“…To analyze the asymptotic behavior of Toeplitz matrices, we define an N × N Toeplitz matrix T N whose elements t k satisfy ∞ k=−∞ |t k | < ∞. According to [32], T N is equivalent to a circulant matrix as N → ∞, and can be expressed as T N (t(λ)) where t(λ) = ∞ k=−∞ t k e jλk . Now we show that H θ is asymptotically equivalent to T N (t(λ)).…”
Section: B Training Sequence Design Via the Crbmentioning
confidence: 99%
“…Now we show that H θ is asymptotically equivalent to T N (t(λ)). First, since both H θ and T N (t(λ)) are banded Toeplitz matrices [32,Sec. 4.3], their strong norms (operator norms) are bounded.…”
Section: B Training Sequence Design Via the Crbmentioning
Full-duplex relays, residual self-interference, maximum likelihood estimation, optimal training sequence, Toeplitz matrix, frequency-selective channels, multiple relays
AbstractChannel estimation and optimal training sequence design for full-duplex one-way relays are investigated.We propose a training scheme to estimate the residual self-interference (RSI) channel and the channels between nodes simultaneously. A maximum likelihood estimator is implemented with Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. In the presence of RSI, the overall source-to-destination channel becomes an intersymbol-interference (ISI) channel. With the help of estimates of the RSI channel, the destination is able to cancel the ISI through equalization. We derive and analyze the Cramer-Rao bound (CRB) in closed-form by using the asymptotic properties of Toeplitz matrices. The optimal training sequence is obtained by minimizing the CRB. Extensions for the fundamental one-way relay model to the frequency-selective fading channels and the multiple relays case are also considered. For the former, we propose a training scheme to estimate the overall channel, and for the latter the CRB and the optimal number of relays are derived when the distance between the source and the destination is fixed. Simulations using LTE parameters corroborate our theoretical results.Xiaofeng Li and Cihan Tepedelenlioglu are with the
“…To analyze the asymptotic behavior of Toeplitz matrices, we define an N × N Toeplitz matrix T N whose elements t k satisfy ∞ k=−∞ |t k | < ∞. According to [32], T N is equivalent to a circulant matrix as N → ∞, and can be expressed as T N (t(λ)) where t(λ) = ∞ k=−∞ t k e jλk . Now we show that H θ is asymptotically equivalent to T N (t(λ)).…”
Section: B Training Sequence Design Via the Crbmentioning
confidence: 99%
“…Now we show that H θ is asymptotically equivalent to T N (t(λ)). First, since both H θ and T N (t(λ)) are banded Toeplitz matrices [32,Sec. 4.3], their strong norms (operator norms) are bounded.…”
Section: B Training Sequence Design Via the Crbmentioning
Full-duplex relays, residual self-interference, maximum likelihood estimation, optimal training sequence, Toeplitz matrix, frequency-selective channels, multiple relays
AbstractChannel estimation and optimal training sequence design for full-duplex one-way relays are investigated.We propose a training scheme to estimate the residual self-interference (RSI) channel and the channels between nodes simultaneously. A maximum likelihood estimator is implemented with Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. In the presence of RSI, the overall source-to-destination channel becomes an intersymbol-interference (ISI) channel. With the help of estimates of the RSI channel, the destination is able to cancel the ISI through equalization. We derive and analyze the Cramer-Rao bound (CRB) in closed-form by using the asymptotic properties of Toeplitz matrices. The optimal training sequence is obtained by minimizing the CRB. Extensions for the fundamental one-way relay model to the frequency-selective fading channels and the multiple relays case are also considered. For the former, we propose a training scheme to estimate the overall channel, and for the latter the CRB and the optimal number of relays are derived when the distance between the source and the destination is fixed. Simulations using LTE parameters corroborate our theoretical results.Xiaofeng Li and Cihan Tepedelenlioglu are with the
“…A well-known property of circulant matrices is the following formula for the determinant (see [16] for a proof of this formula and other properties of circulant matrices):…”
Section: Proof Of Theoremmentioning
confidence: 99%
“…We consider a permutation σ ∈ T 24;3,16 (16,6). We have σ = C 1 C 2 , where (3.5) C 1 =(20, 23, 2, 18, 21, 24, 3, 19, 22, 1, 4) C 2 =(7, 10, 13,5,8,11,14,6,9,12,15), and the two fixed points of σ are 16 and 17.…”
We analyze a canonical construction of group-invariant CR Mappings between hyperquadrics due to D'Angelo. Given source hyperquadric of Q(1, 1), we determine the signature of the target hyperquadric for all finite subgroups of SU (1, 1). We also extend combinatorial results proven by Loehr, Warrington, and Wilf on determinants of sparse circulant determinants.
“…For quantum states with only nearest-neighbor entanglement the correlations appearing in c ( ) N can all be treated classically. The minimum of c ( ) N can then be derived with the help of circulant matrices[23] (see appendix B). As a consequence, all states with entanglement width w 2 satisfy the inequality…”
The size of controllable quantum systems has grown in recent times. Therefore, the spatial degree of freedom becomes more and more important in experimental quantum systems. However, the investigation of entanglement in many-body systems mainly concentrated on the number of entangled particles and ignored the spatial degree of freedom so far. As a consequence, a general concept together with experimentally realizable criteria has been missing to describe the spatial distribution of entanglement. We close this gap by introducing the concept of entanglement width as measure of the spatial distribution of entanglement in many-body systems. We develop criteria to detect the width of entanglement based solely on global observables. As a result, our entanglement criteria can be applied easily to many-body systems since single-particle addressing is not necessary.
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