This paper investigates the feasibility and optimization of 24/7 remote monitoring in the Arctic using Low Earth Orbit (LEO) satellite constellations, specifically Starlink. Through rigorous link analysis incorporating Equivalent Isotropic Radiated Power (EIRP), Gain-to-Noise Temperature ratio (G/T), free space path loss, and rain attenuation, we assess the Carrier-to-Noise Ratio (CNR) for a specified Arctic location. To enhance system reliability, Particle Swarm Optimization (PSO) is applied to optimize satellite constellation parameters, substantially improving visibility from an initial 92.2% to 96.67%. Further optimization via the Walker-Star constellation achieves an unprecedented 100% visibility for a three-month duration. Results highlight the critical impact of atmospheric conditions, satellite configuration, and elevation angles on the overall communication performance, demonstrating a robust methodology for ensuring continuous, reliable Arctic communication. MATLAB (R2024a), MATLAB Satellite Communications Toolbox ver. 24.1, and Aerospace Toolbox ver. 24.1 were used for the analysis in this paper. Some analysis in this paper was done using the computing resources provided by BC DRI Group and Digital Research Alliance of Canada. The satellite data was retrieved from space-track.org.

Massive MIMO can significantly increase the data rate of a wireless communications system. The requirement for even greater data transmission rates in 5G systems has prompted the consideration of the largely unused millimeter wave (mmWave) band. To mitigate the high path loss at mmW frequencies and the poor scattering nature of the mmW channel (fewer paths exist), this paper proposes the use of a uniform planar array (UPA) hybrid beamforming technique with large antenna arrays and the sphere decoding algorithm to improve the performance of mmWave massive MIMO systems. When perfect Channel State Information (CSI) is available at the base Station (BS) and mobile Station (MS), computer simulations have shown that a gain of 10 dB or more can be achieved by using the Sphere Decoding algorithm compared to a system with Zero-forcing (ZF) or minimum mean- square error (MMSE) hybrid beamforming. Moreover, the complexity of the SD algorithm can be reduced by efficiently selecting the radius.

Massive MIMO was recently proposed to achieve the advantage of conventional MIMO but on a greater scale. Massive MIMO can provide a much higher capacity without requiring more wireless spectrum. The need for higher data rate led researchers to consider the Millimeter Wave (mmW) band that offers a much larger unused bandwidth. Because of the higher path loss at mmW frequencies, and the poor scattering nature of the mmW channel (fewer paths exist), a hybrid beamforming technique with large antenna arrays and the Alamouti coding scheme are used in this paper to improve the performance of the single-cell mmW massive MIMO systems. Computer simulations have shown that a gain of 20 dB or more can be achieved using the Alamouti code.

In this paper, we consider an amplify-and-forward-based two-way relaying network, in which the relays need to harvest energy from the received radio frequency signals to remain active in the network and assist data exchange between two transceivers. In particular, considering time-switching architecture, we investigate the problem of joint energy harvesting (EH) time allocation and distributed beamforming in the presence of interference. Specifically, assuming that the perfect knowledge of the interfering links is not available, we study three different design approaches. First, we maximize the sum-rate of the network subject to individual EH power constraints at relays. Resorting to the semi-definite relaxation (SDR) and successive upper-bound minimization techniques, we devise an iterative algorithm that efficiently solves such a challenging problem. Next, we minimize the total power consumed by the relays subject to the rate constraints at the transceivers. Finally, we minimize the EH-phase duration subject to the individual EH power constraints at the relays as well as the rate constraints at the transceivers. The rate constraints, however, make both second and third design optimization problems non-convex and complicated. Although no closed-form solutions are available for these approaches, we propose efficient schemes by applying the SDR technique followed by semi-definite programming problems.

Unlike the existing detectors, which are developed for decode-and-forward (DF) networks in the ideal interference-free case, we consider a more practical scenario where arbitrary interference exists. We consider a DF cooperative network consisting of a source, multiple relays, a destination, and multiple interferers affecting both the relays and the destination. Each relay is equipped with multiple antennas and knows its local instantaneous channel state information (CSI). Assuming that the destination knows the instantaneous CSI of the source-relay, relay-destination, and source-destination channels, we develop, for the first time in the literature, the end-to-end optimum maximum-likelihood (ML) detectors in closed-form for DF systems employing either simultaneous or orthogonal transmissions in the presence of interference. Furthermore, theoretical analysis shows that the proposed detectors achieve full diversity gains in the presence of interference with finite interference-to-noise ratios. Numerical results demonstrate that the proposed optimum detectors substantially outperform the conventional schemes which simply ignore interference.

