Analysis of Wireless Networks With Massive Connectivity

Recent years have witnessed unprecedented growth in wireless networks in terms of both data traffic and number of connected devices. How to support this fast increasing demand for high data traffic and connectivity is a key consideration in the design of future wireless communication systems. With this motivation, in this thesis, we focus on the analysis of wireless networks with massive connectivity. In the first part of the thesis, we seek to improve the energy efficiency (EE) of single-cell massive multiple-input multiple-output (MIMO) networks with joint antenna selection and user scheduling. We propose a two-step iterative procedure to maximize the EE. In each iteration, bisection search and random selection are used first to determine a subset of antennas with the users selected before, and then identify the EE-optimal subset of users with the selected antennas via cross entropy algorithm. Subsequently, we focus on the joint uplink and downlink EE maximization, under a limitation on the number of available radio frequency (RF) chains. With the Jensen\u27s inequality and the power consumption model, the original problem is converted into a combinatorial optimization problem. Utilizing the learning-based stochastic gradient descent framework and the rare event simulation method, we propose an efficient learning-based stochastic gradient descent algorithm to solve the corresponding combinatorial optimization problem. In the second part of the thesis, we focus on the joint activity detection and channel estimation in cell-free massive MIMO systems with massive connectivity. At first, we conduct an asymptotic analysis of single measurement vector (SMV) based minimum mean square error (MMSE) estimation in cell-free massive MIMO systems with massive connectivity. We establish a decoupling principle of SMV based MMSE estimation for sparse signal vectors with independent and non-identically distributed (i.n.i.d.) non-zero components. Subsequently, using the decoupling principle, likelihood ratio test and the optimal fusion rule, we obtain detection rules for the activity of users based on the received pilot signals at only one access point (AP), and also based on the cooperation of the received pilot signals from the entire set of APs for centralized and distributed detection. Moreover, we study the achievable uplink rates with zero-forcing (ZF) detector at the central processing unit (CPU) of the cell-free massive MIMO systems. In the third part, we focus on the performance analysis of intelligent reflecting surface (IRS) assisted wireless networks. Initially, we investigate the MMSE channel estimation for IRS assisted wireless communication systems. Then, we study the sparse activity detection problem in IRS assisted wireless networks. Specifically, employing the generalized approximate message passing (GAMP) algorithm, we obtain the MMSE estimates of the equivalent effective channel coefficients from the base station (BS) to all users, and transform the received pilot signals into additive Gaussian noise corrupted versions of the equivalent effective channel coefficients. Likelihood ratio test is used to acquire decisions on the activity of each user based on the Gaussian noise corrupted equivalent effective channel coefficients, and the optimal fusion rule is used to obtain the final decisions on the activity of all users based on the previous decisions on the activity of each user and the corresponding reliabilities. Finally, we conduct an asymptotic analysis of maximizing the weighted sum rate by joint beamforming and power allocation under transmit power and quality-of-service (QoS) constraints in IRS assisted wireless networks

Energy-Efficient Pilot-Data Power Control in MU-MIMO Communication Systems

Multiple-input multiple-output (MIMO) antenna system is considered as a core technology for wireless communication. To reap the benefits of MIMO at a greater scale, massive MIMO with very large antenna arrays deployed at base station (BS) has recently become the forefront in wireless communication research. Till present, the design and analysis of large-scale MIMO systems is a fairly new subject. On the other hand, excessive power usage in MIMO networks is a crucial issue for mobile operators and the explosive growth of wireless services contributes largely to the worldwide carbon footprint. As such, significant efforts have been devoted to improve the spectral efficiency (SE) as well as energy efficiency (EE) of MIMO communication systems over the past decade, resulting in many energy efficient techniques such as power allocation. This thesis investigates novel energy-efficient pilot-data power control strategies which can be used in both conventional MIMO and massive MIMO communication systems. The new pilot-data power control algorithms are developed based ontwo optimization frameworks: one aims to minimize the total transmit power while satisfying per-user signal-interference-plus-noise ratio (SINR) and power constraints; the other aims to maximize the total EE, which is defined as the ratio of the total SE to the transmit power, under individual user power constraints. The proposed novel pilot-data power allocation schemes also take into account the maximum-ratio combining (MRC) and zero-forcing (ZF) detectors in the uplink together with maximum-ratio transmission (MRT) and ZF precoder in the downlink. Considering that a direct use of such SINR expressions in the power control schemeswould lead to a very difficult optimization problem which is not mathematically tractable, we first investigatethe statistical SINR lower bounds for multi-cell multi-user MIMO (MU-MIMO)communication systemsunder minimum mean square error (MMSE) channel estimation. These lower bounds of the per-user average SINRs are used to replace the true SINRs to simplify the power allocation optimization problems. Such relaxation of the original average SINR yields a simplified problem and leads to a suboptimal solution. Then, based on the derived average SINR lower bounds, two novel energy efficient pilot-data power control problems are formulatedwithin the first optimization framework,aiming to minimize the total transmit power budget subject to the per-user SINR requirement and power consumption constraint in multi-cell MU-MIMO systems. For the EE-optimal power allocation problems with MRT precoder and MRC detector, it is revealed that such minimization problems can be converted to a standard geometric programming (GP) procedure which can be further converted to a convex optimization problem. For the pilot-data power control scheme with ZF precoder and ZF detector, geometric inequality is used to approximate the original non-convex optimization to GP problem. The very large number of BS station situation is also discussed by assuming infinite antennas at BS. Numerical results validate the tightness of the derived SINR lower bounds and the advantages of the proposed energy efficient power allocation schemes. Next, two pilot and data power control schemes are developed based on the second power allocation optimization framework to jointly maximize the total EE for both uplink and downlink transmissions in multi-cell MU-MIMO systems under per-user and BS power constraints. The original power control problems are simplified to equivalent convex problems based on the derived SINR lower bounds along with the Dinkelbach's method and the FrankWolfe (FW) iteration. By assuming infinite antennas at BS, the pilot-data power control in massive MIMO case is also discussed. The performance of the proposed pilot-data power allocation schemes based on the two frameworks, namely total transmit power minimization and total EE maximization, are evaluated and compared with the SE maximization scheme. Furthermore, we investigate the pilot-data power allocation for EE communications in single-cell MU-MIMO systems with circuit power consumption in consideration. The pilot and data power allocation schemes are proposed to minimize the total weighted uplink and downlink transmit power as well as processing circuit power consumption while meeting the per-user SINR and BS power consumption constraints. In our proposed schemes, both fixed and flexible numbers of BS antennas are investigated. For the fixed number of BS antennas case, the non-convex optimization problems are converted to a general GP problem to facilitate the solution. An iterative algorithm is proposed to solve the EE-optimal power control problems in the flexible number of BS antennas casebased on the partial convexity of both the cost function and the constraints. It is shown that the convergence of the proposed iterative algorithm is guaranteed due to the fact that each iteration follows convex optimization

