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The multitier heterogeneous structure of 5G with dense small cells deployment, relays, and device-to-device (D2D) communications operating in an underlay fashion is envisioned as a potential solution to satisfy the future demand for cellular services. However, efficient power allocation among dense secondary transmitters that maintains quality of service (QoS) for macro (primary) cell users and secondary cell users is a critical challenge for operating such radio. In this paper, we focus on the power allocation problem in the multitier 5G network structure using a noncooperative methodology with energy efficiency consideration. Therefore, we propose a distributive intuition-based online learning scheme for power allocation in the downlink of the 5G systems, where each transmitter surmises other transmitters power allocation strategies without information exchange. The proposed learning model exploits a brief state representation to account for the problem of dimensionality in online learning and expedite the convergence. The convergence of the proposed scheme is proved and numerical results demonstrate its capability to achieve fast convergence with QoS guarantee and significant improvement in system energy efficiency. [ABSTRACT FROM AUTHOR]

Terahertz (THz) wireless technology is envisioned to enable terabit-per-second (Tbps) and secure transmissions in sixth-generation (6G) communication networks and has attracted attention from academia and industry in recent years. Because the transmission range of THz radios is restricted compared to that of microwave radios, frequency reuses in the THz band become much more flexible and even possible among transceiver pairs in close proximity. However, without appropriate spatial arrangement and coordination, the frequency reuse in the THz band can also lead to severe co-channel interference and result in a low signal-to-interference-plus-noise ratio (SINR) or a signal-to-interference ratio (SIR), which finally degrades signal detection and network reliability. To thoroughly study the co-channel interference in the THz band, we model the co-channel interference by the compound channel model and analyze it in detail. The adopted channel model captures the key features of THz communication, such as, spreading loss, molecular absorption loss, and dynamic shadowing, which is much different and complicated than those used in the low-frequency band. The resulted SINR and SIR are investigated by approximating the sum of co-channel interference as a gamma distribution. The generalized analytical results are also reduced to specialized forms for two special cases, i.e., the single-interferer case and the case of multiple independent and identically distributed (i.i.d.) interferers. Due to the generalized nature of the THz interference model constructed in this paper, the results play a meaningful role in practical implementation and can be easily extended to advanced performance analyses for THz communication systems. [ABSTRACT FROM AUTHOR]

Future wireless technologies, such as fifth-generation (5G), are expected to support real-time applications with high data throughput, e.g., holographic meetings. From a bandwidth perspective, cognitive radio (CR) is a promising technology to enhance the system's throughput via sharing the licensed spectrum. From a delay perspective, it is well known that increasing the number of decoding blocks will improve system robustness against errors while increasing delay. Therefore, optimally allocating the resources to determine the tradeoff of tuning the length of the decoding blocks while sharing the spectrum is a critical challenge for future wireless systems. In this paper, we minimize the targeted outage probability over the block-fading channels while utilizing the spectrum-sharing concept. The secondary user's outage region and the corresponding optimal power are derived, over two-block and $M$-block fading channels. We propose two suboptimal power strategies and derive the associated asymptotic lower and upper bounds on the outage probability with tractable expressions. These bounds allow us to derive the exact diversity order of the secondary user's outage probability. To further enhance the system's performance, we also investigate the impact of including the sensing information on the outage problem. The outage problem is then solved via proposing an alternating optimization algorithm, which utilizes the verified strict quasi-convex structure of the problem. Selected numerical results are presented to characterize the system's behavior and show the improvements of several sharing concepts. [ABSTRACT FROM AUTHOR]

Hardware distortions (HWDs) render drastic effects on the performance of communication systems. They are recently proven to bear asymmetric signatures; and hence can be efficiently mitigated using improper Gaussian signaling (IGS), thanks to its additional design degrees of freedom. Discrete asymmetric signaling (AS) can practically realize the IGS by shaping the signals’ geometry or probability. In this paper, we adopt the probabilistic shaping (PS) instead of uniform symbols to mitigate the impact of HWDs and derive the optimal maximum a posterior detector. Then, we design the symbols’ probabilities to minimize the error rate performance while accommodating the improper nature of HWD. Although the design problem is a non-convex optimization problem, we simplified it using successive convex programming and propose an iterative algorithm. We further present a hybrid shaping (HS) design to gain the combined benefits of both PS and geometric shaping (GS). Finally, extensive numerical results and Monte Carlo (MC) simulations highlight the superiority of the proposed PS over conventional uniform constellation and GS. Both PS and HS achieve substantial improvements over the traditional uniform constellation and GS with up to one order magnitude in error probability and throughput. [ABSTRACT FROM AUTHOR]

Reliable data transmission is a fundamental challenge in resource-constrained wireless sensor networks. In current implementations, a single bit error requires retransmitting the entire frame. This incurs extra processing overhead and power consumption, especially for large frames. Frame fragmentation into small blocks with individual error detection codes can reduce the unnecessary retransmission of the correctly received blocks. The optimal block size, however, varies based on the wireless channel conditions. In this paper, we propose an interference-aware frame fragmentation scheme called iFrag. iFrag dynamically adjusts the number of blocks inside a frame based on current channel conditions, and effectively addresses all challenges associated with such dynamic partitioning. Through analytical and experimental results, we show that iFrag achieves up to $$3\times$$ improvement in goodput when the channel is noisy, while reducing the delay by 12 % compared to other static fragmentation approaches. On average, it shows a 13 % gain in goodput across all channel conditions used in our experiments. Our testbed results also show that iFrag lowers the energy consumed per useful bit by 60 %, as improved data transmission reliability reduces the number of frame retransmissions which increases the motes energy efficiency. [ABSTRACT FROM AUTHOR]