Energy-efficient estimation of a MIMO channel
© Muñoz and Oberli; licensee Springer. 2012
Received: 21 June 2012
Accepted: 23 October 2012
Published: 26 November 2012
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 multiple-input multiple-output (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 energy-efficient 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.
KeywordsMIMO channel estimation Energy efficiency
Multiple-input multiple-output (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 single-input single-output (SISO) link that attains the same data rate and link reliability. In, 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 energy-efficient than an equivalent SISO system. A detailed energy consumption model for an N×N singular value decomposition-based MIMO system is proposed in. 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 or on maximizing the channel capacity under imperfect channel knowledge. 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.
x H denotes the conjugate transpose operation over x, ∥x∥ is the norm of vector x, 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.Start-up energy consumption, We assume that the transmitter and receiver are by default in a low-power consumption (sleep) mode. Hence, they must be brought online before they can communicate the preamble. We denote as the energy required to start-up the transceivers, which is dominated by the stabilization of the frequency synthesizer . If this component consumes a power P syn has a settling time T trand is shared among all branches (either transmitting or receiving), then the start-up energy of two frequency synthesizers can be expressed as(1)
- 2.Energy consumption of the transmitter electronics, It represents the energy consumption of the digital-to-analog 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(2)
- 3.Energy consumption due to electromagnetic radiation, Each preamble symbol is broadcast from a transmitting antenna with a transmission power provided by the respective power amplifier (PA). The PA’s power consumption is modeled by(3)
- 4.Energy consumption of the receiver electronics, 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,(5)
- 5.Energy consumption due to channel estimation, Every time a packet is received, the channel estimation engine performs K different kinds of arithmetic operations, each of which has an energy consumption , with k=1,…,K and is performed n k times during the execution of the entire channel estimation algorithm. Thus,(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). 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, representing additive white Gaussian noise. The variance depends on the transmission bandwidth W , on the receiver noise figure, N f , and on the link margin M l .
In the following, we analyze the energy consumption and MSE of the LS and MMSE algorithms.
3.1 LS algorithm
As is known a priori, the estimation only requires the product between the matrix S j and an vector. This takes complex products and 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. 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 equal power pilot symbols.
3.2 MMSE algorithm
Number of instructions required by the MMSE estimator
Number of instructions
nsum=4N r N p + 9N t
nprod=4N r N p + 4N r N t + 14N t
where csum, cprod, and cdiv describe the number of cycles required for performing a sum, product, and division, respectively.
Constants of the energy model (19)
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 Ptx. 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 Psyn, Ttr,,, Vdd, I o , fAPU, csum, cprod, cdiv, 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.
In addition, we consider the following restrictions:
R1: In order to ensure a given estimation error, expressions (15) and (18) are upper-bounded by εmax.
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 Ptx(thus, ξ=1) and limited to Pmax.
where k1through k4are 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 must be satisfied. We do this by first solving (20a) by means of Lagrange multipliers in its continuous variable form (see Appendix) and then analyze the integer solution requirement.
The optimal values of the number of pilot symbols and transmission power depend on constant k4:
If N Pmax≥k4, then the constraints (20b) and (20c) are active. Therefore,(21a)
If N Pmax≤k4, then the constraints (20b) and (20d) are active for the non-integer optimization problem, so that Ptx=Pmax and. By imposing the integer constraint over N p , we find(22a)
where ⌈x⌉ denotes the smallest integer larger than x.
The MSE constraint (20b) is active in both cases above because it locks the trade-off between the optimal transmission power and the optimal number of pilot symbols.
5 Numerical evaluation
Generic low-power device parameters
100 μ s
Frequency synthesizer power consumption
Frequency synthesizer settling time
5 μ s
Tx electric power consumption
Rx electric power consumption
Path loss coefficient
Channel path loss
Noise power density
Receiver noise figure
PA drain efficiency
Technical parameters of an arithmetic and logic unit (ALU)
For a standard low-power device, the maximum operation distance is about 50 m. 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.
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 energy-efficiently by multi-hop routes than over single-hop links.
7.1 Optimization problem
with multipliers λ1,λ2, and λ3. After taking derivative of with respect to λ1, λ2,λ3,N u , and Ptxwe find two feasible solutions:
Case I, λ1≠0,λ2≠0,λ3=0The R1 (20b) and R2 (20c) constraints are active, therefore(26a)
This occurs when k4≤N Pmax. In this case, is a feasible integer solution.
Case II, λ1≠0,λ2=0,λ3≠0The R1 (20b) and R3 (20d) constraints are active, therefore, Ptx=Pmax and. This occurs when k4≥N Pmax, but now N u is not necessarily a natural number. Incorporating this constraint, we obtain. Therefore,(27a)
This study was funded by a Master’s degree scholarship and by projects FONDECYT 1110370 and FONDEF D09I1094 from CONICYT.
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