Fig 1 - uploaded by Chintha Tellambura
Content may be subject to copyright.
Source publication
We develop new blind and semi-blind data detectors and channel estimators for orthogonal frequency-division multiplexing (OFDM) systems. Our data detectors require minimizing a complex, integer quadratic form in the data vector. The semi-blind detector uses both channel correlation and noise variance. The quadratic for the blind detector suffers fr...
Contexts in source publication
Context 1
... the OFDM system given in Fig. 1, source data are grouped and/or mapped into multiphase symbols from , which are modulated by the inverse DFT (IDFT) on parallel subcarriers. Note that , are called modulation symbols or transmit data symbols. The input symbol duration is , and the OFDM symbol duration is . We assume that the composite CIR, which includes transmit and ...
Context 2
... the MSE per- formance is degraded by increasing the number of VCs, since the number of information symbols for channel estimation is reduced. In Fig. 10, due to the use of VCs, the complexity of our semi-blind and blind detectors is greatly reduced. Figs. 9 and 10 are plotted assuming that no transmit power normaliza- tion is employed (i.e., a transmitted OFDM symbol has constant energy regardless of the number of VCs). ...
Similar publications
A frequency-hopping (FH) space-frequency (SF)-coded clustered orthogonal frequency division multiple access (OFDMA) system is considered. It is shown that SF coding reduces the number of collisions in OFDMA with FH provided that the subcarrier clusters are formed randomly at each hopping instant. Analytical expressions for the collision rate, which...
Citations
... The channel influence at the receiver, channel state information (CSI), must be precisely estimated to retrieve the transferred symbols. In general, the receiver estimates the CSI by employing pilot symbols that are known to both the receiver and the transmitter [2,3]. ...
Deep learning (DL) algorithms can enhance wireless communication system efficiency and address numerous physical layer challenges. Channel state estimation (CSE) and signal detection (SD) are essential parts of improving the performance of an OFDM wireless system. In this context, we introduce a DL model as an effective alternative for implicit CSE and SD over Rayleigh fading channels in the OFDM wireless system. The DL model is based on the gated recurrent unit (GRU) neural network. The proposed DL GRU model is trained offline using the received OFDM signals related to the transmitted data symbols and added pilot symbols as inputs. Then, it is implemented online to accurately and directly detect the transmitted data. The experimental results using the metric parameter of symbol error rate show that, the proposed DL GRU-based CSE/SD provides superior performance compared with the traditional least square and minimum mean square error estimation methods. Also, the trained DL GRU model exceeds the existing DL channel estimators. Moreover, it provides the highest CSE/SD quality with fewer pilots, short/null cyclic prefixes, and without prior knowledge of the channel statistics. As a result, the proposed DL GRU model is a promising solution for CSE/SD in OFDM wireless communication systems.
... The bit error rate (BER) is determined by comparing the binary data bits generated by the source at the transmitter, dk, with the eventually obtained bits at the receiver, ̂, and then dividing the result by the number length of dk [25]. Figure 2 illustrates the configuration of an OFDM transceiver system (transmitter and receiver) [26], [27]. ...
... As shown in (26) can be rewritten as (27): ...
p>Orthogonal frequency division multiplexing (OFDM) is a multicarrier modulation (MCM) technique that divides the wide bandwidth into parallel narrow bands, each of which is modulated by orthogonal subcarriers. Currently, OFDM is a high-spectral efficiency modulation technique that is used in a variety of wired and wireless applications. The transmitted signal in a wireless communication channel spreads from transmitter to receiver through multiple reflective paths. This triggers multipath fading, which causes variations in the received signal's amplitude and phase. Slow/fast and frequency-selective/frequency-nonselective are the main types of multipath fading channels. Therefore, in this paper, we proposed new models for modeling multipath fading channels, such as the exponential fading channel and the Gamma fading channel. In addition, new bit-error-rate (BER) derivations have been derived. The performance of the OFDM system over proposed channel models has been evaluated using Monte-Carlo simulation and compared to the Rayleigh fading channel model. The obtained results via simulations show that the exponential fading channel at a rate parameter (λ=0.5) outperforms the Rayleigh fading channel by 6 dB for all values of Eb/No, while the Gamma fading channel at (α=2) outperforms the Rayleigh fading channel by 3 dB for all values of Eb/No.</p
... Moreover, some known solutions in the sate of art which make use of ML criteria such as Sphere Decoding (SD) [182][183][184] and QR decomposition with order M Maximum Likelihood Decoding (QRM-MLD) [185] preserve the optimal performance, but they still suffer from major drawbacks. For instance, the SD complexity is highly dependent on the initial value for the search radius: if the search radius is chosen too small, there may be no solution in the hypersphere, but if the search radius is chosen too large, the number of points to explore may become too large, and the algorithm will encounter the same issue as ML-based detection algorithms [186]. ...
