Communication complexity

About: Communication complexity is a research topic. Over the lifetime, 3870 publications have been published within this topic receiving 105832 citations.

Improving coordination with communication in multi-agent reinforcement learning

Abstract: We present a new algorithm for cooperative reinforcement learning in multiagent systems. We consider autonomous and independently learning agents, and we seek to obtain an optimal solution for the team as a whole while keeping the learning as much decentralized as possible. Coordination between agents occurs through communication, namely the mutual notification algorithm. We define the learning problem as a decentralized process using the MDP formalism. We then give an optimality criterion and prove the convergence of the algorithm for deterministic environments. We introduce variable and hierarchical communication strategies which considerably reduce the number of communications. Finally we study the convergence properties and communication overhead on a small example.

Quantum Private Information Retrieval with Sublinear Communication Complexity

Abstract: This note presents a quantum protocol for private information retrieval, in the case of a single (honest) server and with information-theoretical privacy, that has O( p n)- qubit communication complexity, where n denotes the size of the database. In comparison, it is known that any classical protocol must use W(n) bits of communication in this setting.

Low-Complexity Soft-Decision Feedback Turbo Equalization for MIMO Systems With Multilevel Modulations

Abstract: Many communication systems today encounter the problem of data transmission over a channel with intersymbol interference (ISI). The purpose of this paper is to develop a low-complexity iterative soft-decision feedback equalization (SDFE) receiver for severe frequency-selective ISI channels in multiple-input-multiple-output (MIMO) communication systems. The proposed SDFE algorithm offers a novel approach to combat error propagation. In addition, its computational complexity only linearly grows with the number of equalizer coefficients, compared with the quadratic complexity of minimum mean square error (MMSE)-based linear turbo equalizer with time-varying coefficients. The performance of the proposed detection scheme is verified through simulations using different signal constellations. In addition, convergence properties of the proposed SDFE algorithm are also analyzed using an extrinsic information transfer (EXIT) chart and verified by simulations in a severe ISI channel. Simulation results show that our proposed algorithm has significant improvement over the Approximate MMSE linear turbo equalizer proposed by Tuchler et al. Moreover, we show that the performance of the proposed equalization scheme significantly improves when higher order constellations are used for digital modulation.

Communication Complexity Lower Bounds by Polynomials

Abstract: The quantum version of communication complexity allows the two communicating parties to exchange qubits and/or to make use of prior entanglement (shared EPR-pairs). Some lower bound techniques are available for qubit communication complexity, but except for the inner product function, no bounds are known for the model with unlimited prior entanglement. We show that the log-rank lower bound extends to the strongest model (qubit communication + unlimited prior entanglement). By relating the rank of the communication matrix to properties of polynomials, we are able to derive some strong bounds for exact protocols. In particular, we prove both the "log-rank conjecture" and the polynomial equivalence of quantum and classical communication complexity for various classes of functions. We also derive some weaker bounds for bounded-error quantum protocols.

Approximating Private Set Union/Intersection Cardinality With Logarithmic Complexity

Abstract: The computation of private set union/intersection cardinality (PSU-CA/PSI-CA) is one of the most intensively studied problems in privacy preserving data mining (PPDM). However, the existing protocols are computationally too expensive to be employed in real-world PPDM applications. In response, we propose efficient approximate protocols, whose accuracy can be tuned according to application requirements. We first propose a two-party PSU-CA protocol based on Flajolet-Martin sketches. The protocol has logarithmic computational/communication complexity and relies mostly on symmetric key operations. Thus, it is much more efficient and scalable than existing protocols. In addition, our protocol can hide its output. This feature is necessary in PPDM applications, since the union cardinality is often an intermediate result that must not be disclosed. We then propose a two-party PSI-CA protocol, which is derived from the PSU-CA protocol with virtually no cost. Both our two-party protocols can be easily extended to the multiparty setting. We also design an efficient masking scheme for $(^{1}_{n}){-}OT$ . The scheme is used in optimizing the two-party protocols and is of independent interest, since it can speed up $(^{1}_{n}){-}OT$ significantly when $n$ is large. Finally, we show through experiments the effectiveness and efficiency of our protocols.