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 user's 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.

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 work, we consider the downlink of a multi-user 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 user's signal-to-interference-and-noise-ratio (SINR) constraint and the BS's 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 single-cell 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.

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.

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.

Interference management via resource allocation under the quality of service (QoS) provisions is a crucial challenge for next-generation cellular networks. The task becomes more problematic due to emerging services and applications with nonhomogeneous QoS requirements. The resource allocation problem has mostly been investigated using ideal communication links, where it is generally assumed that information can be relayed perfectly between base stations (BSs). The impact of delays and time-outs at communication links during coordination of BSs is not well understood. Furthermore, it is generally assumed that perfect channel state information is available at BSs which may not always be possible due to large channel feedback delays or estimation errors. In this paper, we propose a power adjustment algorithm for heterogeneous networks which considers varying QoS requirements of users. Contrary to the power allocation approaches in the literature, channel state information is not required for power adjustment. The performance of the algorithm is evaluated both analytically and numerically under a setup with non-ideal communication links. The theoretical analysis unveils that the proposed algorithm achieves optimum power allocation with respect to the QoS requirements of users. The numerical results are not only in agreement with theoretical analysis, but also exhibits significant improvement in terms of overall network performance.

Power allocation in small cell networks has mostly been investigated using ideal communication links, where it is generally assumed that information can be relayed perfectly between base stations. In this paper, we propose a model with non-ideal links and a power adjustment algorithm for heterogeneous networks. Contrary to the power allocation approaches in the literature, channel state information is not required for power adjustment. The convergence properties of the proposed algorithm are investigated using tools from switched system theory and it is shown to converge to the max-min solution even with non-ideal communication links. The theoretical analysis is in agreement with the simulation results.

Robust image hashing has been actively researched over the last decade with varied applications in image content authentication and identification under distortions. In the existing literature on robust image hashing, hash algorithms are ignorant of the class of images being hashed. There are however significant application domains such as that of face image hashing where apriori knowledge of the image class as well as permissible distortions can benefit hash algorithm design. In this paper, we present a two stage cascade of dimensionality reduction constructs for face image hashing. The first stage aims to project the face image to a space where geometric distortions manifest approximately as additive noise. For this purpose, we use the non-negative matrix approximations based hash vector developed by Monga et al. which is known to possess excellent geometric attack robustness. In the second stage, we employ oriented principal component analysis (OPCA) based on estimating signal as well as noise statistics in a learning phase and deriving a projection that mitigates the effect of noise. We obtain both experimentally based ROC curves as well as analytical ones via a detection theoretic analysis of the proposed framework. The ROC curves reveal clearly that incorporating such a learning phase greatly reduces error probabilities.