On AVCs with quadratic constraints

In this work we study an Arbitrarily Varying Channel (AVC) with quadratic power constraints on the transmitter and a so-called “oblivious” jammer (along with additional AWGN) under a maximum probability of error criterion, and no private randomness between the transmitter and the receiver. This is in contrast to similar AVC models under the average probability of error criterion considered in [1], [2], and models wherein common randomness is allowed [3] - these distinctions are important in some communication scenarios outlined below. We consider the regime where the jammer's power constraint is smaller than the transmitter's power constraint (in the other regime it is known no positive rate is possible). For this regime we show the existence of stochastic codes (with no common randomness between the transmitter and receiver) that enables reliable communication at the same rate as when the jammer is replaced with AWGN with the same power constraint. This matches known information-theoretic outer bounds. In addition to being a stronger result than that in [1] (enabling recovery of the results therein), our proof techniques are also somewhat more direct, and hence may be of independent interest.

[1]  Anand D. Sarwate,et al.  Relaxing the Gaussian AVC , 2012, ArXiv.

[2]  R. Ahlswede Elimination of correlation in random codes for arbitrarily varying channels , 1978 .

[3]  Meir Feder,et al.  Communication Over Individual Channels , 2009, IEEE Transactions on Information Theory.

[4]  Anant Sahai,et al.  Coding into a source: a direct inverse Rate-Distortion theorem , 2006, ArXiv.

[5]  P. Massart,et al.  Adaptive estimation of a quadratic functional by model selection , 2000 .

[6]  Imre Csiszár,et al.  Arbitrarily varying channels with general alphabets and states , 1992, IEEE Trans. Inf. Theory.

[7]  Prakash Narayan,et al.  The capacity of a vector Gaussian arbitrarily varying channel , 1988, IEEE Trans. Inf. Theory.

[8]  Jon Hamkins,et al.  Asymptotically dense spherical codes - Part II: Laminated spherical codes , 1997, IEEE Trans. Inf. Theory.

[9]  Imre Csiszár,et al.  Capacity of the Gaussian arbitrarily varying channel , 1991, IEEE Trans. Inf. Theory.

[10]  Prakash Narayan,et al.  Gaussian arbitrarily varying channels , 1987, IEEE Trans. Inf. Theory.

[11]  D. Blackwell,et al.  The Capacity of a Class of Channels , 1959 .

[12]  Nelson M. Blachman,et al.  The effect of statistically dependent interference upon channel capacity , 1962, IRE Trans. Inf. Theory.

[13]  A. Wyner Random packings and coverings of the unit n-sphere , 1967 .

[14]  N. Blachman,et al.  On the capacity of a band-limited channel perturbed by statistically dependent interference , 1962, IRE Trans. Inf. Theory.

[15]  A. D. Sarwate,et al.  An AVC perspective on correlated jamming , 2012, 2012 International Conference on Signal Processing and Communications (SPCOM).

[16]  Tamer Basar,et al.  With the Capacity 0.461(bits) and the Optimal Opd Being 'q = , 1998 .

[17]  Imre Csiszár,et al.  The capacity of the arbitrarily varying channel revisited: Positivity, constraints , 1988, IEEE Trans. Inf. Theory.

[18]  D. Blackwell,et al.  The Capacities of Certain Channel Classes Under Random Coding , 1960 .

[19]  Imre Csiszár,et al.  Arbitrarily varying channels with constrained inputs and states , 1988, IEEE Trans. Inf. Theory.

[20]  Claude E. Shannon,et al.  The zero error capacity of a noisy channel , 1956, IRE Trans. Inf. Theory.

[21]  Tamer Basar,et al.  Optimum linear causal coding schemes for Gaussian stochastic processes in the presence of correlated jamming , 1989, IEEE Trans. Inf. Theory.

[22]  Prakash Narayan,et al.  Reliable Communication Under Channel Uncertainty , 1998, IEEE Trans. Inf. Theory.