Joint Pilot Design and Uplink Power Allocation in Multi-Cell Massive MIMO Systems

This paper considers pilot design to mitigate pilot contamination and provide good service for everyone in multi-cell Massive multiple input multiple output (MIMO) systems. Instead of modeling the pilot design as a combinatorial assignment problem, as in prior works, we express the pilot signals using a pilot basis and treat the associated power coefficients as continuous optimization variables. We compute a lower bound on the uplink capacity for Rayleigh fading channels with maximum ratio detection that applies with arbitrary pilot signals. We further formulate the max-min fairness problem under power budget constraints, with the pilot signals and data powers as optimization variables. Because this optimization problem is non-deterministic polynomial-time hard due to signomial constraints, we then propose an algorithm to obtain a local optimum with polynomial complexity. Our framework serves as a benchmark for pilot design in scenarios with either ideal or non-ideal hardware. Numerical results manifest that the proposed optimization algorithms are close to the optimal solution obtained by exhaustive search for different pilot assignments and the new pilot structure and optimization bring large gains over the state-of-the-art suboptimal pilot design.Comment: 16 pages, 8 figures. Accepted to publish at IEEE Transactions on Wireless Communication

Joint Power Allocation and User Association Optimization for Massive MIMO Systems

This paper investigates the joint power allocation and user association problem in multi-cell Massive MIMO (multiple-input multiple-output) downlink (DL) systems. The target is to minimize the total transmit power consumption when each user is served by an optimized subset of the base stations (BSs), using non-coherent joint transmission. We first derive a lower bound on the ergodic spectral efficiency (SE), which is applicable for any channel distribution and precoding scheme. Closed-form expressions are obtained for Rayleigh fading channels with either maximum ratio transmission (MRT) or zero forcing (ZF) precoding. From these bounds, we further formulate the DL power minimization problems with fixed SE constraints for the users. These problems are proved to be solvable as linear programs, giving the optimal power allocation and BS-user association with low complexity. Furthermore, we formulate a max-min fairness problem which maximizes the worst SE among the users, and we show that it can be solved as a quasi-linear program. Simulations manifest that the proposed methods provide good SE for the users using less transmit power than in small-scale systems and the optimal user association can effectively balance the load between BSs when needed. Even though our framework allows the joint transmission from multiple BSs, there is an overwhelming probability that only one BS is associated with each user at the optimal solution.Comment: 16 pages, 12 figures, Accepted by IEEE Trans. Wireless Commu

Employing Antenna Selection to Improve Energy-Efficiency in Massive MIMO Systems

Massive MIMO systems promise high data rates by employing large number of antennas, which also increases the power usage of the system as a consequence. This creates an optimization problem which specifies how many antennas the system should employ in order to operate with maximal energy efficiency. Our main goal is to consider a base station with a fixed number of antennas, such that the system can operate with a smaller subset of antennas according to the number of active user terminals, which may vary over time. Thus, in this paper we propose an antenna selection algorithm which selects the best antennas according to the better channel conditions with respect to the users, aiming at improving the overall energy efficiency. Then, due to the complexity of the mathematical formulation, a tight approximation for the consumed power is presented, using the Wishart theorem, and it is used to find a deterministic formulation for the energy efficiency. Simulation results show that the approximation is quite tight and that there is significant improvement in terms of energy efficiency when antenna selection is employed.Comment: To appear in Transactions on Emerging Telecommunications Technologies, 12 pages, 8 figures, 2 table