We provide distributed algorithms for the radio resource allocation problem in multicell downlink multi-input single-output systems. Specifically, the problems of (1) minimizing total transmit power subject to signal-to-interference-plus-noise ratio (SINR) constraints of each user and (2) SINR balancing subject to total transmit power constraints are considered. We propose a consensus-based distributed algorithm and the fast solution method via alternating the direction method of multipliers. First, we derive a distributed algorithm for minimization of total transmit power. Then, in conjunction with the bracketing method, a distributed algorithm for SINR balancing is derived. Numerical results show that the proposed distributed algorithms achieve the optimal centralized solution.

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The sensitivity of millimeter-wave (mmWave) radio channel to blockage is a fundamental challenge in achieving lowlatency and reliable connectivity. In this paper, we explore the viability of using coordinated multi-point (CoMP) transmission for a delay bounded and reliable mmWave communication. We provide an iterative algorithm for the time-average sumpower-minimization problem by solving a system of Karush-Kuhn-Tucker (KKT) optimality conditions. We use the Lyapunov optimization framework and derive a dynamic control algorithm to transform a time-average stochastic problem into a sequence of deterministic subproblems. Furthermore, for the robust beamformer design, we consider a pessimistic estimate of the userspecific rate, assuming that a portion of CoMP links would be blocked during the data transmission phase, while ensuring the average latency requirements. The numerical examples illustrate that in the presence of random blockages, the proposed method outperforms baseline scenarios and results in energy-efficient, high-reliability and low-latency mmWave communication.

The problem of spectrum sharing between two operators in a dynamic network is considered. We allow both operators to share (a fraction of) their licensed spectrum band with each other by forming a common spectrum band. The objective is to maximize the gain in profits of both operators by sharing their licensed spectrum bands rather than using them exclusively, while considering the fairness among the operators. This is modeled as a two-person bargaining problem, and cast as a stochastic optimization. To solve this problem, we propose centralized and distributed dynamic control algorithms. At each time slot, the proposed algorithms perform the following tasks: 1) determine spectrum price for the operators; 2) make flow control decisions of users data; and 3) jointly allocate spectrum band to the operators and design transmit beamformers, which is known as resource allocation (RA). Since the RA problem is NP-hard, we have to rely on sequential convex programming to approximate its solution. To derive the distributed algorithm, we use alternating direction method of multipliers for solving the RA problem. Numerically, we show that the proposed distributed algorithm achieves almost the same performance as the centralized one. Furthermore, the results show that there is a trade-off between the achieved profits of the operators and the network congestion.Index Terms-Co-primary spectrum sharing, dynamic control, network utility maximization, stochastic optimization, Lyapunov drift, bargaining problem, fairness, sequential convex programming, alternating direction method of multipliers (ADMM), distributed algorithm.

Energy efficient beamforming and power control problem is considered for a MISO (multiple input single output) network. We consider an active femtocell within the coverage area of a macrocell. The femto base station is equipped with multi antennas and the users are considered to be single antenna users. A beamforming and power control problem is formulated in order to minimize the energy consumption per bit in the downlink transmission in the femtocell. The objective function is introduced as "sum power/sum rate" which has the unit J/bit to measure the energy efficiency of the network. The problem is nonconvex. We introduce a novel method to solve this problem with an approximation. We show that the problem can be solved with convex optimization techniques which has more practical interest even though the solution is suboptimal. In order to measure the energy efficiency we apply an existing power model which considers the total energy consumption of a base station. Thus we expect that our solution indicates realistic energy consumption measurements. Then we introduce a beamforming and power control algorithm which minimizes the energy consumption per bit transmission. Finally, the behavior of the objective function is observed in different antenna configurations by varying the user density in different channel environments.

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The problem of spectral efficiency maximization for hybrid analog/digital transceiver architecture, subject to both transmit and receiver power constraints, is considered. With the processing power constraint of the receiver, the number of radio frequency (RF) chains and analog-to-digital (ADC) resolution used greatly affect the achievable transmission rate. We formulate an optimization problem to find this trade-off, and provide an efficient algorithm to solve it. Numerical results show that for power constrained hybrid receiver, there is a trade-off between the number of RF chains and the ADC resolution, that can obtain maximal spectral efficiency.