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Expected Value of Logarithm of a Non-Central Chi-Squared Random Variable

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Expected Value of Logarithm of a Non-Central Chi-Squared Random Variable EXPECTED VALUE OF LOGARITHM OF A NON-CENTRAL CHI-SQUARED RANDOM VARIABLE Abstract. In this note, we derive various closed-form expressions for the expectation of the logarithm of a non-central chi-squared random variable with odd degrees of freedom. A non-central χ2 distributed random variable W with m degrees of freedom and non-centrality Pm 2 parameter ξ = i=1 µi is defined as: m X 2 (1) W = (Xi + µi) ; i=1 m m where fXigi=1 ∼ N (0; 1) and fµigi=1 are positive constants. The goal is to derive tractable expressions for the expected value of logarithm of W . The first result is given below. Theorem 1. Let W be a non-central χ2 distributed random variable with m degrees of freedom and non-centrality parameter ξ. Then it holds that E(log W ) = E( (Z + m=2)) − log 2; where Z is a Poisson random variable with parameter ξ=2. Equivalently: 1 X 1 ξke−ξ=2 ( (Z + m=2)) = (k + m=2); E 2k k! k=0 and (·) is the digamma function, also known as Euler's psi function: Γ0(x) (x) = : Γ(x) Proof. The proof follows closely a similar proof in [1, Lemma 10.1]. The density of W is given by: 1 x(m−2)=4 f (x) = e−(x+ξ)=2I (pξx) x ≥ 0; W 2 ξ m=2−1 where Iν(·) is the modified Bessel function of the first kind of order ν defined as: 1 X 1 x2k+ν I (x) = : ν k!Γ(k + ν + 1) 2 k=0 Therefore using the series expansion of Iν(·), the expected value can be written as: Z 1 (m−2)=4 1 x −(x+ξ)=2 p E(log W ) = log x e Im=2−1( ξx)dx 0 2 ξ Z 1 1 x(m−2)=4 = log x e−(x+ξ)=2 0 2 ξ 1 p 2k+m=2−1 X 1 ξx × dx k!Γ(k + m=2) 2 k=0 1 X 1 ξke−ξ=2 Z 1 = log xe−x=2xk+m=2−1dx 22k+m=2 k!Γ(k + m=2) k=0 0 The last integral can be simplified using [2, 4.352(1)] as Z 1 log xe−x=2xk+m=2−1dx = 2k+m=2Γ(k + m=2)( (k + m=2) − log 2): 0 1 2 EXPECTED VALUE OF LOGARITHM OF A NON-CENTRAL CHI-SQUARED RANDOM VARIABLE The final expectation is given by: 1 1 X 1 ξke−ξ=2 X log 2 ξke−ξ=2 (log W ) = (k + m=2) − E 2k k! 2k k! k=0 k=0 1 X 1 ξke−ξ=2 = (k + m=2) − log 2: 2k k! k=0 The above theorem expresses the integral formulation of the expected value as an infinite series. The expected value can be approximated arbitrarily well using the series, and it is easier to evaluate in general. A further simplification was obtained for even m. For this case, the digamma function writes as: k+m=2−1 X 1 (2) (k + m=2) = − γ; j j=1 where γ ≈ 0:577 is the Euler-Mascheroni constant. This expression was used to derive a simpler expression of the expected value for even m in [3] with the proof in [1]. We provide a similar expression for odd m. The digamma function for odd m writes as: k+(m−1)=2 X 2 (3) (k + m=2) = −γ − 2 log 2 + : 2j − 1 j=1 The following theorem provides a computationally simpler expression than the theorem above for the expected value of log W . Theorem 2. Let W be a non-central χ2 distributed random variable with odd m degrees of freedom and non-centrality parameter ξ. Then it holds that 1 1 ~ E(log W ) = C0 + p h1(ξ=2) − hm(ξ=2) ; Cm π where: (−1)[m=2] C = −γ − 3 log 2;C = ; 0 m Γ(m=2) Z 1 p 1 2 −x2=2 h1(t) = p log(x + 2t) e dx + γ + 2 log(2); 2π −∞ [m=2]−1 X tj+1(−1)j h~ (t) = : m (j + 1)Γ(j + 3=2) j=0 Proof. For m odd, we have: 0Z+(m−1)=2 1 X 2 ( (Z+m=2)) − log 2 = −γ − 3 log 2 + : E E @ 2j − 1A j=1 Define the function hm(t) as: 0Z+(m−1)=2 1 1 0k+(m−1)=2 1 X 2 X e−ttk X 2 h (t) = = ; m E @ 2j − 1A k! @ 2j − 1A j=1 k=0 j=1 and see that: 1 X e−ttk 1 h0 (t) = : m k! k + m=2 k=0 EXPECTED VALUE OF LOGARITHM OF A NON-CENTRAL CHI-SQUARED RANDOM VARIABLE 3 Figure 1. The function h1(t) 0 We find another representation of hm(t) that contains only finite sums and provides a more concise version of hm(t). The following lemma, a generalization of a familiar combinatorial equality, is used for the proof. + Lemma 1. Let x 2 R and k 2 N such that k > x. We have: [x]−1 X (−1)j x(−1)[x] fxg = − + : Γ(j + fxg + 1)(k − j)! (k + fxg)(k − [x])!Γ(x + 1) (k + fxg)Γ(fxg + 1)k! j=0 The previous lemma yields that: [m=2]−1 X (−1)j m(−1)[m=2] 1 (4) = − + : Γ(j + 3=2)(k − j)! 2(k + 1=2)(k − [m=2])!Γ(m=2 + 1) 2(k + 1=2)Γ(3=2)k! j=0 0 Consider the following expression of hm(t): 1 1 X e−ttk 1 X tk 1 h0 (t) = = e−t ; m k! k + m=2 (k − [m=2])! k + 1=2 k=0 k=[m=2] Using (4) with the above equality, we obtain: 1 m(−1)[m=2] X e−ttk 2Γ(m=2 + 1) (k + 1=2)(k − [m=2])! k=[m=2] 1 1 [m=2]−1 X e−ttk X X e−ttk(−1)j = − : 2(k + 1=2)Γ(3=2)k! Γ(j + 3=2)(k − j)! k=[m=2] k=[m=2] j=0 Let us focus on each of these terms. Consider the first term: 1 1 X e−ttk 1 X e−ttk = p 2(k + 1=2)Γ(3=2)k! π (k + 1=2)k! k=[m=2] k=[m=2] [m=2]−1 1 1 X e−ttk = p h0 (t) − p : π 1 π (k + 1=2)k! k=0 4 EXPECTED VALUE OF LOGARITHM OF A NON-CENTRAL CHI-SQUARED RANDOM VARIABLE Note that: 0 (5) h1(t) = E(log W ) + γ + 2 log 2; p where W 0 is equal to (X + 2t)2 for X as a standard normal distributed random variable: Z 1 p 1 2 −x2=2 h1(t) = p log(x + 2t) e dx + γ + 2 log(2): 2π −∞ The second term can be characterized as: 1 [m=2]−1 X X e−ttk(−1)j Γ(j + 3=2)(k − j)! k=[m=2] j=0 1 [m=2]−1 [m=2]−1 k X X e−tti+j(−1)j X X e−ttk(−1)j = − ; Γ(j + 3=2)i! Γ(j + 3=2)(k − j)! i=0 j=0 k=0 j=0 but then: 1 [m=2]−1 1 [m=2]−1 X X e−tti+j(−1)j X e−tti X tj(−1)j = Γ(j + 3=2)i! i! Γ(j + 3=2) i=0 j=0 i=0 j=0 [m=2]−1 X tj(−1)j = : Γ(j + 3=2) j=0 m(−1)[m=2] (−1)[m=2] Assuming Cm = 2Γ(m=2+1) = Γ(m=2) , we have: [m=2]−1 1 1 X e−ttk C h0 (t) = p h0 (t) − p m m π 1 π (k + 1=2)k! k=0 [m=2]−1 [m=2]−1 k X tj(−1)j X X e−ttk(−1)j − + : Γ(j + 3=2) Γ(j + 3=2)(k − j)! j=0 k=0 j=0 It can be seen that: k 1 1 X (−1)j p = ; π (k + 1=2)k! Γ(j + 3=2)(k − j)! j=0 which implies that: [m=2]−1 [m=2]−1 k 1 X e−ttk X X e−ttk(−1)j p = ; π (k + 1=2)k! Γ(j + 3=2)(k − j)! k=0 k=0 j=0 hence: [m=2]−1 1 X tj(−1)j (6) C h0 (t) =p h0 (t) − : m m π 1 Γ(j + 3=2) j=0 A simple integration with adjustment of the respective constant shows that: [m=2]−1 1 X tj+1(−1)j C h (t) =p h (t) − ; m m π 1 (j + 1)Γ(j + 3=2) j=0 with h1(t) given in (5). The function h1(t) is given in Figure 1. The proof follows from the above result. REFERENCES 5 References [1] A. Lapidoth and S. M. Moser, \Capacity bounds via duality with applications to multiple- antenna systems on flat-fading channels," IEEE Transactions on Information Theory, vol. 49, no. 10, pp. 2426{2467, Oct. 2003. [2] I. S. Gradshte˘ınand D. Zwillinger, Table of integrals, series, and products, eng, Eighth edition. Elsevier, Academic Press, 2015. [3] S. M. Moser, \Expectations of a noncentral chi-square distribution with application to IID MIMO Gaussian fading," in 2008 International Symposium on Information Theory and Its Applications, Dec. 2008, pp.
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