Underwater physical medium is a challenging environment for communication using radio frequency (RF) or acoustic waves due to strong attenuation, delay, multi-path fading, power and cost limitations. Discovered a century ago, magneto-inductive (MI) communication technique stands as a strong alternative paradigm due to its independence of environmental impairments including multi-path fading, dynamic channels and high propagation delays experienced by acoustic waves. Furthermore, MI technique yields networking solutions exploiting low-cost, easily-deployable and flexible antenna structures, and the possibility of forming networks of magnetic waveguides defeating path loss. In this work, highly power efficient and fully connected underwater communication networks (UWCNs) composed of transceiver and relay induction coils are presented. Three dimensional (3D) UWCNs are analysed in terms of basic communication metrics, i.e, signal-to-noise ratio, bit-error rate, connectivity and communication bandwidth. The performance studies of realistic 3D networks covering hundreds of meters sea depths and a few km² areas show that fully connected multi-coil networks with communication bandwidths extending from a few to tens of KHz are possible. Furthermore, the performance dependence on coil properties and network size is theoretically modelled. Results show that MI wireless communication is a promising alternative for UWCNs and future research challenges are pointed out.

Cognitive radio (CR) technology helps mitigate wireless resource scarcity problem by dynamically changing frequency spectrum, power and modulation type. Opportunistic spectrum access increases the network capability and quality. Recently, CR applied to wireless sensor networks (WSNs) generated the paradigm of cognitive radio sensor networks (CRSNs) overcoming the challenges posed by event-driven traffic demands of WSNs. To realize advantages of CRSN, spectrum and power allocation, and routing must be jointly considered to maximize the information capacity, resource utilization and the lifetime. In this paper, power and rate adaptation problem is analyzed for a multi-hop CRSN in an information theoretical (IT) capacity maximization framework combined with energy adaptive (EA) mechanisms and utilization of sensor data information correlations (ICs). CRSN characteristics, i.e., fast data aggregation, bursty traffic and node failures, are considered. The capacity optimization problem is defined analytically and practical local schemes are presented showing the superiority of objective functions utilizing ICs and EA mechanisms in terms of the resulting maximum information rate at sink, i.e., R_max, lifetime, and energy utilization. Furthermore, dependence of performance on total bandwidth and various relay energy distributions is explored observing the logarithmic dependence of R_max on total bandwidth.

Magneto-inductive (MI) wireless communications is an emerging subject with a rich set of applications, including local area networks for the Internet-of-Things, wireless body area networks, in-body and on-chip communications, and underwater and underground sensor networks as a low-cost alternative to radio frequency, acoustic or optical methods. Practical MI networks include multiple access channel (MAC) mechanisms for connecting a random number of coils without any specific topology or coil orientation assumptions covering both short and long ranges. However, there is not any information theoretical modeling of MI MAC (MIMAC) capacity of such universal networks with fully coupled frequency selective channel models and exact 3-D coupling model of circular coils instead of long range dipole approximations. In this paper, K-user MIMAC capacity is information theoretically modeled and analyzed, and two-user MIMACs are modeled with explicitly detailed channel responses, bandwidths and coupled thermal noise. K-user MIMAC capacity is achieved through Lagrangian solution with K-user water-filling optimization. Optimum orientations maximizing capacity and received power are theoretically analyzed, and numerically simulated for two-user MIMACs. Constructive gain and destructive interference mechanisms on MIMACs are introduced in comparison with the classical interference based approaches. The theoretical basis promises the utilization of MIMACs in 5G architectures.

Graphene with significant potentials in diverse areas of physical and biological sciences is proposed as a solution to complementary problems of semiconductor and biomedical industries, i.e., the on-chip (OC) interconnect bottleneck and in-body (IB) wireless communications/power transfer (PT), respectively. Emerging nanoscale solutions with radio frequency, optical, ultrasonic, or molecular channels in OC and IB media have various challenges including achievable footprints and frequency, energy consumption, medium dependent features, and interference. In this paper, major challenges are addressed with magneto-inductive (MI) transceivers by combining the advantages of THz operation frequency, unique features of intercalated multi-layer graphene (MLG) coils and range extension with MI waveguides. Our design promises scalable and high performance solutions for the OC interconnect bottleneck while providing biocompatible and universal solutions for challenging IB medium. The proposed solution is theoretically analyzed and numerically compared with the copper-based alternatives, and the practical challenges are discussed. Simulation results achieve high capacity (several Tbit/s) and ultra-low power (500 zJ/bit) wireless communications while providing high (hundreds of kWs) and efficient (10 ⁹ W/mm ² ) wireless PT at several millimeters. In addition, unique properties of MLG such as lightweight structure, biocompatibility, current carrying capacity, and planar manufacturability make the solution more promising for challenging environments.

Graphene with ground breaking properties has tremendous impact on physical sciences as 2-D atomic layer carbon sheet. Its unique electronic and photonic properties lead to applications such as transistors, graphene photodetectors (GPDs), and electronic circuit components. Metal-graphene-metal (MGM) GPDs with single- or multilayer graphene sheets are promising for future nanoscale optical communication architectures because of wide range absorption from far infrared to visible spectrum, fast carrier velocity, and advanced production techniques due to planar geometry. In this paper, signal-to-noise ratio (SNR), bit-error rate (BER), and data rate performances of nanoscale single-layer symmetric MGM photodetectors are analyzed for intensity modulation and direct detection (IM/DD) modulation. Shot and thermal noise limited (NL) performances are analyzed emphasizing graphene layer width dependence and domination of thermal NL characteristics for practical power levels. Tens of Gbit/s data rates are shown to be achievable with very low BERs for single-receiver (SR) GPDs. Furthermore, multireceiver (MR) GPDs and parallel line-scan (PLS) network topology are defined improving the efficiency of symmetric GPDs. SNR performance of SR PLS channels are both improved and homogenized with MR devices having the same total graphene area by optimizing their positions with max-min solutions and using maximal ratio and equal gain diversity combining techniques.

Quantum-approximate optimization algorithm (QAOA) is promising in Noisy Intermediate-Scale Quantum (NISQ) computers with applications for NP-hard combinatorial optimization problems. It is recently utilized for NP-hard maximum-likelihood (ML) detection problem with challenges of optimization, simulation and performance analysis for n × n multiple-input multiple output (MIMO) systems with large n . QAOA is recently applied by Farhi et al. on infinite size limit of Sherrington-Kirkpatrick (SK) model with a cost model including only quadratic terms. In this article, we extend the model by including also linear terms and then realize SK modeling of massive MIMO ML detection. The proposed design targets near ML performance while with complexity including O (16 ^p ) initial operations independent from problem instance and size n for optimizing QAOA angles and O ( n ² p ) quantum operations for each instance. We provide both optimized and extrapolated angles for p ∈ [1, 14] and signal-to-noise (SNR) < 12 dB achieving near-optimum ML performance with p ≥ 4 for 25×25 and 12 × 12 MIMO systems modulated with BPSK and QPSK, respectively. We present two conjectures about concentration properties of QAOA and near-optimum performance for next generation massive MIMO systems covering n < 300.