Optimal Causal Rate-Constrained Sampling for a Class of Continuous Markov Processes

Consider the following communication scenario. An encoder observes a stochastic process and causally decides when and what to transmit about it, under a constraint on bits transmitted per second. A decoder uses the received codewords to causally estimate the process in real time. The encoder and the decoder are synchronized in time. We aim to find the optimal encoding and decoding policies that minimize the end-to-end estimation mean-square error under the rate constraint. For a class of continuous Markov processes satisfying regularity conditions, we show that the optimal encoding policy transmits a 1-bit codeword once the process innovation passes one of two thresholds. The optimal decoder noiselessly recovers the last sample from the 1-bit codewords and codeword-generating time stamps, and uses it as the running estimate of the current process, until the next codeword arrives. In particular, we show the optimal causal code for the Ornstein-Uhlenbeck process and calculate its distortion-rate function

Isospin and a possible interpretation of the newly observed X(1576)

Recently, the BES collaboration observed a broad resonant structure X(1576) with a large width being around 800 MeV and assigned its $J^{PC}$ number to $1^{--}$. We show that the isospin of this resonant structure should be assigned to 1. This state might be a molecule state or a tetraquark state. We study the consequences of a possible $K^*(892)$-${\bar \kappa}$ molecular interpretation. In this scenario, the broad width can easily be understood. By using the data of $B(J/\psi\to X\pi^0)\cdot B(X\to K^+K^-)$, the branching ratios $B(J/\psi\to X\pi^0)\cdot B(X\to \pi^+\pi^-)$ and $B(J/\psi\to X\pi^0)\cdot B(X\to K^+K^-\pi^+\pi^-)$ are further estimated in this molecular state scenario. It is shown that the $X\to \pi^+\pi^-$ decay mode should have a much larger branching ratio than the $X\to K^+K^-$ decay mode has. As a consequence, this resonant structure should also be seen in the $J/\psi\to \pi^+\pi^-\pi^0$ and $J/\psi\to K^+K^-\pi^+\pi^-\pi^0$ processes, especially in the former process. Carefully searching this resonant structure in the $J/\psi\to \pi^+\pi^-\pi^0$ and $J/\psi\to K^+K^-\pi^+\pi^-\pi^0$ decays should be important for understanding the structure of X(1567).Comment: 5 pages, ReVTeX4, 3 figures. Version accepted for publication as a brief report in Phys. Rev.

Optimal Causal Rate-Constrained Sampling of the Wiener Process

We consider the following communication scenario. An encoder causally observes the Wiener process and decides when and what to transmit about it. A decoder makes real-time estimation of the process using causally received codewords. We determine the causal encoding and decoding policies that jointly minimize the mean-square estimation error, under the long-term communication rate constraint of R bits per second. We show that an optimal encoding policy can be implemented as a causal sampling policy followed by a causal compressing policy. We prove that the optimal encoding policy samples the Wiener process once the innovation passes either √(1/R) or −√(1/R), and compresses the sign of the innovation (SOI) using a 1-bit codeword. The SOI coding scheme achieves the operational distortion-rate function, which is equal to D^(op)(R)=1/(6R). Surprisingly, this is significantly better than the distortion-rate tradeoff achieved in the limit of infinite delay by the best non-causal code. This is because the SOI coding scheme leverages the free timing information supplied by the zero-delay channel between the encoder and the decoder. The key to unlock that gain is the event-triggered nature of the SOI sampling policy. In contrast, the distortion-rate tradeoffs achieved with deterministic sampling policies are much worse: we prove that the causal informational distortion-rate function in that scenario is as high as D_(DET)(R)=5/(6R). It is achieved by the uniform sampling policy with the sampling interval 1/R. In either case, the optimal strategy is to sample the process as fast as possible and to transmit 1-bit codewords to the decoder without delay

Optimal Causal Rate-Constrained Sampling for a Class of Continuous Markov Processes

Consider the following communication scenario. An encoder observes a stochastic process and causally decides when and what to transmit about it, under a constraint on bits transmitted per second. A decoder uses the received codewords to causally estimate the process in real time. The encoder and the decoder are synchronized in time. We aim to find the optimal encoding and decoding policies that minimize the end-to-end estimation mean-square error under the rate constraint. For a class of continuous Markov processes satisfying regularity conditions, we show that the optimal encoding policy transmits a 1-bit codeword once the process innovation passes one of two thresholds. The optimal decoder noiselessly recovers the last sample from the 1-bit codewords and codeword-generating time stamps, and uses it as the running estimate of the current process, until the next codeword arrives. In particular, we show the optimal causal code for the Ornstein-Uhlenbeck process and calculate its distortion-rate function