Uplink Non-Orthogonal Multiple Access with Finite-Alphabet Inputs

This paper focuses on the non-orthogonal multiple access (NOMA) design for a classical two-user multiple access channel (MAC) with finite-alphabet inputs. We consider practical quadrature amplitude modulation (QAM) constellations at both transmitters, the sizes of which are assumed to be not necessarily identical. We propose to maximize the minimum Euclidean distance of the received sum-constellation with a maximum likelihood (ML) detector by adjusting the scaling factors (i.e., instantaneous transmitted powers and phases) of both users. The formulated problem is a mixed continuous-discrete optimization problem, which is nontrivial to resolve in general. By carefully observing the structure of the objective function, we discover that Farey sequence can be applied to tackle the formulated problem. However, the existing Farey sequence is not applicable when the constellation sizes of the two users are not the same. Motivated by this, we define a new type of Farey sequence, termed punched Farey sequence. Based on this, we manage to achieve a closed-form optimal solution to the original problem by first dividing the entire feasible region into a finite number of Farey intervals and then taking the maximum over all the possible intervals. The resulting sum-constellation is proved to be a regular QAM constellation of a larger size. Moreover, the superiority of NOMA over time-division multiple access (TDMA) in terms of minimum Euclidean distance is rigorously proved. Furthermore, the optimal rate allocation among the two users is obtained in closed-form to further maximize the obtained minimum Euclidean distance of the received signal subject to a total rate constraint. Finally, simulation results are provided to verify our theoretical analysis and demonstrate the merits of the proposed NOMA over existing orthogonal and non-orthogonal designs.Comment: Submitted for possible journal publicatio

Downlink Precoding for Massive MIMO Systems Exploiting Virtual Channel Model Sparsity

In this paper, the problem of designing a forward link linear precoder for Massive Multiple-Input Multiple-Output (MIMO) systems in conjunction with Quadrature Amplitude Modulation (QAM) is addressed. First, we employ a novel and efficient methodology that allows for a sparse representation of multiple users and groups in a fashion similar to Joint Spatial Division and Multiplexing. Then, the method is generalized to include Orthogonal Frequency Division Multiplexing (OFDM) for frequency selective channels, resulting in Combined Frequency and Spatial Division and Multiplexing, a configuration that offers high flexibility in Massive MIMO systems. A challenge in such system design is to consider finite alphabet inputs, especially with larger constellation sizes such as $M\geq 16$. The proposed methodology is next applied jointly with the complexity-reducing Per-Group Processing (PGP) technique, on a per user group basis, in conjunction with QAM modulation and in simulations, for constellation size up to $M=64$. We show by numerical results that the precoders developed offer significantly better performance than the configuration with no precoder or the plain beamformer and with $M\geq 16$

Radio resource allocation for uplink OFDMA systems with finite symbol alphabet inputs

In this paper, we consider the radio resource allocation problem for uplink orthogonal frequency-division multiple-access (OFDMA) systems. The existing algorithms have been derived under the assumption of Gaussian inputs due to its closed-form expression of mutual information. For the sake of practicality, we consider the system with finite symbol alphabet (FSA) inputs and solve the problem by capitalizing on the recently revealed relationship between mutual information and minimum mean square error (MMSE). We first relax the problem to formulate it as a convex optimization problem, and then, we derive the optimal solution via decomposition methods. The optimal solution serves as an upper bound on the system performance. Due to the complexity of the optimal solution, a low-complexity suboptimal algorithm is proposed. Numerical results show that the presented suboptimal algorithm can achieve performance very close to the optimal solution and that it outperforms the existing suboptimal algorithms. Furthermore, using our proposed algorithm, significant power saving can be achieved in comparison to the case when a Gaussian input is assumed

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

Wireless Information and Power Transfer in Communication Networks: Performance Analysis and Optimal Resource Allocation

Energy harvesting is considered as a prominent solution to supply the energy demand for low-power consuming devices and sensor nodes. This approach relinquishes the requirements of wired connections and regular battery replacements. This thesis analyzes the performance of energy harvesting communication networks under various operation protocols and multiple access schemes. Furthermore, since the radio frequency signal has energy, in addition to conveying information, it is also possible to power energy harvesting component while establishing data connectivity with information-decoding component. This leads to the concept of simultaneous wireless information and power transfer. The central goal of this thesis is to conduct a performance analysis in terms of throughput and energy efficiency, and determine optimal resource allocation strategies for wireless information and power transfer. In the first part of the thesis, simultaneous transfer of information and power through wireless links to energy harvesting and information decoding components is studied considering finite alphabet inputs. The concept of non-uniform probability distribution is introduced for an arbitrary input, and mathematical formulations that relate probability distribution to the required harvested energy level are provided. In addition, impact of statistical quality of service (QoS) constraints on the overall performance is studied, and power control algorithms are provided. Next, power allocation strategies that maximize the system energy efficiency subject to peak power constraints are determined for fading multiple access channels. The impact of channel characteristics, circuit power consumption and peak power level on the node selection, i.e., activation of user equipment, and the corresponding optimal transmit power level are addressed. Initially, wireless information transfer only is considered and subsequently wireless power transfer is taken into account. Assuming energy harvesting components, two scenarios are addressed based on the receiver architecture, i.e, having separated antenna or common antenna for the information decoding and energy harvesting components. In both cases, optimal SWIPT power control policies are identified, and impact of the required harvested energy is analyzed. The second line of research in this thesis focuses on wireless-powered communication devices that operate based on harvest-then-transmit protocol. Optimal time allocation for the downlink and uplink operation interval are identified formulating throughput maximization and energy-efficiency maximization problems. In addition, the performance gain among various types of downlink-uplink operation protocols is analyzed taking into account statistical QoS constraints. Furthermore, the performance analysis of energy harvesting user equipment is extended to full-duplex wireless information and power transfer as well as cellular networks. In full-duplex operation, optimal power control policies are identified, and the significance of introducing non-zero mean component on the information-bearing signal is analyzed. Meanwhile, SINR coverage probabilities, average throughput and energy efficiency are explicitly characterized for wireless-powered cellular networks, and the impact of downlink SWIPT and uplink mmWave schemes are addressed. In the final part of the thesis, energy efficiency is considered as the performance metric, and time allocation strategies that maximize energy efficiency for wireless powered communication networks with non-orthogonal multiple access scheme are determined. Low complex algorithms are proposed based on Dinkelbach’s method. In addition, the impact of statistical QoS constraints imposed as limitations on the buffer violation probabilities is addressed