Wireless Terabits per second (Tbps) link is needed for the new emerging data-hungry applications in Beyond 5G (B5G). Besides, the sub-THz bands are the next frontier for B5G due to scarce and limited bandwidth at lower frequencies. Even though a wider bandwidth and large-scale MIMO are envisioned, but the system and waveform design should consider the channel characteristics, technological limitations, and high RF impairments. Hence, we proposed to use an energy-efficient single carrier modulation with spectral-efficient Index Modulation (IM) and MIMO. First, Spatial Multiplexing (SMX) and Generalized Spatial Modulation (GSM) are studied. We reduced their optimal detection complexity by 99% and the spatial correlation effect on GSM. Then, we proposed dual-polarized (DP)-GSM that provides higher spectral efficiency (SE) via multi-dimensional IM and helps with the latter problem. Afterwards, we proposed the novel filter IM domain that generalizes most existing SISO schemes. We also proposed two novel schemes (Filter Shapes IM (FSIM) and Quadrature FSIM) with their optimal low complexity detectors and specialized equalizers to enhance SE and energy efficiency (EE). Striving for more SE/EE improvement, this IM domain is exploited in MIMO context. To conclude, the proposed SMX-FSIM is compared in sub-THz environment to the previous candidates. The results confirm that SMX-FSIM is the most promising solution for wireless Tbps due to its high SE/EE, robustness to RF impairments, low power consumption, and low complexity/cost with a linear receiver. Finally, the challenging filter bank design imposed by the filter IM domain is tackled by optimization to achieve better results.
... However, a joint channel estimation and data detection is capable of avoiding the use of long training sequences, whilst improving the BER performance [29]. From this perspective, researchers developed several low-complexity near-optimal joint estimation methods [30], [31]. Hence, joint channel estimation and MPA-aided detection is very attractive. ...
... The belief b(h kj ) is obtained by adding the terms m f n j →h kj /v f n j →h kj and 1/v f n j with index n = n into (30) and (31). Then the maximum a posteriori (MAP) estimator can be used for determining the estimate of the channel coefficient byĥ kj = arg max h kj b(h kj ). ...
The sparse code multiple access (SCMA) is a promising candidate for bandwidth-efficient next generation wireless communications, since it can support more users than the number of resource elements. On the same note, faster-than Nyquist (FTN) signaling can also be used to improve the spectral efficiency. Hence in this paper, we consider a combined uplink FTN-SCMA system in which the data symbols corresponding to a user are further packed using FTN signaling. As a result, a higher spectral efficiency is achieved at the cost of introducing intentional inter-symbol interference (ISI). To perform joint channel estimation and detection, we design a low complexity iterative receiver based on the factor graph framework. In addition, to reduce the signaling overhead and transmission latency of our SCMA system, we intrinsically amalgamate it with grant-free scheme. Consequently, the active and inactive users should be distinguished. To address this problem, we extend the aforementioned receiver and develop a new algorithm for jointly estimating the channel state information, detecting the user activity and for performs data detection. In order to further reduce the complexity, an energy minimization based approximation is employed for restricting the user state to Gaussian. Finally, a hybrid message passing algorithm is conceived. Our Simulation results show that the FTN-SCMA system relying on the proposed receiver design has a higher throughput than conventional SCMA scheme at a negligible performance loss.
... Recall that a node obtains CSI via the feedback from the receiver. To realize this, the node first sends pilot signals to the receiver with the aim of performing channel estimation (see [37]- [39] and references therein for pilot designs). Because the pilot length is much shorter than that of the data packets [40], the energy required for channel estimation is much smaller compared to that required for data transmission. ...
We investigate the optimal transmission policy for an energy-harvesting wireless sensor node. The node must decide whether an arrived packet should be transmitted or dropped, based on the packet’s priority, wireless channel gain, and the energy status of the node. The problem is formulated under the Markov decision process (MDP) framework. For such a problem, the conventional method to get the optimal policy is by using a state value function, which is three-dimensional in the considered problem, leading to high complexity. Fortunately, to reduce complexity, we derive an equivalent solution for the optimal policy via a one-dimensional after-state value function. We show that the after-state value function is differentiable and non-decreasing. We also discover a threshold structure of the optimal policy that is derived by the after-state value function. Furthermore, to approximate the after-state value function, we propose a learning algorithm to train a three-layer monotone neural network. The trained network thus finds a near-optimal selective transmission policy of the node. Finally, through simulation, we demonstrate the learning efficiency of the algorithm and the performance of the learned policy.
... Improving the channel estimation performance with fewer pilot symbols is a challenging prospect. One approach is to employ a blind channel estimation method [16]. However, the blind method is too complex computationally to be implemented in small terminals. ...
This study proposes a Peak-to-Average Power Ratio (PAPR) reduction method using an adaptive Finite Impulse Response (FIR) filter in Orthogonal Frequency Division Multiplexing systems. At the transmitter, an iterative algorithm that minimizes the p-norm of a transmitted signal vector is used to update the weight coefficients of the FIR filter to reduce PAPR. At the receiver, the FIR filter used at the transmitter is estimated using pilot symbols, and its effect can be compensated for by using an equalizer for proper demodulation. Simulation results show that the proposed method is superior to conventional methods in terms of the PAPR reduction and computational complexity. It also shows that the proposed method has a trade-off between PAPR reduction and bit error rate performance.
