In this work, we consider the downlink of a multiuser multiple-input multiple-output (MIMO) system and aim to find the jointly optimal number of base station (BS) antennas and transmission powers that minimize the power consumption while satisfying each users signal-to-interference-and-noise-ratio (SINR) constraint and the BSs power constraint. Different from prior work, we consider a power consumption model that takes both transmitted and hardware-consumed power into account. We formulate the joint optimization problem for a singlecell system and derive closed-form expressions for the optimal number of BS antennas and transmission powers. The solution can be utilized in practice to turn on and off antennas depending on the traffic load variations. Substantial power savings are demonstrated by simulation.

Future cellular networks are expected to support new communication paradigms such as machine-type communication (MTC) services along with human-type communication (HTC) services. This requires base stations to serve a large number of devices in relatively short channel coherence intervals which renders allocation of orthogonal pilot sequence per-device approaches impractical. Furthermore. the stringent power constraints, place-and-play type connectivity and various data rate requirements of MTC devices make it impossible for the traditional cellular architecture to accommodate MTC and HTC services together. Massive multiple-input-multiple-output (MaMIMO) technology has the potential to allow the coexistence of HTC and MTC services, thanks to its inherent spatial multiplexing properties and low transmission power requirements. In this work, we investigate the performance of a single cell under a shared physical channel assumption for MTC and HTC services and propose a novel scheme for sharing the time-frequency resources. The analysis reveals that MaMIMO can significantly enhance the performance of such a setup and allow the inclusion of MTC services into the cellular networks without requiring additional resources.

In this paper, we consider the downlink of a multi-cell multiple-input multiple-output system and find the jointly optimal number of base station (BS) antennas and transmission powers that minimize the power consumption while satisfying each users effective signal-to-interference-and-noise-ratio constraint and the BSs power constraints. Different from prior work, we consider a power consumption model that takes both transmitted and hardware-consumed power into account. We formulate the joint optimization problem for both single-cell and multi-cell systems. The closed-form expressions for the optimal number of BS antennas and transmission powers are derived for the single-cell case. The analysis for the multi-cell case reveals that increasing the number of BS antennas in any cell always improves the performance of the overall system in terms of both feasibility and total radiated power. A key contribution of this paper is to show that the joint optimization problem can be relaxed as a geometric programming problem that can be solved efficiently. The solution can be utilized in practice to turn on and off antennas depending on the traffic load variations. The substantial power savings are demonstrated by simulation.

To reach a cost-efficient 5G architecture, the use of remote radio heads connected through a fronthaul to baseband controllers is a promising solution. However, the fronthaul links must support high bit rates as 5G networks are projected to use wide bandwidths and many antennas. Upgrading all of the existing fronthaul connections would be cumbersome, while replacing the remote radio head and upgrading the software in the baseband controllers is relatively simple. In this paper, we consider the uplink and seek the answer to the question: If we have a fixed fronthaul capacity and can deploy any technology in the remote radio head, what is the optimal technology? In particular, we optimize the number of antennas, quantization bits and bandwidth to maximize the sum rate under a fronthaul capacity constraint. The analytical results suggest that operating with many antennas equipped with low-resolution analog-to-digital converters, while the interplay between number of antennas and bandwidth depends on various parameters. The numerical analysis provides further insights into the design of communication systems with limited fronthaul capacity.

This paper compares the sum rates and rate regions achieved by power-domain NOMA (non-orthogonal multiple access) and standard massive MIMO (multiple-input multiple-output) techniques. We prove analytically that massive MIMO always outperforms NOMA in i.i.d. Rayleigh fading channels, if a sufficient number of antennas are used at the base stations. The simulation results show that the crossing point occurs already when having 20-30 antennas, which is far less than what is considered for the next generation cellular networks.

This paper seeks to answer a simple but fundamental question: what role can non-orthogonal multiple access (NOMA) play in massive multi-in multi-out (MIMO)? It is well established that power-domain NOMA schemes can outperform conventional orthogonal multiple access schemes in cellular networks. However, this fact does not imply that NOMA is the most efficient way to communicate in massive MIMO setups, where the base stations have many more antennas than there are users in the cell. These setups are becoming the norm in future networks and are usually studied by assuming spatial multiplexing of the users using linear multi-user beamforming. To answer the above-mentioned question, we analyze and compare the performance achieved by NOMA and multi-user beamforming in both non-line-of-sight and line-of-sight scenarios. We reveal that the latter scheme gives the highestaverage sum rate in massive MIMO setups. We also identify specific cases where NOMA is the better choice in massive MIMO and explain how NOMA plays an essential role in creating a hybrid of NOMA and multi-user beamforming that is shown to perform better than two standalone schemes do.

