The upper mid-band – approximately from 7 to 24 GHz — has recently attracted considerable interest for new cellular services. This frequency range has vastly more spectrum than the highly congested bands below 7 GHz while offering more favorable propagation and coverage than the millimeter wave (mmWave) frequencies. In this regard, the upper mid-band has the potential to provide a powerful and complementary frequency range that balances coverage and capacity. Realizing cellular networks that exploit the full range of these bands, however, presents significant technical challenges. Most importantly, spectrum will likely need to be shared with incumbents including communication satellites, military RADAR, and radio astronomy. Also, the upper mid-band is simply a vast frequency range. Due to this wide bandwidth, combined with the directional nature of transmission and intermittent occupancy of incumbents, cellular systems will likely need to be agile to sense and intelligently use large spatial and frequency degrees of freedom. This paper attempts to provide an initial assessment of the feasibility and potential gains of such adaptive wideband cellular systems operating across the upper mid-band. The study includes: (1) a detailed ray tracing simulation to assess potential gains of multi-band systems in a representative dense urban environment and illustrate the value of a wideband system with dynamic frequency selectivity; (2) an evaluation of potential cross-interference between satellites and terrestrial cellular services and interference nulling to reduce that interference; and (3) design and evaluation of a compact multi-band antenna array structure. Leveraging these preliminary results, we identify potential future research directions to realize next-generation systems in these frequencies.

This paper proposes a deep learning-based intelligent method for detecting and identifying radio signals considering two applications: (1) cognitive radar for identifying micro unmanned aerial systems (micro-UASs) and (2) an automated modulation classification (AMC) scheme for cognitive radio (CR), which can be used for aeronautical communication systems. Our proposed intelligent method is designed of a spectral correlation function (SCF) based feature extractor and a low-complexity deep belief network (DBN) classifier with low FPGA logic utilization.

This paper proposes a low-complexity wideband beamforming subarray for millimeter wave (mmW) 5G wireless communications. The multibeam subarray is based on using a novel delay Vandermonde matrix (DVM) algorithm to efficiently generate analog true-time-delay beams that have no beam squint. A factorization for the DVM leading to low-complexity analog realizations is provided and complexity analysis for real and complex inputs is derived. The DVM is a special case of a Vandermonde matrix but with complex nodes that lack any special properties (unlike the discrete Fourier transform matrix). Error bounds for the DVM are established and then analyzed for numerical stability. Mixed-signal CMOS integrated circuits designs are proposed for the implementation of DVM multibeam algorithms along with low-complexity digital realizations to achieve hybrid beamforming for mmW applications. Analog-digital hybrid mmW multibeam beamforming circuits and systems are designed, for example, with eight beams at 28 GHz and simulated in cadence for functional verification.

Ultra-miniaturized and highly power-efficient ELF (0.3-3 kHz) transmitters are desirable for underground and undersea wireless communications. This paper proposes the use of rotating permanently polarized dipoles (magnets or electrets) for such applications. A power-efficient data modulation scheme based on continuous-frequency FSK (CF-FSK) is also proposed for such electro-mechanical transmitters. Theoretical analysis and simulations show that 7-ary CF-FSK is optimal in the sense that it minimizes average mechanical torque on the mechatronic antenna for a given bit rate. Preliminary experimental results from a prototype ELF magnetic-field based transmitter based on a high-strength rare-earth (NdFeB) magnet and small brushless DC (BLDC) motor are presented. The results highlight local environment challenges such as power line interference that affect the choice of data encoding and modulation schemes. Reliable non-line-of-sight (NLOS) communication through concrete barriers at 100 Hz and ∼0.25 bit/sec is demonstrated at distances up to ∼5 m. The proposed mechatronic antenna and modulation scheme can be scaled up in both frequency and transmit power for more challenging applications in surface-to-undersea, surface-to-cave, and other conductive environments that require ELF channels.

The discrete Fourier transform (DFT) is widely employed for multi-beam digital beamforming. The DFT can be efficiently implemented through the use of fast Fourier transform (FFT) algorithms, thus reducing chip area, power consumption, processing time, and consumption of other hardware resources. This paper proposes three new hybrid DFT 1024-point DFT approximations and their respective fast algorithms. These approximate DFT (ADFT) algorithms have significantly reduced circuit complexity and power consumption compared to traditional FFT approaches while trading off a subtle loss in computational precision which is acceptable for digital beamforming applications in RF antenna implementations. ADFT algorithms have not been introduced for beamforming beyond N = 32, but this paper anticipates the need for massively large adaptive arrays for future 5G and 6G systems. Digital CMOS circuit designs for the ADFTs show the resulting improvements in both circuit complexity and power consumption metrics. Simulation results show similar or lower critical path delay with up to 48.5% lower chip area compared to a standard Cooley-Tukey FFT. The time-area and dynamic power metrics are reduced up to 66.0%. The 1024-point ADFT beamformers produce SNR gains between 29.2–30.1 dB, which is a loss of ≤ 0.9 dB SNR gain compared to exact 1024-point DFT beamformers (worst case) realizable at using an FFT.

Optimal use of scarce radio spectrum is essential in the proliferation of beyond 5G networks, and promising blockchain technology offers various benefits for the spectrum management. However, existing blockchain-based solutions are expensive, nonoptimized, and lack spectrum fraud detection. This article proposes a novel consensus mechanism for a blockchain-based dynamic spectrum access (DSA) system. The proposed “ Proof-of-Sense ” consensus mechanism operates based on spectrum sensing procedures rather than cryptographic calculations. It is specially designed to address fraudulent/unauthorized access to the spectrum by analyzing the sensed spectrum data. The core of the consensus mechanism is a cryptographic key sharing mechanism inspired by Shamir’s secret sharing scheme. Moreover, the proposed DSA system can enable different microservices, such as automated spectrum auctions, payment and penalty handling, and spectrum fraud detection. A proof of concept based on experimental approaches coupled with Matlab simulations is presented to analyze the performance of the proposed consensus mechanism.