}_{p}^{\ast}=N\u2308\frac{{k}_{4}}{N{P}_{\text{max}}}\u2309$(22a)
where ⌈x⌉ denotes the smallest integer larger than x.
The MSE constraint (20b) is active in both cases above because it locks the tradeoff between the optimal transmission power${P}_{\text{tx}}^{\ast}$ and the optimal number of pilot symbols${N}_{p}^{\ast}$.
5 Numerical evaluation
Generic lowpower device parameters
Parameter  Description  Value 

W  Bandwidth  10 kHz 
T _{ s }  Symbol period  100 μ s 
P _{syn}  Frequency synthesizer power consumption  50 mW 
T _{tr}  Frequency synthesizer settling time  5 μ s 
${\stackrel{\u030c}{P}}_{\text{el,tx}}$  Tx electric power consumption  98.2 mW 
${\stackrel{\u030c}{P}}_{\text{el,rx}}$  Rx electric power consumption  112.5 mW 
α  Path loss coefficient  3.5 
A  Channel path loss  30 dB 
N _{0}  Noise power density  −174 dBm/Hz 
N _{f}  Receiver noise figure  10 dB 
M _{l}  Link margin  40 dB 
η  PA drain efficiency  0.35 
Technical parameters of an arithmetic and logic unit (ALU)
Parameter  Description  Value 

f _{ALU}  ALU frequency  20 MHz 
V _{dd}  ALU voltage  3 V 
${I}_{o}({V}_{\text{dd}},{f}_{\text{ALU}})$  Average current  6.37 mA 
c _{sum}  Adding cost  6 cycles 
c _{prod}  Product cost  13 cycles 
c _{div}  Division cost  21 cycles 
For a standard lowpower device, the maximum operation distance is about 50 m[14]. Figure5 shows that at this distance each antenna must send two pilot symbols in order to estimate the channel with an MSE of −10 dB with minimum energy consumption. On the other hand, given that each antenna transmits two preamble symbols, the maximum link distance (which is achieved maximizing the transmission power) is about 53 m.
6 Conclusions
In this study, we present a methodology for determining the length and transmission power of training signals that allow for producing estimates of MIMO channels with a given estimation error and minimal joint energy consumption among transmitter and receiver. We develop a general energy consumption model for the complete process of channel estimation. The model includes energy consumption due to transmission and reception of the training signals and due to the processing required to obtain the channel estimates.
The model was used for studying and optimizing the energy consumption of the LS and MMSE channel estimation algorithms. Both algorithms consume virtually the same energy when operated at their respective optimal training signal configurations of length and transmission power. However, the LS algorithm does not require the knowledge of the channel statistics and of the power of the noise, which makes the LS algorithm the preferred choice.
For link distances of about 50 m, our results show that the channel estimation with minimum energy is achieved using two preamble symbols per transmit antenna when the target estimation MSE is −10 dB. For distances of approximately 120 m, the minimum energy consumption required to achieve the same estimation quality increases tenfold for both algorithms due to path loss. This indicates that longer range MIMO communications can be performed more energyefficiently by multihop routes than over singlehop links.
7 Appendix
7.1 Optimization problem
with multipliers λ_{1},λ_{2}, and λ_{3}. After taking derivative of$\mathcal{\mathcal{L}}$ with respect to λ_{1}, λ_{2},λ_{3},N_{ u }, and P_{tx}we find two feasible solutions:

Case I, λ_{1}≠0,λ_{2}≠0,λ_{3}=0The R1 (20b) and R2 (20c) constraints are active, therefore${N}_{u}^{\ast}=1$(26a)
This occurs when k_{4}≤N P_{max}. In this case,${N}_{p}^{\ast}=N$ is a feasible integer solution.

Case II, λ_{1}≠0,λ_{2}=0,λ_{3}≠0The R1 (20b) and R3 (20d) constraints are active, therefore, P_{tx}=P_{max} and${N}_{u}=\frac{{k}_{4}}{N{P}_{\text{max}}}$. This occurs when k_{4}≥N P_{max}, but now N_{ u }is not necessarily a natural number. Incorporating this constraint, we obtain${N}_{u}^{\ast}=\u2308\frac{{k}_{4}}{N{P}_{\text{max}}}\u2309$. Therefore,${N}_{p}^{\ast}=N\u2308\frac{{k}_{4}}{N{P}_{\text{max}}}\u2309$(27a)
Declarations
Acknowledgements
This study was funded by a Master’s degree scholarship and by projects FONDECYT 1110370 and FONDEF D09I1094 from CONICYT.
Authors’ Affiliations
References
 Cui S, Goldsmith A, Bahai A: Energyefficiency of MIMO and cooperative MIMO techniques in sensor networks. IEEE J. Sel. Areas Commun 2004, 22(6):10891098. 10.1109/JSAC.2004.830916View ArticleGoogle Scholar
 Rosas F, Oberli C: Energyefficient MIMO SVD communications. In PIMRC2012Track 1: PHY and Fundamentals (PIMRC2012PHY) Vol. 1. (Sydney, Australia; 2012.Google Scholar
 Biguesh M, Gershman A: Trainingbased MIMO channel estimation: a study of estimator tradeoffs and optimal training signals. IEEE Trans. Signal Process 2006, 54(3):884893.View ArticleGoogle Scholar
 Hassibi B, Hochwald B: How much training is needed in multipleantenna wireless links. IEEE Trans. Inf. Theory 2003, 49(4):951963. 10.1109/TIT.2003.809594View ArticleMATHGoogle Scholar
 Cui S, Goldsmith A, Bahai A: Energyconstrained modulation optimization. IEEE Trans. Wirel. Commun 2005, 4(5):23492360.View ArticleGoogle Scholar
 Sinha A, Chandrakasan AP: JouleTrack: a web based tool for software energy profiling. In IEEE Design Automation Conference, Vol. 617. (Las Vegas, United States; 2001.Google Scholar
 Yatawatta S, Petropulu A, Graff C: Energy efficient channel estimation in MIMO systems. In IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005 (ICASSP ’05) Vol. 4. (Philadelphia, United States; 2005.Google Scholar
 Rappaport T: Wireless Communications: Principles and Practice. Prentice Hall Communications Engineering and Emerging Technologies Series, Prentice Hall PTR; 2002.MATHGoogle Scholar
 Sayed A: Adaptive Filters. Wiley & Sons, New York; 2011.Google Scholar
 Conway J: Functions of One Complex Variable, Volume 1 of Graduate Texts in Mathematics. Springer Verlag, Berlin; 1978.View ArticleGoogle Scholar
 Muñoz C: Señales de entrenamiento óptimas para estimación de canal MIMO energéticamente eficiente. Master’s thesis, Pontificia Universidad Católica de Chile, 2012Google Scholar
 Instrument T: MSP430x2xx Family. User’s guide.2012. [http://www.ti.com/lit/ug/slau144i/slau144i.pdf]Google Scholar
 Venkat K: Efficient multiplication and division using MSP430. Application report.2006. [http://www.ti.com/lit/an/slaa329/slaa329.pdf]Google Scholar
 Gislason D: ZigBee Wireless Networking. Elsevier, Amsterdam; 2008.Google Scholar
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