Likelihood-based algorithms for the classification of linear digital modulations are systematically investigated for a multiple receive antennas configuration. Existing modulation classification (MC) algorithms are first extended to the case of multiple receive antennas and then a critical problem is identified that the overall performance of the multiple antenna systems is dominated by the worst channel estimate of a particular antenna. To address the performance degradation issue, we propose a new MC algorithm by optimally combining the log likelihood functions (LLFs). Furthermore, to analyze the upper-bound performance of the existing and the proposed MC algorithms, the exact Cramer-Rao Lower Bound (CRLB) expressions of non-data-aided joint estimates of amplitude, phase, and noise variance are derived for general rectangular quadrature amplitude modulation (QAM). Numerical results demonstrate the accuracy of the CRLB expressions and verify that the results reported in the literature for quadrature phase-shift keying (QPSK) and 16-QAM are special cases of our derived expressions. Also, it is demonstrated that the probability of correct classification of the new algorithm approaches the theoretical bounds and a substantial performance improvement is achieved compared to the existing MC algorithm.

We consider the uplink of a distributed antenna system (DAS) in the presence of interference and unknown oscillator offsets, which lead to synchronization errors. For this scenario, we develop a linear receiver which maximizes the output signal-to-interference-plus-noise ratio (SINR). Specifically, we first propose a new structured generalized sidelobe canceller (SGSC) formulation for the commonly used minimum-variance receiver, and derive a general framework to improve the robustness of a linear receiver when oscillator offsets exist. Then a new linear receiver is proposed in closed-form based on the proposed robust SGSC and a ridge regression technique. It is shown by simulations that the proposed linear receiver can provide very robust SINR performance and excellent symbol error rate (SER) performance in the existence of interference and unknown oscillator offsets.

Super-orthogonal space-time trellis codes (SOSTTCs) designed by hand can significantly improve the performance of space-time trellis codes. This paper introduces a new representation of SOSTTCs based on a generator matrix that allows a systematic and exhaustive search of all possible codes. This will verify that some of the known codes are optimal, and provides a means to easily implement encoders and decoders with a large number of states without relying on a graphical representation. New codes with up to 256 states that outperform previously known codes are presented

Space-time trellis codes can significantly improve the error performance or the data rate of wireless communications systems with multiple transmit antennas. These codes are usually generated using a generator matrix with 2 columns and modulo 4 operations for QPSK modulation with two transmit antennas. They can also be implemented as Z4convolutional codes. Another way of representing them is to use a conventional binary convolutional code of rate 2/4. Such a representation is more flexible and allows an easy implementation of space-time encoders and decoders with different modulation schemes and numbers of transmit antennas. Only the rate of the convolutional code needs to be changed using the puncturing technique to accommodate these different schemes. Simulations results have shown that the new codes can achieve an error performance very close to that of the best usual space-time codes.

It has been shown that the design criteria for space-time trellis codes over quasi-static Rayleigh fading channels are the rank criterion and the determinant criterion. Recently, a new design criterion that maximizes only the trace of the distance matrices has been proposed for systems with a large product of the numbers of transmit and receive antennas. These codes are superior when the number of receive antennas is 4 or more. In this paper, the two design criteria are combined so that the codes are good or robust whatever the number of receive antennas is. New QPSK codes that optimize the trace/determinant pair and achieve the full rank are presented. These codes outperform other previously known codes whether there are two receive antennas or more.

In this paper, an adaptive decoding algorithm for convolutional codes, which is a modification of the Viterbi algorithm (VA) is presented. For a given code, the proposed algorithm yields nearly the same error performance as the VA while requiring a substantially smaller average number of computations. Unlike most of the other suboptimum algorithms, this algorithm is self-synchronizing. If the transmitted path is discarded, the adaptive Viterbi algorithm (AVA) can recover the state corresponding to the transmitted path after a few trellis depths. Using computer simulations over hard and soft 3-bit quantized additive white Gaussian noise channels, it is shown that codes with a constraint length K up to 11 can be used to improve the bit-error performance over the VA with K=7 while maintaining a similar average number of computations. Although a small variability of the computational effort is present with our algorithm, this variability is exponentially distributed, leading to a modest size of the input buffer and, hence, a small probability of overflow.