UL-DL duality for cell-free massive MIMO with per-AP power and information constraints

We derive a novel uplink-downlink duality principle for optimal joint precoding design under per-transmitter power and information constraints in fading channels. The information constraints model limited sharing of channel state information and data bearing signals across the transmitters. The main application is to cell-free networks, where each access point (AP) must typically satisfy an individual power constraint and form its transmit signal using limited cooperation capabilities. Our duality principle applies to ergodic achievable rates given by the popular hardening bound, and it can be interpreted as a nontrivial generalization of a previous result by Yu and Lan for deterministic channels. This generalization allows us to study involved information constraints going beyond the simple case of cluster-wise centralized precoding covered by previous techniques. Specifically, we show that the optimal joint precoders are, in general, given by an extension of the recently developed team minimum mean-square error method. As a particular yet practical example, we then solve the problem of optimal local precoding design in user-centric cell-free massive MIMO networks subject to per-AP power constraints

On the MIMO Capacity with Multiple Power Constraints

Themultiple-inputmultiple-output(MIMO)technology has become an essential element of modern communication systems e.g.,3G,4 Gand massive MIMOtechnology has been recently standardized i n3GPPRel-15i.e. ,New Radio(NR)to enhance the spectral efficiency or the capacity of 5G networks. Given a digital communication system, a receiver will suffer from decoding errors if the transmission rate exceeds the capacity. Therefore, the capacity of a MIMO system is an important metric to characterize the system performance. More importantly,an efficient precoder design to achieve that capacity is of great interest. This thesis is dedicated to this fundamental problem under multiple power constraints. From the theoretical perspective, capacity maximization is a classical problem. However efficient algorithms considering realistic scenarios or multiple power constraints, especially for massive MIMO application, are still sparse. In the thesis, the author has sought new methods of determining the capacity under two practical power constraints: 1) per-antenna power constraint (PAPC) 2) linear transmit covariance constraint (LTCC). In particular, the PAPC imposes an individual power limit one ach power amplifier associated with atransmit antenna, thus is much more realistic than the traditional sum power constraint (SPC) in which all transmit antennas collaborate to satisfy a predefined total power budget. In many other practical scenarios, other power constraints can be imposed on a system, not necessarily to either SPC or PAPC. To this end, LTCCs are general enough to include those constraints. In both cases, we have proposed low-complexity approaches to the considered problems and the description of them is in the following. For the problem of capacity maximization under PAPC,two closed-formlow-complexity approaches have been developed for single-user MIMO and multi-user MIMO under different MIMO channels and precoding techniques. More specifically, the first approach is based on fixed-point-iterationtosolvetheproblemdirectlyinthebroadcast channel (BC), whereas the other relies on alternating optimization (AO) together with successive convex optimization (SCA) to solve the equivalent problem in dual multiple access channel (MAC) domain. Interestingly, the latter approach is also applicable to the problem of computing capacity with LTCCs. For the special case of joint SPC and PAPC, we have also derived analytical solutions to this important problem. Last but not least, we have investigated the applications of machine learning to our capacity problems and presented some preliminary results

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

A Survey of Physical Layer Security Techniques for 5G Wireless Networks and Challenges Ahead

Physical layer security which safeguards data confidentiality based on the information-theoretic approaches has received significant research interest recently. The key idea behind physical layer security is to utilize the intrinsic randomness of the transmission channel to guarantee the security in physical layer. The evolution towards 5G wireless communications poses new challenges for physical layer security research. This paper provides a latest survey of the physical layer security research on various promising 5G technologies, including physical layer security coding, massive multiple-input multiple-output, millimeter wave communications, heterogeneous networks, non-orthogonal multiple access, full duplex technology, etc. Technical challenges which remain unresolved at the time of writing are summarized and the future trends of physical layer security in 5G and beyond are discussed.Comment: To appear in IEEE Journal on Selected Areas in Communication

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

A Light Signalling Approach to Node Grouping for Massive MIMO IoT Networks

Massive MIMO is a promising technology to connect very large numbers of energy constrained nodes, as it offers both extensive spatial multiplexing and large array gain. A challenge resides in partitioning the many nodes in groups that can communicate simultaneously such that the mutual interference is minimized. We here propose node partitioning strategies that do not require full channel state information, but rather are based on nodes' respective directional channel properties. In our considered scenarios, these typically have a time constant that is far larger than the coherence time of the channel. We developed both an optimal and an approximation algorithm to partition users based on directional channel properties, and evaluated them numerically. Our results show that both algorithms, despite using only these directional channel properties, achieve similar performance in terms of the minimum signal-to-interference-plus-noise ratio for any user, compared with a reference method using full channel knowledge. In particular, we demonstrate that grouping nodes with related directional properties is to be avoided. We hence realise a simple partitioning method requiring minimal information to be collected from the nodes, and where this information typically remains stable over a long term, thus promoting their autonomy and energy efficiency