This work considers communication networks where individual links can be described as MIMO channels. Unlike orthogonal modulation methods (such as the singular-value decomposition), we allow interference between sub-channels, which can be removed by the receivers via successive cancellation. The degrees of freedom earned by this relaxation are used for obtaining a basis which is simultaneously good for more than one link. Specifically, we derive necessary and sufficient conditions for shaping the ratio vector of sub-channel gains of two broadcast-channel receivers. We then apply this to two scenarios: First, in digital multicasting we present a practical capacity-achieving scheme which only uses scalar codes and linear processing. Then, we consider the joint source-channel problem of transmitting a Gaussian source over a two-user MIMO channel, where we show the existence of non-trivial cases, where the optimal distortion pair (which for high signal-to-noise ratios equals the optimal point-to-point distortions of the individual users) may be achieved by employing a hybrid digital-analog scheme over the induced equivalent channel. These scenarios demonstrate the advantage of choosing a modulation basis based upon multiple links in the network, thus we coin the approach "network modulation".Comment: Submitted to IEEE Tran. Signal Processing. Revised versio
Abstract-The physical-layer network coding (PNC) approach provides improved performance in many scenarios over "traditional" relaying techniques or network coding. This work addresses the generalization of PNC to wireless scenarios where network nodes have multiple antennas. We use a recent matrix decomposition, which allows, by linear pre-and postprocessing, to simultaneously transform both channel matrices to triangular forms, where the diagonal entries, corresponding to both channels, are equal. This decomposition, in conjunction with precoding, allows to convert any two-input multiple-access channel (MAC) into parallel MACs, over which single-antenna PNC may be used. The technique is demonstrated using the two-way relay channel with multiple antennas. For this case it is shown that, in the high signal-to-noise regime, the scheme approaches the cut-set bound, thus establishing the asymptotic network capacity.
The Gaussian parallel relay network problem consists of transmitting a message from a single source node to a single destination node, through a layer of parallel relay nodes. The source is connected to the relays by a Gaussian broadcast channel, while the relays are connected to the destination by a Gaussian multiple access channel. When the channels are all white with the same bandwidth, and the relays cannot decode the message, the best known strategy is "amplify and forward", which achieves the coherence gain of multiple relays. We propose a strategy which achieves this gain even when the noises are colored or the channels have different bandwidths. To that end we use analog modulo-lattice modulation of the codewords in the BC, and then forward the estimated codeword by each of the relays to the MAC. This modulation allows the relays to re-match the signal to the optimal spectrum of the MAC, thus demonstrating how a channel problem can gain from a joint source/channel approach. We show that this strategy is asymptotically optimal in some limiting cases, and that it outperforms the known alternatives in most other cases, where the optimum is unknown. We also demonstrate how to improve the achievable rate in the original white problem, for some signal to noise ratio values.
We consider a discrete-time linear quadratic Gaussian networked control setting where the (full information) observer and controller are separated by a fixed-rate noiseless channel. The minimal rate required to stabilize such a system has been well studied. However, for a given fixed rate, how to quantize the states so as to optimizeperformance is an open question of great theoretical and practical significance. We concentrate on minimizing the control cost for first-order scalar systems. To that end, we use the Lloyd-Max algorithm and leverage properties of logarithmically-concave functions and sequential Bayesian filtering to construct the optimal quantizer that greedily minimizes the cost at every time instant. By connecting the globally optimal scheme to the problem of scalar successive refinement, we argue that its gain over the proposed greedy algorithm is negligible. This is significant since the globally optimal scheme is often computationally intractable. All the results are proven for the more general case of disturbances with logarithmically-concave distributions and rate-limited time-varying noiseless channels. We further extend the framework to event-triggered control by allowing to convey information via an additional "silent symbol", i.e., by avoiding transmitting bits; by constraining the minimal probability of silence we attain a tradeoff between the transmission rate and the control cost for rates below one bit per sample.
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