Future cellular networks will support a massive number of devices as a result of emerging technologies such as Internet-of-Things and sensor networks. Enhanced by machine type communication (MTC), low-power low-complex devices in the order of billions are projected to receive service from cellular networks. Contrary to traditional networks which are designed to handle human driven traffic, future networks must cope with MTC based systems that exhibit sparse traffic properties, operate with small packets and contain a large number of devices. Such a system requires smarter control signaling schemes for efficient use of system resources. In this work, we consider a grant-free random access cellular network and propose an approach which jointly detects user activity and single information bit per packet. The proposed approach is inspired by the approximate message passing (AMP) and demonstrates a superior performance compared to the original AMP approach. Furthermore, the numerical analysis reveals that the performance of the proposed approach scales with number of devices, which makes it suitable for user detection in cellular networks with massive number of devices.

A key challenge of massive MTC (mMTC), is the joint detection of device activity and decoding of data. The sparse characteristics of mMTC makes compressed sensing (CS) approaches a promising solution to the device detection problem. However, utilizing CS-based approaches for device detection along with channel estimation, and using the acquired estimates for coherent data transmission is suboptimal, especially when the goal is to convey only a few bits of data. First, we focus on the coherent transmission and demonstrate that it is possible to obtain more accurate channel state information by combining conventional estimators with CS-based techniques. Moreover, we illustrate that even simple power control techniques can enhance the device detection performance in mMTC setups. Second, we devise a new non-coherent transmission scheme for mMTC and specifically for grant-free random access. We design an algorithm that jointly detects device activity along with embedded information bits. The approach leverages elements from the approximate message passing (AMP) algorithm, and exploits the structured sparsity introduced by the non-coherent transmission scheme. Our analysis reveals that the proposed approach has superior performance compared with application of the original AMP approach.

Machine-type communication (MTC) services are expected to be an integral part of the future cellular systems. A key challenge of MTC, especially for the massive MTC (mMTC), is the detection of active devices among a large number of devices. The sparse characteristics of mMTC makes compressed sensing (CS) approaches a promising solution to the device detection problem. CS-based techniques are shown to outperform conventional device detection approaches. However, utilizing CS-based approaches for device detection along with channel estimation and using the acquired estimates for coherent data transmission may not be the optimal approach, especially for the cases where the goal is to convey only a few bits of data. In this work, we propose a non-coherent transmission technique for the mMTC uplink and compare its performance with coherent transmission. Furthermore, we demonstrate that it is possible to obtain more accurate channel state information by combining the conventional estimators with CS-based techniques.

Non-orthogonal multiple access is a promising technology for the fifth generation systems which exploits the power domain to achieve higher spectral efficiency. The performance of NOMA techniques are usually investigated under an ideal setup with perfect successive interference cancellation. However, the limitations of NOMA techniques under a setup with imperfect successive interference cancellation are not well understood. Contrary to the approaches in the literature, we examine the performance of NOMA under a non-ideal setup and propose two power allocation algorithms. The first algorithm is designed for the max-min problem whereas the second algorithm considers the heterogeneous rate requirements of users and provides solutions based on a novel rate measure. The performance of the algorithms is investigated both theoretically and numerically under a non-ideal setup with channel estimation errors. The theoretical analyses reveal that the algorithms achieve the optimum power allocation for the rate max-min problems. The numerical analyses are not only in agreement with the theoretical analyses, but also show the superiority of the proposed algorithms compared to both the conventional multiple access techniques as well as other NOMA approaches.

In this work, a processing architecture for grant-free machine type communication based on compressive sensing is proposed. The architecture can be adapted for a number of parameters. An instantiation for 128 terminals and 96 antennas is implemented. Without memories it consumes 1.52 W and occupies and area of 5.1 mm(2) in a 28 nm SOI CMOS process. The implemented instance can process about 10k messages per second, each containing four bits.