Beam codebooks are a new feature of massive multiple-input multiple-output (M-MIMO) in 5G new radio (NR). Codebooks comprised of beamforming vectors are used to transmit reference signals and obtain limited channel state information (CSI) from receivers via the codeword index. This enables large arrays that cannot otherwise obtain sufficient CSI. The performance, however, is limited by the codebook design. In this paper, we show that machine learning can be used to train site-specific codebooks for initial access. We design a neural network based on an autoencoder architecture that uses a beamspace observation in combination with RF environment characteristics to improve the synchronization signal (SS) burst codebook. We test our algorithm using a flexible dataset of channels generated from QuaDRiGa. The results show that our model outperforms the industry standard (DFT beams) and approaches the optimal performance (perfect CSI and singular value decomposition (SVD)-based beamforming), using only a few bits of feedback.

Cellular networks continue to trend rapidly towards more bands and carrier frequencies, along with higher base station density, requiring complex decisions to be made when associating a mobile user with a band and cell. This paper develops a novel approach to optimizing frequency band and cell selection while taking into account user mobility and handovers. This is a complex problem because of the uncertain link failure events, handover related overheads, and the significant difference in the propagation characteristics between different frequency bands. The network dynamics due to user mobility are modeled as a Markov decision process, and we develop a recurrent Q-learning framework to exploit the relationship between user trajectories and the history of SINR measurements. The effective cell boundaries are therefore based on user trajectories and velocities rather than just position and signal strength. Detailed system-level simulations show that the proposed learning-based approach improves the throughput of the edge users by 54% and the median throughput by 34% compared to traditional SINR-based association and achieves a superior rate/coverage tradeoff (quantified as sum-log-rate) compared to SINR or signal-strength-based associations.

Wireless cellular networks have many parameters that are normally tuned upon deployment and re-tuned as the network changes. Many operational parameters affect reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference-plus-noise-ratio (SINR), and, ultimately, throughput. In this paper, we develop and compare two approaches for maximizing coverage and minimizing interference by jointly optimizing the transmit power and downtilt (elevation tilt) settings across sectors. To evaluate different parameter configurations offline, we construct a realistic simulation model that captures geographic correlations. Using this model, we evaluate two optimization methods: deep deterministic policy gradient (DDPG), a reinforcement learning (RL) algorithm, and multi-objective Bayesian optimization (BO). Our simulations show that both approaches significantly outperform random search and converge to comparable Pareto frontiers, but that BO converges with two orders of magnitude fewer evaluations than DDPG. Our results suggest that data-driven techniques can effectively self-optimize coverage and capacity in cellular networks.

Massive MIMO is attractive for wireless information and energy transfer due to its ability to focus energy toward desired spatial locations. In this paper, the overall power transfer efficiency (PTE) and the energy efficiency (EE) of a wirelessly powered massive MIMO system are investigated, where a multi-antenna base-station (BS) uses wireless energy transfer to charge single-antenna energy harvesting users on the downlink. The users may exploit the harvested energy to transmit information to the BS on the uplink. The overall system performance is analyzed while accounting for the nonlinear nature of practical energy harvesters. First, for wireless energy transfer, the PTE is characterized using a scalable model for the BS circuit power consumption. The PTE-optimal values for the number of BS antennas and users are derived. Then, for wireless energy and information transfer, the EE performance is characterized. The EE-optimal BS transmit power is derived in terms of the key system parameters, such as the number of BS antennas and the number of users. As the number of antennas becomes large, increasing the transmit power improves the EE for moderate to large number of antennas. Simulation results suggest that it is energy efficient to operate the system in the massive MIMO regime.

Hybrid analog/digital precoding in the downlink of multiuser massive MIMO systems can reduce the number of RF chains hence reducing total cost and improving power efficiency. Having few RF chains, however, makes it difficult for a base station to acquire instantaneous channel state information across all antennas. We develop a hybrid technique that uses only long-term (slowly changing) channel statistics in computing the analog precoding matrix. The proposed analog precoder is designed to maximize signal-to-leakage-plus-noise ratio (SLNR) when combined with a baseband precoder. We also propose a constrained precoder design that reduces the effect of a hardware constraint where the analog precoders are realized with phase shifters.

We propose a new mixed-analog-to-digital convertor (mixed-ADC) architecture for cloud-RAN (C-RAN) systems. The RRH is equipped with a mixed-ADC pool that includes multiple ADC units with various resolutions. In this pool, the RRH selects the appropriate ADCs and connects the selected ADCs to each antenna to quantize the received signals, thereby each antenna can have a different resolution ADC. The quantized signals are sent to a centralized baseband unit (BBU) via a capacity limited fronthaul pipe. To maximize the spectral efficiency of the considered system in a single-user uplink phase, we formulate an optimization problem for ADC selection by exploiting an approximation of the generalized mutual information (GMI). Subsequently we propose a solution. The simulations show the improvement in the GMI by using the proposed ADC selection. Our major findings are: i) In a C-RAN with limited fronthaul capacity, the proposed mixed-ADC makes more efficient use of the fronthaul capacity. ii) In selecting ADCs, assigning a high resolution ADC to a strong channel is beneficial.