... Furthermore, when accessing spectrum holes, an SU must adapt its transmit power depending on channel fading status, which is indicated by channel state information (CSI) [5], [6]. The CSI estimation process is referred to as channel probing: i.e., the SU transmits a pilot sequence (see [7]- [9] and references therein for pilot designs), which enables its receiver to evaluate the channel and provide CSI feedback. Note that this channel probing should take place on an identified spectrum hole. ...
... First, at each time slot, there is probability /2 that the algorithm will choose a S P = 00 to accumulate energy. Therefore, given the battery level b S P t of slot t, we can 7 find a finite T such that prob{b S P t+T ≥ e S + e P } > 0. In other words, at any slot t ≥ T , we have prob{b S P t ≥ e S + e P } > 0. Thus, having sufficient energy for sensing and probing, the algorithm will choose 7 If this condition cannot be satisfied, the underlying energy harvesting process is not sufficient to power the SU. Algorithm 2: Simultaneous Sampling, Learning and Control. ...
This paper considers joint optimization of spectrum sensing, channel probing and transmission power control for a single-channel secondary transmitter which operates with harvested energy from ambient sources. At each time slot, to maximize the expected throughput, the transmitter needs to determine whether or not to perform the operations of spectrum sensing, channel probing, and transmission, according to energy status and channel fading status. First, we model this stochastic optimization problem as a two-stage continuous-state Markov decision process, with a sensing-and-probing stage and a transmit-power-control stage. We simplify this problem by a more useful after-state value function formulation. We then propose a reinforcement learning algorithm to learn the after-state value function from data samples when the statistical distributions of harvested energy and channel fading are unknown. Numerical results demonstrate learning characteristics and performance of the proposed algorithm.
... Therefore, it would be more factual to denote such techniques as conditionallyblind, to distinguish them from fully blind (FB) systems, which do not require pilot symbols and do not have any constraints on the modulation type or order. It is worth noting that conditionally-blind systems are different from semi-blind systems, in which the CE is performed using both, the pilot and data symbols [24]- [26]. ...
This paper presents a blind channel estimation technique for orthogonal frequency division multiplexing (OFDM) communications systems. The proposed system is based on modulating particular pairs of subcarriers using amplitude shift keying (ASK) and phase shift keying (PSK), which enables the realization of a decision-directed (DD) one-shot blind channel estimator (OSBCE), with complexity and accuracy that are comparable to pilot-based channel estimation techniques. The performance of the proposed estimator is evaluated in terms of the mean squared error (MSE), where an accurate analytical expression is derived and verified using Monte Carlo simulation under various channel conditions. The obtained results show that the MSE of the proposed OSBCE is comparable to pilot-based estimators, which confirms the efficiency of the proposed OSBCE. IEEE
... A decision directed iterative channel estimation proposed in [18] improves the computational complexity by avoiding matrix inversion. The number of semi-blind channel estimation schemes are reported for MIMO-OFDM systems as well [19][20][21][22][23][24][25][26]. ...
A joint channel estimation and data detection technique for a multiple input multiple output (MIMO) wireless communication system is proposed. It combines the least square (LS) training based channel estimation (TBCE) scheme with sphere decoding. In this new approach, channel estimation is enhanced with the help of blind symbols, which are selected based on their correctness. The correctness is determined via sphere decoding. The performance of the new scheme is studied through simulation in terms of the bit error rate (BER). The results show that the proposed channel estimation has comparable performance and better computational complexity over the existing semi-blind channel estimation (SBCE) method.
... These methods either suffer from slow convergence, higher computational costs or assume channel to be stationary over several OFDM symbols. These drawbacks make maximum likelihood (ML)-based approaches, e.g., [15,16] more attractive due to their fast convergence despite having the higher computational cost. Usually, suboptimal techniques are employed to reduce the computational cost by restricting the search space of exhaustive ML search. ...
This paper investigates the joint maximum likelihood (ML) data detection and channel estimation problem for Alamouti space-time block-coded (STBC) orthogonal frequency-division multiplexing (OFDM) wireless systems. The joint ML estimation and data detection is generally considered a hard combinatorial optimization problem. We propose an efficient low-complexity algorithm based on branch-estimate-bound strategy that renders exact joint ML solution. However, the computational complexity of blind algorithm becomes critical at low signal-to-noise ratio (SNR) as the number of OFDM carriers and constellation size are increased especially in multiple-antenna systems. To overcome this problem, a semi-blind algorithm based on a new framework for reducing the complexity is proposed by relying on subcarrier reordering and decoding the carriers with different levels of confidence using a suitable reliability criterion. In addition, it is shown that by utilizing the inherent structure of Alamouti coding, the estimation performance improvement or the complexity reduction can be achieved. The proposed algorithms can reliably track the wireless Rayleigh fading channel without requiring any channel statistics. Simulation results presented against the perfect coherent detection demonstrate the effectiveness of blind and semi-blind algorithms over frequency-selective channels with different fading characteristics.
