Globally Optimal Energy-Efficient Power Control and Receiver Design in Wireless Networks

The characterization of the global maximum of energy efficiency (EE) problems in wireless networks is a challenging problem due to the non-convex nature of investigated problems in interference channels. The aim of this work is to develop a new and general framework to achieve globally optimal solutions. First, the hidden monotonic structure of the most common EE maximization problems is exploited jointly with fractional programming theory to obtain globally optimal solutions with exponential complexity in the number of network links. To overcome this issue, we also propose a framework to compute suboptimal power control strategies characterized by affordable complexity. This is achieved by merging fractional programming and sequential optimization. The proposed monotonic framework is used to shed light on the ultimate performance of wireless networks in terms of EE and also to benchmark the performance of the lower-complexity framework based on sequential programming. Numerical evidence is provided to show that the sequential fractional programming framework achieves global optimality in several practical communication scenarios.Comment: Accepted for publication in the IEEE Transactions on Signal Processin

Beyond Massive-MIMO: The Potential of Data-Transmission with Large Intelligent Surfaces

In this paper, we consider the potential of data-transmission in a system with a massive number of radiating and sensing elements, thought of as a contiguous surface of electromagnetically active material. We refer to this as a large intelligent surface (LIS). The "LIS" is a newly proposed concept, which conceptually goes beyond contemporary massive MIMO technology, that arises from our vision of a future where man-made structures are electronically active with integrated electronics and wireless communication making the entire environment "intelligent". We consider capacities of single-antenna autonomous terminals communicating to the LIS where the entire surface is used as a receiving antenna array. Under the condition that the surface-area is sufficiently large, the received signal after a matched-filtering (MF) operation can be closely approximated by a sinc-function-like intersymbol interference (ISI) channel. We analyze the capacity per square meter (m^2) deployed surface, \hat{C}, that is achievable for a fixed transmit power per volume-unit, \hat{P}. Moreover, we also show that the number of independent signal dimensions per m deployed surface is 2/\lambda for one-dimensional terminal-deployment, and \pi/\lambda^2 per m^2 for two and three dimensional terminal-deployments. Lastly, we consider implementations of the LIS in the form of a grid of conventional antenna elements and show that, the sampling lattice that minimizes the surface-area of the LIS and simultaneously obtains one signal space dimension for every spent antenna is the hexagonal lattice. We extensively discuss the design of the state-of-the-art low-complexity channel shortening (CS) demodulator for data-transmission with the LIS.Comment: Submitted to IEEE Trans. on Signal Process., 30 pages, 12 figure

FPGA Implementation of Sphere Detector for Spatial Multiplexing MIMO System

Multiple Input Multiple Output (MIMO (techniquesuse multiple antennas at both transmitter and receiver forincreasing the channel reliability and enhancing the spectralefficiency of wireless communication system.MIMO Spatial Multiplexing (SM) is a technology that can increase the channelcapacity without additional spectral resources. The implementation of MIMO detection techniques become a difficult missionas the computational complexity increases with the number oftransmitting antenna and constellation size. So designing detection techniques that can recover transmitted signals from SpatialMultiplexing (SM) MIMO with reduced complexity and highperformance is challenging. In this survey, the general model ofMIMO communication system is presented in addition to multipleMIMO Spatial Multiplexing (SM) detection techniques. These detection techniques are divided into different categories, such as linear detection, Non-linear detection and tree-search detection.Detailed discussions on the advantages and disadvantages of each detection algorithm are introduced. Hardware implementation of Sphere Decoder (SD) algorithm using VHDL/FPGA is alsopresente

The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review

Network latency will be a critical performance metric for the Fifth Generation (5G) networks expected to be fully rolled out in 2020 through the IMT-2020 project. The multi-user multiple-input multiple-output (MU-MIMO) technology is a key enabler for the 5G massive connectivity criterion, especially from the massive densification perspective. Naturally, it appears that 5G MU-MIMO will face a daunting task to achieve an end-to-end 1 ms ultra-low latency budget if traditional network set-ups criteria are strictly adhered to. Moreover, 5G latency will have added dimensions of scalability and flexibility compared to prior existing deployed technologies. The scalability dimension caters for meeting rapid demand as new applications evolve. While flexibility complements the scalability dimension by investigating novel non-stacked protocol architecture. The goal of this review paper is to deploy ultra-low latency reduction framework for 5G communications considering flexibility and scalability. The Four (4) C framework consisting of cost, complexity, cross-layer and computing is hereby analyzed and discussed. The Four (4) C framework discusses several emerging new technologies of software defined network (SDN), network function virtualization (NFV) and fog networking. This review paper will contribute significantly towards the future implementation of flexible and high capacity ultra-low latency 5G communications

A Primer on MIMO Detection Algorithms for 5G Communication Network

In the recent past, demand for large use of mobile data has increased tremendously due to the proliferation of hand held devices which allows millions of people access to video streaming, VOIP and other internet related usage including machine to machine (M2M) communication. One of the anticipated attribute of the fifth generation (5G) network is its ability to meet this humongous data rate requirement in the order of 10s Gbps. A particular promising technology that can provide this desired performance if used in the 5G network is the massive multiple-input, multiple-output otherwise called the Massive MIMO. The use of massive MIMO in 5G cellular network where data rate of the order of 100x that of the current state of the art LTE-A is expected and high spectral efficiency with very low latency and low energy consumption, present a challenge in symbol/signal detection and parameter estimation as a result of the high dimension of the antenna elements required. One of the major bottlenecks in achieving the benefits of such massive MIMO systems is the problem of achieving detectors with realistic low complexity for such huge systems. We therefore review various MIMO detection algorithms aiming for low computational complexity with high performance and that scales well with increase in transmit antennas suitable for massive MIMO systems. We evaluate detection algorithms for small and medium dimension MIMO as well as a combination of some of them in order to achieve our above objectives. The review shows no single one detector can be said to be ideal for massive MIMO and that the low complexity with optimal performance detector suitable for 5G massive MIMO system is still an open research issue. A comprehensive review of such detection algorithms for massive MIMO was not presented in the literature which was achieved in this work