Massive MIMO, a building block of future 5G systems, is attractive for wireless information and energy transfer. This is largely due to its ability to focus energy towards desired spatial locations. In this paper, the overall energy efficiency of a wirelessly powered massive MIMO system is investigated where a multi-antenna base-station uses wireless energy transfer to charge single- antenna energy harvesting users on the downlink. The users exploit the harvested energy to transmit information to the base-station on the uplink. Using a scalable model for the circuit power consumption at the base-station, the energy efficiency performance (measured in bits/joule) of the overall system is characterized. A closed-form expression is derived for the energy efficiency- optimal downlink transmit power in terms of the key system parameters such as the number of base- station antennas and the number of users. Simulation results suggest that it is energy efficient to operate the system in the massive MIMO regime. As the number of antennas becomes large, increasing the transmit power as well as serving more users help improve the energy efficiency for moderate to large number of antennas.

We propose a mixed analog-to-digital converter ADC (mixed-ADC) structure for a cloud-RAN system, where a single-antenna user terminal communicates with a multi-antenna remote radio head (RRH). In the proposed structure, the RRH is equipped with a mixed-ADC pool that includes multiple ADC units with various resolutions. In this pool, the RRH selects the appropriate ADCs and connects the selected ADCs to each antenna to quantize the received signals; thereby each antenna can have a different resolution ADC. The fronthaul capacity is limited, so that the sum of the bits produced in the selected ADCs is also limited. To maximize the spectral efficiency or the energy efficiency of such a system, we propose algorithms for ADC resolution selection based on an approximation of the generalized mutual information in the low signal-to-noise regime. In the proposed algorithms, we show that for spectral efficiency, using high-resolution ADC on the strong channels is beneficial. The results for energy efficiency maximization are similar, though the largest resolutions are reduced to save power. The simulations show that the proposed method provides significant performance improvement.

Multi-radio heterogeneous networks (Het-Nets) are an important focus area for next generation cellular standards, with the 3GPP community actively developing WiFi/LTE interworking solutions for small-cell deployments. This paper explores coordinated usage of multiple radios (e.g., LTE and WiFi) to improve quality of service (QoS) in multi- radio heterogeneous networks. We focus on heterogeneous network deployments based on co- located WiFi-LTE small cells that allow for tighter multi-radio coordination. To evaluate QoS enhancements, we consider on-time throughput, a metric that captures the deliverable data rate for traffic with delay deadlines. A QoS-aware scheduling algorithm is designed to optimize on-time throughput. Cross-RAT coordination schemes are also explored to further enhance QoS. Through combining the QoS-aware scheduling algorithm and intelligent radio link assignment, we observe significant improvement in per user on-time throughput. Our results indicate that tightly coupled integrated LTE-WiFi small cell architectures can significantly increase the number of user achieving their targeted QoS.

In this paper we analyze pilot-aided channel estimation for the multiple-input multiple-output amplify-and-forward two-way relay channel. We use a linear minimum mean-square estimator for estimating the composite channel at each communicating terminal and by using the notion of “worst-case noise” we derive lower-bounds for the transmission sum-rate in both directions of the established bi-directional relay link.

We present a new perspective on the use of channel state information (CSI) at the transmitter of orthogonal frequency-division multiplexing (OFDM) systems. While conventional closed-loop OFDM systems attempt to pre-equalize the channel through power loading, we propose an average signal- to-noise ratio (SNR) maximizing strategy based on the allocation of pilot locations. Our derivation for the average output SNR of OFDM systems with interpolated channel estimation reveals a combinatorial optimization problem in terms of the pilot- symbol locations. Using this framework, we arrive at the SNR gain achieved in using uniform and non-uniform pilot locations. Further, we present simple sub-optimum solutions based on decoupled optimization and show how minor adjustments of these solutions can also lead to very favorable error rate performances. The proposed solutions are simulated in a wide range of Doppler frequencies and compared to uniform pilot allocation in terms of average symbol error rate.

We present a novel transceiver design for orthogonal frequency-division multiplexing (OFDM) wireless systems employing pilot symbol aided channel estimation (PSACE). The design is based on the insertion of known pilots into the time- frequency OFDM grid in a controlled cyclic fashion to exploit time diversity in the pilot tones without consuming additional bandwidth resource. Semi-blind iterative detection based on the expectation-maximization algorithm along with optimum combining is employed at the receiver. Despite moderately increased transceiver complexity, simulations show that the proposed design has superior error rate performance compared to conventional PSACE methods for the same pilot overhead.

Knowledge of the propagation channel makes it possible for multiple-input multiple-output (MIMO) links to achieve higher system capacities by means of beamforming on the stronger channel eigenvectors. Assigning dedicated low bit rate feedback links is an efficient and practical method of providing transmitters with the required channel state information. One of the key problems in limited feedback precoding is the design of finite indexed codebooks known a priori to both the transmitter and receiver.Previous design methods include signal-to-noise ratio maximization through Grassmannian line packing, distortion minimization via vector quantization and antenna selection algorithms. In this paper we propose a new method that utilizes the dominant right singular vector of the MIMO channel to construct quantized unitary precoders. We show that although sub-optimum, this method can produce full diversity order codebooks. Using Monte-Carlo simulations, we compare the performance of the this method with optimum Grassmannian precoders and also antenna selection and subset selection algorithms.