Iterative Mode-Dropping for the Sum Capacity of MIMO-MAC with Per-Antenna Power Constraint

We propose an iterative mode-dropping algorithm that optimizes input signals to achieve the sum capacity of the MIMO-MAC with per-antenna power constraint. The algorithm successively optimizes each user's input covariance matrix by applying mode-dropping to the equivalent single-user MIMO rate maximization problem. Both analysis and simulation show fast convergence. We then use the algorithm to briefly highlight the difference in MIMO-MAC capacities under sum and per-antenna power constraints.Comment: 6 pages double-column, 5 figure

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

Energy Efficient Reduced Complexity Multi-Service, Multi-Channel Scheduling Techniques

The need for energy efficient communications is essential in current and next-generation wireless communications systems. A large component of energy expenditure in mobile devices is in the mobile radio interface. Proper scheduling and resource allocation techniques that exploit instantaneous and long-term average knowledge of the channel, queue state and quality of service parameters can be used to improve the energy efficiency of communication. This thesis focuses on exploiting queue and channel state information as well as quality of service parameters in order to design energy efficient scheduling techniques. The proposed designs are for multi-stream, multi-channel systems and in general have high computational complexity. The large contributions of this thesis are in both the design of optimal/near-optimal scheduling/resource allocation schemes for these systems as well as proposing complexity reduction methods in their design. Methods are proposed for both a MIMO downlink system as well as an LTE uplink system. The effect of power efficiency on quality of service parameters is well studied as well as complexity/efficiency comparisons between optimal/near optimal allocation

Radio Communications

In the last decades the restless evolution of information and communication technologies (ICT) brought to a deep transformation of our habits. The growth of the Internet and the advances in hardware and software implementations modified our way to communicate and to share information. In this book, an overview of the major issues faced today by researchers in the field of radio communications is given through 35 high quality chapters written by specialists working in universities and research centers all over the world. Various aspects will be deeply discussed: channel modeling, beamforming, multiple antennas, cooperative networks, opportunistic scheduling, advanced admission control, handover management, systems performance assessment, routing issues in mobility conditions, localization, web security. Advanced techniques for the radio resource management will be discussed both in single and multiple radio technologies; either in infrastructure, mesh or ad hoc networks

Resource allocation and optimization techniques in wireless relay networks

Relay techniques have the potential to enhance capacity and coverage of a wireless network. Due to rapidly increasing number of smart phone subscribers and high demand for data intensive multimedia applications, the useful radio spectrum is becoming a scarce resource. For this reason, two way relay network and cognitive radio technologies are required for better utilization of radio spectrum. Compared to the conventional one way relay network, both the uplink and the downlink can be served simultaneously using a two way relay network. Hence the effective bandwidth efficiency is considered to be one time slot per transmission. Cognitive networks are wireless networks that consist of different types of users, a primary user (PU, the primary license holder of a spectrum band) and secondary users (SU, cognitive radios that opportunistically access the PU spectrum). The secondary users can access the spectrum of the licensed user provided they do not harmfully affect to the primary user. In this thesis, various resource allocation and optimization techniques have been investigated for wireless relay and cognitive radio networks

Spectrum Sharing in Wireless Networks via QoS-Aware Secondary Multicast Beamforming

Secondary spectrum usage has the potential to considerably increase spectrum utilization. In this paper, quality-of-service (QoS)-aware spectrum underlay of a secondary multicast network is considered. A multiantenna secondary access point (AP) is used for multicast (common information) transmission to a number of secondary single-antenna receivers. The idea is that beamforming can be used to steer power towards the secondary receivers while limiting sidelobes that cause interference to primary receivers. Various optimal formulations of beamforming are proposed, motivated by different ldquocohabitationrdquo scenarios, including robust designs that are applicable with inaccurate or limited channel state information at the secondary AP. These formulations are NP-hard computational problems; yet it is shown how convex approximation-based multicast beamforming tools (originally developed without regard to primary interference constraints) can be adapted to work in a spectrum underlay context. Extensive simulation results demonstrate the effectiveness of the proposed approaches and provide insights on the tradeoffs between different design criteria

Centralized and partial decentralized design for the Fog Radio Access Network

Fog Radio Access Network (F-RAN) has been shown to be a promising network architecture for the 5G network. With F-RAN, certain amount of signal processing functionalities are pushed from the Base Station (BS) on the network edge to the BaseBand Units (BBU) pool located remotely in the cloud. Hence, partially centralized network operation and management can be achieved, which can greatly improve the energy and spectral efficiency of the network, in order to meet the requirements of 5G. In this work, the optimal design for both uplink and downlink of F-RAN are intensively investigated

Optimal Power Assignment for MIMO Channels Under Joint Total and Per-Group Power Constraints

In this paper we consider a communication system with one transmitter and one receiver. The transmit antennas are partitioned into disjoint groups, and each group must satisfy an average power constraint in addition to the standard overall one. The optimal power allocation (OPA) for the transmit antennas is obtained for the following cases: (i) fixed multiple-input multiple-output (MIMO) orthogonal channel, (ii) i.i.d. fading MIMO orthogonal channel, and (iii) i.i.d. Rayleigh fading multiple-input single-output (MISO) and MIMO channels. The channel orthogonality is encountered in the practical case of the massive MIMO channel under favorable propagation conditions. The closed-form solution to the OPA for a fixed channel is found using the Karush-Kuhn-Tucker (KKT) conditions and it is similar to the standard water-filling procedure while the effect of the per-group average power constraint is added. For a fading channel, an algorithm is proposed to give the OPA, and the algorithm's convergence is proved via a majorization inequality and a Schur-concavity property