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Abstract-We present bounds and a closed-form high-SNR expression for the capacity of multiple-antenna systems affected by Wiener phase noise. Our results are developed for the scenario where a single oscillator drives all the radio-frequency circuitries at each transceiver (common oscillator setup), the input signal is subject to a peak-power constraint, and the channel matrix is deterministic. This scenario is relevant for line-of-sight multipleantenna microwave backhaul links with sufficiently small antenna spacing at the transceivers. For the 2×2 multiple-antenna case, for a Wiener phase-noise process with standard deviation equal to 6• , and at the medium/high SNR values at which microwave backhaul links operate, the upper bound reported in the paper exhibits a 3 dB gap from a lower bound obtained using 64-QAM. Furthermore, in this SNR regime the closed-form high-SNR expression is shown to be accurate.

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We consider a multiple-input multiple-output (MIMO) AWGN channel affected by phase noise. Focusing on the 2 ⇥ 2 case, we show that no MIMO multiplexing gain is to be expected when the phase-noise processes at each antenna are independent, memoryless in time, and with uniform marginal distribution over [0, 2⇡] (strong phase noise), and when the transmit signal is isotropically distributed on the real plane. The scenario of independent phase-noise processes across antennas is relevant for microwave backhaul links operating in the 20-40 GHz range.

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The pragmatic approach to coded continuous-phase modulation (CPM) is proposed as a capacity-achieving lowcomplexity alternative to the serially-concatenated CPM (SC-CPM) coding scheme. In this paper, we first perform a selection of the best spectrally-efficient CPM modulations to be embedded into SC-CPM schemes. Then, we consider the pragmatic capacity (a.k.a. BICM capacity) of CPM modulations and optimize it through a careful design of the mapping between input bits and CPM waveforms. The so obtained schemes are cascaded with an outer serially-concatenated convolutional code to form a pragmatic coded-modulation system. The resulting schemes exhibit performance very close to the CPM capacity without requiring iterations between the outer decoder and the CPM demodulator. As a result, the receiver exhibits reduced complexity and increased flexibility due to the separation of the demodulation and decoding functions. Memoryless Channel Convolutional Convolutional decoder CPE SISO decoder and demapper demodulator Memoryless CPM channel SISO binary encoder modulator CPE P (y|x) CCPM x(t; b) y(t; b) Ĉ d d Π Π −1 Π M Fig. 2. Block diagram of a serially concatenated CPM co-decoder. The block labeled M maps blocks of m bits to M -ary CPM symbols.

Sensor selection has recently received a growing interest in the literature, motivated by the worldwide deployment of wireless sensor networks and by the increase in the number of available applications. In our framework, sensors take remote measurements of a quantity of interest and communicate their observations through a noisy, multiantenna wireless communication link. In this context, we propose a scheme for optimally selecting out of sensor nodes on the basis of the amount of information they convey to a common receiver/actuator. Moreover, a suitable linear precoder is employed and optimized at each transmitter with the aim of maximizing the mutual information between the observed variable and the signal received by the actuator. The sensor selection problem is known to be combinatorial, and several computable relaxations are available in the literature. In this paper, the optimality conditions are formally expressed in an information-theoretic context, and both semi-definite-programming relaxations and greedy schemes, leading to computable techniques for large values of and , are presented. Moreover, specific results for the cases of high and low signal-to-noise ratio on the wireless channel are derived. Numerical simulations show that knowledge of the channel state at the transmitter may lead to an increase of the achievable mutual information and determine a different choice of sensors, thus pointing out that our approach significantly improves upon selection schemes that neglect the characteristics of the communication layer.