In mobile millimeter wave (mmWave) systems, energy is a scarce resource due to the large losses in the channel and high energy usage by analog-to-digital converters (ADC), which scales with bandwidth. In this paper, we consider a communication architecture that integrates the sub-6 GHz and mmWave technologies in 5G cellular systems. In order to mitigate the energy scarcity in mmWave systems, we investigate the rate-optimal and energy-efficient physical layer resource allocation jointly across the sub-6 GHz and mmWave interfaces. First, we formulate an optimization problem in which the objective is to maximize the achievable sum rate under power constraints at the transmitter and receiver. Our formulation explicitly takes into account the energy consumption in integrated-circuit components, and assigns the optimal power and bandwidth across the interfaces. We consider the settings with no channel state information and partial channel state information at the transmitter and under high and low SNR scenarios. Second, we investigate the energy efficiency (EE) defined as the ratio between the amount of data transmitted and the corresponding incurred cost in terms of power. We use fractional programming and Dinkelbach's algorithm to solve the EE optimization problem. Our results prove that despite the availability of huge bandwidths at the mmWave interface, it may be optimal (in terms of achievable sum rate and energy efficiency) to utilize it partially. Moreover, depending on the sub-6 GHz and mmWave channel conditions and total power budget, it may be optimal to activate only one of the interfaces.
Energy efficient transceiver architectures such as the use of low resolution ADCs and hybrid analog digital combining has attracted significant interest. The limits of communications over additive white Gaussian channel with low resolution (1-3 bits) ADCs at the receiver is studied in @cite_22 . The bounds on the capacity of the MIMO channel with 1-bit ADC at high and low SNR regimes are derived in @cite_30 and @cite_0 , respectively. The joint optimization of ADC resolution with the number of antennas in a MIMO channel is studied in @cite_17 . While @cite_5 provides efficient hybrid precoding and combining algorithms for sparse mmWave channels that performs close to full digital solution, @cite_4 combines efficient channel estimation with the hybrid precoding and combining algorithm in @cite_5 .
@cite_30 @cite_4 @cite_22 @cite_0 @cite_5 @cite_17
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Millimeter wave (mmWave) is a viable technology for future cellular systems. With bandwidths on the order of a gigahertz, high-resolution analog-to-digital converters (ADCs) become a power consumption bottleneck. One solution is to employ very low resolution one-bit ADCs. This paper analyzes the flat fading multiple-input multiple-output (MIMO) channel with one-bit ADC. Bounds on the high signal-to-noise ratio (SNR) capacity are derived for the single-input multiple-output (SIMO) channel and the general MIMO channel. The results show how the number of paths, number of transmit antennas, and number of receive antennas impact the capacity at high SNR. As communication systems scale up in speed and bandwidth, the cost and power consumption of high-precision (e.g., 8-12 bits) analog-to-digital conversion (ADC) becomes the limiting factor in modern transceiver architectures based on digital signal processing. In this work, we explore the impact of lowering the precision of the ADC on the performance of the communication link. Specifically, we evaluate the communication limits imposed by low-precision ADC (e.g., 1-3 bits) for transmission over the real discrete-time additive white Gaussian noise (AWGN) channel, under an average power constraint on the input. For an ADC with K quantization bins (i.e., a precision of log2 K bits), we show that the input distribution need not have any more than K+1 mass points to achieve the channel capacity. For 2-bin (1-bit) symmetric quantization, this result is tightened to show that binary antipodal signaling is optimum for any signal-to- noise ratio (SNR). For multi-bit quantization, a dual formulation of the channel capacity problem is used to obtain tight upper bounds on the capacity. The cutting-plane algorithm is employed to compute the capacity numerically, and the results obtained are used to make the following encouraging observations : (a) up to a moderately high SNR of 20 dB, 2-3 bit quantization results in only 10-20 reduction of spectral efficiency compared to unquantized observations, (b) standard equiprobable pulse amplitude modulated input with quantizer thresholds set to implement maximum likelihood hard decisions is asymptotically optimum at high SNR, and works well at low to moderate SNRs as well. We study the performance of multi-input multi-output (MIMO) channels with coarsely quantized outputs in the low signal-to-noise ratio (SNR) regime, where the channel is perfectly known at the receiver. This analysis is of interest in the context of ultra-wideband (UWB) communications from two aspects. First the available power is spread over such a large frequency band, that the power spectral density is extremely low and thus the SNR is low. Second the analog-to-digital converters (ADCs) for such high bandwidth signals should be low-resolution, in order to reduce their cost and power consumption. In this paper we consider the extreme case of only 1-bit ADC for each receive signal component. We compute the mutual information up to second order in the SNR and study the impact of quantization. We show that, up to first order in SNR, the mutual information of the 1-bit quantized system degrades only by a factor of 2 pi compared to the system with infinite resolution independent of the actual MIMO channel realization. With channel state information (CSI) only at receiver, we show that QPSK is, up to the second order, the best among all distributions with independent components. We also elaborate on the ergodic capacity under this scheme in a Rayleigh flat-fading environment. Millimeter wave (mmWave) signals experience orders-of-magnitude more pathloss than the microwave signals currently used in most wireless applications and all cellular systems. MmWave systems must therefore leverage large antenna arrays, made possible by the decrease in wavelength, to combat pathloss with beamforming gain. Beamforming with multiple data streams, known as precoding, can be used to further improve mmWave spectral efficiency. Both beamforming and precoding are done digitally at baseband in traditional multi-antenna systems. The high cost and power consumption of mixed-signal devices in mmWave systems, however, make analog processing in the RF domain more attractive. This hardware limitation restricts the feasible set of precoders and combiners that can be applied by practical mmWave transceivers. In this paper, we consider transmit precoding and receiver combining in mmWave systems with large antenna arrays. We exploit the spatial structure of mmWave channels to formulate the precoding combining problem as a sparse reconstruction problem. Using the principle of basis pursuit, we develop algorithms that accurately approximate optimal unconstrained precoders and combiners such that they can be implemented in low-cost RF hardware. We present numerical results on the performance of the proposed algorithms and show that they allow mmWave systems to approach their unconstrained performance limits, even when transceiver hardware constraints are considered.