This paper studies distributed algorithms for (strongly convex) composite optimization problems over mesh networks, subject to quantized communications. Instead of focusing on a specific algorithmic design, a black-box model is proposed, casting linearly convergent distributed algorithms in the form of fixed-point iterates. The algorithmic model is equipped with a novel random or deterministic Biased Compression (BC) rule on the quantizer design, and a new Adaptive encoding Non-uniform Quantizer (ANQ) coupled with a communication-efficient encoding scheme, which implements the BC-rule using a finite number of bits (below machine precision). This fills a gap existing in most state-of-the-art quantization schemes, such as those based on the popular compression rule, which rely on communication of some scalar signals with negligible quantization error (in practice quantized at the machine precision). A unified communication complexity analysis is developed for the black-box model, determining the average number of bits required to reach a solution of the optimization problem within a target accuracy. It is shown that the proposed BC-rule preserves linear convergence of the unquantized algorithms, and a trade-off between convergence rate and communication cost under ANQ-based quantization is characterized. Numerical results validate our theoretical findings and show that distributed algorithms equipped with the proposed ANQ have more favorable communication cost than algorithms using state-of-the-art quantization rules.

Massive multiple-input multiple-output (massive MIMO) has been studied to improve the throughput in cellular communication systems via spatial degrees of freedom and array gain. However, in massive MIMO systems, due to the hardware cost considerations, it is infeasible to install a radio frequency (RF) chain for each antenna. To deploy massive MIMO within reasonable hardware cost, hybrid precoding is proposed in which the signal is processed by a low-dimensional digital baseband precoder and a high-dimensional analog RF precoder. On the other hand, cooperative transmission is another way to enhance system throughput, in which base stations (BSs) can jointly transmit to a user. In this paper, we first investigate the hybrid precoder design for cooperative multiuser massive MIMO systems. We propose hybrid precoding algorithms for two performance metrics: weighted sum rate and max-min fairness. We also propose a low complexity user pairing for both precoder designs. Numerical results show that the proposed hybrid precoding algorithms outperform existing hybrid precoding algorithms for non-cooperative transmissions, and perform closely compared with existing digital precoding algorithms for cooperative transmissions, when the number of users does not exceed the total number of RF chains of the BSs in cooperative (i.e., the largest possible channel rank). In summary, this paper provides a hybrid precoder design guideline for existing and developing cellular communication systems, including Long Term Evolution-Advanced and the fifth generation cellular systems.

Technologies in millimeter wave (mmWave) communications have gained increasing attention because of its potential to provide substantially extra bandwidth. The main challenges of mmWave communications include high propagation attenuation and sparse scattering propagation channel. A promising technique to cope with propagation attenuation is to adopt antenna array, and use the hybrid transceiver architecture to exploit the array gain for lower hardware costs. On the other hand, the sparse scattering channel causes large fluctuations in link quality. Cooperative transmission is an effective approach for the link quality fluctuation problem, in which multiple Txs can transmit to the same Rx of poor receiving quality. The design of precoder for cooperative transmission in hybrid precoding architecture is crucial in improving system performance. In this paper, we propose the two-stage hybrid precoder design for cooperative multiple input single output (MISO) systems where the design goal is to ensure every Rx has good receiving quality. Because the number of Rx supported by a Tx is limited, we also propose a low computational complexity Tx-Rx pairing algorithm. Numerical results show that performances of the proposed algorithms are near-optimal, and outperform existing hybrid precoding algorithms.

In this paper, the distributed channel access schemes for ALOHA-based cognitive radio networks are considered. In the considered system, time is divided into frames, which are further divided into sensing phase and transmission phase. We derive channel sensing policy in the sensing phase, and channel access policy in the transmission phase for a secondary user (SU) to maximize its throughput. To mitigate high complexities of the above scheme, we propose a threshold-based channel access scheme. In this scheme, an appropriate threshold is set based on channel occupancy information and channel state information, and only channels providing potentially high throughput will be sensed and accessed. The proposed schemes are fully distributed, i.e., no extra information exchange is needed among SUs, which is a highly desired and beneficial property. Simulation results confirm that the proposed schemes outperform prior random access schemes.