In many practical wireless systems, the Signal-to-Interference-and-Noise Ratio (SINR) that is applicable to a certain transmission, referred to as Channel State Information (CSI), can only be learned after the transmission has taken place and is thereby outdated (delayed). For example, this occurs under intermittent interference. We devise the backward retransmission (BRQ) scheme, which uses the delayed CSIT to send the optimal amount of incremental redundancy (IR). BRQ uses fixed-length packets, fixed-rate R transmission codebook, and operates as Markov block coding, where the correlation between the adjacent packets depends on the amount of IR parity bits. When the delayed CSIT is full and R grows asymptotically, the average throughput of BRQ becomes equal to the value achieved with prior CSIT and a fixed-power transmitter; however, at the expense of increased delay. The second contribution is a method for employing BRQ when a limited number of feedback bits is available to report the delayed CSIT. The main novelty is the idea to assemble multiple feedback opportunities and report multiple SINRs through vector quantization. This challenges the conventional wisdom in ARQ protocols where feedback bits are used to only quantize the CSIT of the immediate previous transmission.
We remark here that the issue of delayed CSIT has recently sparkled a significant interest in terms of improvements that can be achieved in terms of Degrees of Freedom at high SNRs @cite_4 @cite_1 . However, that line of work does not deal with HARQ protocols and is directed towards multi-user scenarios, while in our case we consider relatively low SNR and a single link.
@cite_1 @cite_4
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Delay-sensitive traffic services, such as live streaming video, voice over IP and multimedia teleconferencing, requires low end-to-end delay in order to maintain its interactive and streaming nature. In recent years, the popularity of delay-sensitive applications has been rapidly growing. In this study, the authors consider the delay-sensitive user scheduling and power control design for heterogeneous networks (HetNets). They model the problem as an infinite-horizon average cost Markov decision process MDP. Based on the dynamics of channel state information (CSI) and queue state information (QSI), they focus on finding an adaptive user scheduling and power control policy to minimise the average delay of network subjected to the power constraints of heterogeneous base stations. In order to reduce the computational complexity and facilitate the distributive implementation, the authors derive a distributive stochastic learning algorithm which only requires local CSI and local QSI to determine the optimal user scheduling and power control policy. The simulation results confirm that significant delay performance gain can be achieved compared with various baseline schemes. High-fidelity, real-time interactive applications are envisioned with the emergence of the Internet of Things and tactile Internet by means of ultra-reliable low-latency communications (URLLC). Exploiting time diversity for fulfilling the URLLC requirements in an energy efficient manner is a challenging task due to the nontrivial interplay among packet size, retransmission rounds and delay, and transmit power. In this paper, we study the fundamental energy-latency tradeoff in URLLC systems employing incremental redundancy (IR) hybrid automatic repeat request (HARQ). We cast the average energy minimization problem with a finite blocklength (latency) constraint and feedback delay, which is non-convex. We propose a dynamic programming algorithm for energy efficient IR-HARQ optimization in terms of number of retransmissions, blocklength, and power per round. Numerical results show that our IR-HARQ approach could provide around 25 energy saving compared with one-shot transmission (no HARQ). The stringent requirements of ultra-reliable low-latency communications (URLLC) require rethinking of the physical layer transmission techniques. Massive antenna arrays are seen as an enabler of the emerging 5th generation systems, due to increases in spectral efficiency and degrees of freedom for transmissions, which can greatly improve reliability under demanding latency requirements. Massive array coherent processing relies on accurate channel state information (CSI) in order to achieve high reliability. In this paper, we investigate the impact of imperfect CSI in a single-input multiple-output (SIMO) system on the coherent receiver. An amplitude-phase keying (APK) symbol constellation is proposed, where each two symmetric symbols reside on distinct power levels. The symbols are demodulated using a dual-stage non-coherent and coherent detection strategy, in order to improve symbol reliability. By means of analysis and simulation, we find an adequate scaling of the constellation and show that for high signal-to-noise ratio (SNR) and inaccurate CSI regime, the proposed scheme enhances receiver performance. In this paper, we consider a secrecy relaying communication scenario where all nodes are equipped with multiple antennas. An eavesdropper has the access to the global channel state information (CSI), and all the other nodes only know the CSI not associated with the eavesdropper. A new secrecy transmission protocol is proposed, where the concept of interference alignment is combined with cooperative jamming to ensure that artificial noise from transmitters can be aligned at the destination, but not at the eavesdropper due to the randomness of wireless channels. Analytical results, such as ergodic secrecy rate and outage probability, are developed, from which more insightful understanding of the proposed protocol, such as multiplexing and diversity gains, can be obtained. A few special cases, where outage probability cannot be decreased to zero regardless of SNR, are also discussed. Simulation results are provided to demonstrate the performance of the proposed secrecy transmission protocol.